Population Health Management

 

Prior to beginning work on this assignment, read the following articles:

• A Profile in Population Health Management: The Sandra Eskenazi Center for Brain Care Innovation: This Care Model Emphasizes Social, Behavioral, and Environmental Determinants of Health When Treating Dementia
• Accounting for Accountable Care: Value-Based Population Health Management
• How Executives’ Expectations and Experiences Shape Population Health Management Strategies

Watch the following video:

• What Is Population Health? (Links to an external site.)

After the passage of the Patient Protection and Affordable Care Act of 2010, health care organizations have been faced with significant challenges in providing quality care to all Americans.

HealthyPeople.gov (Links to an external site.)

also encourages health care organizations to focus on the relevance of social determinants and health status.                                                                                                                                                

Take on the role of the administrator of a community hospital in your area. You would like to implement a strategic plan to improve the health status of your community. Select a vulnerable population in your area affected by a disease or condition. Examples include aging, COVID-19, diabetes, Ebola, heart disease, opioid epidemics, Zikavirus, and so on. Write a three- to five-page paper that details your strategic plan.

In your paper,

• Describe the population, including demographics and risk factors that determine health in this population.
• Explain the disease or condition prevalent in this population.
• Identify access and barriers to health care and treatment options for this population, including local, state, and federal policies regulating control and prevention of your selected disease or condition in this population.
• Propose at least three strategies to improve the health of the selected population.
• Develop at least three key indicators to measure the success of your proposed population health management.
• Support your response with a minimum of three scholarly sources that were published in the last 5 years.

The Population Health Management assignment

• Must be three to five double-spaced pages in length (not including title and references pages and formatted according to APA Style (Links to an external site.) as outlined in the Writing Center’s APA Formatting for Microsoft Word (Links to an external site.) resource.
• Must include a separate title page with the following:

  Title of paper
  Student’s name
  Ashford University
  Course name and number
  Instructor’s name
  Date submitted

• Must utilize academic voice. See the Academic Voice (Links to an external site.) resource for additional guidance.
• Must include an introduction and conclusion paragraph. Your introduction paragraph needs to end with a clear thesis statement that indicates the purpose of your paper.

  For assistance on writing Introductions & Conclusions (Links to an external site.) as well as Writing a Thesis Statement (Links to an external site.), refer to the Writing Center resources.

• Must use at least three scholarly or peer-reviewed sources published in the past 5 years.

  The Scholarly, Peer-Reviewed, and Other Credible Sources (Links to an external site.) table offers additional guidance on appropriate source types. If you have questions about whether a specific source is appropriate for this assignment, please contact your instructor. Your instructor has the final say about the appropriateness of a specific source for this assignment.
  To assist you in completing the research required for this assignment, view this Ashford University Library Quick ‘n’ Dirty (Links to an external site.) tutorial, which introduces the Ashford University Library and the research process, and provides some library search tips.

• Must document any information used from sources in APA Style as outlined in the Writing Center’s APA: Citing Within Your Paper (Links to an external site.) guide.
• Must include a separate references page that is formatted according to APA Style as outlined in the Writing Center. See the APA: Formatting Your References List (Links to an external site.) resource in the Writing Center for specifications.

https://doi.org/10.1177/0306312719840429

Social Studies of Science
2019, Vol. 49(4) 556 –582

© The Author(s) 2019

Article

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DOI: 10.1177/0306312719840429

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Accounting for accountable
care: Value-based
population health
management

Linda F Hogle
Department of Medical History & Bioethics, University of Wisconsin-Madison, Madison, WI, USA

Abstract
Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the
US. In this model, healthcare providers bear the financial risk of their patients’ health outcomes:
ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized
if improvements are not demonstrated. While the aim is to make providers more accountable
to payers and patients, this is a sea-change in payment and delivery systems, requiring new
infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data
analytics to identify individuals within their populations and sort them using novel categories,
which are then utilized to tailor interventions. The article uses an STS lens to analyze the
assemblage involved in the enactment of population health management through practices of data
collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals
within populations. The processes and practices of implementing accountability technologies thus
produce particular kinds of knowledge and reshape concepts of accountability and care. In the
process, account-giving becomes as much a procedural ritual of verification as an accounting for
health outcomes.

Keywords
Affordable Care Act, big data, dataveillance, population health, risk, US healthcare

This article concerns the way populations are constructed through the processes of
dataveillance and within the set of institutional relations designed to produce value and
accountability. Value-based care (VBC), defined as health outcomes achieved per dol-
lar spent, is becoming a widely embraced policy strategy to contain healthcare costs
while improving patients’ care experience (Porter, 2010; Porter and Teisberg, 2006).

Correspondence to:
Linda F Hogle, Department of Medical History & Bioethics, University of Wisconsin-Madison, 1135 Medical
Sciences Building, 1300 University Avenue, Madison, WI 53706, USA.
Email: lfhogle@wisc.edu

840429 SSS0010.1177/0306312719840429Social Studies of ScienceHogle
research-article2019

Article

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mailto:lfhogle@wisc.edu

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Hogle 557

As one physician remarked: ‘For healthcare providers, value-based care isn’t just an
operational incentive anymore, it’s an imperative for basic survival. [It is] vitally
important to redesign health system services for population health’ (Michael Blackman,
MD as cited in Garvin, 2016). Implicit in this new conceptual landscape is an actuarial
way of thinking, involving both economic and health risk calculations when consider-
ing how to achieve outcomes. Risks affect the health of individuals, populations and
healthcare organizations.

To ensure that ‘value’ outcomes of improved cost and care are achieved, policy ana-
lyst Elliott Fisher contended, an arrangement was needed in which providers share
risks, rewards and penalties with payers. This would make providers more accountable
for the outcomes of their patients, (Fisher et al., 2007). In the US, this led to a new
institutional form, the Accountable Care Organization (ACO). The inherent political
rationalities on which ACOs are built manage risk by compiling comprehensive dossi-
ers on individuals within defined populations and redistributing accountability for their
health outcomes. The complex notion of accountability in contemporary American
medicine is situated within a particular historical, political and sociotechnical moment
with assemblages of technologies, concepts and practices that constitute value-based
care. It takes a unique form in the US, with its market-based healthcare system and no
guaranteed access to care.1

My central argument is that accountability has become a foil through which systems
of managing population health become entangled with particular concepts of value and
risk, in ways that are consequential for population health and clinical care. ACOs consist
of particular kinds of virtual populations to be managed with targeted interventions. This
relies on the ability to make visible individuals who may be harbingers of risk and reas-
sembling them into unique categories. Yet this rests on certain assumptions about what
characteristics constitute current and future risk and how best to ameliorate it.

The administrative and algorithmic techniques to classify individuals and sort them
into groups will determine who may receive what kind of care – something that is very
much at stake in current political efforts to dismantle certain patient protections provided
by recent health law. In the process, relations among providers, payers and patients are
also reordered: Basing care on monetary incentives (and penalties) for the outcomes of
patients puts providers in the position of arbitrating financial and health risks, blurring
their role with that of insurers. Boundaries between clinical care and public health are
blurred as population health management becomes a matter of data-intensive sourcing
about individuals in their everyday lives, and individuals are viewed as risk objects in
relation to others within unconventionally defined populations (Jacobson and Dahlen,
2016). At the same time, infrastructures established to document accountability facilitate
data-intensive sourcing of personal health information for broader purposes of data col-
lection beyond healthcare.

My analysis contributes to STS by bringing together perspectives from valuation
studies and the social study of risk practices to consider the complexities of crossed
economic domains and changing legal and financial practices (Birch, 2017; Dussauge
et al., 2015; Power, 2007, 2016). Hilgartner (1992) distinguishes objects of risk (peo-
ple or things potentially experiencing harm) and risk objects (potential causes of harm).
From a value-based perspective, patients are both, since their health risks may also be

558 Social Studies of Science 49(4)

financial risks to the ACO. The ability of ACOs to identify potential sources of risk is
thus critical.

I use the analytical frame of assemblage – that is, the arrangements of practices, tech-
nologies, and theories that configure action in a sociotechnical space – to analyze inter-
actions shaping the way accountability is manifested in particular institutional forms in
the US (Law and Urry, 2004; Ruppert, 2011). This enables a broader view of value-based
healthcare concepts and activities not only in relation to each other, but also in relation
to activities and structures that pre-figured the current situation, plus phenomena in other
social domains, such as increased dataveillance in consumer and finance domains. The
assemblage includes the big data analytics, changing health information technology
(HIT) infrastructures, novel cost accounting techniques, historical and political policy
contexts, and intensified public health focus on social and behavioral influences on
health as much as genomics. VBC in the US would likely not have existed in its current
form without the interaction among these parts, and within the context of a market-based
healthcare system.

Relations in an assemblage are dynamic: Actants may enter or depart, laws may be
enacted or repealed, benchmarks may move, and measurement tools may be adapted by
local users. I show how interactions in such an unsettled (and potentially unsettling)
assemblage enact concepts such as ‘accountable care’ and ‘population health manage-
ment’ through practices such as data-intensive sourcing (Hoeyer, 2016; Kitchin and
Lauriault, 2014). At the same time, population databases created for accountable care
materialize particular kinds of subjects within framings of future risk and value. Showing
how a particular American version of accountability came to be manifested as algorith-
mic risk sorting of defined populations, I address a fundamental STS question of how
new forms of knowledge and models of social control develop together.

Managing population health entails acquiring much more data about patients, of many
more types, collected and analyzed in new ways. Such intensified data sourcing includes
getting data from sources beyond that which is usually considered to be ‘health-related’
(Hoeyer, 2016; Hogle, 2016a; Van Dijk, 2014). As I show, information about individuals
‘in the wild’, rather than in experimental or treatment spaces in the clinic, is used to
identify, characterize, and intervene in individuals’ health with care coordination, pre-
vention efforts aimed at behaviors, and more.

At the same time, as value-based partnerships, ACOs have to prove they are provid-
ing care that has relative worth according to federally-set benchmarks that measure both
quality and cost-efficiency. To do this requires metrics that order data in particular ways
to demonstrate improved outcomes with which to support their claims to shared sav-
ings. Yet ‘data are not simply “collected”, but are the result of multiple sociotechnical
arrangements of technological and human actors that configure agency and action’
(Ruppert, 2011: 7). It is important, then, to attend to the practices of collecting, measur-
ing, analyzing, sorting and representing data in interaction with value-based payment
and reporting structures. Metrics for measuring outcomes are performative, not only in
terms of generating potential interventions with selected members of populations, but
also in terms of the institutional forms, work practices, and meanings that emerge; in
particular, they perform understandings of what comes to count as ‘population health’,
‘health risk’, and ‘accountability’. The vision may have been to deliver the so-called

Hogle 559

‘Triple Aim’ goals – to improve population health and individuals’ care experience
while reducing per capita costs (Berwick et al., 2008) – but as I show, the way account-
ability practices are being enacted has other effects.

Data analysts are going further than collecting genomic or clinical data to create risk
classifications, using big data analytics to associate social variables with health, deriving
new categories such as ‘superutilizer’, ‘nonadherent’, ‘socially isolated’, or ‘aging-
focused household’. Here the STS literature on classifications is helpful (Bowker and
Star, 1999). Social classifications take on meanings that arise through interactions of
scientific, administrative and popular definitions, and change the way individuals experi-
ence themselves (Hacking, 2006). In the ACO case, patients may be unaware of how
they have been classified (or that they are being classified), but the sorting may nonethe-
less be consequential for their care (Pasquale, 2014; Solove, 2004). In a time of national
policy precarity, practices of measuring and sorting risk thus become a key focus for
study. In VBC, the locus of responsibility becomes more fluid and interactive among the
state, various kinds of public and private corporate entities, and the patients themselves.
As such, it problematizes simplistic understandings of neoliberalism. While explicitly a
market-based model with incentivized competition at its core, responsibility and account-
ability are more complex in the new models and need to be examined.

This article proceeds in two parts. First, I provide background on the US system,
reviewing current and emerging financial and legal infrastructures and including key
laws marrying healthcare payment and delivery with information technologies. These
prefigurations shape the form that accountability takes in the US in distinct ways. Second,
I analyze emerging practices to produce accountability using data-intensive sourcing. As
I show, the question of to whom ACOs are accountable and for what purposes is
debatable.

Methods

This article overviews policy accountability practices in process. I do not include activi-
ties or responses of patients, although this is an area ripe for STS analysis (cf. Lupton
and Michael, 2017). Rather, I focus on providers and payers, the focus of value-based
practice changes. My data come from document analysis of policies and recommenda-
tions from expert governmental and nongovernmental policy advisory bodies (includ-
ing the Institute of Medicine [IOM], Centers for Medicare & Medicaid Innovation
Center, among others), laws (the ACA, the Health Information Technology for Economic
and Clinical Health Act, and other relevant laws) to situate the emergence of ACOs
politically and historically. Third-party white papers plus promotional literature from
analytics companies and VBC consultancies, and reports from ACOs shed light on how
concepts of accountability are framed by various actors. I also interviewed representa-
tives of data analytics companies, and practicing professionals in health informatics,
public health and hospital administration, primarily at clinical medical informatics con-
ferences and workshops between 2015–2017. Population health management and
patient stratification tools (both commercially available and those designed in-house by
providers) were demonstrated at these meetings, using actual patient and provider data.
Patient identifiers were masked, but patient-specific and provider-specific scores and

560 Social Studies of Science 49(4)

comparisons were demonstrated. My observations illuminate the way accountability is
being interpreted and assumptions are being built in to concepts about financial and
health risk, and ultimately, into products that will be used to characterize individuals
and stratify populations.

Background: Conditions of possibility for value-based care

Value-based care concepts are being introduced into many existing healthcare systems,
but each is historically and politically distinct. There are different stakes for providers,
payers, and patients. I therefore begin by reviewing salient features of the US system.

Roots of the cost-quality-outcomes conundrum

Most significantly, the US consistently has among the highest costs of care in the world,
yet worse health indicators than many other countries: the US ranks 50th out of 55 coun-
tries based on cost per person, longevity and health indices (Du and Lu, 2016; Office of
Economic Co-Operation and Development (OECD), 2017). Reform efforts since the sec-
ond half of the 20th century attempted to remedy this conundrum, but have been thwarted
by the fact that without a single-payer system, millions of Americans do not have insur-
ance or are not covered by federal plans, and care is paid on a fee-for-service basis
through many providers operating under various kinds of contracts.

Under fee-for-service, payers (typically government or private insurers) pay, based on
multiple negotiated rates, for healthcare services when ordered by clinicians. The para-
dox of this volume-based model is that patients who are more sick and who are using
more services bring in more revenue for providers. As a result, there is overuse of some
services, especially those with higher revenue margins, while there is under-use by those
who cannot afford certain procedures or medications. Providers are left to pay for
expended services for uninsured or under-insured patients.

Providers have little incentive to contain costs, since their income comes from ser-
vices. Payers, on the other hand, are often more focused on containing cost than improv-
ing quality. Payers are usually unwilling to increase their financial burden and risk, and
quality initiatives may not pay off until far in the future. Private insurers experience sig-
nificant churn (up to a quarter of their customers change insurers each year), so the return
on investment may not be considered worth it. Policymakers saw this essential conflict as
a lack of accountability on both sides, and it became the basis of policies to change pay-
ment and clinical practices. Yet, previous organizational experiments to cap costs (such as
Health Maintenance Organizations [HMOs]) were highly unpopular, and efforts to stand-
ardize quality resulted in a proliferation of thousands of quality measures without substan-
tially improving health indices (IOM, 2006, 2016). Pay-for-performance (P4P) models
tried to incentivize change by paying physicians a bonus to meet or exceed performance
benchmarks, but benchmarks mostly dealt with practice efficiency rather than quality, and
had mixed results (Grossbart, 2006; Porter and Teisberg, 2006). In sum, efforts to improve
quality or reduce cost have been neither comprehensive nor effective.

Between 2008 and 2016, attempts to devise a federally subsidized system for universal
access to care, which might have helped standardized payment and quality requirements,

Hogle 561

met with intense resistance from insurance and medical product industries for which
existing, predictable payment models were deeply entrenched, and from those who
wanted to maintain private markets rather than move to a public, single payer. Ultimately
the Affordable Care Act (PL 111-148, more commonly known as the ACA) was a middle
(albeit potholed) road. Individuals not otherwise covered could gain access to insurance
with subsidies (for some). Until the ACA, private insurers could refuse to cover individu-
als they deemed to be too risky, such as those likely to require more care and incur more
costs because they had pre-existing or chronic conditions, had unhealthy lifestyles, or
had other characteristics associated with higher risks. Likewise, physicians could refuse
to accept certain patients, such as those who had insurance with low provider reimburse-
ment rates (in particular, Medicare). While the ACA guaranteed the possibility of access
to health insurance, there is no guarantee of access to care: individuals can still refuse to
buy insurance and physicians can still limit their patient panels. Payers can no longer
refuse patients due to pre-existing conditions. Plus, of those millions of people added to
insurers’ rolls, many previously had no insurance (so likely had not sought care). The
result was a new mixture of patients, many of whom were sicker and about whom there
was little medical history, hence a different and potentially riskier pool. As I show, payers
and providers are adapting their strategies accordingly, using value-based programs to
adjust formulas for this new landscape.

Most Americans buy health insurance through their employers (who partially subsi-
dize the payments and either contract with insurance companies to provide coverage or
take on the financial risk themselves with their own insurance plan). For older and poorer
citizens, the Department of Health and Human Services (DHHS) Center for Medicare
and Medicaid Services (CMS) contracts with private insurance companies to provide
care. Individuals older than 65 who have worked and paid payroll taxes are covered
under the Medicare program, while the poor and some disabled are covered under the
Medicaid program (these are adults and children, but all must be US citizens, and being
poor alone is insufficient for eligibility).2 While Medicaid was expanded by the ACA to
extend insurance to low-income individuals nationally, as of 2012, states are allowed to
opt out (Kaiser Family Foundation, 2018) – as of this writing, eighteen states have opted
out. As a result, policies and gaps in care vary among states. Finally, about 9% of
Americans are still uninsured (28.8 million) compared to 16% (48.6 million) in 2010
(Zamitti et al., 2017). Medicare patients are sicker, older, and the most costly. Payment
for their care constitutes about one-third of hospital revenues under the current fee-for-
service system, one-quarter of this being for in-patient hospital care. Medicare patients
alone accounted for about 15% of the entire federal budget in 2015, making this program
a prime target for cuts during debates over federal budget deficits. Unlike private insur-
ers, who can create risk pools with which to determine differential policy rates and ben-
efits for members (based on variables such as health status, age, or other factors),
Medicare must pay for ‘reasonable and necessary’ care for all who qualify. Of course, the
interpretation of these terms can vary in practice. The Center for Medicare & Medicaid
Services (CMS) sets reimbursement rates for services, drugs and devices, which also
influence the rates of private payers. Providers complain that their costs for Medicare
patients are often not fully reimbursed (about 12–48% of charges). This has led to a type
of gaming the system called ‘upcharging’ or ‘upcoding’ (classifying patients using more

562 Social Studies of Science 49(4)

lucrative diagnostic billing codes to enhance cost recovery), or outright fraud (claims for
services not rendered, altering medical records) (Dafny and Dranove, 2009). It is unsur-
prising, then, that Medicare is a ripe target for alternative payment plans. Roughly half
of ACOs operate in a Medicare Shared Savings Program (CMS- Aug 2017).

As for providers, just under a quarter of hospitals are for-profit, about 58% are private
but not-for-profit and the remainder are state or locally-owned. In contrast to other coun-
tries, this affects how ‘value’ is tied to revenues and practices. The institutions that per-
form worse on both quality and cost metrics typically care for greater numbers of
vulnerable patients, particularly elderly minority and Medicaid patients. These so-called
‘safety-net’ hospitals consistently look worse on quality and cost measures because of
the complexity of cases, high costs and low revenues (Jha et al., 2011).

The upshot of diverse providers and payers for Americans is that healthcare varies
widely across regions as they receive different levels of quality, cost, and comprehen-
siveness of care. Employers, payers (especially CMS) and providers (especially for-
profit) are intent on lowering their financial risk, particularly with the new mix of
patients, and anything that lowers costs provides competitive advantage. Risk-sharing
initiatives become attractive in this scenario.

It is within these historical and political-economic environments that American
healthcare is transitioning to value-based care, materialized in innovations such as the
ACO. The VBC concept evolved long before passage of the ACA, but serves the so-
called ‘Triple Aim’ goals that were a key feature of the law. Framing best care as best
value is a very different way of thinking about managing a population’s health than uni-
versal care or healthcare as a human right, but fits with the market-economy basis of
American healthcare. Focusing on value also decenters debates about rights to care that
continue to plague American politics. At the time of writing, efforts to dismantle the law
are ongoing; however, infrastructures already installed to execute value-based care will
affect clinical care and public health for years to come.

The road to value-based care is a digital trail

Beyond structures for providing and paying for care, there are relevant histories of laws,
policies, technologies and assumptions about quality paired with legislative require-
ments to embed information technologies bringing cost and quality together, rebranded
as ‘value’ (for a more thorough discussion, see Hogle, 2016b).

In the 1990s, quality care became a priority in the US not only due to poor national
indices, but also because of heightened concerns about medical error and institutional
liability. Yet measuring ‘quality’ is tricky because the term itself is ambiguous. The most
frequently cited definition of quality is: ‘the degree to which health services for individu-
als and populations increase the likelihood of desired health outcomes and are consistent
with current professional knowledge’ (IOM, 1990). This vague definition allowed con-
siderable leeway to institute a plethora of policies, incorporating different values regard-
ing risk under different political and social environments.3

By 2007, recommendations from the IOM (2007) to standardize quality measures
were accompanied by calls for the expanded use of electronic health records (eHR) and
for more data, with which to provide evidence of outcomes, to be collected in each

Hogle 563

clinical encounter.4 The US had been slower than other countries to take up eHR.
However, promoters of the rapidly-evolving healthcare information technology (HIT)
field argued that electronic data capture was crucial to facilitate the transfer of patient
data across clinics, compare treatments or physician practices for the purpose of evi-
dence-based medicine, and to conduct operations or outcomes research (IOM, 2011).

Subsequently, policymakers in President Bush’s administration flagged electronic
medical records as a critical national infrastructure need. By 2009, the Health Information
Technology for Economic and Clinical Health (HITECH) Act was passed, requiring pro-
viders to adopt HIT to achieve ‘meaningful use’ of health information. Providers must
prove that they are using certified eHR to communicate electronically with patients and
other providers in ways that can be quantitatively measured. The stated aim was to
enhance data exchange for purposes of care coordination, population health management
and consumer engagement. In 2018, ‘meaningful use’ was rebranded as ‘promoting
interoperability’, to underscore data sharing. New scoring and measurement policies
were proposed as a condition for participation in Medicare, with penalties for not sharing
data. This directly links to the 21st Century Cures Act of 2016 (dubbed the ‘Cures Act’),
which penalizes data blocking and mandates open application program interfaces (APIs,
the means through which software applications can interact).5 Together, provisions in the
HITECH and Cures Acts laid the infrastructural foundation for facilitating exchange of
data about patients among providers, payers, but also third parties, including data aggre-
gators and analytics companies.

It was also during President Bush’s tenure (2001–09) that healthcare policymakers
began arguing that care should be ‘value-based’: services should have worth relative to
outcomes, not based on cost-cutting alone or simply adding more quality measures. This
occurred in an era of political efforts to ‘downsize government’ and contain federal costs
for welfare and social programs. The trifecta of information technology and data analyt-
ics, a renewed emphasis on quantifiable quality measures, and cost containment pres-
sures in this political climate came together at the starting block in the race to healthcare
reform in 2010.

To ensure that value-based principles would be put into practice, the ACA built in
measures to hold providers accountable for improving patient health outcomes as a con-
dition of receiving payment. Additionally, the Medicare Access and Childrens’ Health
Insurance Program Reauthorization Act of 2015 (MACRA) specified alternative pay-
ment models for Medicare.6 The models offered providers financial incentives if they
could demonstrate improvements in their patients’ outcomes and in cost-efficiency.7
MACRA further stipulated that to get incentives providers must also abide by the mean-
ingful use provisions of the HITECH Act, which, as described above, expanded elec-
tronic data use and sharing. The CMS goal was to convert 50% of providers to some
form of alternate payment model by the end of 2018 – an aggressive timeline.

The reformulation of care-as-value was fully embraced by the time the report ‘Best
Care at Lower Cost’ appeared (IOM, 2013). Significantly, it advocated the creation of a
national, searchable database of information about individuals, real-time collection of
data during each clinical encounter, and greater use of genomics and social indicators. To
deal with such complex, large datasets, advocates heralded the nascent field of big data
analytics in many policy reports (Manyika et al., 2011). Notably, the report was authored

564 Social Studies of Science 49(4)

by the Roundtable for Evidence-Based Medicine (charged with best practices in knowl-
edge production), which then renamed itself the Roundtable on Value and Science-driven
Healthcare. Members include health policy experts plus representatives from CMS, the
Office of National Coordinator for Health IT, the pharmaceutical industry, and the head
of the largest US electronic medical record firm.

To make such sweeping transformations in a system with deeply entrenched payment
structures and revenue streams would take drastic measures to change the way care was
paid for. As Berwick et al. (2003) put it, ‘systematic changes will not come forth quickly
enough unless strong financial incentives are offered to get the attention of managers and
governing boards’ (p. 8). A ‘carrot and stick’ approach was built into alternative payment
systems to incentivize ‘high-value’ care, but, importantly, new models shift responsibil-
ity for costs and risks. Whereas payers (public and private) previously shouldered most
of the financial risk of patients becoming or staying ill, shifting the responsibility to
providers for both patients’ health risks and their own financial risk would make provid-
ers bear consequences for their actions, in the value-based way of thinking. In contrast to
earlier efforts to change payment and service delivery models, risk-sharing constitutes a
substantial rethinking of accounting systems, organizational relationships, and expertise,
entailing novel institutional forms and practices for analyzing and managing care and its
costs (Shortell, 2013).8

Accountable Care Organizations

The Accountable Care Organization (ACO) is one such form. An ACO is most simply
defined as a partnership of healthcare providers (physicians, hospitals, clinics and other
care providers) held jointly accountable to payers (federal government or private insurers)
for the quality, cost and health outcomes of a defined population (McClellan et al., 2010).
ACOs essentially expand the pay-for-performance concept to defined populations
(Grossbart, 2006; Kindig, 2006; Stoto, 2014). They are predominantly organized around
Medicare patients (due to their overall higher costs and risks), but there are also Medicaid
ACOs, and private (commercial) ACOs (typically consisting of large regional healthcare
organizations and large private insurers). ACOs can be physician-based or hospital-based,
but this poses a conundrum for the latter in the US. Hospitals measure overall success by
getting physician business and having patients in hospitals, not keeping them out.

Managing ‘defined populations’ is key. ‘Population’ today usually refers to a sub-
group based on specific characteristics for particular statistical purposes (Armstrong,
2017; Holmberg et al., 2013). For American ACOs, those characteristics are not bounded
by citizenship, geography, similarity of attributes or diseases; rather, population means
individuals who are in an ACO’s defined service area who are attributed to that popula-
tion by the commercial or federal payer. Attribution is done using several methods,
which can affect not only the way populations are constituted (e.g. for whom are provid-
ers accountable), but also how providers may manipulate data.9 About 30 million
Americans are now in ACOs – although many of them would be unaware, since payers
virtually attribute them to an ACO based on what services they have used and where they
have used them (from claims data). How populations are defined has everything to do
with understandings of risk distribution within a group. Furthermore, compensation for

Hogle 565

services in accountability care is based on the health outcomes of individuals in relation
to the defined populations for which the organization is responsible, not the national
population (Porter and Teisberg, 2006).

ACO population management includes coordinating care among the various clinical
entities patients encounter (using social workers or nurses as ‘practice extenders’), and
monitoring patients and promoting health behaviors through continual electronic com-
munications, including data from devices or apps per HITECH mandates. This is consist-
ent with Fisher’s vision of organizations engaging patients in activities that should keep
them healthier so they would not need to use clinical care in the first place. ACOs also
negotiate with pharmaceutical, device and service suppliers to set prices contingent on
clinical outcomes of treatments.10

However, ACO population management also means assessing current and future
health outcomes of patients within an ACO’s attributed population, identifying those
who are high utilizers of costly care, and making interventions based on individual pro-
files (Casalino et al., 2015; Kindig, 2015). This requires more than routine epidemiologi-
cal information, or records of individuals’ past clinical encounters from medical records
or insurance claims data. Rather, more data collected from more sources would be
needed, along with sophisticated analytics. The term population health management has
thus also come to imply the incorporation of data tools used to characterize individuals
as risk objects in relation to others in the defined population. Analytics consultants, for
example, use the following definitions:

any activity that improves the health or care of a single patient by viewing that patient as one
small piece of a broad group of his or her peers. This may mean using a risk score calculator
based on big data analytics to pinpoint one patient’s likelihood of developing heart disease, or
helping another patient manage her diabetes by drawing on engagement and adherence lessons
learned from previous cases. (HealthIT Analytics, 2015)

[population health management is] the information technology (IT) component of the clinical
and administrative aspects of care. This … requires IT resources and tools to collect data on
individual health status; stratify and target populations based on their risk and need for care;
and engage people in their health using patient health records or online portals. (DeVore and
Champion, 2011)

Population health management is directly tied to alternative payment models that require
a provider to statistically prove an improvement in a given population’s health. Several
models exist.11 Some continue to use fee-for-service and have modest incentives for
simply reporting quality and efficiency data (such as hospital readmission rates, total
cost of care, patient satisfaction ratings). More drastic models allow ACOs to take on
greater financial risk in exchange for potentially greater shared savings and bonuses, but
may have additional penalties if they fail to meet targets. Capitation models, similar to
HMOs, give a fixed rate to providers, who bear the consequences of costs being below
or above the negotiated rate. This is a high-stakes game for providers: essentially, hospi-
tals and physician groups who have had a reasonably predictable revenue stream are
betting that any population management measures they take may save them money in the
long run, but they are taking on greater financial uncertainty.

566 Social Studies of Science 49(4)

CMS payments are budget-neutral; there is a single pool of funds for the incentives,
so the penalties on poor performers are used to reward the best performers. An addi-
tional five-star rating system from CMS signifies which providers are the best perform-
ers; this also affects reimbursement rates. Additionally, payers may steer patients to
providers who perform better, by limiting which physicians and hospitals the insured
can use or tiering the amount a patient must self-pay. In fact, several presentations I
attended demonstrated products enabling comparisons of patient outcomes metrics by
physician. These features, along with the way ACOs decide to allocate any savings
among participating providers, set up a competitive environment among providers.
Porter and Teisberg (2006) argue that this would produce the best results, but it is cer-
tain to change relationships among physicians within and across ACOs and may affect
the aggressiveness with which they may pursue value-based objectives. In the uniquely
American assemblage, as described in the background section, value-based care dou-
bles as a strategic marketing tool.

Value-based reimbursements are tied to specific metrics set primarily by the CMS and
are based on recommendations from various sources, including Institute of Medicine
reports (noted above) and the federal Agency for Healthcare Research and Quality,
among others.12 They consist of a complex set of quality and cost efficiency measures
that are weighted to get composite scores. The scores determine how much savings and
incentive rewards an ACO will get, or how much they will be penalized, depending on
the model.

There are currently 23 required quality measures in 4 domains: patient and caregiver
experience, care coordination and patient safety, preventive health, and at-risk popula-
tions (CMS.gov, 2019).13 In 2018, there were 31, about half for clinical outcomes; that
is, assessment of intervention effectiveness (such as whether a diabetic patient’s A1c
level is maintained after being given medications), obtained from payment claims. For
2019, 10 of the 23 required measures relate to patient and caregiver experiences, which
are derived from satisfaction surveys. For example, ACO-45 assesses staff courtesy and
helpfulness as rated by patients. The four domains are now more equally weighted, sug-
gesting that qualitative evaluations are now co-equal to clinical measures in calculating
scores and hence, payments in shared services programs. This shift will likely encourage
changes in administrative procedures and patient experiences, but whether actual health
outcomes are improved is yet to be seen. Measures with particularly high cost impact,
such as adherence to treatment or medication protocols and readmission to the hospital
within 30 days after discharge, are targeted in additional policy documents.14 Other
measures assess structural issues (Were eHR and data infrastructures adopted?) and pro-
cess issues (Were patients immunized? Screened for alcohol abuse?). If scores on these
measures qualify, then the ACO is rewarded accordingly. However, many of the meas-
ures do not speak to actual health outcomes. Rather, they relate to whether procedures
were followed and the percent of times an action was taken. Notably, while some meas-
ures are mandatory, providers may choose from a menu of others to submit for review.
Furthermore, commercial ACOs can set their own metrics and thresholds for achieving
them. ACOs can also select the time period of the minimum 90-day reporting period,
which may affect the metrics. These features can lead to ‘gaming the system’ by control-
ling what gets measured and when.

Hogle 567

Such metrics are inscriptions of risk (Hilgartner, 1992; Power, 2016). That is, they
reflect what kinds of things are viewed as having potential for harm (to patients or
ACOs) and how they might be mitigated. The choice of which things rise to the level of
being important to measure (or not) thus participate in shaping knowledge about what
quality care is. Metrics are also performative organizational artifacts (Power, 2016).
That is, they channel the way organizations enact routines and work flows in a way that
suits CMS reporting requirements: collecting particular kinds of data and entering it
into eHR in a particular way, configuring databases to compare individuals against each
other within a population or to compare providers against each other to get performance
scores, or triggering reports in certain formats. Paradoxically, setting measures as a
target to be met through changes in work flows and protocols makes the measure less
effective, because altered interactions among actors and organizations changes the con-
text. In effect, the system as a whole is being assessed, rather than individual perfor-
mance (Strathern, 1997).15

The checklist of measures provides records to be compared over time and across institu-
tional entities: An audit trail that documents accountability according to value-based ration-
alities. Performance indicators, guidelines and outcomes measures can thus be thought of as
‘accountability devices’ (Jerak-Zuiderent and Bal, 2011; Wallenberg et al., 2016). They pro-
vide accountability to the governance system over ACOs, but the extent to which they pro-
vide accountability to patients or payers is unclear (Fisher and Shortell, 2010).

However, demonstrating value requires different strategies using different kinds of
data than simply documenting whether a procedure is followed or follows quality guide-
lines. A value calculus only works if the worth of outcomes can be measured. Definitions
– and metrics for – outcomes are thus crucial. Yet decisions about what should be meas-
ured and how it should be measured embed values and assumptions about what matters
for the governance of health. Measurements as accountability devices perform several
roles: They provide auditable quantifications for reporting requirements, but they also
visible manifestations of both which health conditions and behaviors are problems in
need of intervention and what data forms come to count as relevant evidence. If account-
able care is as much about shared risk as improving health, then sources of modifiable
risk have to be made visible before they can be made amenable to intervention. In the
emerging biopolitics of population health management, this involves algorithmic tools
and expertise designed to create new types of risk scores and categorizations. In the next
section, I show how intertwined concepts of risk and accountability are materialized with
specific informatic practices. The actants in this part of the assemblage bridge healthcare
with other commercial consumer domains.

Intensified data sourcing for population health
management

Large volumes of data are already being collected for analysis of populations, most eas-
ily from insurance claims and medical records (Halamka, 2014; IOM, 2013; Thompson
et al., 2016), but these have limitations. Diagnostic codes from claims are retrospec-
tive, socio-legally constructed categories for billing purposes, not accountability-work.
Medical records documents what gets ordered and recorded in the clinic (diagnostic

568 Social Studies of Science 49(4)

tests, pain scales, prescriptions, etc), but much of this is unstructured data (text, image,
rating scales). Digital health monitoring with phone apps or dedicated devices is being
used to add real-time data on health behaviors and status (Ruckenstein and Schüll, 2017),
but these are snapshots of specific activities.

If ACOs are accountable for risk and outcomes (that also affect organizational risk
and outcomes), then they need information about what happens beyond clinic walls that
affects their defined population. In particular, social determinants (food insecurity, social
isolation, financial stress) are increasingly linked to health outcomes (Braverman and
Gottlieb, 2014; IOM, 2015; Kindig, 2006). Social determinants, however, are variably
interpreted at the local level. A clinician said that her clinic uses a company to screen
patients for them as a ‘top priority for their agenda’, but explained that this includes
whether they pay their bills regularly, plus motivational factors: ‘How interested are they
in changing? We can get this from social media’(ACO conference attendee, 2017). One
ACO manager in a rural area with a large proportion of ethnic minorities explained that
patients in his ACO ‘are sick because of stupid lifestyles. It’s their food and culture’
(attendee at the same conference, 2017). Structural issues (systematic discrimination,
poverty, exposures to pollutants, working conditions) also clearly affect health, but are
largely out of reach for ACO interventions as they are currently designed for targeted
outcomes. Information is being gathered to act as a proxy for some of these social and
structural effects, and aggregated at both individual and defined population levels to cre-
ate profiles of health status and risk (Gottlieb et al., 2016).

At one extreme, Barrett et al. (2013) propose matching metabolic profiles to socio-
demographic profiles using data produced at all scales, from personal devices to environ-
mental sensing: ‘The social and economic environment can be quantified using spatially
explicit socioeconomic data, such as from the US Census, American Community Survey,
or publicly available crime data. And social connectedness can be assessed through
online social networks’ (p 171). Although similar grand schemes have been proposed by
precision medicine initiatives, the impracticality of collecting and analyzing such vast
troves of data belies the optimism and naiveté of such big data proponents.

Nevertheless, for value-based reporting, providers and payers are collecting data far
beyond what has typically been thought of as ‘medical’ data to serve as proxies for indi-
cators of health status and potential risk. Associating broader, nonclinical information
with health outcomes entails intensified collection of social, behavioral and lifestyle
characteristics (Hoeyer, 2016; Hogle, 2016a). Sources include publicly available data
(education records, property ownership, voter registration, criminal records, by geoloca-
tion for their defined service area). Public sector entities also profit from data sales: 33
states in the US currently sell hospital discharge data (Sweeney, 2015). Individuals’ digi-
tal traces (social media, consumer fitness trackers or apps) are also available from data
aggregators who sell access to individuals’ credit card purchase transactions, loyalty card
records, movie rentals, memberships in smoking cessation or wellness programs, and
more (Singer, 2014; Terhune, 2008).16 Searches can also glean information about social
networks (people and activities with which an individual connects, or absence thereof).

As I will show, such information – structured and collected for entirely different pur-
poses – is being repurposed in efforts to draw conclusions about patients and their health.
Intensified surveillance and big data analytics (natural language processing machine

Hogle 569

learning, and predictive analytics) are used to handle the large volumes of complex data,
and make it possible for information about individuals to be disaggregated and compared
across population-based datasets, to compare individuals against themselves, or to com-
pare providers against each other and to identify misalignments with the ACA quality
and outcomes measures (Bates et al., 2014; Ryan et al., 2016). Once the infrastructure
and arrangements are in place to collect and store data, consulting or insurance compa-
nies or ACO data analysts can then do endless variable querying, well beyond reporting
requirements. It is important to add that entities such as data aggregators and business
intelligence consultants collect patient data to sell for secondary purposes. IQVIA (a
marketing research firm) claims to have more than 120,000 global sources to acquire
patient data. Large pharmaceutical firms, for example, might pay millions of dollars per
year to acquire (de-identified) data to mine for drug reactions or effectiveness. Firms
supplying eHR platforms can also write into their contracts that they can use provider
data for secondary purposes (Tanner, 2017).

The combination of mandated data-reporting requirements and urgency to make radical
system changes have created lucrative markets for health IT products and consultants.
According to several presentations I attended, this burgeoning industry is worth about $6
billion in the US, with about $1.5 billion of this in predictive analytics. Significantly, for
cost accounting, these services will be embedded in cost of care, and so are not visible as
non-direct costs, offsetting some savings that might be earned. Few providers (especially
smaller group practices or hospitals) have the expertise or infrastructures to do the kind of
analytics necessary for ACO reporting requirements. This creates entrepreneurial opportu-
nities, and both small start-ups and large consumer consulting companies are entering the
field. As a line I frequently heard in conferences puts it: ‘Data fishing leads to good fish
stores.’ These new ‘fish stores’ do not sell the data sets they collect; rather, their analyses
are based on proprietary algorithms. The content and designs of algorithms are opaque to
the purchaser-client and to the individuals whose information is extracted.

Some firms offer branded population health management services with copyrighted
algorithms. Many target advertising to ACOs, offering to do both economic risk analyses of
an ACO’s population and to stratify populations for interventions (HealthCatalyst, Optum,
Premier, Philips Wellcentive, Forecast Health). Insurance companies own some of these
firms, so they already have access to claims and medical records data. Other firms aggregate
patient data from provider clients with publicly available or proprietary databases.

Notably, consumer industry companies (which already have millions of data points on
consumers) have entered the space. For example, Experian and FICO are credit rating
firms that have tens of thousands of data on credit card transactions, home or car loans,
income, investments, zip code (a poor but oft-used proxy for wealth) and more. Lexis-
Nexis and Google are search engine firms that track transactions (search terms, online
purchases, memberships, dating services) and aggregate them with publicly available
data (voting records, property ownership, welfare or food aid enrolment). Acxiom claims
to have an estimated 1,600 pieces of data on 98% of individuals in the US. Using big data
analytics, they claim to be able to peg individuals as ‘highly stressed’ (based on credit
rating scores, crime in their neighborhoods or other associated variables), ‘motivated’,
‘diabetes-aware’, ‘senior-oriented’ and other categories (Citron and Pasquale, 2014;
Hogle, 2016a; Pasquale, 2014; Singer, 2014).

570 Social Studies of Science 49(4)

Making accountability devices

Such categories constructed from aggregated digital traces are offered as ways to estimate
current and future health and cost risks. Profiles produced from associative analytics are
being used to characterize individuals’ health status; for example, to see if they are likely
to develop metabolic conditions or heart attacks (Steinberg et al., 2014). Researchers
claim to be able to predict risk of cardiovascular disease by associating health behaviors
with credit scores and factors based on questionable categories of cognitive ability, self-
control and education (Israel et al., 2014). Furthermore, predictive analytics are being
employed to project trajectories: for instance, not only if, but when individuals are likely
to become ill (Weiss et al., 2012). While these claims are yet to be borne out with clinical
evidence, they nonetheless produce hyperbole that stimulates hope that data can be
acquired with which to respond to outcome reporting requirements.

Behavioral, financial and lifestyle information derived from memberships, product
purchases, web surfing and more are being aggregated to create new risk categories
(Citron and Pasquale, 2014; Hogle, 2016a). Such data is also being used as a proxy for
other kinds of conditions, such as indicators that people are denying their diagnosis, not
seeking care or seeking too much care, or not complying with regimens (Halamka, 2014;
IOM, 2013; Murdoch and Detsky, 2013).

Increasingly, predictive analytics are used to stratify patients into groups according to
certain understandings of risk. A 2015 survey by analytics firm Jvion reported that 92%
of providers using predictive analytics use the tools to predict probability of hospital
readmissions for specific patients or to project patient deterioration. Readmission rates
are one of the major quality measures for CMS, so are a focus of attention.17 For exam-
ple, Kaggle competitions paid a $3 million prize for the best algorithm to predict who
would be in the hospital in the following year – using actual (de-identified) patient data.

Many new firms have business models based on predictive analytics for population
health management. Jvion promotes its predictive models thus: ‘The objective is simple
– stop the waste of resources and lives by predicting and stopping losses before they ever
happen’ (Jvion, 2015). Forecast Health illustrated applications on their website (no
longer available after acquisition by Lumeris), using photos of individuals with associ-
ated captions such as “What does her home tell us about her risk of post-op complica-
tions? What does his last vacation tell us about his hospital length of stay? What does his
marital status tell us about his risk of exceeding the bundled price target? More than you
think.” The company claimed to use 4,000 person-specific data points, including indi-
viduals’ car ownership or public transportation use, retail purchasing habits (clothes,
toiletries), ability to pay (student loan or other debt), lifestyle data (alcohol consumption,
exercise) to predict health and cost outcomes. Companies add such data to medical
records to predict first hospital admission, readmissions, and post-operative complica-
tions (among other risk factors). What is notable is that while much of analytics utilizes
de-identified data and might be used for small cohorts of similarly-grouped patients, for
many kinds of interventions, specific identity rather than de-identified data is necessary,
raising questions about privacy.

A high priority for quality measures is whether patients are likely to become non-
adherent to medications or treatments. Attacking patient adherence is also low-hanging

Hogle 571

fruit for cost-savings to the medical industry and providers because it becomes an easy
way to decrease system costs without having to decrease or renegotiate the cost of drugs
or other products and services. Adherence was by far the biggest topic of a number of
conferences I attended on value-based medicine and health IT. It is estimated that non-
adherent patients cost the system about $100–300 billion annually, due to re-admissions
and continued symptoms with subsequent use of services (Sulzicki et al., 2012; Volpp
et al., 2008). Companies such as IMS and Intelliscript have long tracked prescription
orders, obtained from pharmacies, physicians, and pharmacy benefits managers for mar-
keting research. These databases contain information about not only who has filled what
kind of prescriptions (and who has failed to fill or continue their prescription), but physi-
cians’ prescribing trends and comparative costs. ACOs (and drug companies that may
participate in ACO through purchasing contracts) are very interested in this data, espe-
cially for more expensive drugs. The quality measures to ensure electronic communica-
tions and patient engagement also get put to use here, encouraging the creation of ways
to monitor potentially non-adherent patients and then to intervene. Such efforts are also
being monetized: For example, the University of Pennsylvania hospital uses tracking
devices both to remind patients of their need to take their blood thinning medications and
to track when it was not taken. Those who did were entered into a lottery which gave
them a chance to win up to $100, and those who did not were told how much they would
have won if they had complied (O’Kane et al., 2012).

According to the CMS, 5% of Medicare and Medicaid patients account for 50% of
the costs of health care (CMS.gov, 2018). Much of this is due to complex cases with co-
morbidities, but a considerable portion is due to so-called ‘super-utilizers’ (those who dis-
proportionately use health services). Public health experts suggest that much of this is due to
behavioral and lifestyle factors in which an intervention could be made – and if so, this is a
prime target to demonstrate ‘improved outcome’ metrics (Braverman and Gottlieb, 2014).

However, while targeting high utilizers seems self-evident, analysts now advocate
more fine-grained targeting for value accounting. Many super-utilizers, such as some
older or chronically ill patients, are unlikely ever to get better, but others may change
their risk status over time. For the purpose of demonstrating quality outcome improve-
ment, it is strategically advantageous to make interventions with patients who are more
likely to improve, to demonstrate better outcomes in their metrics. For this reason, ana-
lysts are using predictive analytics to examine risk dynamics; that is, conditions in which
individuals have rising or declining risk. ACOs can then employ interventions that are
‘worth it’. For example, Forecast Health creates ‘impactable risk’ scores to indicate
patients for whom an intervention is likely to demonstrate an improved outcome.
Strategic interventions on those who will likely demonstrate improved outcomes will
produce more reportable value than interventions on those who will not.

Accountability devices and social sorting

Such scores and measures are opaque, arbitrary and discriminatory (Citron and Pasquale,
2014; Pasquale, 2014). Significantly, the examples I have given may provide precise
information (e.g. more data points with which to profile an individual), but may not be
accurate. Information sources used are partial at best, and can be misleading or spurious.

572 Social Studies of Science 49(4)

Contexts in which individuals’ transactions are digitally traced can shape behaviors in
ways that algorithms may not discern. As individuals upload new information, buy
things, do online searches, or fail to pay bills on time, they enact social categories that
may or may not be reflected in the algorithm (Cheney-Lippold, 2011). In the example of
adherence, decisions not to adhere might include ability to pay, access to treatment cent-
ers and transportation, other socio-cultural issues, or side effects. These are highly rele-
vant contexts that are missing from such analyses.

Categories based on associative data are problematic (Hoffman and Podgurski, 2013).
One firm claims that mail-order and online shoppers were more likely to use emergency
services. Another claims that buying a smaller home flags financial insecurity – and
hence, likelihood of becoming non-adherent (Hogle, 2016a). Were the algorithm design-
ers presuming sedentary lifestyles of online shoppers? Might a downsizer simply be an
empty-nester? Could there be other spurious relationships among such data? Categories
such as creditworthiness or proneness to adherence cannot directly be measured; rather,
proxy information imputes characteristics with which to categorize and stratify popula-
tions. Nevertheless, the categories are artifacts of the way a problem has been defined
and the way healthcare constructs systems of accountability, risk and value. Regardless,
it is unlikely that most people know which of their behaviors or transactions affect their
scores. While some behaviors can be gamed by altering such things as gym membership,
reported dietary consumption, or time of day of glucose monitoring, other factors used to
design scoring algorithms are obscure. When it comes to credit and property or life insur-
ance scoring, it is virtually impossible for individuals to make changes to their scores.
There is also disparate impact on low-income people who already suffer discrimination
(Barocas and Selbst, 2016; Madden et al., 2017).

STS literature demonstrates how social classifications have taken on meanings that
arise through interactions of scientific, administrative and popular definitions, and
change the way individuals experience themselves (Bowker and Star, 1999). In this
case, classifications in the service of accountability create a new form of automated
social sorting (Lyon, 2003). The stigma and potentially unjustified classifications can
have profound ramifications (Pasquale, 2014; Solove, 2004). Problematic categories as
produced by associative analytics may be reified as proxies to explain social and bio-
logical phenomena and become part of the permanent record: a ‘health deny-er’ or a
future ‘non-adherent’ person will likely carry that label for payers and providers for
some time.

Outcomes of outcome-based systems and implications for
accountability

The report card on ACOs is mixed (Grossbart, 2006; McWilliams et al., 2016). Some
reports tout increases in numbers of participants covered and cost savings for some
Medicare models (Muhlestein and McClellan, 2016). Others lost money, and most were
unable to increase both quality scores and cost containment (Kocot and White, 2016;
Muhlestein and Hall, 2014). Unsurprisingly, providers complain that quality measures
are not based on solid methodology and are too difficult to achieve. In fact, some ACOs
with significant cost savings nonetheless failed to meet CMS targets, so dropped out of

Hogle 573

the program (Blackstone and Fuhr, 2016). In a survey of 411 clinic and hospital manag-
ers, only 21% had achieved objectives (Numerof & Associates, 2018). The main problem
cited was difficulty with outcome measures: While 78 percent could track standardized
clinical outcomes (A1C or blood pressure), only 43 percent were able to track required
‘quality’ outcomes.

More than three-quarters of surveyed physicians felt the new models would not
improve care and half felt that using metrics to assess provider performance has a nega-
tive impact on care. Ninety percent felt they could not spend the extra time or investment
to implement new IT systems (Humana News, 2015; Kaiser Family Foundation, 2015).
Many providers hesitate to enter risk-based agreements when their revenues depend on
what patients do outside of the purview of the clinic.

Organizationally, the upfront expense and disruption of such major innovations can
be difficult to justify, especially for smaller organizations, and benefits may not appear
for years. New personnel are required to coordinate care across organizations and inter-
vene directly with high-risk patients, and new forms of expertise and information tech-
nology infrastructures are necessary for managing and sharing sensitive data on the scale
described. Data-sharing across facilities is expensive to implement and maintain, even
with cloud-based data warehouses, and in the competitive American healthcare market,
providers and vendors balk at sharing, even with the new CMS emphasis on interopera-
bility. Administrative burdens to meet recent federal requirements designed to establish
value-based systems are being blamed for declines in smaller independent practices and
consolidations with hospital systems.

Public health advocates and social scientists have long argued that impactful issues
such as violence, transportation and food insecurity should be included in health status
assessments, but clinical institutions have little expertise and capacity for interventions
(Casalino et al., 2015; Tannenbaum, 2017). Some health issues are beyond the control
of providers and may be beyond the control of patients as well. Not everyone has the
option of moving away from neighborhoods in toxic or unsafe environments to avoid
being triggered by asthma or being stressed. Some patients may not have enough money
near the end of a pay period to refill a prescription or buy healthier food, even if
prompted to change behaviors to make outcomes measures look better. Dealing with
problems of food insecurity issues and affordable transportation requires a government
willing to make major investments in infrastructures and a long-term commitment
beyond clinical care. It is unlikely that the political will to sustain such services will
exist in the near future.

If some risks are addressed, others are not. Data (whether collected by payers,
third parties, or well-meaning care providers) may be used for or against vulnerable
groups. Medicare and Medicaid patients (the primary targets of ACO population
management and most likely to be high utilizers of care) are targets for major fund-
ing cuts under the current US political administration. Proposals to repeal the ACA
not only decrease support for these patients, but make coverage conditional on
behavioral attributes and predicted risk. Specifically, one proposal allows insurers to
use predictive analytics to identify potentially high-risk, high-cost patients and cede
them to a high-risk pool, for which the federal government will pay a capped amount
(Hall and Bagley, 2017).

574 Social Studies of Science 49(4)

At the time of this writing, cuts to cancer screening and other preventive care, social
security, disability and child health programs, and more than $800 billion to Medicaid
are proposed. Federal funding to help with social determinants (poverty, violence, hous-
ing and food insecurity, environmental toxins) is also being cut in the current administra-
tion. Additional severe cuts to environmental and consumer protection agencies undercut
any downstream efforts to alleviate damaging health effects from social and environ-
mental harms. Together, these represent the defunding of support to some of the most
vulnerable in the US. Paradoxically, market-based healthcare qua value-based care is
being asked to shoulder large-scale social justice issues the federal government demurs
to take on.

Conclusions

In this article, I have shown how the assemblage of actants organized around value-based
care enact accountability and value through practices of population health management
using data-intensive sourcing, and I illuminated policy and political trajectories under-
pinning these new configurations. The socio-technical arrangements involved emerge
within historical and political particularities of US market-based healthcare and the
embedding of data-driven solutions to social problems such as public health.

Accountability devices such as outcomes measures and predictive analytics create par-
ticular kinds of groupings of individuals into populations, with the goal of managing
population health and producing value in ways formulated by VBC. The unconventional
processes of data collection, interpretation and category-making may change dynamically
in the complex interactions of ACO assemblages, yet they have significant implications
for patients and their care. Outcomes measures developed for official reporting require-
ments embed notions of what kinds of things come to count as risk objects, inscribe rules
about how risk should be managed, and embody interpretations about what accountability
means at a particular historical and political-economic moment. Patients are not passive
in this picture: They are still held to account for their behaviors and health status. Value-
creating activities rely on both producing new forms of knowledge through data-intensive
sourcing and getting patients to act on ACOs’ filtered interpretations of those knowledge
products in order to meet institutional benchmarks.

The practices of producing profiles for outcome measures may generate value accord-
ing to defined outcomes per dollars spent, but they also generate a trove of data about
individuals, which can be monetized and variably used for alternative purposes
(Ruckenstein and Schüll, 2017). The process of implementing accountability devices
thus reshapes the values they were supposed to set forth (Dussauge et al., 2015). The aim
may have been to generate patient-centered improved outcomes and responsible cost-
efficiency by determining what interventions on which individuals are prudent, but prac-
tices to instantiate the alternative payment system and document processes arguably
serves the system as much as patients’ wellbeing.

In the ACO example, participants are pushed to change their practices rapidly to adapt
to value-based care – but to do so under conditions shaped by interactions with other
parts of the assemblage: laws and regulations changing not only healthcare payment
systems but also mandating particular kinds of information technologies and circuits,

Hogle 575

expanded use of data analytics from consumer and finance industries, changing notions
of acceptable evidence (risk modeling based on associative and machine-learning analyt-
ics rather than causal data and conventional epidemiological logics). While some of
these conditions may exist elsewhere, the US assemblage also includes market competi-
tion for patients, entrepreneurial and proprietary control over databases, historical failed
efforts to secure comprehensive healthcare reform and more.

The assemblage is already modulating. Since the first version of this article, CMS
announced an overhaul of both MACRA and HITECH ‘meaningful use’ provisions,
ostensibly to reduce regulatory burden. On closer inspection, this announcement by
CMS head Seema Varma at the 2018 Health Information Management Systems Society
conference is more of a reorientation from Federal reporting requirements to require-
ments to share data more broadly. As Jared Kushner (son-in-law and advisor to President
Trump) put it: ‘The time is now to align every facet of the federal government and the
private sector to ensure information is communicated and shared seamlessly.’ Varma
plans to leverage CMS payment regulations and contracts with insurers to crack down on
data blocking, with a primary focus on providing interfaces to allow the private sector to
derive value from insurance claims data.

STS scholars have expertly analyzed the epistemology of emerging HIT and data
analytics in specific scientific domains, and specific algorithmic means of digitizing per-
sons and populations and the resulting effects. However, I encourage future STS scholar-
ship to examine how emerging data infrastructures increasingly integrate domains. As I
have shown, population health management spans clinical care, public health/epidemiol-
ogy, biomedical research and behavioral economics. Following the data – as well as the
money – shows how information flows beyond institutional or knowledge domain
boundaries into everyday life. At the same time, modes of rationality, the social relations
involved, and systems for governing and financing phenomena can be exposed by fol-
lowing data flows.

To this end, I used the idea of assemblage to point out some of the complex and
dynamic interactions to bring an STS perspective to the analysis of public health.
Assemblage makes the co-evolution of data and organizational infrastructures with
value-based medicine more visible, and opens the way to follow such phenomena as they
travel beyond activities related to ACOs themselves. In particular, more than merely
creating an audit trail for accountability to public and private payers, the very acts of
measuring and documenting outcomes serve to establish a broader networked platform
with which to collect data about individuals far beyond any specific aims of population
health management. Finally, if ACOs are accountable for creating value, then one has to
ask whose sense of value counts, and to whom are ACOs really accountable?

Acknowledgements

I gratefully acknowledge the healthcare professionals, company representatives and other
attendees at several value-based medicine conferences who shared their experiences, as well as
helpful comments from anonymous reviewers. An early version of this article was presented at
the 2016 4S/EASST conference in Barcelona Spain. Comments from attendees, and in particu-
lar, panel organizers Klaus Hoeyer and Martyn Pickersgill, were valuable contributions for the
final article.

576 Social Studies of Science 49(4)

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.

Notes

1. For some international comparisons, see National Health Service (NHS) England (2018) for
the UK, Bonde et al. (2018) for Denmark, and McClellan et al. (2017) more broadly.

2. Medicare covers about 55 million and Medicaid currently serves 70 million people, including
32 million low-income children, 7 million elderly, 20 million non-elderly low-income, and 10
million with disabilities. At the time of this writing, expanded Medicaid coverage provided by
the ACA is likely to be reduced so Medicaid recipients can ‘have skin in the game’ as Seema
Varma (CMS chief) puts it.

3. After complaints about complexity and burden, these policies were pared down (IOM, 2006,
2007, 2015, 2016; Krumholz et al., 2008). The politics of quality measures and how they
come to gain authority are an important area of empirical inquiry (see, e.g. Jerak-Zuiderent
and Bal, 2011).

4. Electronic medical records (eMR) include medical history (hospital and clinic diagnostic
data, physician notes, etc.). Electronic health records (eHR) includes more information about
general health (such as immunizations and other preventive care, wellbeing, and so on). For
the purpose of this article, I use eHR.

5. HITECH was a provision of the American Reinvestment and Recovery Act (2009), an eco-
nomic stimulus bill to invigorate identified critical national infrastructures. The 21st Century
Cures Act (PL114-255) is better known for opening access to experimental treatments, but
contains provisions that fundamentally alter data use and exchange.

6. Section 3022 of the Affordable Care Act amended the 1935 Social Security Act, adding S1899
(‘Shared Savings Program’), which became the statutory basis of value-based cost-sharing
and ACOs. Providers of Medicare-covered services and supplies (e.g. physicians, hospitals
and others) are encouraged (not mandated) to create ACOs. The text of MACRA (H.R. 2,
Pub.L.114-10) is found at: https://www.congress.gov/114/plaws/publ10/PLAW-114publ10.
pdf (accessed 20 June 2017).

7. These incentive models include the Merit-Based Incentive Payment System (MIPS) and
Advanced Alternative Payment Models (AAPMs). Previous programs requiring various qual-
ity and data use measures were combined into MIPS. Eligible providers are rewarded or
penalized based on quality, resource use, clinical practice improvement and ‘meaningful use’
of eHR.

8. Nevertheless, ACO skeptics often repeat the joke that ‘ACO’s are just HMO’s in drag’
(Kelly Evers, Urban Institute Senior Fellow, in an interview with one such HMO, Kaiser
Healthcare).

9. Attribution can be done prospectively, based on a patient’s use of services over the past year.
In this case, providers are notified which patients they are responsible for the next year.
Providers can then make interventions to identified patients in advance; however, they can
also choose to avoid certain patients to influence overall ACO population outcomes. When
attributed retrospectively, patients are identified who have actually received services over
the past year. Providers can’t know in advance who they are, but patients who have left the
practice are removed, so there is less risk of providers being held responsible for outcomes of
patients they no longer care for. For Medicare ACOs, the CMS now uses a hybrid method that
reconciles patient lists (Lewis et al., 2013). This adds a level of complexity that is beyond the
reach of this article, but nonetheless contributes to possibilities to ‘game’ the system.

https://www.congress.gov/114/plaws/publ10/PLAW-114publ10

https://www.congress.gov/114/plaws/publ10/PLAW-114publ10

Hogle 577

10. Value-based contracting negotiates prices based on efficacy rather than product cost or attrib-
utes. For example, insurance-based ACOs pursue value-based contracting especially for
expensive drugs (cancer or chronic diseases) since they can negotiate lower prices or refuse
drugs failing to demonstrate efficacy and improved outcomes (according to healthcare con-
sulting firm Avalere, which surveyed 42 health plans in 2015). Device company Medtronic
recently penned an agreement with insurer Aetna to base payment for insulin pumps on the
A1c levels of diabetic patients using the pumps (Berkrot, 2017).

11. Models are described in the Alternative Payment Model Framework and Progress Tracking
(APMFPT) Work Group (2016) report.

12. The prioritization of metrics has been put in accounting terms befitting care-as-value perspec-
tives: ‘Net health benefit refers to gains in [population] health from an intervention compared
to an alternative intervention, after subtracting improvements in health that may be forgone
because of the costs of the intervention’ (Meltzer and Chung, 2014: 133).

13. Measures are compared to a benchmark of per capita expenditures for the 3 years prior to
forming the ACO. Commercial ACOs may establish their own metrics for quality. In 2018,
ACOs had to demonstrate minimum 2% reduction over the benchmark to qualify for shared
savings.

14. Section 3025 of the ACA added the Hospital Readmissions Reduction Program S1886(q) to
the Social Security Act. Good adherence scores earn ACOs more points in the CMS star rating
system.

15. I am indebted to Sergio Sismondo for this point.
16. The Family Educational Rights and Protection Act (FERPA) (20 USC § 1232g; 34 CFR 99)

has no provisions for protecting personal information in education records from third party
health researchers. In fact, school and college records are often mined for population health
purposes. Loyalty programs include Walgreen’s drug stores, which offers rewards for custom-
ers to use wellness trackers that can be automatically linked to customer purchases (prescrip-
tions, over-the-counter drugs and other).

17. Section 3025 of the ACA added the Hospital Readmissions Reduction Program S1886(q) to
the Social Security Act.

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G E N E R AT IO N S – Journal of the American Society on Aging

68 | Spring 2019

A Profile in Population Health
Management: The Sandra Eskenazi
Center for Brain Care Innovation
By Malaz Boustani, Lindsey
Yourman, Richard J. Holden,
Peter S. Pang, and Craig A. Solid

This care model emphasizes social, behavioral,
and environmental determinants of health when
treating dementia.

abstract This article describes how key aspects of the Sandra Eskenazi Center for Brain Care
Innovation’s (SECBCI) care model can inform other entities on the development of new models of
population health management, through a framework that emphasizes social, behavioral, and envi-
ronmental determinants of health, as well as biomedical aspects. The SECBCI is a collaboration with
Eskenazi Health and community-based organizations such as the Central Indiana Council on Aging
Area Agency on Aging and the Greater Indianapolis Chapter of the Alzheimer’s Association in Central
Indiana. | key words: Sandra Eskenazi Center for Brain Care Innovation, Alzheimer’s, dementia, social
determinants of health

Alzheimer’s disease and related dementias (ADRD) impose significant challenges upon
older adults and their caregivers (Friedman et al.
2015; Alzheimer’s Association, 2017), who often
provide unpaid care. Most physicians providing
treatment know that effective care for ADRD and
supporting unpaid caregivers requires a more
sophisticated framework than is offered by the
traditional primary care model. Such a frame-
work values biomedical aspects of health, but
places as much emphasis on social, behavioral,
and environmental determinants of health, recog-
nizing them as major players in the health of indi-
viduals and the population as a whole (Taylor
et al., 2016).

Social, behavioral, and environmental deter-
minants influence health directly and indirectly,
manifesting as individual behaviors and habits,
but also as disparities in access to care (Galea et
al., 2011). Through targeted efforts, beginning

in 2007, to improve ADRD care for underserved
populations in central Indiana, we established
the Sandra Eskenazi Center for Brain Care Inno-
vation (SECBCI)—which is affiliated with Indiana
University in Indianapolis—in collaboration with
Eskenazi Health and community-based organi-
zations such as the Central Indiana Council on
Aging Area Agency on Aging and the Greater
Indianapolis Chapter of the Alzheimer’s Associa-
tion. This article describes how key aspects of our
care model can inform the development of new
models of population health management.

Creating a Successful Population Health
Management Model
The Eskenazi Health System is a safety-net
healthcare system serving a diverse, low-income
population in Marion County, Indianapolis. In
2007, SECBCI used strategies that would ulti-
mately become the Agile Implementation model

A Primer on Managed Care: Multiple Chronic Conditions

supplement 3 | 69

(Boustani, Alder, and Solid, 2018) to identify and
implement evidence-based solutions for manag-
ing ADRD. The model’s minimum specifications
were patient and unpaid caregiver education and
support, regular biopsychosocial needs assess-
ment, prevention and treatment of comorbid
conditions, medication management, and care
coordination among clinical providers and com-
munity resources.

During SECBCI’s decade-plus existence, we
have witnessed first-hand how these specifi-
cations allow for more personalized and more
effective individual and whole population care.
A key factor in the SECBCI’s success is that our
care for ADRD extends beyond that which is
given in the primary care setting, acknowledg-

ing and addressing the influence of social deter-
minants in the health and wellness of those with
ADRD and their unpaid caregivers. In short, the
model has improved care for people with ADRD
because of its wider view of care for a defined
population.

To expand these lessons to other populations,
Eskenazi Health leadership recently convened
an interdisciplinary team to discuss elements of a
successful population health management model
with the following four priorities: an accountable
health community; an interdisciplinary, diverse,
and scalable workforce; evidence-based care pro-
tocols; and a data warehouse with a comprehen-
sive performance feedback loop at the individual
and the population levels.

Definitions of these elements and how they
work together are as follows:

The accountable health community is a
fully integrated (i.e., owned by the same entity
or connected through a joint venture) system of
community-based and healthcare delivery orga-
nizations in a defined community that informs

the size and scope of subsequent elements
needed to fully support its members.

The interdisciplinary, diverse, and scal-
able workforce is a team-based approach
involving providers and community partners
outside the healthcare system. In addition to pri-
mary and specialty care clinicians, other criti-
cal team members include counselors and health
coaches, care coordinators, community health
workers and resource navigators, administra-
tors, business developers, and researchers. The
diverse skill sets and collaboration with commu-
nity partners emphasize the importance of social
determinants of health. It is a more affordable,
scalable, and sustainable approach than clini-
cian-only models. These partnerships between
health systems and community services reduce
costs by reducing duplicative or unnecessary
care, or connecting people with appropriate
community services, which may reduce the need
for subsequent interventions or hospitalizations,
without sacrificing quality.

Evidence-based care protocols ensure the
highest quality of care and incorporate multiple
determinants of health, including those related
to cognitive, physical, medical, genetics, and
behavior, as well as non-clinical aspects related
to communication and documentation, and
social circumstances.

The data warehouse with a comprehen-
sive performance feedback loop requires sev-
eral characteristics. The first is a reliable and
valid sensor, i.e., a means for collecting, monitor-
ing, and alerting about modifiable (e.g., substance
abuse, weight, employment) and non-modifiable
(e.g., age, sex, race) biopsychosocial informa-
tion about each population member. The sensor
is a set of algorithms that automatically iden-
tifies when certain events occur (e.g., a health
encounter) or when there are certain combi-
nations of data elements indicating that a per-
son may require additional attention or may be
at increased risk for other conditions or adverse
events. For example, if a person living alone
is diagnosed with cognitive impairment and

The model has improved ADRD patient
care because of its wider view of care
for a defined population.

G E N E R AT IO N S – Journal of the American Society on Aging

70 | Spring 2019

receives a prescription for medication, the sen-
sor would note that the person may be less likely
to adhere to their medication schedule. Then
provider(s) can be informed of this in real time.

The sensor may encompass multiple data col-
lection methods, such as specific fields in the
electronic health record and-or specific informa-
tion from administrative and claims databases.
It is important that the sensor can collect data on
social determinants of health, as well as infor-
mation related to a person’s physical and cogni-
tive functioning. Additionally, the sensor should
collect healthcare use and cost data as a way to
track care and provide feedback regarding the
model’s effectiveness.

As mentioned, in addition to collecting these
data, the sensor would identify when certain
combinations of values indicate that a popula-
tion member has experienced a significant event
or has an increased risk for an adverse outcome.
Although the data need to be accessible to pro-
viders and those coordinating care, it is crucial
that the data also are secure and confidential.

Finally, the data require a specialty unit
of qualified individuals to oversee the entire
accountable healthcare system and provide
a centralized mechanism to coordinate care,
which we refer to as the Mission Care Coor-
dination Center, or MC3. This specialty unit
of individuals involved in running the MC3
includes an interdisciplinary team involving,
at a minimum, a nurse, a social worker, an ana-
lyst, and a healthcare administrator to carry
out necessary tasks. The MC3 dynamically cat-
egorizes and triages the biopsychosocial needs
of the population and optimally dispatches the
diverse workforce accordingly, while provid-
ing timely feedback to that workforce at both
the individual case management and population
levels. The MC3 is supported by patient-, clini-
cian-, and dual-facing technologies that collect
and visualize information and support better
decision-making.

The MC3 model reflects recommendations
made by the American College of Physicians to

routinely screen for and respond to social deter-
minants of health, and account for complexity
and variation in how social determinants link to
outcomes in different conditions (Daniel, Born-
stein, and Kane, 2018).

The advanced track of the Accountable
Health Communities model includes a “back-
bone” organization to “facilitate data collec-

tion and sharing among all partners to enhance
service capacity” (Alley et al., 2016). As speci-
fied in the Accountable Health Communities
model, the organization would operate indepen-
dently from the accountable health community
and may not have the ability to determine where
the resources are needed the most, or have the
authority to get them to the right people, at the
right time.

The MC3, in contrast, is an integrated, cen-
tralized unit. We believe such a centralized
method of care coordination is not only more
efficient, but also leads to greater equity within
populations, as well as more support for the
healthcare providers who care for the most
socially complex individuals.

How the Model Functions
To provide an example of how these four pro-
posed elements of a population health model
function in practice, consider the fictional case
of Mr. Smith, a 72-year-old man who lives with
his wife. Mr. Smith presents to the emergency
department with a chronic obstructive pulmo-
nary disease (COPD) exacerbation after running
out of his scheduled inhalers. He is known to the
SECBCI and the larger accountable health com-
munity through previous encounters. In addition
to cognitive impairment, his past medical his-
tory includes Type 2 diabetes, with retinopathy
and major depressive disorder.

The team-based approach involves
providers and community partners
outside the healthcare system.

A Primer on Managed Care: Multiple Chronic Conditions

supplement 3 | 71

The four elements of the system work in con-
cert to provide Mr. Smith the best possible care,
as follows:

Upon Mr. Smith’s arrival at the emergency
department, the electronic health record system
(the sensor) alerts the MC3, which notifies an
interdisciplinary healthcare team (diverse work-
force), including his primary care geriatrician,
pharmacist, nurse, and social worker.

The emergency department physician sta-
bilizes Mr. Smith with prednisone and inhalers
(evidence-based care), the social worker identi-
fies that Mr. Smith is no longer driving due to his
cognitive impairment and notes that his wife is
in the hospital for pneumonia (social determi-
nants of care collected by the sensor and stored
in the data warehouse).

The pharmacist arranges for Mr. Smith to
have automated mail refills of inhalers, ensures
proper inhaler technique, and adjusts his dia-

betes medication while on prednisone. Addi-
tionally, the pharmacist is informed of Mr.
Smith’s cognitive impairment and understands
the challenges this poses for medication adher-
ence. Thus, the pharmacist checks with a social
worker about the current plan to ensure Mr.
Smith has the necessary help with his medi-
cations, and provides additional instructions
regarding the prescription changes.

The social worker also coordinates Mr.
Smith’s transportation for a follow-up appoint-
ment with his geriatrician, evaluates and ad –
dresses any safety concerns regarding his safe –
ty at home alone, and arranges for Meals on
Wheels to ensure he has access to food while
his wife is absent.

As part of the population health registry for
people with COPD, diabetes, and a recent emer-
gency department visit, Mr. Smith is sched-

uled to receive a follow-up call by a nurse. The
nurse checks on his breathing, daily blood sug-
ars, and nutrition, and knows he is being sup-
plied with Meals on Wheels and that no meal
adjustments need to be made for his diabetes.
However, through the SECBCI-provided care
management, he already receives regular follow-
ups in person and over the phone that the MC3
schedules and tracks. Instead of separate, unre-

lated follow-ups for individual conditions, the
information from the emergency department
visit is relayed to the nurse following up from
the SECBCI, and inquiries regarding all condi-
tions are made during a single follow-up call in
the next week. Further, additional follow up is
scheduled to evaluate his wife’s condition upon
her discharge to determine whether her ability
to care for her husband has diminished, and if so
what additional services are required.

The MC3 tracks the percentage of patients
with one or more emergency department visits
in the past ninety days, and therefore the emer-
gency department visit represents a significant
event in his care. Through review of Mr. Smith’s
ongoing care use and costs, the MC3 analyst
team is able to assess his care’s effectiveness,
and strategize with the nurse and social worker
regarding any additional care needed.

The MC3 team can review whether or not
Mr. Smith fills his prescriptions, if he routinely
misses appointments, or if he has repeated emer-
gency department visits—patterns of care use
that warrant consideration of further cogni-
tive decline, relapse of depression, or inadequate
social support. If any of these were present, the
MC3 nurse would contact the geriatrician to
ensure the issues have been identified and there

We believe such a centralized method
of care coordination leads to greater
equity within populations.

The MC3 tracks the percentage of
patients with one or more emergency
department visits in the past
ninety days.

G E N E R AT IO N S – Journal of the American Society on Aging

72 | Spring 2019

is a plan to address them. If necessary, the geria-
trician can draw upon the interdisciplinary team
for assistance and specialized care. In this con-
tinuous cycle, all elements remain dynamic and
adjust appropriately to changes in Mr. Smith’s
social and medical determinants of health, the
population’s needs as a whole, the available work-
force, and evidence-based healthcare protocols.

Conclusion
Whether caring for people suffering from
chronic conditions such as ADRD or designing a
larger population health management model, we
can effectively and efficiently incorporate infor-
mation on social determinants of health into
better care for all patients in the system. Under-
standing how the key components function in
concert with one another can allow administra-

tors and providers to fully appreciate their roles
and the roles of others within the continuum of
care, with the goal of improving overall popula-
tion health.

Malaz Boustani, M.D., M.P.H., is Richard M. Fairbanks
Professor of Aging Research at Indiana University
School of Medicine, in Indianapolis. Lindsey Yourman,
M.D., is an assistant professor of Medicine in the
division of Geriatrics and Gerontology at UC San
Diego Health, in California. Richard J. Holden, Ph.D., is
an associate professor of Medicine in the Division of
General Internal Medicine and Geriatrics at Indiana
University School of Medicine. Peter S. Pang, M.D., is
an associate professor of Emergency Medicine at
Indiana University School of Medicine. Craig A. Solid,
Ph.D., is owner and principal of Solid Research Group,
LLC, in St. Paul, Minnesota.

References
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able Health Communities—
Addressing Social Needs Through
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374(1): 8–11.

Alzheimer’s Association. 2018.
“2017 Alzheimer’s Disease Facts
and Figures.” tinyurl.com/yc5h
blbe. Retrieved December 20, 2017.

Boustani, M., Alder, C. A., and
Solid, C. A. 2018. “Agile Implemen-
tation: A Blueprint for Implement-
ing Evidence-based Healthcare
Solutions.” Journal of the American
Geriatrics Society 66(7): 1372–76.

Daniel, H., Bornstein, S. S., and
Kane, G. C. 2018. “Addressing
Social Determinants to Improve
Patient Care and Promote Health
Equity: An American College of
Physicians Position Paper.” Annals
of Internal Medicine 168(8): 577–8.

Friedman, E. M., et al. 2015. “U.S.
Prevalence And Predictors of
Informal Caregiving for Demen-
tia.” Health Affairs 34(10): 1637–41.

Galea, S., M., et al. 2011. “Estimated
Deaths Attributable to Social Fac-
tors in the United States.” Ameri-
can Journal of Public Health 101(8):
1456–65.

Taylor, L. A., et al. 2016. “Lever-
aging the Social Determinants of
Health: What Works?” PLoS One
11(8): e0160217.

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individual use.

RESEARCH ARTICLE Open Access

How executives’ expectations and
experiences shape population health
management strategies
Betty M. Steenkamer1* , Hanneke W. Drewes2, Natascha van Vooren2, Caroline A. Baan1,2, Hans van Oers1,2 and
Kim Putters3,4

Abstract

Background

: Within Population Health Management (PHM) initiatives, stakeholders from various sectors apply PHM
strategies, via which services are reorganised and integrated in order to improve population health and quality of
care while reducing cost growth. This study unravelled how stakeholders’ expectations and prior experiences
influenced stakeholders intended PHM strategies.

Methods

: This study used realist principles. Nine Dutch PHM initiatives participated. Seventy stakeholders (mainly
executive level) from seven different stakeholder groups (healthcare insurers, hospitals, primary care groups,
municipalities, patient representative organisations, regional businesses and program managers of the PHM
initiatives) were interviewed. Associations between expectations, prior experiences and intended strategies of the
various stakeholder groups were identified through analyses of the interviews.

Results

: Stakeholders’ expectations, their underlying explanations and intended strategies could be categorized
into four themes: 1.

Regional collaboration

; 2.

Governance structures and stakeholder roles

; 3. Regional learning
environments, and 4. Financial and regulative conditions. Stakeholders agreed on the long-term expectations of
PHM development. Differences in short- and middle-term expectations, and prior experiences were identified
between stakeholder groups and within the stakeholder group healthcare insurers. These differences influenced
stakeholders’ intended strategies. For instance, healthcare insurers that intended to stay close to the business of
care had encountered barriers in pushing PHM e.g. lack of data insight, and expected that staying in control of the
purchasing process was the best way to achieve value for money. Healthcare insurers that were more keen to
invest in experiments with data-technology, new forms of payment and accountability had encountered positive
experiences in establishing regional responsibility and expected this to be a strong driver for establishing
improvements in regional health and a vital and economic competitive region.

Conclusion

: This is the first study that revealed insight into the differences and similarities between stakeholder
groups’ expectations, experiences and intended strategies. These insights can be used to improve the pivotal
cooperation within and between stakeholder groups for PHM.

Keywords: Population health management strategies, Realist method, Executives’ expectations

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

* Correspondence: betty.steenkamer@rivm.nl; betty.steenkamer@gmail.com
1Tilburg School of Social and Behavioural Sciences, Tilburg University, Tranzo,
PO Box 90153, 5000 LE Tilburg, The Netherlands
Full list of author information is available at the end of the article

Steenkamer et al. BMC Health Services Research (2019) 19:757
https://doi.org/10.1186/s12913-019-4513-3

http://crossmark.crossref.org/dialog/?doi=10.1186/s12913-019-4513-3&domain=pdf

http://orcid.org/0000-0003-1285-2860

http://creativecommons.org/licenses/by/4.0/

http://creativecommons.org/publicdomain/zero/1.0/

mailto:betty.steenkamer@rivm.nl

mailto:betty.steenkamer@gmail.com

Background
Population Health Management (PHM) refers to large-scale
transformations required for the reorganisation and integra-
tion of services across public health, health care, social care
and community services in order to achieve simultaneous im-
provements in population health, quality of care and reduction
in cost growth (Triple Aim (TA)) [1] (Steenkamer B, Drewes
HW, Baan CA, Putters K, van Oers H. Reorganising and inte-
grating public health, health care, social care and community
services: a theory-based framework for collaborative adaptive
health networks to achieve the triple aim. Provisionally ac-
cepted for publication). To stimulate PHM, a wide range of
stakeholders work together to design place-based initiatives
and explore which strategies will not only strengthen connec-
tions across different sectors, but also transform how health
care is delivered in order to address the full range of health de-
terminants and build more healthier communities [2] (Steen-
kamer B, Drewes HW, Baan CA, Putters K, van Oers H.
Reorganising and integrating public health, health care, social
care and community services: a theory-based framework for
collaborative adaptive health networks to achieve the triple
aim. Provisionally accepted for publication).
It seems likely that the success of place-based initiatives

is influenced by the alignment between the expectations
of the various stakeholders on how initiatives should be
developed and the strategies that the various stakeholder
groups intend to implement. Previous research has indi-
cated that stakeholders’ intended strategies are based on
prior experiences regarding which strategies worked in
which contexts and how and why they worked [3–8].
However, it remains unclear what the differences and
similarities in prior experiences of the various stakeholder
groups that participate in these place-based initiatives
were with regard to PHM development. Nor is it clear
what their expectations are with regard to how best to de-
velop PHM. This can be related to the dominant focus of
previous research on the impact of strategies and on what
factors facilitate or inhibit the development of multi-
sector partnerships for health [5, 9–11]. Therefore, this
study aims to answer the following research question:

How are expectations and prior experiences of the
various stakeholder groups that participate in place-
based initiatives associated with stakeholders’ intended
strategies to further develop PHM?

Practice leaders and policymakers can use the insights
into the differences and similarities between the expecta-
tions, experiences and intended strategies of the various
stakeholder groups, to influence and shape how initiatives
could be further developed. Moreover, insight into stake-
holders’ experiences regarding which strategies work in
which contexts and how and why they work will add to
the theoretical understanding of PHM strategies.

Methods
This explorative study used realist principles. The hallmark
of realist inquiry is its understanding of causality, linking in-
terventions, hereafter referred to as strategies (S), contextual
factors (C) mechanisms (M) and outcomes of strategies (O)
[3, 4, 6, 12]. These links are the so-called SCMO configura-
tions [3, 4, 7]. From a realist point of view, strategies imple-
mented in a specific situation will change this context due to
the resources and opportunities these strategies offer or de-
duct [4, 12, 13]. Due to this changed context, people will
change their reasoning or behaviour, which will influence the
outcomes of these strategies [6, 12, 13]. For PHM oriented
definitions of ‘strategies’, ‘contexts’, ‘mechanism’, outcomes’,
and SCMO configurations, see Table 1.
The process steps within this study were as follows: 1.

Identifying the expectations with regard to the development
of PHM of various stakeholder groups that participate in
place-based initiatives; 2. revealing the deeper explanations,
i.e. the SCMO configurations, upon which these expectations
are based, and, 3. exploring how expectations and prior expe-
riences are associated with intended PHM strategies.

Data collection
Nine Dutch PHM initiatives that together serve over two
million people, took part in this research project (see Add-
itional file 1. for details on the Dutch PHM pioneer sites).
To gain maximum insight into the expectations, prior expe-
riences and intended strategies, representatives (executive
level) of all stakeholder groups that participated in the
steering committee or that were otherwise involved in re-
gional PHM development, were invited to participate in a
(face to face) interview. Three persons declined to partici-
pate due to logistical reasons and ten persons were included
at the request of the initial invitees, e.g. some preferred to
be assisted by their staff. Between June 2016 and February
2017, 55 interviews were conducted with 70 stakeholders of
nine Dutch PHM initiatives. The interviews were foremost
individual interviews conducted face to face except for 3 in-
terviews that were performed via telephone. The 70 stake-
holders were part of seven different stakeholder groups:
representatives of hospitals (N = 16) (including a long-term
care organization); primary care groups (N = 11), patient
representative organizations (N = 5), municipalities (N = 17
of which 7 aldermen and 1 representative of the Regional
Public Health Service), healthcare insurance companies
(N = 12), local businesses (N = 2), and program managers of
the nine PHM initiatives (N = 7).
In preparation for the interviews, the authors collected

documents concerning the vision, mission and ambitions
that were stated by the PHM initiatives at the start of
the PHM programs. A semi-structured interview guide
was used to support the interview process (see Add-
itional file 2). The interviews consisted of three steps.
First, based on the authors’ assumption that as PHM

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 2 of 15

initiatives develop in time, interviewees might have expecta-
tions spread over time, interviewees were asked to write
down their short (until 2018, 5 years after the start of the
PHM initiative); middle- (until 2023, 10 years after the start)
and long-term (until 2033, 20 years after the start) expecta-
tions with regard to the development of PHM in a specific
data extraction form. Second, the expectations were then dis-
cussed by asking interviewees to focus on explanations
underlying their expectations. These explanations, which
were based on prior experiences of what strategies had
worked, how and why (i.e. the contextual factors and mecha-
nisms by which these strategies operated), were first asked
using open questions. Next, a document was shown to the
interviewees that visualized the theoretical framework for
PHM named the Collaborative Adaptive Health Network
(CAHN) (Steenkamer B, Drewes HW, Baan CA, Putters K,
van Oers H. Reorganising and integrating public health,
health care, social care and community services: a theory-
based framework for collaborative adaptive health networks
to achieve the triple aim. Provisionally accepted for publica-
tion). CAHN summarizes the insights into how and why
PHM can successfully be developed. CAHN describes eight
components (Relation, Social forces, Accountability, Leader-
ship, Resources, Finance, Regulations and Market) that con-
tain the insights into the relationships between PHM
strategies, their outcomes, and the contextual factors and
mechanisms that explain how and why these outcomes were
reached, and the theories underling these relationships. This
document was used to discuss any additional prior experi-
ences underlying the expectations regarding the development
of the PHM initiative that were not put forward in the first
instance (see second step in which the authors used an open
question to gain insight into the prior experiences). Third, in-
terviewees were asked for their intended strategies. The re-
searchers discussed with the interviewees how expectations
and prior experiences were associated with these strategies.

Analyses, synthesis and interpretation of the data
The semi-structured interviews containing the expectations,
prior experiences and intended strategies were transcribed
verbatim and this data was analysed using MaxQDA soft-
ware. In addition, the expectations with regard to the short-,
middle- and long-term period that interviewees had written
down in the data-extraction form, were put into a Microsoft
Excel sheet and structured along the three time periods,
stakeholder groups and PHM initiatives. As the expectations
were formulated from the perspective of 70 interviewees and
varied on a detail level, they were clustered on the basis of
recognition of similarities to ensure richness of data and
broad representation of perspectives [16]. Structuring the
data in this way, enabled identification of which expectations
were limited to one or more time periods, and which expec-
tations were mentioned by the majority of the stakeholder
groups and PHM initiatives. Since the 70 interviewees were
not equally distributed among the stakeholder groups in
terms of numbers, the expectations that were mentioned by
a majority of stakeholder groups (at least 4 out of 7) involv-
ing a majority of the PHM initiatives (at least 5 out of 9),
were included in the further analysis process to ensure suffi-
cient generic validity. As there was a very limited number of
perspectives that were shared by less than half of the stake-
holder groups, almost all the different perspectives of the
stakeholders of the PHM initiatives were included in
MaxQDA. Using MaxQDA, these included expectations and
their prior experiences (i.e. the relationships between
strategies-contexts-mechanism-outcomes (SCMO)) and the
intended strategies that were related to these expectations,
were identified. By relating the expectations, the prior experi-
ences and intended strategies in an integrated way, themes
emerged. Subsequently, for each theme the expectations and
underlying prior experiences and intended strategies were
put into a Microsoft Excel sheet per stakeholder group. This
overview enabled insight into the range and variations per

Table 1 PHM oriented definitions of realist concepts

PHM Strategy Refers to the intended plan of action [3, 13, 14]. Intended plans of action attempt to create changes by offering (or deducting)
resources or opportunities in a given context [15]. In this study strategies refer to the reorganization and integration of public
health, health care, social care and community services including ‘partner’ sectors (e.g. housing, transport), to promote the TA.

Context Pertains to the ‘backdrop’ of programs [13]. For example, the different multilevel sociocultural, historical, economic, political or
relational conditions connected to the development of PHM by PHM initiatives that are also changed as a result of the
implemented strategies and, which may cause certain mechanisms to be triggered.

Mechanism Refers to the generative force that leads to outcomes [14]. It denotes the changes in reasoning or behaviour of the various
stakeholders (e.g. feelings of multi-disciplinary accountability triggered by the introduction of new financial incentives). Strat-
egies should not be mistaken for mechanisms. Whereas strategies are the intended plans of action, mechanisms are the re-
sponses to the intentional resources that are offered [13].

Outcome Pertains to intended or unintended outcomes of strategies [13]. In this study, the reported outcomes are the measured
outcomes as stated in the studies included in this review, e.g. changes in knowledge or new financial arrangements.

SCMO
configurations

SCMOs are heuristics that portray the relationships between strategies, context, mechanism, and outcome [3, 4, 7]. The SCMO
configurations in the current study present the relationships between the strategies for PHM that, when implemented in a
specific context, triggers mechanisms to cause certain outcomes.

Intended PHM
strategies

Refers to strategies based on stakeholders’ expectations and prior experiences regarding which strategies work and how and
why they work; i.e. the relationships between strategies-contexts-mechanism-outcomes (SCMO configurations).

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 3 of 15

expectation and in the prior experiences and intended strat-
egies within and between stakeholder groups.

Ethics approval
Approval and consent for this study was provided by the Eth-
ical Review Committee at Tilburg University (EC-2016.27).

Results
This study identified four overarching themes with regard to
the expectations, prior experiences and intended PHM strat-
egies. These themes were: 1. Regional collaboration as a basis
for PHM; 2. Governance structures and stakeholder roles; 3.
Learning environments that stimulate PHM, and 4. Financial
and regulative conditions that stimulate PHM. The themes
were intertwined and were represented in each time period.
However, short-term expectations were mainly represented
within theme 1. Theme 2 and 3 also highlighted short-term
expectations, but middle term expectations were more
prominent. Expectations within theme 4 mostly represented
the middle- and long-term. With regard to the intended
PHM strategies, most intended strategies related to the
short-term, and no intended PHM strategies were men-
tioned in relation to the long-term expectations.
Per theme, the variations within and between stake-

holder groups’ expectations are described, including the
time frame (short-middle-long term). In addition, under
the headings ‘prior experiences’ and ‘intended strategies’
per theme is described how the contexts of the various
stakeholder groups influenced their reasoning (the mecha-
nisms), and thus the outcomes of prior implemented strat-
egies, and how this influenced the outlined intended PHM
strategies of the various stakeholder groups. Furthermore,
per theme, reference is made to the respective Tables 2, 3,
4 and 5. The top row of Tables 2, 3, 4 and 5 provides
insight into the expectations and intended strategies of the
various stakeholder groups participating in PHM initia-
tives. In the bottom row of each table, the prior experi-
ences on what strategies reached which outcomes
(strategies-outcomes), how and why these outcomes were
reached (context-mechanism) are described.

Regional collaboration
Expectations
Overall, the majority of stakeholder groups expected an
increase in regional collaboration via expansion of target
groups within the PHM program (e.g. youth, frail elderly
and people with mental health problems) with an in-
creasing number of stakeholder organizations and sec-
tors (e.g. care and nursing homes, home care,
municipalities and businesses) in the upcoming years.
(see Table 2.). In addition, stakeholders expected a re-
gional health policy that was based on a regional vision
that integrated health with other domains (e.g. educa-
tion, housing, economics) in the long-term (2033) to

support the development of a healthy, vital and eco-
nomic thriving region.

Prior experiences
This study identified that stakeholder groups’ prior experi-
ences differed the most within healthcare insurers, between
healthcare insurers and municipalities, and between hospi-
tals and primary care groups (see Table 2). With regard to
the healthcare insurers, at one extreme, this stakeholder
group had encountered negative experiences with pushing
PHM in contexts they in hindsight regarded as too com-
plex and which jeopardized their control over the purchas-
ing process and their wish to establish value for money.
For instance, PHM initiatives within highly competitive
markets, and with PHM governance structures containing
many different providers and other payers, were considered
strong inhibitors for the development of PHM. This was
due to e.g. the difficulty of aligning interests of all involved
stakeholders. At the other extreme, healthcare insurers,
specifically in those areas in which they had a dominant
market position, had experienced that investments in re-
gional relationships with municipalities, regional providers,
businesses and educational institutions, were strong driver
for regional collaboration. In addition, although they had
experienced that positive business cases were important,
these were more and more viewed from the perspective
that in order to address the social determinants of health,
which they acknowledged as being strong drivers for
health, regional collaboration was necessary.
With regard to municipalities, interviewees indicated

that due to the decentralization of tasks from the central
to the local government in 2015 (see Table 2.), the role
of municipalities on regional health and social issues had
increased, which was increasingly reflected in the PHM
initiatives. Interviewees experienced differences in how
municipalities and healthcare insurers approached PHM
development within initiatives. Municipalities, program
managers, businesses and primary care groups stated
that municipalities’ contexts as local governments of-
fered for instance much more latitude to invest in a
healthy and prosperous region than healthcare insurers
due to e.g. differences in decision-making processes in
the purchase of healthcare.

If we believe in a project, then politically we can move
much faster than health care insurers, no cost-benefit
analysis but much more if it’s good then we’ll invest in it.
Also, we as a municipality are much more aware of what
is happening in our region, we know the people. [ … ] For
us as a municipality it has to do with seeing the connec-
tion between health, education, employment, participa-
tion, and to connect the citizens themselves to the higher
goal of better health and vitality (Social innovation
official Municipality; I26).

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 4 of 15

Table 2 Regional collaboration as a basis for Population Health Management: expectations, intended PHM strategies and prior
experiences as reported by stakeholders

Stakeholder groups’*expectation (short (5-), middle (10-),
long (20 years) term)

Stakeholder groups’ intended strategies (short (5-), middle (10-),
long (20 years) term)

Short HCI, M, PCG, PM: Increased collaboration across
sectors with an increasing number of stakeholders
with an increasing number of target groups.
H, HCI: Increased collaboration within the care
sector on specific population groups. Value for
money is established.

B, H, HCI, M, PCG, PM, PRO: Collaboration
is increasingly based on a regional vision –
M, PRO, B: and increasingly based on
(above) regional coordination mechanisms
from a social-economic perspective.

HCI: Main focus on care sector. Sharpen hospital profiles by allocation,
substitution and concentration of specific care. Slowly increase collaboration
with municipalities. H: Keep complex care in hospital. Delay the shift of low
complex care.

HCI: Investments in regional relationships. Intensify collaboration
with municipalities.
B, M, HCI, PCG, PM, PRO: Investments in shared responsibility
based on a long-term vision – data and funding that support an integral
policy (H, HCI, PCG, PM) – from a social-economic perspective (B, M, PRO).

Middle HCI, M, PCG, PM: Collaboration across
sectors with an increasing number of
stakeholders with an increasing number
of target groups continuous.
M, HCI, PCG, PM: Increased collaboration
between municipalities and healthcare insurers.
Shift from curative to preventive care and
self-management.
B, HCI, M, PCG, PRO: Stabilization of
decentralization** via sustainable collaboration
between regional stakeholders.

HCI: idem short term.
PCG: Expand collaboration with hospitals on current projects and in Public Private
Partnerships and with other stakeholders in social sector.
H, HCI, M, PCG, PM, PRO: Organize larger projects and projects that have more impact
on TA, more prevention, more stakeholders using concepts such as Positive Health.

Long B, H, HCI, M, PCG, PM, PRO: Regional health
policy is based on a regional vision.

–

Prior strategies and outcomes contextual factors-mechanisms

HCI: Investments in PHM initiatives. PHM
is too costly and time consuming.
HCI: Investments in regional relationships
in order to establish regional responsibility
for addressing the social determinants of health.
Positive experiences.
M: Collaboration with healthcare insurers
for risk groups. Difficulties with establishing
business cases. Slow progress.

H: Mergers of hospitals. Mergers continued.
PCG: -Substitution of care and professionalizing
of PCG organizations. Slow progress.
-PCG pacts to influence politics in order to
cut hospital budgets, were unsuccessful.

HCI: Hindering factors for investments in PHM are highly competitive markets, to many involved
stakeholders, too little regional market power of the insurer, no collaborative agenda especially
with municipalities, no insight into data to support business cases. B, HCI, M, PCG, PM:
Hindering factors for PHM are top down management culture within healthcare insurer,
differences in legitimacy between healthcare insurer and municipalities***, differences in financial
interests, differences in culture (e.g. decision-making structure), differences in operational scale
(too small numbers of insured people within one municipality), and high turnovers within
healthcare insurers which prohibits understanding the regional situation. B, M, PCG, PM: Munici-
palities have more freedom to invest in projects when purchasing from the Social Support Act,
the Participation Act and Youth Aid, compared to healthcare insurers when purchasing from the
Healthcare law, which allowed healthcare insurers to only compensate prevention for patients
with health problems to prevent worse. H, HCI: Business cases are drivers for collaboration. M:
The healthcare insurers have commercial interests, which municipalities have not.

H: hospitals experience difficulties regarding the induced 0% growth by the government,
high market competition, the demand for more transparency, quality and efficiency, continu-
ous pressures to match supply and demand, financial bottlenecks (i.e. real estate problems),
internal resistance to concentration, redistribution and substitution of care. The preconditions
of hospital directors’ and MSBs**** to get agreement on a new hospital profile, which health-
care insurers demand, are: more focus, time and upfront financial guarantees.
HCI. PHM development which is assigned to specific managers within several
healthcare insurance organisations facilitated going beyond care. Investments in
providers and municipalities are important to address the social determinants of health.
B, HCI, M, PCG, PRO: Relationships and regional coordination of PHM are the drivers
for collaboration.
PCG, PM: Increased tasks and paperwork do not weigh up to financial uncertainties.
Hospitals are too internally focused. Rigorous cuts in hospital budgets are necessary for
real transition and real responsibility of healthcare insurers to control hospital budgets.
Political pressure on gatekeeper function during the national elections has made PCGs
more aware that building on and PHM experiences and showing results was pivotal for
their profession.

*B = Businesses; H = Hospital; HCI = Health care insurer; M = Municipality; PM = Program manager; PCG = Physician care group; PRO = Patient representative organization
**Decentralization of tasks from the central to the local government (since 2015) entails safeguarding 1. the wellness of children up to 18 years, 2. the support
people need to be able to work, 3. the care and social support people need to live in their own homes as long as possible
***Insurers’ legitimacy: ensure public interests: quality, affordability and accessibility of care to safeguard the macro care-budget and safety and quality norms;
municipality’s legitimacy: ensure interest of regional societal issues
****MSBs = Associations of medical specialists within a hospital

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 5 of 15

In addition, particularly municipalities, and business
but also healthcare insurers that focused on regional re-
lationships, primary care groups including program
managers and patient representative organizations situ-
ated in economic less prosperous regions had experi-
enced that to establish a vital and economic competitive
region, strategies had to be based on a social and eco-
nomic perspective. Hospitals and primary care groups

had experienced uncertainties surrounding the national
budgetary boundaries (zero growth for hospitals and a
small growth for primary care) set by the National gov-
ernment in 2016. In addition, hospitals had experienced
internal and external pressures on the hospital market
(see Table 2.). These pressures negatively influenced pro-
gression on substitution of care to primary care groups
within the PHM initiatives. Interviewees stated that this

Table 3 Governance structures and roles: expectations, intended Population Health Management strategies and prior experiences as
reported by stakeholders

Stakeholder groups’*expectation (short (5-), middle (10-)
long (20 years) term)

Stakeholder groups’ intended strategies (short (5-), middle (10-),
long (20 years) term)

Short B, H, HCI, M, PCG, PM, PRO: Decreased role of
individual local and regional organizations, a regional
governance structure is increasingly seen as the right
scale for regional responsibility. Increasingly other regional
stakeholders will enter. Governance structures will
continuously change. Structures will range from
informal to formal collaborative networks (on specific subjects).
H, HCI, PCG, PRO: Different ideas on who will play a lead role.

B, H, M, PM. Citizens solve local problems (e.g. loneliness)
as co-creators with support of professionals.
H, HCI, PCG, PM, PRO: Increased citizens’ awareness of
responsibility for their own health via big data-technologies.
The role of ‘co-creative citizens’ is increasingly anchored in
the regional healthcare policy (H, HCI, PCG, PM, PRO)
(strategic, tactical, operational) (PRO).

H, HCI.: Invest in hospital learning networks.
H, HCI, PCG: Invest in the bundling of high complex care in
hospital networks and low complex care in multidisciplinary
centres. Invest in the development of regional governance structures.

B, H, HCI, M, PCG, PM, PRO: Develop meaningful engagement
of citizens.
HCI, M, PCG, PRO: Inventory of citizens’- patient’s wishes
and needs regarding regional health and wellbeing
H, M, PCG, PM, PRO: Establish a citizens’ cooperative.
B, HCI, M, PCG, PM, PRO: Activate community building
so citizens can self-manage.

Middle HCI, PCG: The beginning of ACO Dutch style.
H, HCI, PM, PCG: PHM networks are responsible
for regional PHM

H, HCI, PM, PCG: The community is more in the lead.
B, M, PCG, PM, PRO: A bigger role for municipalities in
directing regional health care, while the role of the healthcare
insurers is expected to decrease as it insufficiently fits the
transformation movement. Citizens are co-creators in the
regional healthcare policy.

H, HCI: Idem short term.
H, HCI, PCG: Idem short term.

B, H, HCI, M, PCG, PM, PRO: Idem short term.
HCI, M, PCG, PRO: Ensure citizens-patients are co-creators.
B, HCI, M, PCG, PM, PRO: Idem short-term.

Long HCI, PCG, PM: Accountable Care Organisation – Health
Management Organisations.

–
Prior strategies and outcomes contextual factors-mechanisms

HCI, H: Investments in sharper profiles. Hospitals take
matters more and more into their own hands.
PCG: Exert upward and outward influence. In some
areas regional agendas were increasingly coupled.

B, H, HCI, M, PCG, PM, PRO:
-Make patient representative organizations part of the
PHM governance structure. Limited patient influence
was noticed.
-Organise that citizens take co-director-producer roles
of social initiatives. Citizens are increasingly active in the public domain.

HCI, H: Technological developments will build organizational power for
hospital networks. H: primary care groups might be marginalized as they
lack professional capacity and knowledge. Hospitals are capable of taking
the integrator role.
PCG: Agendas have become increasingly ambitious by engaging local,
regional influential stakeholders and national policymakers. Integrator role is
seen as a powerful strategy to influence future governance structure.

B, H, HCI, M, PCG, PM, PRO: -Stakeholders were in doubt if this
organizational representation equalized the representation of citizens. Also,
questions remained with regard to what governance structures would be
appropriate for patient-citizens engagement.
-The political-social relations between the government, the market and the
community are changing. Engagement of citizens is necessary to ensure
that services are being arranged according to their needs.
B, M, PCG, PM, PRO: Municipalities are obliged by law to support that
more people participate and find work (also people with an occupational
disability in collaboration with regional businesses), and to support citizens
to arrange matters themselves in the public domain (‘Do-democracy’).
Democratization and decentralization will erode healthcare insurers’ role in
time.

*B = Businesses; H = Hospital; HCI = Health care insurer; M = Municipality; PM = Program manager; PCG = Physician care group; PRO = Patient
representative organization

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 6 of 15

was mostly due to the high priority within hospitals to-
wards the inner organization and perceived financial
risks (see also Themes 2, 3, 4). Furthermore, at the time
of interviewing, primary care groups had experienced
political pressures in the run-up to the national elections
for a new government in 2016, as political parties dis-
cussed several new options regarding the organization of
healthcare and the position of providers including
diminishing the gatekeeper function of general
practitioners.

Intended strategies
This study identified that although all stakeholders ex-
pected an increase in regional collaboration within PHM
initiatives, prior experiences with regional collaboration
influenced the focus and speed of the intended collab-
orative strategies. As a result, the intended strategies dif-
fered between healthcare insurers, municipalities’ and
providers. The focus of healthcare insurers at the one
extreme was to stay close to ‘the business of care’, using
positive business cases underlying multiyear contracts

Table 4 Learning environments that stimulate Population Health Management: expectations, intended PHM strategies and prior
experiences as reported by stakeholders

Stakeholder groups’*expectation (short (5-), middle (10-),
long (20 years) term)
Stakeholder groups’ intended strategies (short (5-), middle (10-),
long (20 years) term)

Short H, HCI, M, PCG, PM: Learning environment are
being developed. H, HCI: Learning hospital networks
will be established.
B, HCI, M, PCG, PM: Municipalities and healthcare
insurers will more and more exchange data and share
purchase knowledge and expertise to gain insight
into costs and benefits.
PCG, PM, PRO: Start of regional IT structure.

H: Invest in technological developments, education, knowledge and
employment of staff and in creating and strengthening an innovation culture.

B, H, HCI, M, PCG, PM, PRO: Invest in technological means and training.
Provide insight into needs, quality and costs for clear decision support.
Investigate what indicators and data are needed for Value Based Health
Care and Positive Health.
HCI, PCG, PRO: Invest in business cases and the Plan-Do-Study-Act cycle
at all levels. Invest in value for money, i.e. by linking Patient-Reported
Outcome MeasureS to data, introducing nudging (e.g. care-miles).

Middle H, HCI, PCG, PM, M: increase in E-health,
personalized health and start of patient-ownership
of health files. More care is delivered closer to home
with use of technology. Patients have an active role
in shared decision-making based on data.
H, PCG, PM: Technology will lead to a higher
demand for technical staff and a need for other
competences and training. Staffing will be a challenge.

H: Appoint an intermediate between the user of technology and the supplier of
technology.
H, HCI, PCG, PM: Organize patient ownership of health files and technical devices.

Long PRO, PM, H, PCG: Technology has changed
professionals’ and patients’ roles. Regional
health policy is based on population data and
matching financial arrangements. National IT structure

–
Prior strategies and outcomes contextual factors-mechanisms

H, HCI, M, PCG, PM: low investments in technology.
Investments are just enough to meet the requirements
of electronic patient files, quality records and the
existing method of financing.
H, HCI, M, PCG: Efforts to share data. This is
difficult within initiatives, especially when 2/more
healthcare insurers take part or between healthcare
insurers and municipalities.

H, PCG, PRO: Stimulation of more insight into
health records and needs, costs and quality of
care and support. This subject is high on the
agenda of the public.

HCI, PCG, H, PM: Organizations work on timely and targeted feedback to providers and
administrators. Organizations increasingly understand that this can contribute to insight
into the demand and needs of the population, quality of care, and cost-effectiveness and
to the willingness to choose the best treatment-support at the lowest price, to innovate
consistently and to organize (long-term) financial arrangements. H: investments in technol-
ogy are necessity to achieve a shaper hospital profile. B, H, HCI, M, PCG, PM: The data-
technology lacks behind the desired information need, which induced tenseness. H, HCI,
PCG: for hospitals investments in technology were key. Hospitals were reluctant to share
data with primary care groups and healthcare insurers as this could influence their financial
budget. HCI: Some organizations are reluctant to share cost data with the healthcare in-
surer because opening their books will set back their bargaining power. Continuous leader-
ship support is important when sharing data to support a learning environment. M, HCI:
lack of insight into data produced tensions between municipalities and healthcare insurers.
H, HCI, PCG: lack of clarity on regulative restrictions on specific types of data-
sharing between healthcare insurers, hospitals, primary care groups and between
health care insurers.
B, HCI, M, PCG, PM, PRO: Care and support is increasingly planned around
patients. Organizations are more aware that, in principle, patients or their family
have control. In addition, as citizens-patients are co-creators of their own health,
insight into health records and needs, and the quality and costs of prevention, care
and community services is necessary to enable this co-creatorship. The influence of
citizens-patients will increasingly be supported by modern technology. B, H, HCI,
M, PCG, PM, PRO: The real upheaval in healthcare will only take place if patients in-
creasingly use this technology.

*B = Businesses; H = Hospital; HCI = Health care insurer; M = Municipality; PM = Program manager; PCG = Physician care group; PRO = Patient
representative organization

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 7 of 15

Table 5 Financial and regulative conditions that stimulate Population Health Management: expectations, intended PHM strategies
and prior experiences, as reported by stakeholders

Stakeholder groups’*expectation (short (5-), middle (10-),
long (20 years) term)
Stakeholder groups’ intended strategies (short (5-), middle (10-),
long (20 years) term)

Short B, HCI, M, PCG, PM, PRO: No changes in the
finance system, certainly no payment models for
the total population as originally planned.
First TA results will be achieved on intervention
level. Business cases based on the TA model.

B, M, PCG, PRO. First TA results on intervention level.
Shared savings as incentives on an increasing number
of projects; first experiences with regional budgets.

PM, PCG, HCI, H: The purchasing procedures will change.
H, HCI, PCG, PM: Regulations restricting the data-sharing
will not be changed shortly.

H, HCI, PCG: Organize multi-year contracts.
H, HCI: Invest in business cases based on value-based health care.
Integral payment model for mental health care, frail elderly and birth care,

HCI, PCG, PM, PRO: Invest in incentives such as shared savings and
use revenues for investments in the PHM initiative.

PCG, PM: Determine the purchasing process together with the
health care insurers and providers and pay more attention to prevention:
HCI, M, PCG: Pull funds together for specific interventions for specific
populations in light of positive health in specific neighborhoods.

Middle B, M, PCG, PM, PRO: Bundling of budgets across
sectors-TA outcomes for the whole regional population.
Regulations are changed for closer collaboration,
combining budgets, payment model for the total population.
H: Payment of complete pathways instead of payment
of separate parts of the pathway.
B, H, HCI, M, PCG, PM, PRO: Rules are changed
for data sharing

HCI: Experiment with subscription fees that are in line with the practices’ population,
combined with a bonus on outcomes that are of joint interest to the entire population.
H, HCI, PCG, PM, PRO: Keep experimenting with data optimization.
HCI, PCG, PM, PRO: Engage politicians.

Long B, H, M, PCG, PM, PRO: Citizens’ coordination
of regional health’ financial arrangements.

B, H, HCI, M, PCG, PM, PRO: Regional health
policy is based on big data with matching
financial arrangements.

–
Prior strategies and outcomes contextual factors-mechanisms

HCI: Investments in multi-year contracts with hospitals
to reduce volume and costs of care. Shared savings
incentives for specific projects. Resistance to outcome
funding and new payment models and shared
savings agreements based on the total population
of the PHM initiative.

B, H, HCI, M, PCG, PM, PRO: Improve efficiency
and quality motivated by financial incentives.
Business cases that are positive from a societal
perspective but negative from an organizational
perspective are a problem.

PHCI, M, PCG: Exchange of data to develop
business cases for PHM development. This has
challenged the purchasing procedures. Exchange
of data sensitive to competition between
healthcare insurers is prohibited.

HCI: Hospitals received budget guarantees via multi-year contracts to adjust the company for sub-
stitution of care to primary care groups. Contracts could be brokered if the quality of care was re-
duced and requirements were included within contracts, e.g. to cooperate in data-infrastructure
development. Furthermore, no savings incentives for the total population were made due to lack
of upfront financial investments, lack of data and knowledge to measure total population’ effects,
and insurers did nor prefer interference of an integrator needed to divide the savings. Limited ex-
perience with alternative ways of payment. Insurers did not prefer outcome payment due to the
danger of patient selection. No preference for region wide population payment due to fear of a
shift in responsibility to an integrator. Insurers feared that shifting accountability to providers would
increase the information asymmetry in favor of providers, and would lead to loss of control over
providers, and weaken their purchasing power.

B, H, HCI, M, PCG, PM, PRO: Leadership and trust are preconditions for financial experiments.
Fragmented financing and market forces inhibit structural change. H, HCI, PM, PCG: Current pol-
icy and purchasing process cannot guarantee efficiency and affordability, accessibility of care and
support. The NZa** sets the payment infrastructure, however rational business cases sometimes
do not fit into the system, then the NZa should redefine payment structures. Also, the market in
which providers have to compete does not fit their need to collaborate for PHM.
PCG, PM: Budgets allocated to specific compartments such as hospital care within the
budgetary framework of the government, hinder substitution of secondary care to
primary care.

B, HCI, PCG, PM, PRO: The Competition Act (ACM ***rules) has rules on data exchange
between stakeholders in light of maintaining a level playground. Market competition
and payments must be based on health gains. However, the privacy legislation is about
privacy protection but not about care optimization. The question is whether it is not the
other way around: is it not against the law to not use possibilities that exist for
optimization of care, as the law on the medical treatment contract (WBGO) says that
professional should present the best treatment to patients. Rigorous changes are
necessary in the payment system, legislation and regulations for true transitions in health
care. Professionals have experienced that confidence and experimental space and an
upfront guarantee that their actions are in line with the legal frameworks or are
permitted by supervising organization(s), is necessary.

*B = Businesses; H = Hospital; HCI = Health care insurer; M = Municipality; PM = Program manager; PCG = Physician care group; PRO = Patient
representative organization
**NZa: The NZa establishes descriptions of the treatments (performance, e.g. maximum rates), and supervises healthcare providers and
healthcare insurers
***ACM: The Dutch Authority for Consumers and Markets is a Dutch independent public regulator charged with the supervision of competition,
telecommunication and consumer law

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 8 of 15

preferably with organizations where the most value for
money could be reached such as hospitals or mental
health care organizations. In addition, they intended to
slowly organize collaboration with municipalities in
small scale interventions with sufficient return on invest-
ments in the short and middle term. The healthcare in-
surer at the other extreme intended to increasingly
invest in preventive activities e.g. in larger neighbour-
hoods or regional projects that would have more impact
on TA outcomes for the population in the next years,
using concepts such as ‘positive health’ [17], as instru-
ments to success.

[ … ] and we as a health care insurer, want to
demonstrate our added social value. Investing in
developments such as ‘Positive Health’ and ‘Value
Based Health Care’ are important to determine this
added value. We are working hard on this to see how
quality can be defined differently, like happiness and
well-being of people. [ … ] Therefore, collaboration
with municipalities is becoming more intense because
you have to be careful that you do not throw your
problems over the wall (CEO Healthcare insurer; I16).

Municipalities stated that, supported by the
decentralization movement, they intended to (further)
develop PHM in order to focus and invest in a healthy,
vital and economic competitive region together with
healthcare insurers, providers, regional businesses and
educational institutions. With regard to collaboration
between hospitals and primary care groups, the financial
uncertainties mostly influenced hospitals’ strategies to-
wards substitution of care on the short term. Inter-
viewees stated that hospitals intended to obtain
sufficient financial-contractual latitude with healthcare
insurers to develop a new and sharper hospital profile,
and in the meantime delay the shift of (low) complex
care until more financial certainty and more certainty
about the regional spread of specializations and planning
of tasks and personnel within the regional hospital sec-
tor was reached. Meanwhile, primary care groups saw
the importance of building on the experiences in the
PHM initiatives so far and safeguarding the position of
primary care i.e. the gatekeeping function of general
practitioners in the future. Therefore, they focused on a
two-pronged strategy. First of all, primary care groups
intended to expand their collaboration with hospitals on
current and new patient groups by investing in ways that
were of interest from an entrepreneurial perspective as
well as from a medical developmental perspective, such
as setting up Public Private Partnerships around new
medical technological developments. Second, primary
care groups intended to expand their PHM strategies to-
ward stakeholders within the social domain. The

upcoming concept ‘positive health’ was viewed as a good
starting point.

[ … ] if everybody would consider the social
determinants of health, then I expect that the majority
of what we now see in the physician practices has
nothing to do with care. It has to do with poverty, not
having a job [ … ] (Executive physician care group;
I28)

Governance structures and stakeholder roles
Expectations
All stakeholder groups expected a decrease of the role of
individual organisations in the near future and envi-
sioned that the collaborative of stakeholders within
PHM initiatives would eventually carry full regional re-
sponsibility for the health and well-being of the total
regional population by 2033 (see Table 3.). In addition,
stakeholders expected that PHM initiatives would con-
tinue to adapt their governance structures to fit this re-
gional responsibility. Hospitals, healthcare insurers and
primary care groups expected highly complex healthcare
to be distributed across hospital networks, and low com-
plex hospital care to be bundled in multidisciplinary
centres within Health Management Organisation
(HMO) – Accountable Care Organisation (ACO) struc-
tures (2033). Most hospitals and half of the primary care
groups expected to play a leading role in PHM. In
addition, all stakeholder groups expected that the en-
gagement of citizens and patients in the governance
structure of the PHM initiatives and its participating or-
ganizations would ensure the needs of the regional
population in the future.

Prior experiences
In recent years, hospitals increasingly had to counter fi-
nancial cuts in overhead. According to representatives of
hospitals one of the consequences was that their main
focus had been to increasingly bundle knowledge,
technological investments and organizational power in
hospital networks and public private partnerships (see
Table 3.). They had experienced that these develop-
ments, in combination with technological developments,
already had led to more cooperation between hospitals
and physician care groups. However, with regard to the
latter the majority of hospitals stated that physician care
groups lacked in professionalizing their policy and man-
agement activities to fit their new role related to substi-
tution of care such as providing sufficient GPs with
expertise in a specific sub-specialism.

[ … ] the technological developments for the large
group of chronic patients will lead to a 40% decrease

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 9 of 15

in the total of outpatient visits […] Maybe this will
lead to even more collaboration with general
practitioners but it could also lead to the erosion of
care as the ‘gatekeeper’ function could for the most
part be taken over by software devices, and it is
questionable if primary care groups are capable of
developing beyond a facility company for general
practitioners, which they currently are. [ … ] These
technological developments and our organisational
power could work to our advantage with regard to our
leading role and could negatively affect the role of
primary care groups in the future. (Senior executive
hospital; I39).

Meanwhile, several leading general practitioners of
frontrunning primary care groups that participated in
PHM initiatives had accomplished influential leading
positions within regional executive ‘table of tables’, in
which strategic priorities for the region as a whole were
discussed. This development had opened up possibil-
ities for expansion towards collaborative health net-
works, upon which new governance structures could be
built in the future.
With regard to the role of patients-citizens on a gov-

ernance level, earlier experiences with engaging patient
representative organizations within the PHM initiatives’
governance structures was limited and according to in-
terviewees had had limited success due to these organi-
zations’ lack of (specific) expertise on a strategic level.
The influence of patients-citizens was foremost limited
to the operational level, e.g. sharing their experiences
and using these as an inspiration for the transformation
of health service pathways. At the time of interviewing,
in one PHM initiative, stakeholders had recently intro-
duced a citizen’s cooperative to engage citizens in the
development of PHM. The idea was that citizens could
use this legal entity to influence what care and support
will be delivered in the region. The instrument that
would make this possible was a regional health insurance
policy. However, the legal entity was still in its infancy.
Except healthcare insurers, stakeholder groups specu-
lated that if these and other developments regarding the
promotion of citizens’ participation as well as the
decentralization of tasks from central government to
municipalities continued, the role of the healthcare in-
surer would no longer be needed.

Intended strategies
Stakeholders’ prior experiences highly influenced their
intended strategies. Hospitals intended to continue the
chosen path mentioned above and organize high com-
plex care in higher volumes in fewer regional hospital
networks and play a leading role in PHM development.
In addition, hospitals intended to slowly organize (low)

complex care for specific target groups in alignment
with regional stakeholders in multi-disciplinary centres.
Meanwhile, leaders of frontrunning primary care groups
that had experienced that PHM initiatives did not stand
on their own but operated in a wider regional transition
field, intended to further build upon regional tables to-
wards regional collaborative health network structures.
Also, with regard to engagement of patient and citi-

zens, prior experiences highly influenced stakeholders’
intended strategies. Due to the limited success in en-
gaging patients and citizens based on specific expertise,
all stakeholder groups were still thinking about how best
to engage citizens in the PHM governance structure.
Most stakeholder groups were leaning towards engaging
citizens in the role of a more moral authority and not
necessarily on the basis of specific expertise.

Regional learning environments

Expectations
All stakeholders expected that the development of a
learning environment would go along with the incre-
mental development of PHM (see Table 4.). In addition,
all stakeholders expected that the real transition in the
health system will take place due to patients’- citizens’
increased use of technology, as a result of which pro-
viders need training and knowledge to coach patients
and provide them with good, objective information in
order to decide on the most optimal treatment.

Prior experiences
Interviewees from all stakeholder groups indicated that
they had gained experiences in setting up a data-
infrastructure, in training healthcare professionals in the
use of the data-infrastructure and in giving timely and
targeted feedback to individual care providers and ad-
ministrators in order to support the operationalization
and implementation of new interventions (see Table 4.).
This had contributed to more awareness and willingness
to change how and what care is offered, and to experi-
ments in shared decision-making based on real time
data. However, according to interviewees, the IT devel-
opments still lagged behind the desired information
needs needed to take further steps towards PHM. At the
time of the interviews, this had led to tensions between
hospitals, primary care groups and healthcare insurers,
between municipalities and healthcare insurers and be-
tween healthcare insurers themselves. Stakeholders indi-
cated these tensions were associated with contextual
factors such as securement of (financial) interests (hospi-
tals), inability – hesitation to give insight into the neces-
sary data upon which business cases surrounding
substitution of care could be built (healthcare insurers),
lack of knowledge or consensus on which data – indica-
tors were needed to support the transformation of health

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 10 of 15

pathways (healthcare insurers, hospitals, physician care
groups), and lack of clarity about what is legally permit-
ted regarding linkage of data (healthcare insurers, hospi-
tals, physician care groups) (see theme 4).

Quality is what we need, comparing data,
benchmarking and not the resistance of professionals
[…], the fear organizations have that their interests
may be at stake, and that we deal with them in such a
clumsy way that they get away with it and maybe if
we are not careful they will get away with it in the
next five years. These are the real problems.
(Innovation manager healthcare insurer; I54)

Intended strategies
Because hospitals needed to secure their (financial)
interest (see themes 1, 2 and 4), their main focus was to
further invest in technological developments and
specialization and planning of medical technical staff in
the short and middle term, in order to sharpen their
profile and realize specific person-centred network care
within hospital networks. The other stakeholder groups
indicated that despite the experienced difficulties men-
tioned above, they intended to further invest in gaining
insight into supply and demand, quality and costs of pre-
vention, care and welfare, as they realized this was essen-
tial for establishing continuous improvements. However,
primary care groups in particular stressed that know-
ledge and support for instance from knowledge institu-
tions, clarity from the government about the linking of
data (see theme 4) and a financial reward for care pro-
viders for the delivery of meaningful data, were neces-
sary to realize a learning environment.

Financial and regulative conditions that suit the
stimulation of PHM
Expectations
All stakeholder groups expected changes in the middle-
and long-term with regard to the current financial sys-
tem, laws and regulations, and accountability procedures
that would stimulate improvements in the TA (see
Table 5.). All stakeholders expected that between 2023
and 2033 the current funding and payment models
within the health care system would be replaced by
other models. However, while healthcare insurers were
more cautious with regard to their expectations of a par-
ticular model, the other stakeholder groups disagreed
about which model was most suitable for realizing the
TA: payment models for the total regional population,
payment per (care) activity with shared savings – bo-
nuses, or integral payment models. In addition, stake-
holders expected changes in laws and regulations for
organizations to: 1. Work more closely together without

changing the freedom of choice of providers; 2. To share
data; and 3. To combine budgets across sectors, or to be
held responsible for the health of the total population.

If you really want to take steps in substitution, the
Ministry of Health, Welfare and Sport has to change
their policy by rigorously removing money from the
hospitals and partly allocating this to primary care.
Currently, no one dares to take the lead because they
don’t want to risk their reputation (CEO physician
care group: I2).

Prior experiences
Healthcare insurers indicated that their cautions towards
new forms of payment and funding were based on a lack
of experiments in the past in alternative payment models
in which questions such as what and how much risk or-
ganizations could take or which outcome measures
would be most suitable, were addressed (see Table 5.). In
addition, the healthcare insurers that intended to stay
close to ‘the business of care’ (see theme 1.), believed that
new ways of payment and funding would increase infor-
mation asymmetry, which would imply shifts in account-
ability to providers that could result in loss of control
over the providers and weaken healthcare insurers’ pur-
chasing process. Furthermore, all stakeholders had expe-
rienced that leadership and trust were necessary
conditions for experimenting with new forms of pay-
ment and funding. Stakeholders indicated they had been
able to build trusted relationships in the last 5 years
since the PHM initiatives had started and during which
the first positive results on the TA for specific interven-
tions and subpopulations were achieved. However,
during this period of time, leaders of stakeholder organi-
zations ran into issues that hampered the development
of PHM, such as restrictions on data sharing (see theme
3), the lack of invoicing codes for new types of services,
and the way budgets within the national budget frame-
work are distributed, which hindered the substitution of
care. Additional questions that also needed answers were
for example how to take financial risks for the total
health care costs of the regional population and how to
compete while at the same time cooperate between
organizations without risking loss of freedom of choice
for patients.

Intended strategies
Although stakeholders were of the opinion that the
current payment and funding models did not sufficiently
stimulate simultaneous improvement in the TA, they
(i.e. primary care groups, businesses, and patient repre-
sentative organizations), intended to continue to
organize care and support in a more coherent way to

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 11 of 15

impact the full range of health determinants, as they
hopefully expected that payment models and funding
will be adjusted in the middle-term. Meanwhile,
intended strategies between healthcare insurers differed.
The healthcare insurer that predominantly intended to
‘stay close to the business of care’, primarily focused on
providing multi-year contracts to hospitals that showed
trusted leadership, clinical responsibility and which were
able to achieve healthy financial conditions. In addition,
they intended to stay in control of the purchasing
process by investing in continuous monitoring to pre-
vent information asymmetry. Furthermore, they
intended to experiment in integral payment models (e.g.
birth care, mental health, frail elderly). Healthcare in-
surers that predominantly intended to invest in regional
relationships and responsibility were, just like municipal-
ities, more focused on experimenting with a combin-
ation of models, demonstrating returns on investment,
identify ways of overcoming administrative barriers to
coverage, align administrative processes and distribute
data to stimulate efficiency and evaluation.
In addition, to stimulate changes in laws and regula-

tions, several front running primary care groups’ strategies
were aimed at continuously influencing the ministry of
Health, Welfare and Sport and national interest groups on
subjects that hindered PHM development. Subjects that
were put forward were e.g. restrictions in data integration
due to the current privacy law (see theme 3), municipal-
ities having more latitude than healthcare insurers in or-
ganizing business cases that bridged sectors (also see
themes 1, 2, 3), and restrictions in substitution of second-
ary care to primary care due to the current budgetary
framework of the government.

Discussion

This study identified stakeholder groups’ short-, middle-
and long-term expectations of PHM development, the
underlying explanations for these expectations and their
intended strategies. These expectations, their underlying
explanations and intended strategies could be categorized
into four themes: 1. Regional collaboration as a basis for
PHM; 2. Governance structures and stakeholder roles; 3.
Learning environments that stimulate PHM, and 4. Finan-
cial and regulative conditions that suit PHM. These
themes are intertwined. Although stakeholders mostly
agreed on long term-overall expectations, the short and
middle term expectations and prior experiences largely
differed between stakeholder groups and within the stake-
holder group healthcare insurers. These differences influ-
enced stakeholders’ intended strategies towards PHM
development. Healthcare insurers that highly valued con-
trol over the purchasing process and value for money,
intended to stay close to the business of care, in compari-
son to insurers that valued regional relationships in order

to establish regional responsibility for health and social is-
sues. The latter were more keen to invest in data-sharing,
and in experiments with data-technology, new forms of
payment, funding and accountability. Of all providers,
hospitals’ strategies were the most internally focused. This
internal focus was mostly due to ongoing financial pres-
sures that hindered the shift of low complex care to pri-
mary care groups, data-technology development and the
sharing of data, and experiments with new forms of pay-
ment. Of all stakeholders, municipalities and regional
businesses were the most driven to address health and
social issues from a socio-economic perspective and on a
regional scale in order to establish a vital and economic
competitive region. This was mainly based on municipal-
ities’ decentralization tasks and on businesses’ interest to
support healthy behaviour of employees.
The current study showed that collaboration between

an increasing number of stakeholders and extension of
the portfolio of the PHM initiatives were mainly
expected in the short term, while more experiments with
new payment models and funding were mainly expected
in the middle and long term. These results are in line
with previous literature, which has shown that specific
activities are associated with specific phases in PHM
development [18, 19].
As described in theme 1, the way healthcare insurers

operationalized their tasks to safeguard the quality, af-
fordability and accessibility of care, influenced how
PHM development. These findings are in line with previ-
ous literature [20–22]. PHM initiatives in which the
healthcare insurers interpreted their role as ‘regional fi-
nancial manager’ from a relational point of view, could
make more progress with regard to the focus and speed
of PHM development than PHM initiatives in which the
healthcare insurers primarily focused on staying close to
the business of care. In addition to previous literature,
this study has given insight into the underlying experi-
ences, i.e. the conditions and motivations that influenced
the choices of stakeholders’ intended strategies. For in-
stance, the insurers that had had negative experiences in
pushing PHM, which had jeopardized their control over
the purchasing process, intended to stay close to the
business of care as they expected this strategy was the
best way to achieve value for money.
The governance structures of the pioneer sites have

been adapted over time to guide the development of
PHM and all stakeholders expected this trend to con-
tinue. However, up until now there is still no clear
picture of how the governance structures of PHM initia-
tives will further evolve and how the roles between the
organizations will be divided and who will take responsi-
bility for the total population in the future. This is com-
parable to place-based initiatives in for instance the
United States, the United Kingdom, Canada or Germany,

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 12 of 15

which have shown that governance structures are divers
and that changes in governance structures have many
reasons such as lack of commitment, lack of interest and
lack of resources [2, 18, 23]. In addition, in line with pre-
vious literature, PHM initiatives do not stand on their
own but operate in a wider transition field in which
PHM initiatives connect nodes within the network and
build upon regional developments towards regional col-
laborative health network structures [2, 18] (Steenkamer
B, De Weger E, Drewes HW, Putters K, van Oers H,
Baan CA: Implementing Population Health Manage-
ment: An international comparative study, submitted for
publication).
Moreover, as described in theme two, despite stake-

holders’ conviction that involving patients/communities
can help ensure that services are more tailored to their
needs and thus ultimately improve community health out-
comes, all stakeholder groups remained unsure on how to
implement more ‘meaningful’ community engagement
within their own contexts. These findings are in line with
previous literature [24, 25]. Based on the results of this
study the discrepancy between the intended strategies and
expectation towards future roles could be attributed to
prior negative experiences with patient engagement. To en-
gage patients/communities more meaningfully, PHM initia-
tives could draw inspiration from previous studies (e.g. de
Weger et al., 2018). For example, in the Netherlands, some
communities and municipalities have been experimenting
with involving citizens in the planning and decision-making
of how municipalities’ budgets should be spent [26]. The
experiences gained in citizens involvement can serve as ex-
amples on how communities can be involved in developing
a regional health policy based on a shared vision (Theme 1),
be involved in initiatives’ leadership and management struc-
tures (Theme 2), in helping to identify citizens’ needs
(Theme 3) and in setting financial priorities (Theme 4).
Moreover, in addition to preconditions such as trust

and leadership, investments in data infrastructures and
technologies and additional knowledge, expertise and
capacity are needed for the introduction of new ways of
payment and funding. Alternative payment models
seem effective to actually realize the TA, however, these
take a long time to iron out and the pros and cons need
to be properly monitored to make adjustments possible
[20, 21, 27]. In addition, the consequences of techno-
logical developments to utilize existing information
systems of professionals and citizens are linked to ad-
justments in privacy- and other laws, and to adjust-
ments in accountability procedures. To further speed
up PHM development there is a need for government
support such as clarity about data integration and fi-
nancial support such is the case for the Accountable
Health Communities in the United States, that receive
resources, including financial support and technical

assistance specifically intended for aspects of PHM de-
velopment such as setting up a learning environment
[28]. This could contribute to reducing the tensions
stakeholder groups encounter (ed).
This study has several limitations. One being that the

information provided by the interviewees is subjective
information formulated from the perspective of the
stakeholder. In addition, the 70 interviewees were not
equally divided over stakeholder groups. Therefore, the
analysis was set up to only include the perspectives that
emerged in at least half of the stakeholder groups and
PHM initiatives. However, as there was a very limited
number of perspectives that were shared by less than
half the stakeholder groups, this study contains almost
all the different perspectives of the stakeholders of the
PHM initiatives. In addition, the analysis and synthesis
of the data was performed by two researchers and veri-
fied by the research team which renders confidence to
the reliability of the results. Furthermore, PHM strat-
egies for 2023 were less put forward by interviewees in
comparison to those for 2018. For 2033, PHM strategies
were lacking completely. This can be explained by the
fact that especially for the long-term time frame, stake-
holders indicated that from a political and economic
perspective 20 years was too unpredictable.
This research contributes to the theoretical under-

standing of PHM strategies by giving insight into what
strategies work and how and why they work. In addition,
practice leaders and policymakers can use the insights
into the expectations on the future development of
PHM of a diverse range of stakeholder groups, their
prior experiences and their intended PHM strategies to
better stimulate and coordinate PHM development. Fu-
ture research should investigate how regional financial
management can best be executed and what the roles of
healthcare insurers, municipalities and third parties (in-
tegrators) should be in order to further push PHM, and
who best can take responsibility for the health of the
total population in the future. In addition, future re-
search should investigate in what way citizens can best
be involved in PHM development. Furthermore, it
should be investigated how the government and super-
vising organizations can best stimulate investments in
regional learning environment such as data-technology
and knowledge-development, and how best to stimulate
market-collaboration and new payment models that pro-
mote simultaneous improvements in the TA. An ex-
ample of a program that could be investigated is the
Dutch National Program ‘The right care at the right
place’, which e.g. provides a regional basic dataset that
can help healthcare insurers municipalities and providers
in mapping the current and future care and support
needs and the current offerings [29]. Moreover, research
could further investigate differences in values and

Steenkamer et al. BMC Health Services Research (2019) 19:757 Page 13 of 15

convictions of the various stakeholder groups that could
hinder PHM.

Conclusion
The differences in intended strategies between stake-
holder groups and within the stakeholder group health-
care insurers were mostly based on differences in prior
experiences i.e. specific contextual factors that stake-
holders had experienced and that hindered progress in
PHM. Barriers that stakeholder groups encountered
were related to e.g. differences in values and convictions,
information asymmetries which could endanger the pur-
chasing process, lack of insight into data to support
business cases or financial uncertainties due to political
pressures. These barriers made stakeholders more reluc-
tant to take steps beyond their usual practice and push
PHM further. In addition, stakeholders indicated that
government support was needed to e.g. reduce barriers
between stakeholder groups related to restrictions within
laws and regulations such as providing clarity about data
integration, market-collaboration and also (financial)
support intended for specific aspects of PHM such as
new payment models that stimulate PHM, and setting
up and improving learning environments. Policymakers
and practice leaders can use these insights to reduce
these uncertainties and establish more comfort in order
for all stakeholder groups to jointly establish PHM.

Supplementary information

The online version of this article (https://doi.org/10.1186/s12913-019-4513-3)
contains supplementary material, which is available to authorized users.

Additional file 1. Characteristics of the nine Dutch PHM initiatives
(DOCX 16 kb)

Additional file 2. Interview guideline (DOCX 54 kb)

Abbreviations

ACO: Accountable Care Organisation; CAHN: Collaborative Adaptive Health
Network framework; CEO: Chief Executive Officer; HMO: Health Management
Organisation; PHM: Population health Management; SCMO: Strategy-Context-
Mechanism-Outcome configuration; TA: Triple Aim

Acknowledgements

Not applicable.

Authors’ contributions

BS, HD, NvV, CB, HvO and KP have contributed to the design of the research.
BS, HD and NvV have made substantial contributions to the acquisition of
data, and to the analysis and interpretation of the results. All authors (BS, HD,
NvV, CB, HvO and KP) have been involved in drafting the manuscript and
revising it critically for important intellectual content. All authors (BS, HD,
NvV, CB, HvO and KP) have read and approved the manuscript.

Funding

Not applicable.

Availability of data and materials

The datasets used and analysed during the current study are available from
the corresponding author on request.

Ethics approval and consent to participate

Approval for this study was provided by the Ethical Review Committee at
Tilburg University (EC-2016.27). All participants provided a written informed
consent for participation and publication.

Competing interests

The authors declare that they have no competing interests.

Author details

1Tilburg School of Social and Behavioural Sciences, Tilburg University, Tranzo,
PO Box 90153, 5000 LE Tilburg, The Netherlands. 2National Institute for Public
Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The
Netherlands. 3Erasmus School of Health Policy & Management (ESHPM), P.O.
Box 1738, 3000 DR Rotterdam, The Netherlands. 4The Netherlands Institute
for Social Research, PO Box 16164, 2500 BD The Hague, The Netherlands.

Received: 1 June 2019 Accepted: 4 September 2019

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Abstract
Background
Methods
Results
Conclusion
Background
Methods
Data collection
Analyses, synthesis and interpretation of the data
Ethics approval
Results
Regional collaboration
Expectations
Prior experiences
Intended strategies
Governance structures and stakeholder roles
Expectations
Prior experiences
Intended strategies
Regional learning environments
Expectations
Prior experiences
Intended strategies
• Financial and regulative conditions that suit the stimulation of PHM

Expectations
Prior experiences
Intended strategies
Discussion
Conclusion
Supplementary information
Abbreviations
Acknowledgements
Authors’ contributions
Funding
Availability of data and materials
Ethics approval and consent to participate
Competing interests
Author details
References
Publisher’s Note