Home Uncategorized Rough Draft Qualitative Research Critique and Ethical Considerations

Uncategorized

Rough Draft Qualitative Research Critique and Ethical Considerations

Write a critical appraisal that demonstrates comprehension of the two qualitative research studies listed below.

Must use the following “Research Critique Guidelines” document to organize your essay. Successful completion of this assignment requires that you provide rationale, include examples, and reference content from the studies in your responses.

Use your practice problem and the two qualitative, peer-reviewed research articles you choose.

***Practice Problem that needs to be included*** PICOT: In mental health patients with substance use disorders (P), does treatment, (I) as compared to non-treatment, (C), reduce readmissions, (O) within 90 days? (T).

In a 1,000–1,250 word essay, summarize the two qualitative studies, using the nursing research guide template and explain the ways in which the findings might be used in nursing practice, and address ethical considerations associated with the conduct of the study. APA format.

ResearchCritique Guidelines – Part I

Use this document to organize your essay. Successful completion of this assignment requires that you provide a rationale, include examples, and reference content from the studies in your responses.

Qualitative Studies

Background of Study

1. Summary of studies. Include problem, significance to nursing, purpose, objective, and research question.

How do these two articles support the nurse practice issue you chose?

1. Discuss how these two articles will be used to answer your PICOT question.

. Describe how the interventions and comparison groups in the articles compare to those identified in your PICOT question.

Method of Study:

1. State the methods of the two articles you are comparing and describe how they are different.

2. Consider the methods you identified in your chosen articles and state one benefit and one limitation of each method.

Results of Study

1. Summarize the key findings of each study in one or two comprehensive paragraphs.

What are the implications of the two studies in nursing practice?

Ethical Considerations

1. Discuss two ethical consideration in conducting research.

2. Describe how the researchers in the two articles you choose took these ethical considerations into account while performing their research.

1Antony SM,

et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

Qualitative study of perspectives
concerning recent rehospitalisations
among a high-risk cohort of veteran
patients in Connecticut, USA

Sheila M Antony,1 Lauretta E Grau,1,2 Rebecca S Brienza1,3

To cite: Antony SM, Grau LE,
Brienza RS. Qualitative study of
perspectives concerning recent
rehospitalisations among a high-
risk cohort of veteran patients
in Connecticut, USA. BMJ Open
2018;8:e018200. doi:10.1136/
bmjopen-2017-018200

► Prepublication history for
this paper is available online.
To view these files, please visit
the journal online (http:// dx. doi.
org/ 10. 1136/ bmjopen- 2017-
018200).

Received 13 June 2017
Revised 27 April 2018
Accepted 4 May 2018

1VA Connecticut Healthcare
System, West Haven,
Connecticut, USA
2Yale School of Public Health,
New Haven, Connecticut, USA
3Section of General Internal
Medicine, Yale School of
Medicine, New Haven,
Connecticut, USA

Correspondence to
Dr Lauretta E Grau;
lauretta. grau@ yale. edu

Research

AbstrACt
Objectives Veterans Affairs (VA) patients are at risk for
rehospitalisation due to their lower socioeconomic status,
older age, poor social support or multiple comorbidities.
The study explored inpatients’ perceptions about
factors contributing to their rehospitalisation and their
recommendations to reduce this risk.
Design Thematic qualitative data analysis of interviews
with 18 VA inpatients.
setting VA Connecticut Healthcare System, West Haven
Hospital medical inpatient units.
Participants All were aged 18+ years, rehospitalised
within 30 days of most recent discharge, medically stable
and competent to provide consent.
Measurements Interviews assessed inpatients’ health
status after last discharge, reason for rehospitalisation,
access to and support from primary care providers (PCP),
medication management, home support systems and
history of substance use or mental health disorders.
results The mean age was 71.6 years (11.1 SD); all
were Caucasian, living on limited budgets, and many had
serious medical conditions or histories of mental health
disorders. Participants considered structural barriers to
accessing PCP and limited PCP involvement in medical
decision-making as contributing to their rehospitalisation,
although most believed that rehospitalisation had
been inevitable. Peridischarge themes included beliefs
about premature discharge, inadequate understanding
of postdischarge plans and insufficiently coordinated
postdischarge services. Most highly valued their
VA healthcare but recommended increasing PCPs’
involvement and reducing structural barriers to accessing
primary and specialty care.
Conclusions Increased PCP involvement in medical
decision-making about rehospitalisation, expanded clinic
hours, reduced travel distances, improved communications
to patients and their families about predischarge and
postdischarge plans and proactive postdischarge outreach
to high-risk patients may reduce rehospitalisation risk.

IntrODuCtIOn
The issue of hospital readmission has come
to national attention, and although the
link between readmissions and quality of
care is controversial, readmissions lead to
increased cost, and interventions to reduce

readmissions have been correlated with
reduced mortality.1 Overall cost for 30-day
readmissions within the Veterans Affairs (VA)
is estimated at US$6000 to US$8000 for an
average medical admission,2 although some
studies suggest higher rates of readmission
within the Department of Veterans Affairs
health system than in non-VA hospitals.3 4 In
addition to overall costs associated with read-
missions, rehospitalised patients are more
likely to suffer from chronic comorbidities
and impaired functional status that place
them at increased risk of death.5 6 Systems
level factors such as hospital size have been
found to be negatively associated with the
patient outcomes of rehospitalisation and
death.7

VA patients may be at higher risk for rehos-
pitalisation due to their lower socioeconomic
status, older age, poor social support and
multiple comorbidities.8–11 Among seriously
ill veterans receiving palliative care, a recent

strengths and limitations of this study

► Collecting data on Veterans Affairs (VA) patients’
perspectives about their recent rehospitalisation
can identify important contextual factors that are not
typically or easily assessed in quantitative studies
and may suggest other issues to target in interven-
tions to reduce rehospitalisation risk.

► Inpatients’ recommendations about how to reduce
rehospitalisation risk may uncover structural/sys-
tems issues not recognised by providers or other
hospital staff that may be important to target in in-
terventions to reduce rehospitalisation risk.

► Although saturation was achieved, the non-probabi-
listic sampling strategy and small sample size limits
the potential generalisability and should be verified
in a larger, quantitative study.

► The study sample included mostly Caucasian males
from one VA hospital in the Northeast, and it is pos-
sible that other themes exist among non-Caucasian
or female patients or in other regions of the USA.

on A
ugust 18, 2020 by guest. P

rotected by copyright.
http://bm

jopen.bm
j.com

/
B

M
J O

pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D

ow
nloaded from

http://bmjopen.bmj.com/

http://dx.doi.org/10.1136/bmjopen-2017-018200

http://crossmark.crossref.org/dialog/?doi=10.1136/bmjopen-2017-018200&domain=pdf&date_stamp=2018-06-29

http://bmjopen.bmj.com/

2 Antony SM, et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

qualitative study found that issues with self-care and
poor support systems may contribute to readmissions.12
Chronic disease, distance from the VA and age are also
associated with increased readmission risk in veterans.8 9
Studies of transitions of care suggest that difficulty navi-
gating the healthcare system, disempowerment to make
health decisions and complex psychosocial factors may
contribute to readmissions.13 Studies of non-VA patients
who are readmitted within 30 days of last discharge reveal
that patients often have difficulty in understanding
their discharge plans, issues with self-care and difficulty
resolving these barriers.14 15 Functional impairment14 16 17
and polypharmacy18 are associated with preventable post-
operative complications (eg, infection, thromboem-
boli)19 and, among bariatric surgical patients, have higher
presurgical basal metabolic index scores.20

The VA system has sought to reduce readmission risk,
primarily via identification of risk factors in quantitative
studies—both within3 21 and beyond the VA system2 14 22—
and testing interventions to reduce readmissions.10 23–25
For example, rehospitalisation rates were reduced by
implementing nursing-led interventions to improve
delivery of discharge instructions in patients following
hip replacement and pharmacist-led interventions to
provide postdischarge medication reconciliation.23 25 And
improved contact with primary care correlated with fewer
readmissions among older veterans.10 Yet rehospitalisa-
tion rates remain high.

To date, few studies have examined veterans’ percep-
tions regarding the readmission experience.12 13 The
current study was undertaken in response to VA interest
in exploring patients’ perceptions about factors that
possibly contributed to their recent rehospitalisation and
how to potentially reduce the likelihood of readmission.
Identification of the unique challenges and perspectives
of these patients may inform future healthcare policies
and guide development of interventions aimed at further
reducing readmission risk, enhancing quality of health-
care and improving transitions of care.

MethODs
setting
The VA Connecticut (VACT) healthcare system comprises
six community-based outpatient clinics (CBOCs), an
ambulatory care centre and the 216-bed main hospital
and ambulatory care clinics at West Haven and serves
57 884 patients.26 The study was conducted on the inpa-
tient medicine units at the main hospital, which is staffed
by Yale internal medicine residents, full-time hospitalists
and rotating primary care and subspecialty attending
providers from the West Haven VA.

Patient and public involvement
The study was not a randomised controlled trial. The
research question was based on patients’ preference
not to be readmitted frequently and the VACT priority
to potentially reduce readmissions within 30 days of

the last hospitalisation. Neither patients nor the public
were involved in the study design. The study goal was to
understand the reasons for readmission from patients’
perspectives and determine whether we could develop
interventions to prevent or reduce readmission. Our
initial assumption was that patients would have important
insights due to their recent readmission, and the open-
ended interviews allowed participants to shape the
discussion according to their own priorities. In addition,
patients were not involved in the recruitment or conduct
of the study due to patient privacy issues, the uniqueness
of the study population (ie, inpatient, readmitted) and
the specialised skills required for conducting qualitative
interviews. We did not ask participants’ permission to
contact them after the study due to privacy and HIPAA
concerns. The study results will be disseminated through
the literature. We will also attempt to distribute the results
through regular publications for veterans.

Participant eligibility and recruitment
Purposive sampling was used to recruit VA patients who
were (1) 18+ years of age, (2) rehospitalised to internal
medicine within 30 days of last discharge, (3) medically
stable and (4) mentally competent to provide consent.
Potential participants were identified via retrospective
chart review and discussion with the patient’s ward nurse
to confirm eligibility criteria. Participants were then
consented and interviewed during their hospitalisation.
Interviews were audiotaped and lasted 20–30 min. Written
consent was obtained from all participants.

Data collection and analysis
An interview guide, previously used in a non-VA setting,27
was adapted to include VA-specific questions regarding
pharmacy services, telephone triage, inpatient and
primary care services and probes to contextualise factors
surrounding rehospitalisation. Key domains included
perceived postdischarge health status, factors believed
to be associated with readmission, medication manage-
ment, access to and support from primary care, home
support systems, resources (eg, housing, transportation)
and history of substance use or mental health disorders.
Basic demographic information (ie, age, ethnicity, sex)
was also recorded. The interviews occurred between
September 2013 and October 2014 and were conducted
by internal medicine residents with training on qualita-
tive interviewing skills.

All interviews were audiotaped, transcribed verbatim
and subsequently deidentified. Interviews continued until
data saturation was achieved as determined during regu-
larly scheduled research team meetings. Codebook devel-
opment and data analysis followed an iterative process
and were grounded in the text. The coding and analytic
team (SA, LG) met weekly throughout the process of
codebook development, coding and analysis. A total of
32 codes were created based on the content expressed
during the interviews. Both team members held postgrad-
uate degrees in clinical fields and were experienced in

on A
ugust 18, 2020 by guest. P
rotected by copyright.
http://bm
jopen.bm
j.com
/
B
M
J O
pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D
ow
nloaded from

http://bmjopen.bmj.com/

3Antony SM, et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

qualitative research methods; one (SA) had interviewed
some participants and was able to bring that experience
to the analytic discussion. Both independently coded all
transcripts, and any coding discrepancies were resolved by
consensus during the weekly meetings. The team’s epis-
temological position was constructionist and used prag-
matism as the interpretive framework. Using ATLAS. ti
(V.7.1.7) and thematic analysis,28 29 we identified common
patterns across the dataset, grouped them into themes,
and sought ‘negative’ instances where the data did not
fit the existing themes. Reports of all quotes subsumed
under each code were generated and discussed iteratively
to identify themes that transcended individual codes. The
analyses were reviewed by the research team iteratively
during the entire coding and analytic period of the study.

results
Four major themes were identified. The first related to
participants’ thoughts about what they valued in their
healthcare and providers. Two themes concerned factors
that may have contributed to their rehospitalisation, the
first primarily identifying factors most closely associated

with the actual readmission and the second with factors
related to difficulties surrounding discharge and postdis-
charge services. The final theme concerned their recom-
mendations to reduce rehospitalisation risk.

sample characteristics
Table 1 describes the study sample. Of the 18 participants,
there were 17 men and 1 woman, proportions that reflect
the overall VACT patient population.30 All were Cauca-
sian; this is also consistent with the overall racial compo-
sition of the West Haven VA patient population. Most
were elderly (mean age 71.6; SD 11.1 years), reported
being financially secure although often living on limited
budgets; approximately half lived with family members
or spouses. Many had pre-existing chronic or serious
medical conditions (eg, diabetes, pulmonary or cardio-
vascular disease, neurological disorders, cancer) or histo-
ries of affective disorders (eg, depression, post-traumatic
stress disorder, anxiety); a few had alcohol or substance
use disorders.

Participants could be generally classified into four
patient types based on their description of events,
behaviours and attitudes: (1) loners, (2) ‘hardcore’,

Table 1 Sample characteristics (N=18)

Age (years) Mean 71.6 (SD 11.1)
Range=57–90

► <59 (2) ► 60–69 (8) ► 70–79 (3) ► 80–89 (3) ► >=90 (2)

Male 94% (17)

White, non-Hispanic 100% (18)

Living situation ► 7 subjects live independently
► 6 live with family (non-spouse)
► 4 live with a spouse
► 1 lives in a nursing facility

Homecare ► 10 reported having home services

Medication management ► 9 reported needing assistance
– 4 from family member
– 5 from nurses or nursing facility

► 8 were independent in medication management
► 1 did not take medications

Patient type

Loner “I deal with my own problems by myself. I’ve always kept issues to myself”. (Male, 65 years)
“Well, I’m kind of a lonie, you know? I keep to myself”. (Male, 64 years)

‘Hardcore’ “I don’t go for a hangnail or anything like that. The only time I go over [to the clinic] is when [the
doctor] says, ‘It’s time for your checkup and they do a little blood work, stuff like that. I’m not the
type of person to go, I’m hardcore”. (Male, 57 years)
“I tough it out and just, there’s no reason to call a doctor because you stub your toe”. (Male,
64 years)

Resilient “Because I know the different medical complications that I have that make things—they muddy the
waters a bit. So I know how to tweak them around”. (Female, 59 years)
“I’m pretty independent on what’s going on and everything so”. (Male, 60 years)

Passive/accepting “I take what they give me. I don’t judge…I figure they know what they’re doin’.” (Male, 74 years)

on A
ugust 18, 2020 by guest. P
rotected by copyright.
http://bm
jopen.bm
j.com
/
B
M
J O
pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D
ow
nloaded from

http://bmjopen.bmj.com/

4 Antony SM, et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

(3) engaged or (4) passive/accepting. Loners can
be described as independently managing their lives
(including medical problems) with little or no outside
input. ‘Hardcore’ patients tended to minimise medical
problems and delay seeking healthcare services. The
engaged patients tended to actively participate in active
discussions about their health with their providers.
Passive/accepting patients tended to unquestioningly
accept medical decisions made by their providers or
families. The first three patient types took an active
interest in the medical decision-making process, and
the fourth and smallest group tended to accept any
medical decisions made by their providers or family.
Quotes that exemplify each of these patient types are
provided in table 1. The patient types did not neatly
‘map’ onto the themes, but were thought to influence
decision-making processes and activities at times.

healthcare values and priorities
Most participants were very satisfied with their healthcare
and described their VA providers as ‘(a) caring person’,
‘(an) excellent team of doctors’, ‘(a) wonderful person’
and ‘pays attention’. As one male patient aged 69 years
noted, ‘I think the veterans get a better support system
than most [patients] do’.

But [my primary care provider] really does pay a lot
of attention. And, to the details. To the minor details
that you wouldn’t think of. (male, 90 years)

The valued qualities were sometimes explicitly identi-
fied or could be inferred from negative statements about
their care. Trust was an important quality that participants
highly valued, particularly in their primary care provider
(PCP).

Because the only one that I want, the only person
I want to see when it comes to my health, that are
people who I trust. Okay and I know that my primary
doctor, I trust, okay. She’s very, very good and she’s
always concerned about me when I walk through that
door and she always has a good word to say to me.
(Male, 57 years)

And like I told you, I have a lot of faith in [my PCP].
So whatever she says, I go along. (Male, 90 years)

Respectful and attentive attitudes were other commonly
valued qualities.

[The PCP is] very good, she’ll pick up stuff and she’ll
call me and check with me and find out what’s good
and I like that too. (Male, 69 years)

The driver was very disrespectful to me and I says,
I don’t want you to go and pick on this driver but
you get a hold of all your drivers and you sit down
with them and they have to respect the veteran who is
handicapped. (Male, 57 years)

Participants also valued providers who communicated
and listened well and were thoroughly knowledgeable

about their case. They appreciated clear and consistent
explanations about their medical care, especially in cases
involving multiple specialties during their inpatient care.

Well, I always feel better after I talk to my primary
care doctor because he knows everything about me.
He knows the meds I’m on, he knows about my histo-
ry. (Male, 64 years)

I mean she was easy to talk to. She, you would tell
her your deepest secrets and everything … that you
wouldn’t tell nobody else. You just felt comfortable
with her, just her personality, her attitude. (Male, 60
years)

[The inpatient medical team will] listen to you for
10 min and then, okay, I’ll get back to you this after-
noon and they never do. (Male, 64 years)

Continuity of care was another important issue for
participants. For complicated medical problems involving
multiple providers or a hospitalisation, some partici-
pants questioned the extent of communication between
providers and regretted the limited involvement of their
PCP during their hospitalisation.

If I’m in a hospital, I think my primary doctor should
be one of the first people to come here. (Male, 90
years)

So, it just seems like there’s so many people on your
case that things get kinda mixed up between the peo-
ple… I like their 1-on-1, I like the 1-on-1 rather than
a team of doctors. (Male, 64 years)

[The] bigger question I have is whether the doctors
who prescribe them are talking to each other. (Male,
69 years)

themes linked to the readmissions event
Perceptions about the readmission situation included
three themes: (1) logistical/structural barriers to
accessing their PCP, (2) limited involvement of PCPs
in the medical decision-making process and (3) the
perceived inevitability of the readmission.

Logistical/structural barriers
Logistical/structural barriers were the most frequently
cited problem and mainly concerned challenges to
connecting with their PCP in a timely fashion. Partici-
pants reported often feeling worse after clinic hours or
on weekends when the possibility of speaking to a clinic
provider was reduced. Beliefs about limited clinic hours
and heavily booked outpatient schedules were other
perceived barriers to accessing primary care services.

What happens sometimes, things happen on the
weekend…You can’t get nobody. (Male, 82 years)

I went over there to see [the PCP] and they told me
I couldn’t see him. I could only see him by appoint-
ment. And they wanted to give me an appointment
in, like, a month and a half. (Male, 64 years)

When you get hurt, you get hurt. And if it comes
down to it, they’re not open, they’re only open from

on A
ugust 18, 2020 by guest. P
rotected by copyright.
http://bm
jopen.bm
j.com
/
B
M
J O
pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D
ow
nloaded from

http://bmjopen.bmj.com/

5Antony SM, et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

7:00, 7:30 in the morning till 4:00 in the, then you
have to go to the Emergency Room. (Male, 57 years)

For others, the lack of specialty services at the CBOCs
necessitated travelling long distances to the central
clinic. Perceived transportation difficulties or ineffi-
ciencies may have caused patients to attempt handling
their medical problems at home. This phenomenon was
more likely to occur among loner or ‘hardcore’ patient
types.

I have to depend on somebody to give me a ride and
both my brothers that give me a ride down here,
they’re both working so they can’t just bring me down
any time they want. (Male, 64 years)

So I set up a ride with the VA Transport and that’s
a pain in the ass because if they got ten people in
there, I can’t go in there with my walker and my leg
wrapped up. I tried it once and it was devastating.
(Male, 57 years)

Limited PCP involvement in the medical decision to return to the
hospital
Some participants considered the decision to seek
urgent care without input from the PCP—the person
ostensibly the most knowledgeable about and most
trusted by the patient—was due to these logistical/
structural barriers or tendencies to ignore symptoms
(especially for the loner and ‘hardcore’ patients) until
there seemed to be no option but to go to the emer-
gency department.

It happened on a weekend and I had no choice but to
go to the Emergency Room… Well, like this last time.
I started getting pains in my stomach and naturally,
my daughter and my son said, “You better go”. (Male,
82 years)

Well, like sometimes I let it go too long [and so go
to the Emergency Room]. I don’t get to where I’m
supposed to be till, it’s not too late but it’s late. (Male,
60 years)

Perceived inevitability of the rehospitalisation
Perhaps the most surprising issue was that most patients
believed that their readmission was inevitable because
they had a chronic, degenerative or terminal illness. Only
two participants believed that their readmission could
have been prevented. One attributed his kidney failure
to what he believed to be inadequate monitoring of his
lab data, and the other was convinced that he had been
discharged too soon.

I was following [the doctor’s] orders to take MiraLax
twice a day and still I wound up here. I don’t get it.
(Male, 82 years)

I don’t think [the doctor] could’ve done anything…
Because it was— my heart hadn’t ever done anything
like that before. So there’s no way any doctor would
have known. (Male, 76 years)

Discharge-associated themes
Several themes that may have contributed to participants’
readmission concerned the discharge process and postdis-
charge services. These included beliefs about premature
discharge, inadequate or poorly understood information
about postdischarge plans or poorly coordinated postdis-
charge services.

Perceived premature discharge
Participants noted persistent symptoms or insufficient
rehabilitation as contributing to their rehospitalisation.
For some, their strongly expressed desire to return home
may have played an important role in early discharge and
their ultimate readmission. For example, it was unclear in
the case of one participant whether he had shared with
his providers that he continued to be symptomatic at the
time of his first discharge. In another case resulting in
readmission after falling at home, it was unclear whether
the patient would have received training on using his
walker had he not left the hospital against medical advice.

I come down with C. diff. They kept me in the hos-
pital for approximately a week and a half. They sent
me home, even though I still had C. diff. They never
tested me and…I was at home. I had a problem with
diarrhea and so on and so forth. (Male, 64 years)

My congestive heart failure was so bad that they start-
ed treating that. And treated that for about 9 weeks.
And then sorta had fully recovered, but I hadn’t. I was
too weak. And I left the hospital 1 day, and when I got
home, I realized I shouldn’t be home, so I came back.
(Male, 74 years)

Response to question about the readmission: I
needed more (inpatient) physical therapy, rehab.
(Male, 65 years)

Insufficient or poorly understood information about postdischarge
plans
Several participants thought that insufficient or unclear
information at the time of discharge may have contrib-
uted to their readmission. It is possible that some (eg,
passive/accepting or ‘hardcore’ patient types) may not
have asked questions to clarify any confusion concerning
the discharge plans.

I know exactly what I can eat, how to prepare my
foods, and a little bit more than I did the very first
time they released me. (Male, 64 years)

There’s about 15 pills that I have to take and God
knows what they are and for what they’re used for,
because nobody has explained what they are used for.
(Male, 87 years)

Inadequate postdischarge services
Finally, some complained that their postdischarge treat-
ment was either poorly coordinated, inadequate to
address their postdischarge service needs or not covered
by their insurance.

on A
ugust 18, 2020 by guest. P
rotected by copyright.
http://bm
jopen.bm
j.com
/
B
M
J O
pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D
ow
nloaded from

http://bmjopen.bmj.com/

6 Antony SM, et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

[VA staff] told me, “You go home and take care of
your treatment at home. Okay? When you get there,
your nurse will be waiting for you. They’ll come ev-
ery day to do it for you. Put in the machine and get
it ready”. They lied. Nobody showed up. (Male, 64
years)

Yes, they set me up for a visiting nurse or visiting phys-
ical therapist but it lasted for about 3 days each, which
didn’t accomplish much. (Male, 69 years)

Patient recommendations about how to reduce readmission
risk
Most participants were pleased with their VA healthcare
but recommended several ways to further improve care
and reduce risk of rehospitalisation. These included
increasing involvement of PCPs, reducing transporta-
tion and distance barriers to primary and specialty care,
expanding predischarge services and improving coordi-
nation of postdischarge services.

Improve access to PCP and to expedite more urgent cases
Most participants had great respect and trust for the
primary care providers, and many suggestions concerned
ways in which to reduce challenges to interacting
with them both within and outside the hospital. These
included requiring a call-back within 24 hours for urgent
complaints or being able to ‘rush the system’ in cases
where quicker scheduling of services or procedures are
required.

I wish I could talk directly [to the primary care team],
pick up the phone and you gotta go through like with
the Veteran’s Service…Everything goes to an answer-
ing machine. (Male, 57 years)

Instead of, maybe have two or three appointments
or testings or something set up real close together to
find out, to get a good grip on what’s going on and
then you know is it something that we need to really
move with or is it something we can kind of put the
brakes on and slow down a bit. (Male, 60 years)

I mean you’ve got a 48 hours I think system for noti-
fication or for where they got to return the informa-
tion back and, if anything, I would like to see some
way where there’s a red button that you could hit
that would be an emergency; in other words if I got
a problem that needs to be addressed right away, is
there something in the system, the computer or tele-
phone where that can happen. (Male, 68 years)

And now [because of delays in scheduling the proce-
dure], they’re looking at removing the complete bile
duct system, so they kind of went from a minor surgery
to a very complex, major surgery. (Male, 60 years)

Reduce barriers associated with long distances or limited
transportation services
Recommendations to reduce challenges to accessing
primary and specialty outpatient services involved

arranging for efficient and courteous transportation
services and reducing travel distances for such services.

…the transportation with the VA is a very poor system
when it comes to handicapped persons like myself.
They have to come up with something better for us
because we are handicapped. (Male, 57 years)

They never asked me, Do you need money for trans-
portation?…Like at some places, they got the cashier
downstairs and I see some guys get money for trans-
portation. (Male, 82 years)

Improve postdischarge service provision
Participants believed that better coordination and expan-
sion of predischarge and postdischarge services could
reduce readmission rates. They thought that patients at
high risk for readmission (eg, recent infections, serious
disease) should have more aggressive follow-up and
in-home care. They also suggested expanding inpatient
nutrition and physical therapy services to better prepare
them for returning home, expanding postdischarge
services and improving the efficiency of the process for
covering such services.

And what they should’ve done was…keep me in the
hospital long enough to know or, what they should’ve
done, they should’ve told me not to eat solid food.
They should’ve told me I should be eating a pureed
diet. (Male, 64 years)

In response to a question about whether visiting nurse
services would have prevented one participant’s read-
mission: I would have ended up in the same position
[of being readmitted], but sooner. (Male, 65 years)

DIsCussIOn
Current VA discharge procedures require providing
patients with telephone numbers and instructions about
reasons to call or return to the hospital. The VA has
mandated that all patients be contacted by their primary
care team within 48 hours of discharge, yet our major
finding was that participants largely did not appear to
interact with the medical system prior to deciding to
return to the hospital. Rather, they remained at home
until they or their family felt the person was ‘sick enough’
to warrant hospitalisation. This finding is consistent with
other studies noting that individuals from disadvan-
taged27 31 or veteran13 32 groups often prefer to access
healthcare through the emergency department or make
triage decisions independently. Interventions to improve
health literacy may help patients better understand
discharge instructions and reasons to contact their health
providers postdischarge.33 34 However, the tendency of
patients such as the loners or ‘hardcore’ identified in our
study to delay contacting their provider may be so firmly
entrenched that it may be difficult to change attitudes or
behaviour.

on A
ugust 18, 2020 by guest. P
rotected by copyright.
http://bm
jopen.bm
j.com
/
B
M
J O
pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D
ow
nloaded from

http://bmjopen.bmj.com/

7Antony SM, et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

Although further research is needed to determine the
generalisability of our findings, the data suggest that
greater involvement of and interaction with the primary
care team may help to reduce rehospitalisation rates. This
can be addressed in several ways. First, to address gaps in
postdischarge care, we suggest placing a second postdis-
charge follow-up call 5–7 days after the first for high-risk
patients such as those with frailty-related diagnoses10 or
complicated medication regimens. This could identify
patients at risk of readmission earlier and connect them
with needed outpatient services when appropriate. For
patients who are receiving home-based care—be they in
their own home or in a long-term assisted living facility,
follow-up calls to the private or community-based organ-
isation providing services may further decrease risk of
readmission. Periodic calls during the first few weeks after
discharge could verify patients’ status and that they are
receiving the services ordered in the discharge plan.

Our participants reported trusting their PCPs, valued
receiving unified messages when there were multiple
providers, and were disappointed that their PCP had
virtually no input during their hospitalisation. Limited
interaction with primary care was also reported among
non-VA patients,35 36 where only 24% of geriatric, family
or internal medicine physicians care for their patients
both in and out of the hospital.37 Given the VA goal of
creating patient-centred medical homes,38 we recom-
mend initiatives to increase PCP involvement in both the
inpatient and outpatient settings. Currently, no formal
procedure or requirement exists for involving PCPs in
their VA patients’ inpatient care. Some VA systems have
implemented interventions to improve transitions of care
between the inpatient and outpatient settings.39 Although
logistically complex, formal involvement of PCPs at
admission and during hospitalisation may further reduce
readmission risk and should be evaluated in further work.

The perceived logistical/structural challenges appeared
to influence decisions about seeking emergency versus
primary care services. Although VA data indicate that
VACT exceeds benchmark standards in access to care
for primary care urgent visits,40 interventions to expand
access to primary care have shown mixed results in
reducing readmissions.10 41 Our data suggest the need for
expanding such access. This could be accomplished by
adding an on-call medical provider 24/7 for patients to
consult in cases needing timely action but not necessarily
emergency services.

Similarly and consistent with another study,8 physical
distance from the main VA medical centre appeared to be
a barrier to seeking care. Since the time of our study, the
VA has expanded local access to non-VA care.42 However,
given veterans’ appreciation of VA care and desire for
coordinated care, implementing specialty and ancillary
services within the CBOCs might also be considered.

It was unclear whether perceptions about not receiving
adequate services predischarge and postdischarge repre-
sented a lack rather than misunderstanding of discharge
instructions. This issue should be studied further, possibly

using ethnographic observation methods. We also recom-
mend carefully assessing patients’ discharge readiness,
predischarge service needs (eg, nutrition education, phys-
ical therapy, polypharmacy), postdischarge living situation
and support system, medication management strategies
and need for assistance. This assessment process should
begin at admission and continue through discharge when
discharge instructions should be reviewed with the patient
and persons who will assist with their care. As mentioned
previously, it should also involve interactions with private
or community-based service organisations when needed.

Several study limitations exist. Our interviews were
conducted from September 2013 to October 2014.
Since then, the VA system has addressed some of the
barriers noted by our participants. These changes (which
occurred at approximately the same time as this study)
included reducing call centre response times, instituting
weekend hours and PCP urgent visits and expanding
access to specialty services with the Veterans Choice
bill. It will be important to assess whether the perceived
barriers noted in our study have been reduced with these
interventions. Second, the study included mainly older
and Caucasian participants from a single medical centre,
and as such, the findings may not apply to other veteran
populations. Future research is needed to determine
whether additional readmission themes exist among
women and minority populations or in other geographic
regions. Third, we did not collect data from patients’
medical records and were therefore unable to validate
their reports of medical events; the self-reported data may
have been subject to recall and social desirability bias.
Finally, as in any qualitative study, interpretation of the
study results, although grounded in the data, is viewed
through the epistemological and ontological lenses of the
researchers performing the analysis. Given the multidisci-
plinary nature of the research team, we believe that our
perspectives and training were diverse and permitted a
broader and more balanced consideration of the data.

This study identified perceived barriers that may
contribute to rehospitalisation among a sample of older
veteran patients such as limited contact with the medical
system prior to returning to the hospital, limited access to
care due to clinic hours and distance and the adequacy
of services prior to and following discharge. The data
further identified strategies that may reduce risk of rehos-
pitalisation, including proactive postdischarge outreach
to high-risk patients, patient education regarding the role
of primary care, increased access to outpatient care locally
and during non-business hours and improved discharge
planning.

Acknowledgements The authors would like to thank the participants for
providing their unique perspectives on possible factors contributing to their recent
readmissions and to Drs Amish Desai, Alexandra Norcott and Alexander Pine, the
internal medicine residents who served as additional interviewers on this project.

Contributors SMA provided substantial contribution to the research design and
data collection and analysis. She led the manuscript development process, provided
final approval of the document and agrees to be accountable for all aspects
of the work. LEG led the data analysis and provided substantial contribution to

on A
ugust 18, 2020 by guest. P
rotected by copyright.
http://bm
jopen.bm
j.com
/
B
M
J O
pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D
ow
nloaded from

http://bmjopen.bmj.com/

8 Antony SM, et al. BMJ Open 2018;8:e018200. doi:10.1136/bmjopen-2017-018200

Open access

manuscript preparation, research design and data collection and analysis. She
provided final approval of the document and agrees to be accountable for the data
analysis portion of the work. RSB provided substantial contribution to the research
design of the study and provided final approval of the document. She agrees to be
accountable for all aspects of the work.

Funding This study was funded by VHA Office of Academic Affiliations (OAA).

Competing interests None declared.

Patient consent Obtained.

ethics approval VA Connecticut Healthcare System West Haven IRB.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement The interview guide and codebook used during data
analysis are available to anyone on email request to the corresponding author.

Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-commercially,
and license their derivative works on different terms, provided the original work is
properly cited and the use is non-commercial. See: http:// creativecommons. org/
licenses/ by- nc/ 4. 0/

© Article author(s) (or their employer(s) unless otherwise stated in the text of the
article) 2018. All rights reserved. No commercial use is permitted unless otherwise
expressly granted.

reFerenCes
1. Benbassat J, Taragin M. Hospital readmissions as a measure of

quality of health care: advantages and limitations. Arch Intern Med
2000;160:1074–81.

2. Carey K, Stefos T. The cost of hospital readmissions: evidence from
the VA. Health Care Manag Sci 2016;19:241–8.

3. Nuti SV, Qin L, Rumsfeld JS, et al. Association of admission to
veterans affairs hospitals vs non-veterans affairs hospitals with
mortality and readmission rates among older men hospitalized with
acute myocardial infarction, heart failure, or pneumonia. JAMA
2016;315:582–92.

4. Weeks WB, West AN, Wallace AE, et al. Comparing the
characteristics, utilization, efficiency, and outcomes of VA and non-
VA inpatient care provided to VA enrollees: a case study in New York.
Med Care 2008;46:863–71.

5. Neupane B, Walter SD, Krueger P, et al. Predictors of inhospital
mortality and re-hospitalization in older adults with community-
acquired pneumonia: a prospective cohort study. BMC Geriatr
2010;10:22.

6. Targher G, Dauriz M, Laroche C, et al. In-hospital and 1-year
mortality associated with diabetes in patients with acute heart failure:
results from the ESC-HFA heart failure long-term registry. Eur J Heart
Fail 2017;19:54–65.

7. Farsi M, Ridder G. Estimating the out-of-hospital mortality rate using
patient discharge data. Health Econ 2006;15:983–95.

8. Holloway JJ, Medendorp SV, Bromberg J. Risk factors for early
readmission among veterans. Health Serv Res 1990;25:213–37.

9. Moore CD, Gao K, Shulan M. Racial, income, and marital status
disparities in hospital readmissions within a veterans-integrated
health care network. Eval Health Prof 2015;38:491–507.

10. Pugh JA, Wang CP, Espinoza SE, et al. Influence of frailty-related
diagnoses, high-risk prescribing in elderly adults, and primary care
use on readmissions in fewer than 30 days for veterans aged 65 and
older. J Am Geriatr Soc 2014;62:291–8.

11. Tang VL, Halm EA, Fine MJ, et al. Predictors of rehospitalization after
admission for pneumonia in the veterans affairs healthcare system. J
Hosp Med 2014;9:379–83.

12. Enguidanos S, Coulourides Kogan AM, Schreibeis-Baum H, et al.
“Because i was sick”: seriously ill veterans’ perspectives on reason
for 30-day readmissions. J Am Geriatr Soc 2015;63:537–42.

13. Stephens C, Sackett N, Pierce R, et al. Transitional care challenges
of rehospitalized veterans: listening to patients and providers. Popul
Health Manag 2013;16:326–31.

14. Greysen SR, Stijacic Cenzer I, Auerbach AD, et al. Functional
impairment and hospital readmission in medicare seniors. JAMA
Intern Med 2015;175:559–65.

15. Greysen SR, Harrison JD, Kripalani S, et al. Understanding patient-
centred readmission factors: a multi-site, mixed-methods study. BMJ
Qual Saf 2017;26:33–41.

16. Burke LG, Jha AK. Patients’ functional status and hospital
readmissions. JAMA Intern Med 2015;175:565–6.

17. Hoyer EH, Needham DM, Atanelov L, et al. Association of
impaired functional status at hospital discharge and subsequent
rehospitalization. J Hosp Med 2014;9:277–82.

18. Morandi A, Bellelli G, Vasilevskis EE, et al. Predictors of
rehospitalization among elderly patients admitted to a rehabilitation
hospital: the role of polypharmacy, functional status, and length of
stay. J Am Med Dir Assoc 2013;14:761–7.

19. Dickinson H, Carico C, Nuño M, et al. Unplanned readmissions and
survival following brain tumor surgery. J Neurosurg 2015;122:61–8.

20. Holzwarth R, Huber D, Majkrzak A, et al. Outcome of gastric bypass
patients. Obes Surg 2002;12:261–4.

21. Chaiyachati KH, Gordon K, Long T, et al. Continuity in a VA patient-
centered medical home reduces emergency department visits. PLoS
One 2014;9:e96356.

22. Singh S, Lin YL, Kuo YF, et al. Variation in the risk of readmission
among hospitals: the relative contribution of patient, hospital and
inpatient provider characteristics. J Gen Intern Med 2014;29:572–8.

23. Green UR, Dearmon V, Taggart H. Improving transition of care for
veterans after total joint replacement. Orthop Nurs 2015;34:79–86.

24. Kansagara D, Chiovaro JC, Kagen D, et al. Transitions of care
from hospital to home: an overview of systematic reviews and
recommendations for improving transitional care in the veterans
health administration. Washington DC: Department of Veterans
Affairs, 2015.

25. Paquin AM, Salow M, Rudolph JL. Pharmacist calls to older adults
with cognitive difficulties after discharge in a tertiary veterans
administration medical center: a quality improvement program. J Am
Geriatr Soc 2015;63:571–7.

26. U.S. Department of Veterans Affairs. 2015 annual report. USA: VA
New England Healthcare system: U.S. Government Printing Office,
2016.

27. Long T, Genao I, Horwitz LI. Reasons for readmission in an
underserved high-risk population: a qualitative analysis of a series of
inpatient interviews. BMJ Open 2013;3:e003212.

28. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res
Psychol 2006;3:77–101.

29. Braun V, Clarke V. Successful qualitative research: a practical guide
for beginners. Thousand Oaks CA: SAGE Publications Ltd, 2013.

30. U.S. Department of Veterans Affairs. Celebrating women’s history
month. Secondary 664 Celebrating women’s history month 2015.
2015 http://www. connecticut. va. gov/ CONNECTICUT/ features/
Women_ Veterans_ Health_ Program. asp

31. Kangovi S, Barg FK, Carter T, et al. Understanding why patients of
low socioeconomic status prefer hospitals over ambulatory care.
Health Aff 2013;32:1196–203.

32. Stevenson CW, Pori D, Payne K, et al. Hearing the veteran’s
voice in congestive heart failure readmissions. Prof Case Manag
2015;20:177–85.

33. Maniaci MJ, Heckman MG, Dawson NL. Functional health literacy
and understanding of medications at discharge. Mayo Clin Proc
2008;83:554–8.

34. Rosenow EC. Patients’ understanding of and compliance with
medications: the sixth vital sign? Mayo Clin Proc 2005;80:983–7.

35. Horwitz LI, Moriarty JP, Chen C, et al. Quality of discharge practices
and patient understanding at an academic medical center. JAMA
Intern Med 2013;173:1715–22.

36. McAlearney AS. Hospitalists and family physicians: understanding
opportunities and risks. J Fam Pract 2004;53:473–81.

37. Jones KC, Whatley MM. Hospitalists: a growing part of the primary
care workforce. Secondary hospitalists: a growing part of the primary
care workforce. 2016 https://www. aamc. org/ download/ 462728/ data/
june 2016 hosp ital ists agro wing part ofth epri mary care work force. pdf

38. Rosland AM, Nelson K, Sun H, et al. The patient-centered medical
home in the veterans health administration. Am J Manag Care
2013;19:e263–72.

39. Moy NY, Lee SJ, Chan T, et al. Development and sustainability of an
inpatient-to-outpatient discharge handoff tool: a quality improvement
project. Jt Comm J Qual Patient Saf 2014;40:219–27.

40. US Department of Veterans Affairs. Strategic analytics for
improvement and learing (SAIL). Secondary Strategic analytics
for improvement and learing (SAIL). 2016 https://www. va.
gov/ QUALITYOFCARE/ measure- up/ Strategic_ Analytics_ for_
Improvement_ and_ Learning_ SAIL. asp

41. Leppin AL, Gionfriddo MR, Kessler M, et al. Preventing 30-day
hospital readmissions. A systematic review and meta-analysis of
randomaized trials. JAMA Int Med 2014;174:1095–107.

42. HealthNet Federal Services. Secondary December 15, 2016. 2014
https://www. hnfs. com/ content/ hnfs/ home/ va/ home/ provider/
veterans- choice- program. html

on A
ugust 18, 2020 by guest. P
rotected by copyright.
http://bm
jopen.bm
j.com
/
B
M
J O
pen: first published as 10.1136/bm
jopen-2017-018200 on 30 June 2018. D
ow
nloaded from

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

http://www.ncbi.nlm.nih.gov/pubmed/10789599

http://dx.doi.org/10.1007/s10729-014-9316-9

http://dx.doi.org/10.1001/jama.2016.0278

http://dx.doi.org/10.1097/MLR.0b013e31817d92e1

http://dx.doi.org/10.1186/1471-2318-10-22

http://dx.doi.org/10.1002/ejhf.679

http://dx.doi.org/10.1002/hec.1150

http://www.ncbi.nlm.nih.gov/pubmed/2109741

http://dx.doi.org/10.1177/0163278713492982

http://dx.doi.org/10.1111/jgs.12656

http://dx.doi.org/10.1002/jhm.2184

http://dx.doi.org/10.1111/jgs.13238

http://dx.doi.org/10.1089/pop.2012.0104

http://dx.doi.org/10.1001/jamainternmed.2014.7756

http://dx.doi.org/10.1136/bmjqs-2015-004570

http://dx.doi.org/10.1001/jamainternmed.2014.7759

http://dx.doi.org/10.1002/jhm.2152

http://dx.doi.org/10.1016/j.jamda.2013.03.013

http://dx.doi.org/10.3171/2014.8.JNS1498

http://dx.doi.org/10.1381/096089202762552476

http://dx.doi.org/10.1371/journal.pone.0096356

http://dx.doi.org/10.1007/s11606-013-2723-7

http://dx.doi.org/10.1097/NOR.0000000000000124

http://dx.doi.org/10.1111/jgs.13315

http://dx.doi.org/10.1136/bmjopen-2013-003212

http://dx.doi.org/10.1191/1478088706qp063oa

http://www.connecticut.va.gov/CONNECTICUT/features/Women_Veterans_Health_Program.asp

http://dx.doi.org/10.1377/hlthaff.2012.0825

http://dx.doi.org/10.1097/NCM.0000000000000080

http://dx.doi.org/10.1016/S0025-6196(11)60728-3

http://dx.doi.org/10.4065/80.8.983

http://dx.doi.org/10.1001/jamainternmed.2013.9318

http://www.ncbi.nlm.nih.gov/pubmed/15189722

https://www.aamc.org/download/462728/data/june2016hospitalistsagrowingpartoftheprimarycareworkforce

http://www.ncbi.nlm.nih.gov/pubmed/23919446

http://dx.doi.org/10.1016/S1553-7250(14)40029-1

https://www.va.gov/QUALITYOFCARE/measure-up/Strategic_Analytics_for_Improvement_and_Learning_SAIL.asp

http://dx.doi.org/10.1001/jamainternmed.2014.1608

https://www.hnfs.com/content/hnfs/home/va/home/provider/veterans-choice-program.html

http://bmjopen.bmj.com/

Qualitative study of perspectives concerning recent rehospitalisations among a high-risk cohort of veteran patients in Connecticut, USA

Abstract
Methods
Setting
Patient and public involvement
Participant eligibility and recruitment
Data collection and analysis
Results
Sample characteristics
Healthcare values and priorities
Themes linked to the readmissions event
Logistical/structural barriers
Limited PCP involvement in the medical decision to return to the hospital
Perceived inevitability of the rehospitalisation
Discharge-associated themes
Perceived premature discharge
Insufficient or poorly understood information about postdischarge plans
Inadequate postdischarge services
Patient recommendations about how to reduce readmission risk
Improve access to PCP and to expedite more urgent cases
Reduce barriers associated with long distances or limited transportation services
Improve postdischarge service provision

Discussion
References

ORIGINAL RESEARCH

Contribution of Psychiatric Illness and Substance Abuse to 30-Day
Readmission Risk

Robert E. Burke, MD1,2*, Jacques Donz�e, MD, MSc3,4, Jeffrey L. Schnipper, MD, MPH3,4,5

1Hospital Medicine Section, Department of Veterans Affairs Medical Center, Eastern Colorado Health Care System, Denver, Colorado; 2Division of
General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Denver, Colorado; 3Division of General Medicine and
Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts; 4Department of Medicine, Harvard Medical School, Boston, Massachusetts;
5Brigham and Women’s Hospital (BWH) Hospitalist Service, Boston, Massachusetts.

BACKGROUND: Little is known about the contribution of
psychiatric illness to medical 30-day readmission risk.

OBJECTIVE: To determine the independent contribution of
psychiatric illness and substance abuse to all-cause and
potentially avoidable 30-day readmissions in medical
patients.

DESIGN: Retrospective cohort study.

SETTING: Patients discharged from the medicine services
at a large teaching hospital from July 1, 2009 to June 30,
2010.

MEASUREMENTS: The main outcome of interest was 30-
day all-cause and potentially avoidable readmissions; the
latter determined by a validated algorithm (SQLape) in both
bivariate and multivariate analysis. Readmissions were cap-
tured at 3 hospitals where the majority of these patients are
readmitted.

RESULTS: Of 6987 discharged patients, 1260 were read-
mitted within 30 days (18.0%); 388 readmissions were
potentially avoidable (5.6%). In multivariate analysis, 2 or

more prescribed outpatient psychiatric medications (odds
ratio [OR]: 1.1, 95% confidence interval [CI]: 1.01-1.20) or
any prescription of anxiolytics (OR: 1.16, 95% CI: 1.00–
1.35) were associated with increased all-cause readmis-
sions, whereas discharge diagnoses of anxiety (OR: 0.82,
95% CI: 0.68-0.99) or substance abuse (OR: 0.80, 96% CI:
0.65-0.99) were associated with fewer all-cause readmis-
sions. These findings were not replicated as predictors of
potentially avoidable readmissions; rather, patients with dis-
charge diagnoses of depression (OR: 1.49, 95% CI: 1.09-
2.04) and schizophrenia (OR: 2.63, 95% CI: 1.13-6.13) were
at highest risk.

CONCLUSIONS: Our data suggest that patients treated
during a hospitalization for depression and for schizophre-
nia are at higher risk for potentially avoidable 30-day read-
missions, whereas those prescribed more psychiatric
medications as outpatients are at increased risk for
all-cause readmissions. These populations may represent
fruitful targets for interventions to reduce readmission risk.
Journal of Hospital Medicine 2013;8:450–455. VC 2013
Society of Hospital Medicine

Readmissions to the hospital are common and costly.1

However, identifying patients prospectively who are
likely to be readmitted and who may benefit from
interventions to reduce readmission risk has proven
challenging, with published risk scores having only
moderate ability to discriminate between patients
likely and unlikely to be readmitted.2 One reason for
this may be that published studies have not typically
focused on patients who are cognitively impaired, psy-
chiatrically ill, have low health or English literacy, or
have poor social supports, all of whom may represent
a substantial fraction of readmitted patients.2–5

Psychiatric disease, in particular, may contribute to
increased readmission risk for nonpsychiatric (medi-
cal) illness, and is associated with increased utilization

of healthcare resources.6–11 For example, patients
with mental illness who were discharged from New
York hospitals were more likely to be rehospitalized
and had more costly readmissions than patients with-
out these comorbidities, including a length of stay
nearly 1 day longer on average.7 An unmet need for
treatment of substance abuse was projected to cost
Tennessee $772 million of excess healthcare costs in
2000, mostly incurred through repeat hospitalizations
and emergency department (ED) visits.10

Despite this, few investigators have considered the
role of psychiatric disease and/or substance abuse in
medical readmission risk. The purpose of the current
study was to evaluate the role of psychiatric illness
and substance abuse in unselected medical patients to
determine their relative contributions to 30-day all-
cause readmissions (ACR) and potentially avoidable
readmissions (PAR).

METHODS
Patients and Setting

We conducted a retrospective cohort study of consecu-
tive adult patients discharged from medicine services
at Brigham and Women’s Hospital (BWH), a 747-bed
tertiary referral center and teaching hospital, between

*Address for correspondence and reprint requests: Robert E. Burke,
MD, Denver VA Medical Center, Medical Service (111), 1055 Clermont
Street, Denver, CO 80220-3808; Telephone: 303-399-8020; Fax: 303-
393-5199; E-mail: Robert.Burke5@va.gov

Additional Supporting Information may be found in the online version of
this article.

Received: November 8, 2012; Revised: February 27, 2013;
Accepted: March 7, 2013
2013 Society of Hospital Medicine DOI 10.1002/jhm.2044
Published online in Wiley Online Library (Wileyonlinelibrary.com).

450 An Official Publication of the Society of Hospital Medicine Journal of Hospital Medicine Vol 8 | No 8 | August 2013

July 1, 2009 and June 30, 2010. Most patients are
cared for by resident housestaff teams at BWH
(approximately 25% are cared for by physician assis-
tants working directly with attending physicians), and
approximately half receive primary care in the Part-
ners system, which has a shared electronic medical
record (EMR). Outpatient mental health services are
provided by Partners-associated mental health profes-
sionals including those at McLean Hospital and
MassHealth (Medicaid)-associated sites through the
Massachusetts Behavioral Health Partnership. Exclu-
sion criteria were death in the hospital or discharge to
another acute care facility. We also excluded patients
who left against medical advice (AMA). The study
protocol was approved by the Partners Institutional
Review Board.

Outcome

The primary outcomes were ACR and PAR within 30
days of discharge. First, we identified all 30-day read-
missions to BWH or to 2 other hospitals in the Part-
ners Healthcare Network (previous studies have
shown that 80% of all readmitted patients are read-
mitted to 1 of these 3 hospitals).12 For patients with
multiple readmissions, only the first readmission was
included in the dataset.

To find potentially avoidable readmissions, adminis-
trative and billing data for these patients were proc-
essed using the SQLape (SQLape s.a.r.l., Corseaux,
Switzerland) algorithm, which identifies PAR by
excluding patients who undergo planned follow-up
treatment (such as a cycle of planned chemotherapy)
or are readmitted for conditions unrelated in any way
to the index hospitalization.13,14 Common complica-
tions of treatment are categorized as “potentially
avoidable,” such as development of a deep venous
thrombosis, a decubitus ulcer after prolonged bed
rest, or bleeding complications after starting anticoa-
gulation. Although the algorithm identifies theoreti-
cally preventable readmissions, the algorithm does not
quantify how preventable they are, and these are thus
referred to as “potentially avoidable.” This is similar
to other admission metrics, such as the Agency for
Healthcare Research and Quality’s prevention quality
indicators, which are created from a list of
ambulatory care-sensitive conditions.15 SQLape has
the advantage of being a specific tool for readmis-
sions. Patients with 30-day readmissions identified by
SQLape as planned or unlikely to be avoidable were
excluded in the PAR analysis, although still included
in ACR analysis. In each case, the comparison group
is patients without any readmission.

Predictors

Our predictors of interest included the overall preva-
lence of a psychiatric diagnosis or diagnosis of sub-
stance abuse, the presence of specific psychiatric
diagnoses, and prescription of psychiatric medications

to help assess the independent contribution of these
comorbidities to readmission risk.

We used a combination of easily obtainable inpa-
tient and outpatient clinical and administrative data
to identify relevant patients. Patients were considered
likely to be psychiatrically ill if they: (1) had a psychi-
atric diagnosis on their Partners outpatient EMR
problem list and were prescribed a medication to treat
that condition as an outpatient, or (2) had an Interna-
tional Classification of Diseases, 9th Revision diagno-
sis of a psychiatric illness at hospital discharge.
Patients were considered to have moderate probability
of disease if they: (1) had a psychiatric diagnosis on
their outpatient problem list, or (2) were prescribed a
medication intended to treat a psychiatric condition as
an outpatient. Patients were considered unlikely to
have psychiatric disease if none of these criteria were
met. Patients were considered likely to have a sub-
stance abuse disorder if they had this diagnosis on
their outpatient EMR, or were prescribed a medica-
tion to treat this condition (eg, buprenorphine/
naloxone), or received inpatient consultation from a
substance abuse treatment team during their inpatient
hospitalization, and were considered unlikely if none
of these were true. We also evaluated individual cate-
gories of psychiatric illness (schizophrenia, depression,
anxiety, bipolar disorder) and of psychotropic medica-
tions (antidepressants, antipsychotics, anxiolytics).

Potential Confounders

Data on potential confounders, based on prior litera-
ture,16,17 collected at the index admission were
derived from electronic administrative, clinical, and
billing sources, including the Brigham Integrated
Computer System and the Partners Clinical Data Re-
pository. They included patient age, gender, ethnicity,
primary language, marital status, insurance status, liv-
ing situation prior to admission, discharge location,
length of stay, Elixhauser comorbidity index,18 total
number of medications prescribed, and number of
prior admissions and ED visits in the prior year.

Statistical Analysis

Bivariate comparisons of each of the predictors of
ACR and PAR risk (ie, patients with a 30-day ACR
or PAR vs those not readmitted within 30 days) were
conducted using v2 trend tests for ordinal predictors
(eg, likelihood of psychiatric disease), and v2 or Fisher
exact test for dichotomous predictors (eg, receipt of
inpatient substance abuse counseling).

We then used multivariate logistic regression analy-
sis to adjust for all of the potential confounders noted
above, entering each variable related to psychiatric ill-
ness into the model separately (eg, likely psychiatric
illness, number of psychiatric medications). In a sec-
ondary analysis, we removed potentially collinear var-
iables from the final model; as this did not alter the
results, the full model is presented. We also conducted

Patients at Risk for 30-Day Readmission | Burke et al

An Official Publication of the Society of Hospital Medicine Journal of Hospital Medicine Vol 8 | No 8 | August 2013 451

a secondary analysis where we included patients who
left against medical advice (AMA), which also did not
alter the results. Two-sided P values <0.05 were consid- ered significant, and all analyses were performed using the SAS version 9.2 (SAS Institute, Inc., Cary, NC).

RESULTS
There were 7984 unique patients discharged during
the study period. Patients were generally white and
English speaking; just over half of admissions came
from the ED (Table 1). Of note, nearly all patients
were insured, as are almost all patients in Massachu-
setts. They had high degrees of comorbid illness
and large numbers of prescribed medications. Nearly
30% had at least 1 hospital admission within the
prior year.

All-Cause Readmissions

After exclusion of 997 patients who died, were dis-
charged to skilled nursing or rehabilitation facilities,
or left AMA, 6987 patients were included (Figure 1).
Of these, 1260 had a readmission (18%). Approxi-
mately half were considered unlikely to be psychiatri-
cally ill, 22% were considered moderately likely, and
29% likely (Table 2).

In bivariate analysis (Table 2), likelihood of psychi-
atric illness (P < 0.01) and increasing numbers of pre- scribed outpatient psychiatric medications (P < 0.01) were significantly associated with ACR. In multivari- ate analysis, each additional prescribed outpatient psy- chiatric medication increased ACR risk (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.01-1.20) or any prescription of an anxiolytic in particular (OR: 1.16, 95% CI: 1.00–1.35) was associated with increased risk of ACR, whereas discharge diagnoses of anxiety (OR: 0.82, 95% CI: 0.68-0.99) and substance abuse (OR: 0.80, 95% CI: 0.65-0.99) were associated with lower risk of ACR (Table 3).

Potentially Avoidable Readmissions

After further exclusion of 872 patients who had
unavoidable readmissions according to the SQLape
algorithm, 6115 patients remained. Of these, 388 had
a PAR within 30 days (6.3%, Table 1).

In bivariate analysis (Table 2), the likelihood of
psychiatric illness (P 5 0.02), number of outpatient
psychiatric medications (P 5 0.04), and prescription of
anxiolytics (P 5 0.01) were significantly associated
with PAR, as they were with ACR. A discharge diag-
nosis of schizophrenia was also associated with

PAR

(P 5 0.03).

In multivariate analysis, only discharge diagnoses of
depression (OR: 1.49, 95% CI: 1.09-2.04) and schizo-
phrenia (OR: 2.63, 95% CI: 1.13-6.13) were associ-
ated with PAR.

DISCUSSION
Comorbid psychiatric illness was common among
patients admitted to the medicine wards. Patients with

documented discharge diagnoses of depression or
schizophrenia were at highest risk for a potentially
avoidable 30-day readmission, whereas those pre-
scribed more psychiatric medications were
at increased risk for ACR. These findings were

TABLE 1. Baseline Characteristics of the Study
Population

Characteristic

All

Patients,

N (%)

Not

Readmitted,

N (%)

ACR,

N (%)
PAR

N (%)*

Study cohort 6987 (100) 5727 (72) 1260 (18) 388 (5.6)
Age, y
<50 1663 (23.8) 1343 (23.5) 320 (25.4) 85 (21.9) 51–65 2273 (32.5) 1859 (32.5) 414 (32.9) 136 (35.1) 66–79 1444 (20.7) 1176 (20.5) 268 (18.6) 80 (20.6) >80 1607 (23.0) 1349 (23.6) 258 (16.1) 87 (22.4)

Female 3604 (51.6) 2967 (51.8) 637 (50.6) 206 (53.1)
Race

White 5126 (73.4) 4153 (72.5) 973 (77.2) 300 (77.3)
Black 1075 (15.4) 899 (15.7) 176 (14.0) 53 (13.7)
Hispanic 562 (8.0) 477 (8.3) 85 (6.8) 28 (7.2)
Other 224 (3.2) 198 (3.5) 26 (2.1) 7 (1.8)

Primary language
English 6345 (90.8) 5180 (90.5) 1165 (92.5) 356 (91.8)

Marital status
Married 3642 (52.1) 2942 (51.4) 702 (55.7) 214 (55.2)
Single, never married 1662 (23.8) 1393 (24.3) 269 (21.4) 73 (18.8)
Previously married† 1683 (24.1) 1386 (24.2) 289 (22.9) 101 (26.0)

Insurance
Medicare 3550 (50.8) 2949 (51.5) 601 (47.7) 188 (48.5)
Medicaid 539 (7.7) 430 (7.5) 109 (8.7) 33 (8.5)
Private 2892 (41.4) 2344 (40.9) 548 (43.5) 167 (43.0)
Uninsured 6 (0.1) 4 (0.1) 2 (0.1) 0 (0)

Source of index admission
Clinic or home 2136 (30.6) 1711 (29.9) 425 (33.7) 117 (30.2)
Emergency department 3592 (51.4) 2999 (52.4) 593 (47.1) 181 (46.7)
Nursing facility 1204 (17.2) 977 (17.1) 227 (18.0) 84 (21.7)
Other 55 (0.1) 40 (0.7) 15 (1.1) 6 (1.6)

Length of stay, d
0–2 1757 (25.2) 1556 (27.2) 201 (16.0) 55 (14.2)
3–4 2200 (31.5) 1842 (32.2) 358 (28.4) 105 (27.1)
5–7 1521 (21.8) 1214 (21.2) 307 (24.4) 101 (26.0)
>7 1509 (21.6) 1115 (19.5) 394 (31.3) 127 (32.7)

Elixhauser comorbidity index score
0–1 1987 (28.4) 1729 (30.2) 258 (20.5) 66 (17.0)
2–7 1773 (25.4) 1541 (26.9) 232 (18.4) 67 (17.3)
8–13 1535 (22.0) 1212 (21.2) 323 (25.6) 86 (22.2)
>13 1692 (24.2) 1245 (21.7) 447 (35.5) 169 (43.6)

Medications prescribed as outpatient
0–6 1684 (24.1) 1410 (24.6) 274 (21.8) 72 (18.6)
7–9 1601 (22.9) 1349 (23.6) 252 (20.0) 77 (19.9)
10–13 1836 (26.3) 1508 (26.3) 328 (26.0) 107 (27.6)
>13 1866 (26.7) 1460 (25.5) 406 (32.2) 132 (34.0)

Number of admissions in past year
0 4816 (68.9) 4032 (70.4) 784 (62.2) 279 (71.9)
1–5 2075 (29.7) 1640 (28.6) 435 (34.5) 107 (27.6)
>5 96 (1.4) 55 (1.0) 41 (3.3) 2 (0.5)

Number of ED visits in past year
0 4661 (66.7) 3862 (67.4) 799 (63.4) 261 (67.3)
1–5 2326 (33.3) 1865 (32.6) 461 (36.6) 127 (32.7)

NOTE: Abbreviations: ACR, all-cause readmission; ED, emergency department; PAR, potentially avoidable
readmission. PAR cohort excludes patients with unavoidable readmissions. *Percentages may not add up
to 100% due to rounding or when subcategories were very small (<0.5%). †Previously married includes patients who were divorced or widowed.

Burke et al | Patients at Risk for 30-Day Readmission

452 An Official Publication of the Society of Hospital Medicine Journal of Hospital Medicine Vol 8 | No 8 | August 2013

independent of a comprehensive set of risk factors
among medicine inpatients in this retrospective cohort
study.

This study extends prior work indicating patients
with psychiatric disease have increased healthcare uti-
lization,6–11 by identifying at least 2 subpopulations
of the psychiatrically ill (those with depression and
schizophrenia) at particularly high risk for 30-day
PAR. To our knowledge, this is the first study to iden-
tify schizophrenia as a predictor of hospital readmis-
sion for medical illnesses. One prior study
prospectively identified depression as increasing the
90-day risk of readmission 3-fold, although medica-
tion usage was not assessed,6 and our report strength-
ens this association.

There are several possible explanations why these
two subpopulations in particular would be more pre-
disposed to readmissions that are potentially avoid-
able. It is known that patients with schizophrenia,
for example, live on average 20 years less than the
general population, and most of this excess mortality
is due to medical illnesses.19,20 Reasons for this may

include poor healthcare access, adverse effects of
medication, and socioeconomic factors among
others.21,22 All of these reasons may contribute to
the increased PAR risk in this population, mediated,
for example, by decreased ability to adhere to
postdischarge care plans. Successful community-
based interventions to decrease these inequities have
been described and could serve as a model for
addressing the increased readmission risk in this
population.23

Our finding that patients with a greater number of
prescribed psychiatric medications are at increased
risk for ACR may be expected, given other studies
that have highlighted the crucial importance of medi-
cations in postdischarge adverse events, including
readmissions.24 Indeed, medication-related errors and
toxicities are the most common postdischarge adverse
events experienced by patients.25 Whether psychiatric
medications are particularly prone to causing postdi-
scharge adverse events or whether these medications
represent greater psychiatric comorbidity cannot be
answered by this study.

TABLE 2. Bivariate Analysis of Predictors of Readmission Risk

All-Cause Readmission Analysis Potentially Avoidable Readmission Analysis

No. in Cohort (%) % of Patients With ACR P Value* No. in Cohort (%) % of Patients With PAR P Value*

Entire cohort 6987 18.0 6115 6.3
Likelihood of psychiatric illness

Unlikely 3424 (49) 16.5 3026 (49) 5.6
Moderate 1564 (22) 23.5 1302 (21) 7.1
Likely 1999 (29) 16.4 1787 (29) 6.4
Likely versus unlikely 0.87 0.20
Moderate 1 likely versus unlikely 0.001 0.02

Likelihood of substance abuse 0.01 0.20
Unlikely 5804 (83) 18.7 5104 (83) 6.5
Likely 1183 (17) 14.8 1011 (17) 5.4 0.14

Number of prescribed outpatient psychotropic medications <0.001 0.04 0 4420 (63) 16.3 3931 (64) 5.9 1 1725 (25) 20.4 1481 (24) 7.2 2 781 (11) 22.3 653 (11) 7.0 >2 61 (1) 23.0 50 (1) 6.0

Prescribed antidepressant 1474 (21) 20.6 0.005 1248 (20) 6.2 0.77
Prescribed antipsychotic 375 (5) 22.4 0.02 315 (5) 7.6 0.34
Prescribed mood stabilizer 81 (1) 18.5 0.91 69 (1) 4.4 0.49
Prescribed anxiolytic 1814 (26) 21.8 <0.001 1537 (25) 7.7 0.01 Prescribed stimulant 101 (2) 26.7 0.02 83 (1) 10.8 0.09 Prescribed pharmacologic treatment for substance abuse 79 (1) 25.3 0.09 60 (1) 1.7 0.14 Number of psychiatric diagnoses on outpatient problem list 0.31 0.74

0 6405 (92) 18.2 5509 (90) 6.3
1 or more 582 (8) 16.5 474 (8) 7.0

Outpatient diagnosis of substance abuse 159 (2) 13.2 0.11 144 (2) 4.2 0.28
Outpatient diagnosis of any psychiatric illness 582 (8) 16.5 0.31 517 (8) 8.0 0.73
Discharge diagnosis of depression 774 (11) 17.7 0.80 690 (11) 7.7 0.13
Discharge diagnosis of schizophrenia 56 (1) 23.2 0.31 50 (1) 14 0.03
Discharge diagnosis of bipolar disorder 101 (1) 10.9 0.06 92 (2) 2.2 0.10
Discharge diagnosis of anxiety 1192 (17) 15.0 0.003 1080 (18) 6.2 0.83
Discharge diagnosis of substance abuse 885 (13) 14.8 0.008 803 (13) 6.1 0.76
Discharge diagnosis of any psychiatric illness 1839 (26) 16.0 0.008 1654 (27) 6.6 0.63
Substance abuse consultation as inpatient 284 (4) 14.4 0.11 252 (4) 3.6 0.07

NOTE: Abbreviations: ACR, all-cause readmission, PAR, potentially avoidable readmission. *All analyses performed with v2 trend test for ordinal variables in more than 2 categories or Fisher exact test for dichotomous variables.
Comparison group is patients without a readmission in all analyses. PAR analysis excludes patients with nonpreventable readmissions as determined by the SQLape algorithm.

Patients at Risk for 30-Day Readmission | Burke et al

An Official Publication of the Society of Hospital Medicine Journal of Hospital Medicine Vol 8 | No 8 | August 2013 453

It was surprising but reassuring that substance
abuse was not a predictor of short-term readmissions
as identified using our measures; in fact, a discharge
diagnosis of substance abuse was associated with
lower risk of ACR than comparator patients. It seems
unlikely that we would have inadequate power to find
such a result, as we found a statistically significant
negative association in the ACR population, and 17%
of our population overall was considered likely to
have a substance abuse comorbidity. However, it is
likely the burden of disease was underestimated given
that we did not try to determine the contribution of
long-term substance abuse to medical diseases that
may increase readmission risk (eg, liver cirrhosis from
alcohol use). Unlike other conditions in our study,
patients with substance abuse diagnoses at BWH can
be seen by a dedicated multidisciplinary team while
an inpatient to start treatment and plan for postdi-
scharge follow-up; this may have played a role in our
findings.

A discharge diagnosis of anxiety was also somewhat
protective against readmission, whereas a prescription
of an anxiolytic (predominantly benzodiazepines)
increased risk; many patients prescribed a benzodiaze-
pine do not have a Diagnostic and Statistical Manual
of Mental Disorders–4th Edition (DSM-IV) diagnosis
of anxiety disorder, and thus these findings may reflect
different patient populations. Discharging physicians
may have used anxiety as a discharge diagnosis in
patients in whom they suspected somatic complaints
without organic basis; these patients may be at lower
risk of readmission.

Discharge diagnoses of psychiatric illnesses were
associated with ACR and PAR in our study, but out-
patient diagnoses were not. This likely reflects greater
severity of illness (documentation as a treated diagno-
sis on discharge indicates the illness was relevant dur-
ing the hospitalization), but may also reflect
inaccuracies of diagnosis and lack of assessment of se-
verity in outpatient coding, which would bias toward
null findings. Although many of the patients in our
study were seen by primary care doctors within the
Partners system, some patients had outside primary
care physicians and we did not have access to these
records. This may also have decreased our ability to
find associations.

The findings of our study should be interpreted in
the context of the study design. Our study was retro-
spective, which limited our ability to conclusively
diagnose psychiatric disease presence or severity (as is
true of most institutions, validated psychiatric screen-
ing was not routinely used at our institutions on hos-
pital admission or discharge). However, we used a
conservative scale to classify the likelihood of patients
having psychiatric or substance abuse disorders, and
we used other metrics to establish the presence of ill-
ness, such as the number of prescribed medications,
inpatient consultation with a substance abuse service,
and hospital discharge diagnoses. This approach also
allowed us to quickly identify a large cohort unaf-
fected by selection bias. Our study was single center,
potentially limiting generalizability. Although we cap-
ture at least 80% of readmissions, we were not able
to capture all readmissions, and we cannot rule out
that patients readmitted elsewhere are different than
those readmitted within the Partners system. Last, the
SQLape algorithm is not perfectly sensitive or specific
in identifying avoidable readmissions,13 but it does
eliminate many readmissions that are clearly unavoid-
able, creating an enriched cohort of patients whose
readmissions are more likely to be avoidable and
therefore potentially actionable.

We suggest that our study findings first be consid-
ered when risk stratifying patients before hospital dis-
charge in terms of readmission risk. Patients with
depression and schizophrenia would seem to merit
postdischarge interventions to decrease their poten-
tially avoidable readmissions. Compulsory community
treatment (a feature of treatment in Canada and Aus-
tralia that is ordered by clinicians) has been shown to
decrease mortality due to medical illness in patients
who have been hospitalized and are psychiatrically ill,
and addition of these services to postdischarge care
may be useful.23 Inpatient physicians could work to
ensure follow-up not just with medical providers but
with robust outpatient mental health programs to
decrease potentially avoidable readmission risk, and
administrators could work to ensure close linkages
with these community resources. Studies evaluating
the impact of these types of interventions would need

TABLE 3. Multivariate Analysis of Predictors of
Readmission Risk

ACR, OR

(95% CI)

PAR, OR

(95% CI)*

Likely psychiatric disease 0.97 (0.82-1.14) 1.20 (0.92-1.56)
Likely and possible psychiatric disease 1.07 (0.94-1.22) 1.18 (0.94-1.47)
Likely substance abuse 0.83 (0.69-0.99) 0.85 (0.63-1.16)
Psychiatric diagnosis on outpatient problem list 0.97 (0.76-1.23) 1.04 (0.70-1.55)
Substance abuse diagnosis on outpatient problem list 0.63 (0.39-1.02) 0.65 (0.28-1.52)
Increasing number of prescribed psychiatric medications 1.10 (1.01-1.20) 1.00 (0.86-1.16)

Outpatient prescription for antidepressant 1.10 (0.94-1.29) 0.86 (0.66-1.13)
Outpatient prescription for antipsychotic 1.03 (0.79-1.34) 0.93 (0.59-1.45)
Outpatient prescription for anxiolytic 1.16 (1.00–1.35) 1.13 (0.88-1.44)
Outpatient prescription for methadone or buprenorphine 1.15 (0.67-1.98) 0.18 (0.03-1.36)

Discharge diagnosis of depression 1.06 (0.86-1.30) 1.49 (1.09-2.04)
Discharge diagnosis of schizophrenia 1.43 (0.75-2.74) 2.63 (1.13-6.13)
Discharge diagnosis of bipolar disorder 0.53 (0.28-1.02) 0.35 (0.09-1.45)
Discharge diagnosis of anxiety 0.82 (0.68-0.99) 1.11 (0.83-1.49)
Discharge diagnosis of substance abuse 0.80 (0.65-0.99) 1.05 (0.75-1.46)
Discharge diagnosis of any psychiatric illness 0.88 (0.75-1.02) 1.22 (0.96-1.56)
Addiction team consult while inpatient 0.82 (0.58-1.17) 0.58 (0.29-1.17)

NOTE: Abbreviations: ACR, all-cause readmissions; CI, confidence interval; OR, odds ratio; PAR, poten-
tially avoidable readmissions. *All analyses performed by multivariate logistic regression adjusting for
patient age, gender, ethnicity, language spoken, marital status, insurance source, discharge location, length
of stay, comorbidities (Elixhauser), number of outpatient medications, number of prior emergency depart-
ment visits, and admissions in the prior year. Analyses were performed by entering each exposure of interest
into the model separately while adjusting for all covariates. Comparison group is patients without any read-
mission for all analyses.

Burke et al | Patients at Risk for 30-Day Readmission

454 An Official Publication of the Society of Hospital Medicine Journal of Hospital Medicine Vol 8 | No 8 | August 2013

to be conducted. Patients with polypharmacy, includ-
ing psychiatric medications, may benefit from inter-
ventions to improve medication safety, such as
enhanced medication reconciliation and pharmacist
counseling.26

Our study suggests that patients with depression,
those with schizophrenia, and those who have
increased numbers of prescribed psychiatric medica-
tions should be considered at high risk for readmis-
sion for medical illnesses. Targeting interventions to
these patients may be fruitful in preventing avoidable
readmissions.

Acknowledgements
The authors thank Dr. Yves Eggli for screening the database for poten-
tially avoidable readmissions using the SQLape algorithm.

Disclosures: Dr. Donz�e was supported by the Swiss National Science
Foundation and the Swiss Foundation for Medical–Biological Scholar-
ships. The authors otherwise have no conflicts of interest to disclose.
The content is solely the responsibility of the authors and does not nec-
essarily represent the official views of the US Department of Veterans
Affairs.

References
1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among

patients in the Medicare fee-for-service program. N Engl J Med. 2009;
360(14):1418–1428.

2. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models
for hospital readmission: a systematic review. JAMA. 2011;
306(15):1688–1698.

3. Axon RN, Williams MV. Hospital readmission as an accountability
measure. JAMA. 2011;305(5):504–505.

4. Arbaje AI, Wolff JL, Yu Q, Powe NR, Anderson GF, Boult C. Postdi-
scharge environmental and socioeconomic factors and the likelihood
of early hospital readmission among community-dwelling Medicare
beneficiaries. Gerontologist. 2008;48(4):495–504.

5. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low
health literacy and health outcomes: an updated systematic review.
Ann Intern Med. 2011;155(2):97–107.

6. Kartha A, Anthony D, Manasseh CS, et al. Depression is a risk factor
for rehospitalization in medical inpatients. Prim Care Companion J
Clin Psychiatry. 2007;9(4):256–262.

7. Li Y, Glance LG, Cai X, Mukamel DB. Mental illness and hospitaliza-
tion for ambulatory care sensitive medical conditions. Med Care.
2008;46(12):1249–1256.

8. Raven MC, Carrier ER, Lee J, Billings JC, Marr M, Gourevitch MN.
Substance use treatment barriers for patients with frequent hospital
admissions. J Subst Abuse Treat. 2010;38(1):22–30.

9. Shepard DS, Daley M, Ritter GA, Hodgkin D, Beinecke RH. Managed
care and the quality of substance abuse treatment. J Ment Health Pol-
icy Econ. 2002;5(4):163–174.

10. Rockett IR, Putnam SL, Jia H, Chang CF, Smith GS. Unmet substance
abuse treatment need, health services utilization, and cost: a popula-
tion-based emergency department study. Ann Emerg Med. 2005;
45(2):118–127.

11. Brennan PL, Kagay CR, Geppert JJ, Moos RH. Elderly Medicare inpa-
tients with substance use disorders: characteristics and predictors of
hospital readmissions over a four-year interval. J Stud Alcohol. 2000;
61(6):891–895.

12. Schnipper JL, Roumie CL, Cawthon C, et al. Rationale and design of
the Pharmacist Intervention for Low Literacy in Cardiovascular Dis-
ease (PILL-CVD) study. Circ Cardiovasc Qual Outcomes. 2010;
3(2):212–219.

13. Halfon P, Eggli Y, Pretre-Rohrbach I, Meylan D, Marazzi A, Burnand
B. Validation of the potentially avoidable hospital readmission rate as
a routine indicator of the quality of hospital care. Med Care. 2006;
44(11);972–981.

14. Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand
B. Measuring potentially avoidable hospital readmissions. J Clin Epi-
demiol. 2002;55:573–587.

15. Agency for Healthcare Research and Quality Quality Indicators.
(April 7, 2006). Prevention Quality Indicators (PQI) Composite Mea-
sure Workgroup Final Report. Available at: http://www.qualityindica-
tors.ahrq.gov/modules/pqi_resources.aspx. Accessed June 1, 2012.

16. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in
general medicine patients: a prediction model. J Gen Intern Med.
2010;25(3):211–219.

17. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of
an index to predict early death or unplanned readmission after dis-
charge from hospital to the community. CMAJ. 2010;182(6):
551–557.

18. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity meas-
ures for use with administrative data. Med Care. Jan 1998;36(1):
8–27.

19. Parks J, Svendsen D, Singer P, Foti ME, eds. Morbidity and mortality in peo-
ple with serious mental illness. October 2006. National Association of State
Mental Health Directors, Medical Directors Council. Available at: http://
www.nasmhpd.org/docs/publications/MDCdocs/Mortality%20and%20 Mo
rbidity%20Final%20Report%208.18.08 . Accessed January 13, 2013.

20. Kisely S, Smith M, Lawrence D, Maaten S. Mortality in individuals
who have had psychiatric treatment: population-based study in Nova
Scotia. Br J Psychiat. 2005;187:552–558.

21. Kisely S, Smith M, Lawrence D, Cox M, Campbell LA, Maaten S.
Inequitable access for mentally ill patients to some medically necessary
procedures. CMAJ. 2007;176(6):779–784.

22. Mitchell AJ, Malone D, Doebbeling CC. Quality of medical care for
people with and without comorbid mental illness and substance mis-
use: systematic review of comparative studies. Br J Psychiat. 2009;
194:491–499.

23. Kisely S, Preston N, Xiao J, Lawrence D, Louise S, Crowe E. Reducing
all-cause mortality among patients with psychiatric disorders: a popu-
lation-based study. CMAJ. 2013;185(1):E50–E56.

24. van Walraven C, Jennings A, Taljaard M, et al. Incidence of poten-
tially avoidable urgent readmissions and their relation to all-cause
urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.

25. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The inci-
dence and severity of adverse events affecting patients after discharge
from the hospital. Ann Intern Med. 2003;138(3):161–167.

26. Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based
medication reconciliation practices: a systematic review. Arch Intern
Med. 2012;172(14):1057–1069.

Patients at Risk for 30-Day Readmission | Burke et al

An Official Publication of the Society of Hospital Medicine Journal of Hospital Medicine Vol 8 | No 8 | August 2013 455

http://www.qualityindicators.ahrq.gov/modules/pqi_resources.aspx

http://www.nasmhpd.org/docs/publications/MDCdocs/Mortality%20and%20Morbidity%20Final%20Report%208.18.08

Turn in your highest-quality paper
Get a qualified writer to help you with

“ Rough Draft Qualitative Research Critique and Ethical Considerations ”

Get high-quality paper

NEW! AI matching with writer

Hire a Writer

Client Reviews

4.9

Sitejabber

4.6

Trustpilot

4.8

Our Guarantees

100% Confidentiality

Information about customers is confidential and never disclosed to third parties.

Original Writing

We complete all papers from scratch. You can get a plagiarism report.

Timely Delivery

No missed deadlines – 97% of assignments are completed in time.

Money Back

If you're confident that a writer didn't follow your order details, ask for a refund.

New to Your Trusted Assignment Help Service? Sign up & Save

Calculate the price of your order

Type of paper needed:

Pages:

You will get a personal manager and a discount.

Academic level:

We'll send you the first draft for approval by at

Total price:

$0.00

Power up Your Academic Success with the
Team of Professionals. We’ve Got Your Back.

Power up Your Study Success with Experts We’ve Got Your Back.

Order Now Order Now