A Quality Improvement Collaborative to Improve the Discharge Process for Hospitalized ChildrenSusan Wu, MD, a, b Amy Tyler, MD, c, d Tina Logsdon, MS, e Nicholas M. Holmes, MD, MBA, f, g Ara Balkian, MD, MBA, a, b Mark Brittan, MD, MPH, c, d LaVonda Hoover, BSN, CPN, MS, b Sara Martin, RN, BSN, d Melisa Paradis, MSN, RN, CPN, h Rhonda Sparr-Perkins, RN, MBA, g Teresa Stanley, DNP, RN, i Rachel Weber, MSIE, g Michele Saysana, MDi, j
Although discharge from the hospital
for many pediatric patients means the
child is clinically improving, it also
creates potential risk because of the
transition of care. 1 At a minimum this
care may include medications and
follow-up appointments, but it may
also include home care, wound care,
or therapy. The discharge process
has historically been fragmented
and variable, leading to errors. 2 – 4
In 1 adult study, as many as 49% of
patients had ≥1 medication error at
discharge, which could increase their
likelihood for readmission.5 In other
studies, 10% to 20% of patients had
an adverse event after discharge, with
about half of these events deemed to
be preventable. 6, 7
Most of the work on improving
discharge processes to date has
abstractOBJECTIVE: To assess the impact of a quality improvement collaborative on
quality and efficiency of pediatric discharges.
METHODS: This was a multicenter quality improvement collaborative including
11 tertiary-care freestanding children’s hospitals in the United States,
conducted between November 1, 2011 and October 31, 2012. Sites selected
interventions from a change package developed by an expert panel. Multiple
plan–do–study–act cycles were conducted on patient populations selected
by each site. Data on discharge-related care failures, family readiness for
discharge, and 72-hour and 30-day readmissions were reported monthly
by each site. Surveys of each site were also conducted to evaluate the use of
various change strategies.
RESULTS: Most sites addressed discharge planning, quality of discharge
instructions, and providing postdischarge support by phone. There was
a significant decrease in discharge-related care failures, from 34% in the
first project quarter to 21% at the end of the collaborative (P < .05). There
was also a significant improvement in family perception of readiness for
discharge, from 85% of families reporting the highest rating to 91%
(P < .05). There was no improvement in unplanned 72-hour (0.7% vs 1.1%,
P = .29) and slight worsening of the 30-day readmission rate (4.5% vs 6.3%,
P = .05).
CONCLUSIONS: Institutions that participated in the collaborative had lower
rates of discharge-related care failures and improved family readiness
for discharge. There was no significant improvement in unplanned
readmissions. More studies are needed to evaluate which interventions are
most effective and to assess feasibility in non–children’s hospital settings.
QUALITY REPORTPEDIATRICS Volume 138 , number 2 , August 2016 :e 20143604
To cite: Wu S, Tyler A, Logsdon T, et al. A Quality
Improvement Collaborative to Improve the
Discharge Process for Hospitalized Children.
Pediatrics. 2016;138(2):e20143604
aDepartment of Pediatrics, University of Southern
California Keck School of Medicine, Los Angeles, California; bChildren’s Hospital Los Angeles, Los Angeles, California; cDepartment of Pediatrics, University of Colorado School of
Medicine, Denver, Colorado; dChildren’s Hospital Colorado,
Aurora, Colorado; eChildren’s Hospital Association,
Overland Park, Kansas; fDepartment of Surgery, Division
of Urology, University of California San Diego, San Diego,
California; gRady Children’s Hospital San Diego, San
Diego, California; hChildren’s Hospital & Medical Center,
Omaha, Nebraska; iRiley Hospital for Children at Indiana
University Health, Indianapolis, Indiana; and jDepartment
of Pediatrics, Indiana University School of Medicine,
Indianapolis, Indiana
Dr Wu conceptualized and designed the study,
participated in data collection, assisted in data
analysis, drafted the initial manuscript, and
critically reviewed and revised the manuscript;
Drs Tyler, Brittan, and Saysana and Ms Hoover,
Ms Martin, and Ms Stanley conceptualized and
designed the study, participated in data collection,
drafted the initial manuscript, and critically
reviewed and revised the manuscript; Ms Logsdon
conceptualized and designed the study, supervised
data collection and analysis, drafted the initial
manuscript, and critically reviewed and revised
the manuscript; Dr Holmes conceptualized and
designed the study, participated in development of
data collection instruments, and drafted the initial
manuscript; Dr Balkian, Ms Paradis, and Ms Sparr-
Perkins conceptualized and designed the study,
participated in data collection, and drafted the
initial manuscript; Ms Weber conceptualized and
designed the study, participated in development of
data collection instruments, participated in data
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focused on the adult population.
Examples of these projects include
the Better Outcomes for Older Adults
Through Safe Transitions Project,
sponsored by the Society for Hospital
Medicine; Project Re-Engineered
Discharge, sponsored by the Agency
for Healthcare Research and Quality,
National Heart, Lung and Blood
Institute, Blue Cross Blue Shield
Foundation, and the Patient-Centered
Outcomes Research Institute; and
the State Action on Avoidable
Rehospitalizations initiative of
the Commonwealth Fund and the
Institute for Healthcare Improvement
(IHI). All these projects recommend
strategies to improve the discharge
process, including scheduling
follow-up appointments before
discharge, medication plans, written
patient discharge instructions,
patient education about diagnosis
and medications, follow-up telephone
calls to the patient, communication
to the outpatient primary provider
at discharge, and others. 8 – 11 Recently
White et al12 improved discharge
efficiency in a children’s hospital by
creating a common set of discharge
goals for 11 different pediatric
diseases. Although this intervention
did decrease the length of stay, the
readmission rate was not changed.
To date, the only published pediatric
discharge improvement collaborative
focused on improving communication
to primary care providers after
hospital discharge. 13
About 20% of older Medicare
patients who are hospitalized are
readmitted to the hospital within 30
days after discharge. 14 Because of
the high cost of readmissions, adult
hospitals with high readmission rates
receive reduced Medicare payments
under the Affordable Care Act. 15
Reimbursement rate penalties for
Medicaid patients, including children,
are already being implemented
in some states. In an analysis of
>550 000 pediatric admissions in 72
hospitals, Berry et al 16 found that the
30-day unplanned readmission rate
in pediatric patients was 6.5%, which
is much lower than in adults. Recent
publications have reported that most
children who were readmitted had an
underlying chronic disease, and only
a small percentage of readmissions
were found to be preventable. 17, 18
Interestingly, 1 study suggested that
children who had a documented
follow-up scheduled with their
primary care provider were more
likely to be readmitted to the hospital
than those who did not. 19
Because of the potential for errors
and variability in the discharge
process, Children’s Hospital
Association (CHA) formed the first
pediatric improvement collaborative
to examine whether shared
improvement strategies would affect
discharge-related care failures,
parent-reported readiness for
discharge, and readmission.
METHODS
Setting
The CHA invited its members
to participate in a multicenter
collaborative project addressing
the discharge process for pediatric
inpatients. Eleven hospitals
participated in the collaborative.
One hospital did not submit data
on interventions and therefore was
excluded from analysis. All hospitals
were tertiary-care freestanding
children’s hospitals in the United
States that were members of the
CHA. A specified target population
was selected at the discretion of
the participating site ( Table 1). The
participants selected populations by
specific disease processes, level of
clinical complexity, or specific units
in the hospital.
Intervention
The study was patterned after the
standard methods used by the CHA
in many of its other collaborative
projects. 20 –24 The model for this
improvement process was based
on previous work developed by
the IHI and has been used
successfully in pediatric settings. 25 –29
A multidisciplinary advisory panel
of experts with previous experience
in discharge processes was recruited
from across the CHA. The panel
evaluated the existing literature
and adopted tools and change
concepts from previous discharge
programs. 2, 3, 8 – 11, 30 They also
incorporated lessons learned
e2
TABLE 1 Participating Hospitals and Areas of Project Focus
Site Target Patient Population
Nationwide Children’s Hospital, Columbus, OH Patients with sickle cell disease readmitted within
30 d for acute chest pain or pain crisis
Children’s Hospital Colorado, Aurora, CO Patients with asthma and seizure managed by
hospitalists
Riley Hospital for Children at Indiana University
Health, Indianapolis, IN
Unit-based patients on 7W managed by
hospitalists, complex care patients on 8E
Children’s Hospital Los Angeles, Los Angeles, CA Medical/surgical unit, cystic fi brosis admissions
and cardiovascular acute unit
New York–Presbyterian Morgan Stanley Children’s
Hospital, New York, NY
NICU and oncology–bone marrow transplant
Children’s Hospital & Medical Center, Omaha, NE Nonchronic patients on medical/surgical unit
Children’s Hospital of Pittsburgh of UPMC,
Pittsburgh, PA
Patients scheduled for discharge on medical and
surgical unit
The Children’s Hospital of Philadelphia, Philadelphia,
PA
All patients scheduled for discharge
Rady Children’s Hospital San Diego, San Diego, CA Patients with asthma on medical unit,
appendectomy on surgical unit, cardiac surgery
in critical care unit, all patients on hematology/
oncology, and all patients in NICU
Children’s National Medical Center, Washington, DC Medical patients managed by resident trainees
and hospitalists
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PEDIATRICS Volume 138 , number 2 , August 2016
from previous CHA collaboratives,
including the “Improving Inpatient
Throughput” and “Improving Patient
Handoffs” programs. 20 This panel
developed a change package covering
6 broad areas, which included the
following strategies:
• Proactive discharge planning
throughout the hospitalization.
• Improve throughput.
• Arrange postdischarge treatment.
• Communicate postdischarge plan
to providers.
• Communicate postdischarge plan
to patients and families.
• Postdischarge support.
Sites formed multidisciplinary
teams and were required to
have an executive-level sponsor.
The collaborative held 4 virtual
learning sessions and monthly Web
conferences. In between the learning
sessions were 3 action periods,
during which each site performed
small tests of change using the plan–
do–study–act method. During the
learning sessions, training on quality
improvement methods was provided
by national experts. High performers
also shared their successes, and
participants were given opportunities
to ask questions. Sites also presented
their progress and challenges during
monthly Web conferences. Teams
could communicate with each other
and share tools and resources via
an electronic mailing list and a
shared Web site. Teams were guided
through improvement efforts by an
experienced improvement coach.
Measures and Data Collection
The primary aim of the study was
to reduce discharge-related care
failures by 50% in 12 months.
Discharge-related care failures were
measured by using phone calls to
families 2 to 7 days after discharge.
Failure was a composite all-or-none
measure; if any problem related to
discharge occurred, the discharge
was counted as a failure. Required
components of the measure included
understanding of diagnosis, receiving
discharge instructions, receiving
discharge education, compliance
with instructions, receiving
necessary equipment, having a
plan to follow up pending tests,
receiving help with appointments,
and not needing a related unplanned
visit. A discharge phone call script
adapted by the expert panel from
Project Re-Engineered Discharge
was provided, and each site was
permitted to modify the script to
meet their local needs and capacity. 10
All other measures were
optional and selected by the
individual sites depending on
the change strategies targeted
(see Supplemental Information
and Supplemental Tables 1–6 for
definition of measures). Readiness
for discharge and readmission
rates were priority measures
and were highly recommended
although not required. Patient
and family readiness for discharge
was defined as the percentage of
families rating the highest category
on the hospital’s standard patient
satisfaction survey. Readmission at
72 hours and 30 days was defined
as unplanned rehospitalization for
the same diagnosis. Baseline data
were collected from August through
October 2011 if available. If baseline
data were not available (eg, outreach
calls), the first 3 months of project
data were used as baseline. From
November 2011 to October 2012,
the hospitals participated by using
Deming’s plan–do–study–act cycles to
perform tests of change, implement
improvements, and sustain results.
Each site selected changes based
on local capabilities and priorities.
Standardized reporting of data
occurred on a monthly basis via an
electronic data repository managed
by The CHA and did not include
any patient identifiers. Monthly
reports also included a narrative
section that included information
on successes, challenges, and next
steps. In addition to collecting project
measures, CHA staff scored each
site based on improvement activity
and performance by using the IHI
Assessment Scale for Collaboratives.
The scale rates teams between 0.5
and 5.0, with 0.5 defined as being
signed up to participate and 5.0
demonstrating major change in all
areas, outcome measures at national
benchmark levels, and spread under
way. (See Supplemental Table 7 for
rating scale.) 31
Data Analysis
Measures were plotted on run charts
(Minitab version 17.1, State College,
PA), with the first 3 months of data
reported used as baseline. Only
months where ≥3 sites reported
data were included. Run charts were
interpreted according to standard
probability-based rules for α level
P < .05. 32, 33 Data for both individual
hospital and overall hospital were
also aggregated to the quarterly level
for analysis in SAS version 9.3 (SAS
Institute, Inc, Cary, NC). Comparisons
between the entire baseline period
and postbaseline values for the
aggregated hospital data were
made with χ2 tests. Within each
quarter, first observation carried
forward or last observation carried
back imputation was conducted for
missing data in SAS.
This study was determined to be
exempt by the Children’s Hospital Los
Angeles Institutional Review Board
(CHLA-14-00111).
RESULTS
Elements of the collaborative change
package were adopted by each
institution at varying levels ( Table 2).
All sites chose to work on educating
families on diagnosis and plans for
discharge. Several sites also used
discharge checklists, with discharge
milestones and barriers. Eight out
of 10 sites improved the written
discharge instructions given to
families. Some of these improvements
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included designing standardized
discharge instructions for certain
diagnoses, making instructions more
user-friendly, and creating new
discharge instruction forms in the
electronic medical record. Almost all
sites (9 of 10) used postdischarge
follow-up phone calls to reinforce
discharge instructions. Most sites also
reported working on identifying and
obtaining discharge medications. Few
sites addressed communication with
primary care providers.
Aggregate data for all hospitals
combined are depicted in monthly
run charts. Run charts with individual
hospital trends are available online
(Supplemental Figures 4–8).
Eight hospitals reported rates of
discharge-related care failures.
Because precollaborative data
were not available at most sites,
the first quarter of the project was
used as baseline data. The run
chart demonstrated a shift, with 10
consecutive points below the baseline
median line (Supplemental Figure 4).
The statistical process control chart
( Fig 1B) also confirms this finding,
with 9 postintervention points below
the baseline mean and the final
postintervention point below the
lower control limit. The aggregate
rate of care failures was overall 34%
in the first project quarter; the rate at
the end of the collaborative was 21%,
or a reduction of 40% (P < .05). Top-
performing hospitals were able to
achieve even lower care failure rates
with the use of varying interventions
( Fig 1B).
Only 4 hospitals reported data on
family feeling ready for discharge
( Fig 2). For these hospitals, there
was a statistically significant
increase in the percentage of
patients who rated the readiness for
discharge in the highest category.
The precollaborative baseline was
85% of patients giving the highest
rating; during the last quarter of the
collaborative it was 91% (P < .05).
The run chart showed a shift of 6
points above the median line in the
last 2 quarters.
Five hospitals reported unplanned
readmission rates for the same
diagnosis, at 72 hours ( Fig 3A) and
at 30 days ( Fig 3B). Four hospitals
reported both rates. There was no
improvement in unplanned 72-hour
(0.7% vs 1.1%, P = .29) and slight
worsening of the 30-day readmission
rate (4.5% vs 6.3%, P = .05).
Of the 11 participating sites, 4
achieved an IHI Assessment Scale
for Collaboratives score of 5.0
at the end of the collaborative
(Hospitals A, B, C, D), indicating
outstanding improvement. One site
obtained a score of 4.5 (sustainable
improvement, Hospital E), and 4
sites achieved a 4.0 (significant
improvement, Hospitals F, G, H, I).
Two sites were able to test
changes but did not demonstrate
measurable improvement. Common
characteristics of the sites that
achieved a score of 5.0 included
strong multidisciplinary involvement;
close collaboration with electronic
medical record (EMR) teams;
dedicated staff time for discharge
phone calls, discharge education,
and discharge rounding; and use of
discharge checklists.
e4
TABLE 2 Change Strategies Used by Participating Sites
Change Strategy Change Ideas Number of Sites Using Strategy
Proactive discharge planning throughout
hospitalization.
Educate the patient and family about diagnosis and plans for discharge. 10
Include discharge planning in rounds and other staff communication. 7
Establish and continuously update anticipated discharge date and time. 4
Ensure fi nancial problems will not impede discharge. 3
Improve throughput. Complete the discharge process promptly. 7
Create specifi c conditional or contingency discharge orders. 7
Proactively prevent and manage delays. 5
Work with essential partners (eg, laboratory, radiology, social work). 4
Spread discharges across the day. 3
Arrange for postdischarge treatment. Identify the correct medicines and a plan to obtain and take them. 9
Make appointments for follow-up medical appointments and postdischarge
tests.
7
Organize postdischarge home-based services and medical equipment. 6
Plan for the follow-up of results from laboratory tests or studies that are
pending at discharge.
3
Anticipate planned readmissions for additional treatment (eg, chemotherapy
treatments).
2
Communicate postdischarge plans to
providers.
Transmit discharge summary to clinicians accepting care of the patient. 5
Develop physician discharge summary for next providers. 4
Initiate verbal communication with outpatient caregivers as needed. 2
Communicate postdischarge plans to
patients and families.
Create or improve written discharge instructions for the patient and family. 8
Review the written discharge instructions with the patient and family. 6
Review with the patient and family what to do if a problem arises. 6
Postdischarge support. Provide telephone reinforcement of the discharge plan via outreach calls. 9
Provide opportunities for patients and families to ask questions after
discharge.
4
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PEDIATRICS Volume 138 , number 2 , August 2016
DISCUSSION
Adverse events related to poor
hospital discharge planning are well
described, 34 and to our knowledge
this is the first multicenter
collaborative to target the hospital
discharge process for pediatric
inpatients. Because the discharge
process is complex, involving
multiple clinical microsystems,
achieving large-scale change is
particularly challenging. Although the
collaborative did not meet its target
of 50% reduction in care failures,
significant progress was made. We
found a decrease in discharge care
failures and improvement in patient
readiness for discharge. However,
there was no impact on 72-hour
unplanned readmissions and even
a slight increase in the 30-day
readmission rate.
A wide variety of change strategies
were adopted by the participating
sites to achieve results. One of the
most commonly adopted strategies
was proactive discharge planning
throughout the hospitalization.
Several change ideas were used to
accomplish this planning, such as
educating the patient and family
about diagnosis and plans for
discharge, including discharge
planning in rounds, establishing and
continuously updating anticipated
discharge time, and ensuring that
financial problems did not impede
discharge. Other key change areas
were improving communication
of postdischarge plans to families
and providing postdischarge
support via outreach phone calls.
Previous studies have shown that
postdischarge contacts via home
visits or follow-up phone calls were
effective in decreasing health care
utilization and improving satisfaction
with care. 35 – 38 Although most sites
made postdischarge phone calls
during the collaborative period, not
all were able to continue doing so.
The standardized phone call script
used during the collaborative could
take <5 minutes to 20 minutes,
depending on the patient. If
interpretation was needed, the call
could take even longer. Some sites
found this script unworkable and
shortened it significantly. Follow-up
studies must be done to evaluate
the cost and benefit of phone calls to
support their sustainability. Few sites
e5
FIGURE 1Discharge-related care failures (n = 8 sites reporting; 5895 discharges). Percentage of discharges where ≥1 discharge-related care failure was identifi ed during postdischarge phone call. First 3 months of available data used as baseline. A, Statistical process control p-chart. B, Annotated run chart, top-performing hospitals. Horizontal line represents the hospital’s baseline from the fi rst 3 months of data collection. LCL, lower confi dence level (3 standard deviations below the mean); NP, nurse practitioner; RN, registered nurse; UCL, upper confi dence level (3 standard deviations above the mean).
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chose to implement interventions
related to communication and
coordination with outpatient
primary care physicians. Future
efforts focused on this strategy may
demonstrate more improvements in
discharge-related outcomes.
Despite improvements in discharge-
related outcome measures, there
was no improvement in readmission
rates during the collaborative. In
fact, we saw a slight increase in
30-day unplanned readmissions.
This could result from seasonal
variability in readmissions. Also,
readmission rates vary by diagnosis,
leading to high variability in this
measure. For example, 1 site focused
on management of patients with
sickle cell disease, who have 30-day
readmission rates between 10%
and 20%, and another site focused
on patients with asthma, with much
lower readmission rates of <2%. 39 – 41
Also, our method was able to assess
only revisits to the same facility. 42
Another possibility is that improving
throughput and discharge timeliness
led to earlier discharge, with the
unintended consequence of
increased readmission; however,
we did not collect data on length
of stay. There is also significant
variability in the definition
of readmissions. We defined
readmissions as unplanned
readmissions for the same condition;
however, even within these
parameters, each site used different
methods to collect the
data. Even unplanned readmission
may be unavoidable and therefore
an insensitive measure for discharge
quality. The 3M Potentially
Preventable Readmissions algorithm
is a promising tool that can be
used in future improvement
efforts, but it has not yet been
prospectively evaluated and may
still overestimate preventability.43
Average unplanned readmission
rates were very low in the population
studied: <1% for 3 days and 5% for
30 days. This finding adds to recent
evidence that readmissions may
not be a good indicator of hospital
quality in the pediatric setting. 44
Readmission rates are not solely an
indicator of discharge quality; they
are a measure of the entire health
system, as well as socioeconomic
factors and patient disease. 38, 45, 46
There is also no consensus on the
optimal readmission interval. The
Centers for Medicare and Medicaid
Services uses 30 days for adult
readmissions measures; however,
some studies have used 7, 14, or
15 days. Future studies should
establish standardized frameworks
and measures for evaluating
discharge care quality.47, 48
The limitations of this collaborative
are consistent with other initiatives
to improve care across multiple
sites. 49, 50 First, the participating sites
were all tertiary-care freestanding
children’s hospitals, so the results
may not be generalizable to
community hospitals or pediatric
care provided in general hospitals.
Second, we were not able to measure
the impact of specific change
strategies, because each site chose
different targets and implemented
a bundle of several strategies
simultaneously. Randomization
of the interventions across
sites would have increased our
ability to draw conclusions about
the effectiveness of individual
interventions but would not
have allowed sites to choose the
strategies most relevant to their
populations and feasible in their
local environments. Third, for most
measures sites did not have baseline
data before implementing changes.
In addition, charts had only 11 to 15
data points, with the first 3 points
serving as baseline, leaving only
8 to 12 postintervention points.
Therefore, we had insufficient points
to accurately calculate control limits.
Also, because prestudy baseline
data were not available for most
measures, it is possible that the teams
may have made early improvements
that were not reflected in the
data. This discrepancy is likely
to underestimate the true effect
of the project. Nearly every site
had difficulty obtaining data, and
some sites were ultimately not
able to submit data on some of the
measures. Hospitals need better data
systems and analytic resources to
e6
FIGURE 2Proportion of families who felt ready for discharge (n = 4 sites reporting; 2824 discharges). Percentage of families giving the highest rating of readiness for discharge on hospital patient satisfaction surveys.
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PEDIATRICS Volume 138 , number 2 , August 2016
more effectively plan and monitor
progress of quality improvement
work. Finally, each site used different
patient populations and different
tools to collect data, making the
data heterogeneous and difficult to
compare.
Participating sites reported several
benefits of the collaborative model
that were consistent with previous
studies. 51, 52 Teams enjoyed the
opportunity to learn from national
experts, share challenges and
successes, learn and adapt from
different settings and patient
populations, and share tools such
as checklists and call scripts.
The collaborative approach also
helped sites develop urgency for
change at the institutional level
and fostered friendly competition
and accountability. Teams were
also able to leverage collaborative
participation to secure financial
resources and staff time. Several
innovations were also developed
and tested during the collaborative
period and made available to
others. Some examples include
sickle cell action plans, seizure
actions plans, a “discharge lounge, ”
whiteboards in patient rooms
with home schedules, and peer
mentoring programs. Although
teams cited difficulties in making
timely modifications in the EMR,
many sites shared the same EMR
platform and were able to exchange
technical assistance and screen
shots of changes made such as
automated discharge readiness
reports, conditional discharge
order sets, and standardized
discharge instructions.
CONCLUSIONS
This study shows the potential
benefit of the collaborative
approach to improve quality of
inpatient discharges by using an
intervention bundle implemented
in pediatric hospital settings. The
spread of such interventions has the
potential to improve care transition
outcomes for all hospitalized
children.
ACKNOWLEDGMENTS
Expert panel members: Lori
Armstrong, MSN, RN, NEA-BC; Mary
Daymont, RN, MSN, CCM, CPUR;
Pamela Kiessling, RN, MSN; Cheryl
Missildine, RN, MSN, NEA-BC; Karen
Tucker, MSN, RN. Data analysis: Cary
Thurm, PhD, Children’s Hospital
Association.
ABBREVIATIONS
CHA: Children’s Hospital
Association
EMR: electronic medical record
IHI: Institute for Healthcare
Improvement
e7
FIGURE 3Unplanned readmission for the same condition. A, Within 72 hours; B, within 30 days (n = 5 sites reporting; 7654 discharges).
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collection, assisted with data analysis, drafted the initial manuscript, and critically reviewed and revised the manuscript; and all authors approved the fi nal
manuscript as submitted.
DOI: 10.1542/peds.2014-3604
Accepted for publication Mar 14, 2016
Address correspondence to Susan Wu, MD, Division of Hospital Medicine, Department of Pediatrics, Children’s Hospital Los Angeles, 4650 Sunset Blvd, Los Angeles,
CA 90027. E-mail: [email protected]
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2016 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have no fi nancial relationships relevant to this article to disclose.
FUNDING: Funding for the collaborative was provided by the Children’s Hospital Association and participating member hospitals.
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential confl icts of interest to disclose.
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