A Systematic Review of Hospital Readmissions Among Patients With Cancer
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176 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM
Systematic Review of Hospital Readmissions
Among Patients With Cancer in the United States
Janice F. Bell, PhD, MN, MPH, Robin L. Whitney, RN, PhD, Sarah C. Reed, MSW, MPH,
Hermine Poghosyan, PhD, MPH, Rebecca S. Lash, PhD, MPP, RN,
Katherine K. Kim, PhD, MPH, MBA, Andra Davis, RN, MN, PhD, Richard J. Bold, MD,
and Jill G. Joseph, MD, PhD
ARTICLE
C ancer care has been declared a crisis in the United States because of
the growing demand for services, increasing complexity of treatment,
and dramatically rising costs of care (Institute of Medicine [IOM],
2013). Some 1.6 million individuals are diagnosed with cancer each
year, and the number of cancer survivors is projected to increase
dramatically because of the aging population and improvements in treatment
(American Cancer Society [ACS], 2016; IOM, 2013). By 2020, cancer care costs
are expected to reach $173 billion, reflecting a considerable increase from $72
billion in 2004 (ACS, 2014; Smith & Hillner, 2011). At the same time, national
reports criticize the quality of cancer care, calling for greater patient-centered
focus; improved care coordination, with management of care transitions across
settings; and cost containment through the reduction of preventable healthcare
use (IOM, 2013; Smith & Hillner, 2011).
Programs and policies to reduce hospital readmissions are increasingly viewed
as promising avenues to reduce spending and improve healthcare quality and
efficiency as well as patient experiences (Naylor, Aiken, Kurtzman, Olds, &
Hirschman, 2011; Robert Wood Johnson Foundation [RWJF], 2013; Schoen, Os-
Purpose/Objectives: To review the existing literature on readmission rates, predictors,
and reasons for readmission among adults with cancer.
Data Sources: U.S.-based empirical studies reporting readmission rates from January 2005
to December 2015 were identified using four online library databases—PubMed, CINAHL®,
EconLit, and the online bibliography of the National Cancer Institute’s Surveillance Epide-
miology and End Results Program. Some articles were identified by the authors outside
the database and bibliography searches.
Data Synthesis: Of the 1,219 abstracts and 271 full-text articles screened, 56 studies
met inclusion criteria. The highest readmission rates were observed in patients with blad-
der, pancreatic, ovarian, or liver cancer. Significant predictors of readmission included
comorbidities, older age, advanced disease, and index length of hospital stay. Common
reasons for readmission included gastrointestinal and surgical complications, infection,
and dehydration.
Conclusions: Clinical efforts to reduce the substantial readmission rates among adults
with cancer may target high-rate conditions, infection prevention, proactive management
of nausea and vomiting, and nurse-led care coordination interventions for older adult
patients with multiple comorbid conditions and advanced cancer.
Implications for Nursing: Commonly reported reasons for readmission were nursing-sensitive
patient outcomes (NSPOs), amenable to nursing intervention in oncology settings. These
findings underscore the important role oncology nurses play in readmission prevention by
implementing evidence-based interventions to address NSPOs and testing their impact
in future research.
Bell is an associate professor in the Betty
Irene Moore School of Nursing at the Uni-
versity of California (UC) Davis; Whitney is
an associate professor in the Department
of Internal Medicine at UC San Francisco
in Fresno; Reed is a doctoral candidate in
the Betty Irene Moore School of Nursing
at UC Davis; Poghosyan is an assistant
professor in the School of Nursing in
the Bouvé College of Health Sciences at
Northeastern University in Boston, MA;
Lash is a nursing publications manager
in the Department of Nursing Practice,
Research, and Education at UCLA Health
System; Kim is an assistant professor in
the Betty Irene Moore School of Nursing at
UC Davis; Davis is an assistant professor
in the College of Nursing at Washington
State University in Vancouver; Bold is a
physician at the UC Davis Comprehensive
Cancer Center in Sacramento; and Joseph
is an associate dean for research and a
professor in the Betty Irene Moore School
of Nursing at UC Davis.
Bell, Poghosyan, Lash, Kim, Bold, and
Joseph contributed to the conceptualiza-
tion and design. Bell, Whitney, Reed,
Poghosyan, and Lash completed the data
collection. Bell and Poghosyan provided
statistical support. Bell, Whitney, Reed,
Poghosyan, Lash, Kim, and Joseph provid-
ed the analysis. Bell, Whitney, Reed, Lash,
Kim, Davis, Bold, and Joseph contributed
to the manuscript preparation.
Bell can be reached at jfbell@ucdavis.edu,
with copy to editor at ONFEditor@ons.org.
Submitted May 2016. Accepted for publi-
cation July 12, 2016.
Keywords: clinical practice; nursing re-
search quantitative; outcomes research
ONF, 44(2), 176–191.
doi: 10.1011/17.ONF.176-191Downloaded on 12 05 2018. Single-user license only. Copyright 2018 by the Oncology Nursing Society. For permission to post online, reprint, adapt, or reuse, please email pubpermissions@ons.org
Systematic Review of Hospital Readmissions
Among Patients With Cancer in the United States
Janice F. Bell, PhD, MN, MPH, Robin L. Whitney, RN, PhD, Sarah C. Reed, MSW, MPH,
Hermine Poghosyan, PhD, MPH, Rebecca S. Lash, PhD, MPP, RN,
Katherine K. Kim, PhD, MPH, MBA, Andra Davis, RN, MN, PhD, Richard J. Bold, MD,
and Jill G. Joseph, MD, PhD
ARTICLE
C ancer care has been declared a crisis in the United States because of
the growing demand for services, increasing complexity of treatment,
and dramatically rising costs of care (Institute of Medicine [IOM],
2013). Some 1.6 million individuals are diagnosed with cancer each
year, and the number of cancer survivors is projected to increase
dramatically because of the aging population and improvements in treatment
(American Cancer Society [ACS], 2016; IOM, 2013). By 2020, cancer care costs
are expected to reach $173 billion, reflecting a considerable increase from $72
billion in 2004 (ACS, 2014; Smith & Hillner, 2011). At the same time, national
reports criticize the quality of cancer care, calling for greater patient-centered
focus; improved care coordination, with management of care transitions across
settings; and cost containment through the reduction of preventable healthcare
use (IOM, 2013; Smith & Hillner, 2011).
Programs and policies to reduce hospital readmissions are increasingly viewed
as promising avenues to reduce spending and improve healthcare quality and
efficiency as well as patient experiences (Naylor, Aiken, Kurtzman, Olds, &
Hirschman, 2011; Robert Wood Johnson Foundation [RWJF], 2013; Schoen, Os-
Purpose/Objectives: To review the existing literature on readmission rates, predictors,
and reasons for readmission among adults with cancer.
Data Sources: U.S.-based empirical studies reporting readmission rates from January 2005
to December 2015 were identified using four online library databases—PubMed, CINAHL®,
EconLit, and the online bibliography of the National Cancer Institute’s Surveillance Epide-
miology and End Results Program. Some articles were identified by the authors outside
the database and bibliography searches.
Data Synthesis: Of the 1,219 abstracts and 271 full-text articles screened, 56 studies
met inclusion criteria. The highest readmission rates were observed in patients with blad-
der, pancreatic, ovarian, or liver cancer. Significant predictors of readmission included
comorbidities, older age, advanced disease, and index length of hospital stay. Common
reasons for readmission included gastrointestinal and surgical complications, infection,
and dehydration.
Conclusions: Clinical efforts to reduce the substantial readmission rates among adults
with cancer may target high-rate conditions, infection prevention, proactive management
of nausea and vomiting, and nurse-led care coordination interventions for older adult
patients with multiple comorbid conditions and advanced cancer.
Implications for Nursing: Commonly reported reasons for readmission were nursing-sensitive
patient outcomes (NSPOs), amenable to nursing intervention in oncology settings. These
findings underscore the important role oncology nurses play in readmission prevention by
implementing evidence-based interventions to address NSPOs and testing their impact
in future research.
Bell is an associate professor in the Betty
Irene Moore School of Nursing at the Uni-
versity of California (UC) Davis; Whitney is
an associate professor in the Department
of Internal Medicine at UC San Francisco
in Fresno; Reed is a doctoral candidate in
the Betty Irene Moore School of Nursing
at UC Davis; Poghosyan is an assistant
professor in the School of Nursing in
the Bouvé College of Health Sciences at
Northeastern University in Boston, MA;
Lash is a nursing publications manager
in the Department of Nursing Practice,
Research, and Education at UCLA Health
System; Kim is an assistant professor in
the Betty Irene Moore School of Nursing at
UC Davis; Davis is an assistant professor
in the College of Nursing at Washington
State University in Vancouver; Bold is a
physician at the UC Davis Comprehensive
Cancer Center in Sacramento; and Joseph
is an associate dean for research and a
professor in the Betty Irene Moore School
of Nursing at UC Davis.
Bell, Poghosyan, Lash, Kim, Bold, and
Joseph contributed to the conceptualiza-
tion and design. Bell, Whitney, Reed,
Poghosyan, and Lash completed the data
collection. Bell and Poghosyan provided
statistical support. Bell, Whitney, Reed,
Poghosyan, Lash, Kim, and Joseph provid-
ed the analysis. Bell, Whitney, Reed, Lash,
Kim, Davis, Bold, and Joseph contributed
to the manuscript preparation.
Bell can be reached at jfbell@ucdavis.edu,
with copy to editor at ONFEditor@ons.org.
Submitted May 2016. Accepted for publi-
cation July 12, 2016.
Keywords: clinical practice; nursing re-
search quantitative; outcomes research
ONF, 44(2), 176–191.
doi: 10.1011/17.ONF.176-191Downloaded on 12 05 2018. Single-user license only. Copyright 2018 by the Oncology Nursing Society. For permission to post online, reprint, adapt, or reuse, please email pubpermissions@ons.org
ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 177
born, How, Doty, & Peugh, 2009). Hospital stays are
stressful and inconvenient for patients and their fam-
ilies, and substantially contribute to out-of-pocket
healthcare costs. One aim of the Patient Protection
and Affordable Care Act is to reduce healthcare
spending through improved outpatient management
of chronic disease and reduced hospital readmis-
sions (Carroll & Frakt, 2013; Kocher & Adashi, 2011).
Likewise, the Center for Medicaid and Medicare
Innovation instituted a five-year Community Care
Transitions Program to test models for improving
patient transitions from hospitals to other settings
and avoiding unnecessary readmissions (Agency
for Healthcare Research and Quality [AHRQ], 2014;
Kocher & Adashi, 2011). Such initiatives are built on
the assumption that some readmissions are prevent-
able; the validity of readmission rates as indicators
of healthcare quality depends on this premise (Gold-
field et al., 2008).
Oncology nurses play important roles in prevent-
ing readmission from the moment patients are ad-
mitted to hospitals by identifying and addressing
complications and adverse inpatient events that
may increase readmission risk, assessing patient and
family knowledge, providing education throughout
the hospital stay and in preparation for discharge,
assisting with medication management, support-
ing advanced care planning, and coordinating care
transitions between inpatient and community-based
providers and services (Feigenbaum et al., 2012;
Naylor et al., 2011). Indeed, a growing body of evi-
dence suggests that multicomponent interventions
focused on care transitions and incorporations of
strategies—such as comprehensive discharge plan-
ning and instructions with follow-up, home visits,
individualized care planning, clinical management,
education, and behavioral support—may be effec-
tive in reducing readmission rates (Coleman, Parry,
Chalmers, & Min, 2006; Epstein, Jha, & Orav, 2011;
Feigenbaum et al., 2012; Hansen, Young, Hinami,
Leung, & Williams, 2011; Hari & Rosenzweig, 2012;
Jack et al., 2009; Naylor et al., 2011; Peikes, Chen,
Schore, & Brown, 2009; VanSuch, Naessens, Stroebel,
Huddleston, & Williams, 2006).
Successful nursing interventions to reduce re-
admission depend on identifying groups at risk for
preventable readmission; however, the burden of
readmissions for patients with cancer is not well
described in extant literature, nor is the extent to
which readmissions are preventable in this popula-
tion. To date, cancer-specific readmission rates are
not publicly reported, and the Centers for Medicare
and Medicaid Services (CMS) penalties for readmis-
sions do not apply to cancer hospitals (Horwitz et
al., 2012). In a predictive model of avoidable read-
missions developed at a large academic medical
center (Donzé, Aujesky, Williams, & Schnipper, 2013),
discharge from an oncology service was a significant
risk factor, even when excluding planned readmis-
sions for chemotherapy. Similarly, a Canadian study
(Ji, Abushomar, Chen, Qian, & Gerson, 2012) found
that the all-cause readmission rates of patients with
cancer were higher than the rates of patients with
other conditions. Whether these findings are relevant
to the unique U.S. clinical, payment, and healthcare
policy environment is unknown.
Studies of readmissions among patients with cancer
in the United States are needed to ascertain the extent
of this population’s risk for readmission, to identify
subgroups that might benefit from interventions to
reduce readmissions, and to provide benchmarks
against which to measure the success of such
interventions. Accordingly, this systematic literature
review had three related aims focused on patients
with cancer: (a) to examine the proportion of patients
with cancer who are readmitted to the hospital within
30 days of discharge, (b) to enumerate the reasons
for and predictors of readmissions, and (c) to assess
whether and how current studies identify potentially
preventable readmissions.
Methods
Following the Preferred Reporting Items for System-
atic Reviews and Meta-Analyses (PRISMA) guidelines
(Moher, Liberati, Tetzlaff, & Altman, 2009), the authors
of the current study searched three electronic library
databases (PubMed, CINAHL®, EconLit) and the online
bibliography of the National Cancer Institute’s Surveil-
lance Epidemiology and End Results (SEER) Program.
The Medical Subject Heading (MeSH) terms patient
readmission and neoplasms or neoplasm metastasis
or carcinoma were employed in the PubMed search.
The keywords readmission(s) or rehospitalization(s)
were used in the EconLit search, which was limited
to publications in analysis of healthcare markets,
health, government policy, regulation, public health,
and health production. The subject headings readmis-
sion and neoplasms were employed in the CINAHL
search. The SEER bibliography search focused on
the keywords readmission(s) or rehospitalization(s) in
abstracts and titles. In addition, the authors identified
relevant articles outside the database and bibliogra-
phy searches.
Inclusion and Exclusion Criteria
The inclusion criteria included (a) peer-reviewed
empirical studies conducted in the United States, (b)
articles published from January 1, 2005, to Decem-
ber 31, 2015, (c) articles with sample sizes of 50 or
born, How, Doty, & Peugh, 2009). Hospital stays are
stressful and inconvenient for patients and their fam-
ilies, and substantially contribute to out-of-pocket
healthcare costs. One aim of the Patient Protection
and Affordable Care Act is to reduce healthcare
spending through improved outpatient management
of chronic disease and reduced hospital readmis-
sions (Carroll & Frakt, 2013; Kocher & Adashi, 2011).
Likewise, the Center for Medicaid and Medicare
Innovation instituted a five-year Community Care
Transitions Program to test models for improving
patient transitions from hospitals to other settings
and avoiding unnecessary readmissions (Agency
for Healthcare Research and Quality [AHRQ], 2014;
Kocher & Adashi, 2011). Such initiatives are built on
the assumption that some readmissions are prevent-
able; the validity of readmission rates as indicators
of healthcare quality depends on this premise (Gold-
field et al., 2008).
Oncology nurses play important roles in prevent-
ing readmission from the moment patients are ad-
mitted to hospitals by identifying and addressing
complications and adverse inpatient events that
may increase readmission risk, assessing patient and
family knowledge, providing education throughout
the hospital stay and in preparation for discharge,
assisting with medication management, support-
ing advanced care planning, and coordinating care
transitions between inpatient and community-based
providers and services (Feigenbaum et al., 2012;
Naylor et al., 2011). Indeed, a growing body of evi-
dence suggests that multicomponent interventions
focused on care transitions and incorporations of
strategies—such as comprehensive discharge plan-
ning and instructions with follow-up, home visits,
individualized care planning, clinical management,
education, and behavioral support—may be effec-
tive in reducing readmission rates (Coleman, Parry,
Chalmers, & Min, 2006; Epstein, Jha, & Orav, 2011;
Feigenbaum et al., 2012; Hansen, Young, Hinami,
Leung, & Williams, 2011; Hari & Rosenzweig, 2012;
Jack et al., 2009; Naylor et al., 2011; Peikes, Chen,
Schore, & Brown, 2009; VanSuch, Naessens, Stroebel,
Huddleston, & Williams, 2006).
Successful nursing interventions to reduce re-
admission depend on identifying groups at risk for
preventable readmission; however, the burden of
readmissions for patients with cancer is not well
described in extant literature, nor is the extent to
which readmissions are preventable in this popula-
tion. To date, cancer-specific readmission rates are
not publicly reported, and the Centers for Medicare
and Medicaid Services (CMS) penalties for readmis-
sions do not apply to cancer hospitals (Horwitz et
al., 2012). In a predictive model of avoidable read-
missions developed at a large academic medical
center (Donzé, Aujesky, Williams, & Schnipper, 2013),
discharge from an oncology service was a significant
risk factor, even when excluding planned readmis-
sions for chemotherapy. Similarly, a Canadian study
(Ji, Abushomar, Chen, Qian, & Gerson, 2012) found
that the all-cause readmission rates of patients with
cancer were higher than the rates of patients with
other conditions. Whether these findings are relevant
to the unique U.S. clinical, payment, and healthcare
policy environment is unknown.
Studies of readmissions among patients with cancer
in the United States are needed to ascertain the extent
of this population’s risk for readmission, to identify
subgroups that might benefit from interventions to
reduce readmissions, and to provide benchmarks
against which to measure the success of such
interventions. Accordingly, this systematic literature
review had three related aims focused on patients
with cancer: (a) to examine the proportion of patients
with cancer who are readmitted to the hospital within
30 days of discharge, (b) to enumerate the reasons
for and predictors of readmissions, and (c) to assess
whether and how current studies identify potentially
preventable readmissions.
Methods
Following the Preferred Reporting Items for System-
atic Reviews and Meta-Analyses (PRISMA) guidelines
(Moher, Liberati, Tetzlaff, & Altman, 2009), the authors
of the current study searched three electronic library
databases (PubMed, CINAHL®, EconLit) and the online
bibliography of the National Cancer Institute’s Surveil-
lance Epidemiology and End Results (SEER) Program.
The Medical Subject Heading (MeSH) terms patient
readmission and neoplasms or neoplasm metastasis
or carcinoma were employed in the PubMed search.
The keywords readmission(s) or rehospitalization(s)
were used in the EconLit search, which was limited
to publications in analysis of healthcare markets,
health, government policy, regulation, public health,
and health production. The subject headings readmis-
sion and neoplasms were employed in the CINAHL
search. The SEER bibliography search focused on
the keywords readmission(s) or rehospitalization(s) in
abstracts and titles. In addition, the authors identified
relevant articles outside the database and bibliogra-
phy searches.
Inclusion and Exclusion Criteria
The inclusion criteria included (a) peer-reviewed
empirical studies conducted in the United States, (b)
articles published from January 1, 2005, to Decem-
ber 31, 2015, (c) articles with sample sizes of 50 or
178 VOL. 44, NO. 2, MARCH 2017 • ONCOLOGY NURSING FORUM
more, and (d) studies that identified the proportion
of readmissions among patients with cancer aged 18
years or older. Articles were excluded if they were
(a) reports of a literature review, meta-analysis,
commentary, or case study; (b) focused solely on
health service use at the end of life, given higher
expected rates of readmissions attributed to con-
founding by progression of disease; or (c) presented
readmission rates that were not exclusive to patients
with cancer.
Screening Process
All citations were managed in EndNote X7, and
duplicates were discarded. A two-stage screening
process was applied to assess whether articles met
inclusion criteria, with all articles screened by the
lead author and at least one other investigator. In
the first stage, the authors searched all EndNote
fields, including titles and abstracts, for the keywords
readmission(s) or rehospitalization(s). Articles were
retrieved and the full text examined if they could
not be included or excluded based on the EndNote
keyword search, as in the case of scanned papers.
In the second stage of the review, the full text of all
included papers from the first stage was obtained and
examined against the inclusion and exclusion criteria
independently by at least two investigators. All the
references of the included articles, meta-analyses,
and review papers identified during the review were
iteratively examined.
Data Abstraction
Included studies were sorted into one
of two groups according to their focus on
a single institution (hospital or medical
center) or multiple institutions. A stan-
dardized abstraction form was developed
to systematically collect and summarize
key data elements from each article. The
authors of the current study calculated
30-day readmission rates for articles pre-
senting readmission rates in time frames
other than 30 days, assuming a constant
rate of readmission over time. This ap-
proach yielded conservative 30-day read-
mission estimates because most readmis-
sions occur within the first 30 days and
decline afterward (Benbassat & Taragin,
2000). Most studies using alternative time
frames reported readmissions within time
frames longer than 30 days. Significant
predictors of readmission from the results
of multivariate regression models were re-
corded, as were the most common reasons
for readmission, if specified in the articles.
Finally, the authors examined the studies
to ascertain whether the readmissions were classified
as potentially preventable and, if so, they recorded the
definition. At least 90% agreement was reached in each
stage of the review, with discrepancies resolved by the
consensus of all participating authors.
Results
After duplicates were discarded, a total of 1,219
articles were collected from the combined searches
of PubMed, EconLit, CINAHL, the SEER bibliographic
database, and studies found outside the search crite-
ria by the authors (see Figure 1). Of these, 948 studies
were excluded based on a review of the abstracts,
titles, and keywords. The full text of the remaining 271
articles was reviewed, and 215 were excluded, primar-
ily because they did not measure readmission, the
readmission data were not specific to patients with
cancer, or they were not based in the United States. In
total, 56 studies met the inclusion criteria, including
24 single-institution and 32 multiple-institution stud-
ies (see Table 1).
Characteristics of the Studies
Almost all the studies examined readmissions fol-
lowing surgical (n = 53) rather than medical index ad-
missions. Most used retrospective cohort designs (n =
52), with the remainder using prospective consecutive
cohort designs. Most single-institution studies relied
on a review of medical records, while cancer registry
FIGURE 1. Selection of Studies Examining Hospital Readmissions
Records identified
through database
searching
(n = 1,864)
Additional records
identified through
other sources
(n = 74)
Records screened after
duplicates were removed
(n = 1,219)
Full-text articles assessed
for eligibility
(n = 271)
Included in review
(N = 56)
Excluded (n = 948)
Based on review of abstract,
titles, and keywords
Excluded (n = 215)
• Readmission proportion
not reported (n = 93)
• Readmission proportion
not cancer-specific (n = 53)
• Not based in the United
States (n = 46)
• Did not meet other inclu-
sion criteria (n = 23)
more, and (d) studies that identified the proportion
of readmissions among patients with cancer aged 18
years or older. Articles were excluded if they were
(a) reports of a literature review, meta-analysis,
commentary, or case study; (b) focused solely on
health service use at the end of life, given higher
expected rates of readmissions attributed to con-
founding by progression of disease; or (c) presented
readmission rates that were not exclusive to patients
with cancer.
Screening Process
All citations were managed in EndNote X7, and
duplicates were discarded. A two-stage screening
process was applied to assess whether articles met
inclusion criteria, with all articles screened by the
lead author and at least one other investigator. In
the first stage, the authors searched all EndNote
fields, including titles and abstracts, for the keywords
readmission(s) or rehospitalization(s). Articles were
retrieved and the full text examined if they could
not be included or excluded based on the EndNote
keyword search, as in the case of scanned papers.
In the second stage of the review, the full text of all
included papers from the first stage was obtained and
examined against the inclusion and exclusion criteria
independently by at least two investigators. All the
references of the included articles, meta-analyses,
and review papers identified during the review were
iteratively examined.
Data Abstraction
Included studies were sorted into one
of two groups according to their focus on
a single institution (hospital or medical
center) or multiple institutions. A stan-
dardized abstraction form was developed
to systematically collect and summarize
key data elements from each article. The
authors of the current study calculated
30-day readmission rates for articles pre-
senting readmission rates in time frames
other than 30 days, assuming a constant
rate of readmission over time. This ap-
proach yielded conservative 30-day read-
mission estimates because most readmis-
sions occur within the first 30 days and
decline afterward (Benbassat & Taragin,
2000). Most studies using alternative time
frames reported readmissions within time
frames longer than 30 days. Significant
predictors of readmission from the results
of multivariate regression models were re-
corded, as were the most common reasons
for readmission, if specified in the articles.
Finally, the authors examined the studies
to ascertain whether the readmissions were classified
as potentially preventable and, if so, they recorded the
definition. At least 90% agreement was reached in each
stage of the review, with discrepancies resolved by the
consensus of all participating authors.
Results
After duplicates were discarded, a total of 1,219
articles were collected from the combined searches
of PubMed, EconLit, CINAHL, the SEER bibliographic
database, and studies found outside the search crite-
ria by the authors (see Figure 1). Of these, 948 studies
were excluded based on a review of the abstracts,
titles, and keywords. The full text of the remaining 271
articles was reviewed, and 215 were excluded, primar-
ily because they did not measure readmission, the
readmission data were not specific to patients with
cancer, or they were not based in the United States. In
total, 56 studies met the inclusion criteria, including
24 single-institution and 32 multiple-institution stud-
ies (see Table 1).
Characteristics of the Studies
Almost all the studies examined readmissions fol-
lowing surgical (n = 53) rather than medical index ad-
missions. Most used retrospective cohort designs (n =
52), with the remainder using prospective consecutive
cohort designs. Most single-institution studies relied
on a review of medical records, while cancer registry
FIGURE 1. Selection of Studies Examining Hospital Readmissions
Records identified
through database
searching
(n = 1,864)
Additional records
identified through
other sources
(n = 74)
Records screened after
duplicates were removed
(n = 1,219)
Full-text articles assessed
for eligibility
(n = 271)
Included in review
(N = 56)
Excluded (n = 948)
Based on review of abstract,
titles, and keywords
Excluded (n = 215)
• Readmission proportion
not reported (n = 93)
• Readmission proportion
not cancer-specific (n = 53)
• Not based in the United
States (n = 46)
• Did not meet other inclu-
sion criteria (n = 23)
ONCOLOGY NURSING FORUM • VOL. 44, NO. 2, MARCH 2017 179
TABLE 1. Studies of Readmissions Among Patients With Cancer (N = 56)
Readmission
Study Samplea Data Source Definition Rateb
Single Institution (n = 24)
Ahmad et al.,
2014
419 patients with gastric cancer, 49% at an advanced stage,
with a median age of 68 years. Patients underwent surgery
related to their cancer; about 50% reported comorbidities.
Hospital database,
medical records
30 days 15%
AlHilli et al.,
2015
538 patients with ovarian cancer, 77% at an advanced stage,
with a mean age of 63 years. Patients underwent surgery
related to their cancer; about 58% reported comorbidities.
Hospital database,
medical records
30 days 19%
Clark et al.,
2013
460 patients with ovarian cancer, 87% at an advanced stage,
with a median age of 61 years. Patients underwent surgery
related to their cancer; 65% reported comorbidities.
Medical records 30 days 12%
Dedania et
al., 2013
70 patients with pancreatic cancer, 54% at an advanced stage,
with a mean age of 66 years. Patients underwent surgery
related to their cancer.
Hospital database,
medical records
30 days 29%
Dickinson et
al., 2015c
362 patients with brain cancer, with a median age of 63 years.
Patients underwent surgery related to their cancer.
Hospital database,
medical records
30 days 8%
Doll et al.,
2014
152 patients with gynecologic cancer, 30 at an advanced
stage, with a median age of 59 years. About 64% reported
comorbidities.
CRPR, hospital data-
base, medical records
30 days 12%
Fauci et al.,
2011
207 patients with ovarian cancer, 84% at an advanced stage,
with a mean age of 64 years. Patients underwent surgery
related to their cancer.
Hospital database 30 days 16%
Glasgow et
al., 2014
53 patients with gynecologic cancer, 90% at an advanced
stage, with a median age of 63 years. Patients underwent
surgery related to their cancer; about 42% reported comor-
bidities.
Medical records 30 days 34%
Grant et al.,
2005
100 patients with hematologic cancer, with a mean age of 45
years. Patients underwent a medical procedure related to their
cancer; 34% reported comorbidities.
Medical records 180 days 8%
Gustafson et
al., 2012c
76 patients with hepatic cancer, with a mean age of 57 years.
Patients underwent surgery related to their cancer.
CRPR, research
database
30 days 15%
Hari & Rosen-
zweig, 2012
62 patients with pancreatic cancer underwent surgery related
to their cancer.
Medical records,
research database
90 days 9%
Kastenberg
et al., 2013
257 patients with pancreatic cancer, with a mean age of 65
years. Patients underwent surgery related to their cancer.
Medical records 30 days 18%
Kimbrough et
al., 2014
245 patients with hepatic cancer, with a median age of 59
years. Patients underwent surgery related to their cancer;
about 41% reported comorbidities.
Medical records 30, 60, and
90 days
11%
Klos et al.,
2014
235 patients with colon cancer, 64% at an advanced stage,
with a mean age of 72 years. Patients underwent surgery
related to their cancer; 91% reported comorbidities.
Medical records 30 days 8%
Liang et al.,
2013
395 with endometrial cancer, with a mean age of 61 years.
Patients underwent surgery related to their cancer; 62% re-
ported comorbidities.
Medical records 90 days < 3%
Offodile et
al., 2015
249 patients with head and neck cancer, 46% at an advanced
stage, with a mean age of 59 years. Patients underwent surgery
related to their cancer; 74% reported comorbidities.
Medical records 30 days 15%
Continued on the next page
TABLE 1. Studies of Readmissions Among Patients With Cancer (N = 56)
Readmission
Study Samplea Data Source Definition Rateb
Single Institution (n = 24)
Ahmad et al.,
2014
419 patients with gastric cancer, 49% at an advanced stage,
with a median age of 68 years. Patients underwent surgery
related to their cancer; about 50% reported comorbidities.
Hospital database,
medical records
30 days 15%
AlHilli et al.,
2015
538 patients with ovarian cancer, 77% at an advanced stage,
with a mean age of 63 years. Patients underwent surgery
related to their cancer; about 58% reported comorbidities.
Hospital database,
medical records
30 days 19%
Clark et al.,
2013
460 patients with ovarian cancer, 87% at an advanced stage,
with a median age of 61 years. Patients underwent surgery
related to their cancer; 65% reported comorbidities.
Medical records 30 days 12%
Dedania et
al., 2013
70 patients with pancreatic cancer, 54% at an advanced stage,
with a mean age of 66 years. Patients underwent surgery
related to their cancer.
Hospital database,
medical records
30 days 29%
Dickinson et
al., 2015c
362 patients with brain cancer, with a median age of 63 years.
Patients underwent surgery related to their cancer.
Hospital database,
medical records
30 days 8%
Doll et al.,
2014
152 patients with gynecologic cancer, 30 at an advanced
stage, with a median age of 59 years. About 64% reported
comorbidities.
CRPR, hospital data-
base, medical records
30 days 12%
Fauci et al.,
2011
207 patients with ovarian cancer, 84% at an advanced stage,
with a mean age of 64 years. Patients underwent surgery
related to their cancer.
Hospital database 30 days 16%
Glasgow et
al., 2014
53 patients with gynecologic cancer, 90% at an advanced
stage, with a median age of 63 years. Patients underwent
surgery related to their cancer; about 42% reported comor-
bidities.
Medical records 30 days 34%
Grant et al.,
2005
100 patients with hematologic cancer, with a mean age of 45
years. Patients underwent a medical procedure related to their
cancer; 34% reported comorbidities.
Medical records 180 days 8%
Gustafson et
al., 2012c
76 patients with hepatic cancer, with a mean age of 57 years.
Patients underwent surgery related to their cancer.
CRPR, research
database
30 days 15%
Hari & Rosen-
zweig, 2012
62 patients with pancreatic cancer underwent surgery related
to their cancer.
Medical records,
research database
90 days 9%
Kastenberg
et al., 2013
257 patients with pancreatic cancer, with a mean age of 65
years. Patients underwent surgery related to their cancer.
Medical records 30 days 18%
Kimbrough et
al., 2014
245 patients with hepatic cancer, with a median age of 59
years. Patients underwent surgery related to their cancer;
about 41% reported comorbidities.
Medical records 30, 60, and
90 days
11%
Klos et al.,
2014
235 patients with colon cancer, 64% at an advanced stage,
with a mean age of 72 years. Patients underwent surgery
related to their cancer; 91% reported comorbidities.
Medical records 30 days 8%
Liang et al.,
2013
395 with endometrial cancer, with a mean age of 61 years.
Patients underwent surgery related to their cancer; 62% re-
ported comorbidities.
Medical records 90 days < 3%
Offodile et
al., 2015
249 patients with head and neck cancer, 46% at an advanced
stage, with a mean age of 59 years. Patients underwent surgery
related to their cancer; 74% reported comorbidities.
Medical records 30 days 15%
Continued on the next page
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