Evaluating the Effectiveness of Communityand Hospital Medica

Evaluating the Effectiveness of Community
and Hospital Medical Record Integration
on Management of Behavioral Health
in the Emergency Department
Stephanie Ngo, MD
Mohammad Shahsahebi, MD, MBA
Sean Schreiber, MSED, LPC
Fred Johnson, MBA
Mina Silberberg, PhD
Abstract
This study evaluated the correlation of an emergency
department embedded care coordinator
with access to community and medical records in decreasing
hospital and emergency
department use in patients with behavioral health issues. This
retrospective cohort study
presents a 6-month pre-post analysis on patients seen by the
care coordinator (n=524). Looking
at all-cause healthcare utilization, care coordination was
associated with a significant median
decrease of one emergency department visit per patient (p G
0.001) and a decrease of 9.5 h in
emergency department length of stay per average visit per
patient (pG0.001). There was no
significant effect on the number of hospitalizations or hospital
length of stay. This intervention
demonstrated a correlation with reducing emergency department
use in patients with behavioral
health issues, but no correlation with reducing hospital

utilization. This under-researched
approach of integrating medical records at point-of-care could
serve as a model for better
emergency department management of behavioral health
patients.
Address correspondence to Mohammad Shahsahebi, MD, MBA,
Department of Community and Family Medicine, Duke
University, Durham, NC, USA. Phone: (919) 342-8845; Email:
[email protected]
Stephanie Ngo, MD, Department of Community and Family
Medicine, Duke University, Durham, NC, USA.
Fred Johnson, MBA, Department of Community and Family
Mina Silberberg, PhD, Department of Community and Family
Mohammad Shahsahebi, MD, MBA, Northern Piedmont
Community Care, Durham, NC, USA. Phone: (919) 342-8845;
Email: [email protected]
Fred Johnson, MBA, Northern Piedmont Community Care,
Durham, NC, USA.
Sean Schreiber, MSED, LPC, Alliance Behavioral Health,
Raleigh, NC, USA.
Journal of Behavioral Health Services & Research, 2017. 651–
658. c)2017 National Council for Behavioral Health. DOI
10.1007/s11414-017-9574-7
Evaluating the effectiveness of community NGO ET AL. 651
Introduction
Background

Patients with behavioral health issues often require more
resource-intensive care and are more
likely to be frequent users of health services.1–7 Brennan et al.
found that patients with at least one
primary psychiatric visit to the emergency department (ED)
were 4.6 times more likely than those
without a primary psychiatric visit to be classified as high
utilizers of health services overall, and
that on average, high utilizers with a primary psychiatric visit
had a significantly higher number of
ED visits than non-psychiatric high utilizers.7
Furthermore, Bboarding^ of patients with behavioral health
issues has become a serious problem
for patients who require psychiatric attention and has
overburdened EDs that already struggle with
overcrowding and lack of sufficient resources.1,8,9 Boarding is
often defined as the length of stay
(LOS) in the ED greater than 4h after medical clearance and is
normally due to awaiting placement
at another inpatient facility.8,9 EDs are poorly equipped at
providing mental healthcare, and
boarding leads to substandard quality of patient care, poor
patient and provider satisfaction, and
lost hospital revenue and negatively impacts patient throughput.
1,3,8–10.
Patients with behavioral health issues have been
disproportionately affected by boarding due to a
national decrease in psychiatric inpatient facilities and lack of
appropriate increase in community-
based resources.1–3,10 Patients with behavioral health needs
are a vulnerable population who can be
particularly difficult to serve because of their complex needs
and scarcity in resources for their

care. The quality of care that these patients receive can
potentially improve with better integration
and coordination among medical services, mental health care,
and community resources. Case
management is defined as Ba collaborative process of
assessment, planning, facilitation, care
coordination, evaluation, and advocacy for options and services
to meet an individual’s and
family’s comprehensive health needs through communication
and available resources to promote
quality, cost-effective outcomes,^ and can be utilized in
initiatives focused on coordinating patient
care.8,11 In studies targeting high utilizer populations, the
results have been mixed but generally,
case management and care coordination has been found to
reduce hospital costs, decrease
healthcare utilization, and improve clinical (e.g., alcohol and
substance use, psychiatric symptoms,
mortality) and social outcomes (e.g., homelessness, insurance
status, social security support).12–14
With the development of new information technology systems,
there is the potential for case
managers and care coordinators to have simultaneous access to
a broader range of patient data than
has been available in the past and, therefore, be more effective.
This specific intervention of
information sharing has not been studied in depth. Though
integrated access may not be a novel
intervention, many barriers such as privacy concerns and EHR
fragmentation have prevented
broadscale implementation, thus limiting the quantification of
its impact. This paper reports an
evaluation of an intervention that allowed a case manager to
have integrated access to multiple
community stakeholder records to improve care coordination

and to alleviate the psychiatric
boarding burden and frequent ED use often associated in
patients with behavioral health issues.
The goal of this study was to evaluate whether an embedded ED
care coordinator with access to
multiple electronic health records (EHRs) is effective in
reducing hospital and ED utilization in
patients with behavioral health issues using a pre-post analysis.
Methods
Study Setting and Program Description
Duke University Hospital (DUH), a part of the Duke University
Health System (DUHS), is a
teaching hospital and tertiary and quaternary care hospital with
938 beds. It is the designated Level
1 Trauma Center for Durham County. The ED has
approximately 65,000 visits per year.
652 The Journal of Behavioral Health Services & Research 45:4
October 2018
DUHS is the predominant medical provider in Durham County
and the sole provider of
emergency and inpatient care. Like many insurance entities,
North Carolina Medicaid separates
behavioral healthcare from traditional medical care. As a result,
few patients receive the totality of
their care within one health system or EHR.
A collaboration was developed in Durham, NC, between
Alliance Behavioral Healthcare (ABH),
a community mental health managed care organization; North

Piedmont Community Care (NPCC),
an organization owned and operated by Duke Health System and
one of Community Care North
Carolina (CCNC) networks (the North Carolina Medicaid
medical home and population health
management organization); and DUHS. A care coordinator,
funded and employed collectively by
these entities was embedded into the DUH ED from February
2013 to April 2014. Of note, the
position was filled again in July 2014 and the care coordinator
continues to provide services at the
DUH ED. The program has since been expanded to include
pediatric ED patients. Patients with
behavioral health needs who visited the DUH ED and were
referred to the ED psychiatry team
received services from the care coordinator as part of the
standard of care.
At the highest level, patients who receive services from a
provider within the Alliance network
range from 3years old and up are either Medicaid eligible or are
considered indigent, defined by
Alliance as at or below 300% of the federal poverty level. In
order to receive services beyond an
assessment or psychological testing, during the time of the
study, they must have a behavioral
health diagnosis under the International Classification of
Diseases, 9th Revision (ICD-9) of 219 to
317. If a consumer was previously served in the care
coordination program, in addition to a
behavioral health diagnosis, including intellectual and
developmental disabilities, they would need
to have a history of crisis service utilization, a recent history of
incarceration, and a recent history
of inpatient psychiatric services, pregnancy, and substance
abusing or enrolled in the Innovations

waiver, meaning the individual’s functioning made them
eligible for an institutional level of care in
an intermediate care facility.
The care coordinator had traditional case management duties,
such as making community and
medical referrals, assisting with inpatient or community
placement, and coordinating appropriate
follow up. However, this position was unique in that the care
coordinator was able to enhance
patient care by accessing the separate EHRs and claims data of
ABH, CCNC, and DUHS in order
to give ED providers information regarding patients’ outpatient
providers, current medications,
community resources, etc. This can be especially important for
patients who are in active
psychiatric crisis and are unable or unwilling to provide a
history. Therefore, the care coordinator
was poised to Bconnect the dots^ when providers were
unfamiliar with the care a patient was
receiving in the community.
The goal of the care coordinator was to act as a bridge between
the different community and
medical entities. By having an understanding of these different
entities with access to their separate
records, the care coordinator aimed to improve the ED team’s
ability to provide care and determine
the most proper patient disposition, which would then translate
to increased quality of care and
efficiency in serving patients.
The data accessible on the DUH medical record systems
included medical notes, labs, imaging,
and other studies done in encounters with DUHS. Many patients
saw psychiatric providers outside

of DUHS, so these records were not available to the ED
psychiatric providers. The additional data
that the care coordinator had access to included these patient
assessments, outside hospital
discharge summaries, and other psychological testing.
Study Design
This study was a retrospective, observational cohort study using
patients as their own controls
and was reviewed and approved by the Institutional Review
Board of Duke University. Approval
was also granted by Alliance Behavioral Healthcare.
Data were obtained via Duke Enterprise Data Unified Content
Explorer (DEDUCE), a web-
based research query tool and centralized database that collects
encounter-level data from hospitals
and clinics within DUHS. We did not obtain data from the
outside community and hospital
providers or claim data from payers for the purposes of this
evaluation.
DUHS, ABH, and NPCC worked collaboratively on this study.
Population
The cohort consisted of patients whom the care coordinator
served as part of the Duke ED
psychiatry team (n=527). Since data collection predated the
evaluation, there were some
discrepancies in how the data were recorded, although these

were small. Patients who could not
be identified in the electronic medical record system via chart
review using the recorded patient
information were removed from the analysis (n=3) giving a final
sample size of n=524.
Measures and Outcomes
Integration was achieved by giving the care coordinator access
to both EHRs. Unfortunately, the
two EHR systems were not allowed to directly interface or share
information. Currently, providers
in the DUH ED do not have access to the majority of these
patients behavioral health history at the
point-of-care. Though release of information is typically
requested, the pace and 24-h nature of ED
care make it less likely that this information is available to the
ED teams in a timely manner. The
primary benefit of the current, siloed system is protection of
patient health data. This, however,
may negatively impact the ability to provide whole-person care.
DUH and Alliance employ similar strategies to ensure restricted
access. The Alliance consumer
management system uses a role-based security system where
users are granted access to parts of
the system based on their role within the agency. Care
coordinators typically have access to all
clinical data within the system, unless there is a reason to
restrict access to an identified consumer’s
record. The system has the ability to lockdown these records
and limit access to specific users.
While the care coordination staff have access to clinical data,
they are not permitted to see
consumer grievances or network provider details beyond
information need to facilitate referrals. In

the DUH EHR, a password re-entry and reason must be given
prior to accessing behavioral health
records. Both systems require users to change their password
every 90days and require all users to
undergo annual privacy training and client rights training.
Data from patients seen by the care coordinator from February
2013 to April 2014 were utilized,
with their first recorded encounter with the care coordinator as
the enrollment date. For each
patient, 6months of data before and after his/her enrollment date
were collected, giving a total time
frame for the data of August 2012 to October 2014.
Data collected included demographic information (age, gender,
race, and ethnicity); patient
comorbidity, as determined by the International Classification
of Diseases, Ninth Revision (ICD-9)
codes in all diagnostic fields (primary and secondary diagnoses)
in all ED and inpatient encounters in the
patient’s 12-month time frame; and encounter visit types
(hospitalizations, ED visits, and LOS).
The primary outcomes examined were number of ED visits,
number of inpatient hospitalizations,
ED length of stay (LOS) (measured in hours), and hospital LOS
(measured in days). Length of stay
was averaged as opposed to calculated as a sum total time due
to lack of LOS data for some
encounters.
Data Analysis
To assess the impact of care coordination on health utilization,
a pre-post analysis was performed
comparing outcome measures in the 6months before and after

the patients’ initial contact with the
October 2018
care coordinator (enrollment date). Wilcoxon signed-rank tests
between the pre and post data were
utilized to obtain the median difference for each metric,
reported with interquartile ranges (IQR)
due to lack of normality of data distribution. A probability p
value of G0.05 was considered
significant. Statistical analysis was conducted using R 3.2.0
software package.
Results
During February 2013 to April 2014, the care coordinator
served 527 patients, with a total of
524 patients included in the analysis. The cohort was
predominantly male (59%) and Black/African
American (62%), and had a mean age of 40.3years (Table 1).
The most prevalent psychiatric diagnoses among this cohort
were substance-related and
addictive disorders (58%); schizophrenia and other psychotic
disorders (48%); depressive disorders
(47%); disruptive, impulse-control, and conduct disorders
(30%); and bipolar disorders (28%)
(Table 2).
During the 6-month pre period, there was a range of 0–116 ED
visits and 0–37 hospitalizations.
The 6-month pre-intervention medians were one
hospitalization/patient, 0.5days for LOS of

hospitalization/patient, two ED visits/patient, and 15.4h for
LOS of ED visit/patient. Analysis of
the 6-month pre-post all-cause healthcare utilization data
demonstrated a strongly significant
median decrease of one ED visit per patient (pG0.001) and a
decrease of 9.5 h in the emergency
department length of stay per average visit per patient
(pG0.001) (Table 3). There was no
significant change in either median difference of
hospitalizations (median = 0, p=0.17) or median
difference in hospital LOS (median = 0, p=0.21) (Table 3).
Discussion
The goal of this study was to assess the impact of a care
coordinator with increased EHR access
on ED and hospital utilization. We found that there was an
associated reduction in ED utilization
with a decrease in both number of ED visits and ED LOS. This
is consistent with the majority of
studies that have looked at a variety of case management
intervention strategies in reducing ED
Table 1
Demographic characteristics (n=524)
Number (%)
Age, mean 40.3±13.9
Male gender 309 (5%)
Race
Black/African American 325 (62%)
White/Caucasian 152 (29%)
Multiracial 15 (2.9%)
American Indian 4 (0.8%)

Asian 2 (0.4%)
Other/unknown/declined 26 (5%)
Ethnic group
Hispanic/Latino 19 (4%)
Other/Unknown/Declined 505 (96%)
Percentages may not add up to 100% due to rounding
use.12,13 The decrease in ED LOS may be related to the ability
of the care team to more effectively
and efficiently plan disposition and follow up for patients not
requiring immediate psychiatric
hospitalization. Fewer ED visits could be a result of better
coordination of behavioral health care
leading to less frequent crises. Reductions in ED visits and LOS
for this population can translate to
the ED’s ability to decrease overcrowding and increase patient
throughput, which can ultimately
lead to a reduction in lost revenue, reduction in wait times, and
improved delivery of care. This
intervention may assist in alleviating the psychiatric boarding
burden that many EDs face.
However, it appears that the impact on healthcare utilization
was limited to ED use. We found no
significant change in all-cause hospital utilization in either
difference in pre-post hospitalizations
and hospital LOS. Fewer studies have looked at hospital
utilization as an outcome than ED, but
most, like ours, report a lack of significant change.12,13 These
results looked at all-cause

hospitalizations, not only psychiatric-related hospitalizations.
Because the care coordinator targeted
patients specifically when they interacted with the psychiatry
ED team and had minimal interaction
with medically related ED visits, the lack of change in
hospitalizations may be due to patients that
Table 2
Psychiatric comorbidities
Percentage (n)
Substance-related and addictive disorders 58% (294)
Schizophrenia and psychotic disorders 48% (243)
Depressive disorders 47% (239)
Disruptive, impulse-control, and conduct disorders 30% (151)
Bipolar disorders 28% (143)
Trauma- and stressor-related disorders 22% (110)
Neurodevelopmental disorders 21% (109)
Anxiety disorders 19% (98)
Personality disorders 19% (96)
Neurocognitive disorders 10% (53)
Obsessive compulsive disorders 2% (11)
Somatic symptom and related disorders 1% (6)
Psychiatric comorbidities were determined using the psychiatric
diagnosis classifications and associated ICD-9
codes in the Diagnostic and Statistical Manual of Mental
Disorders (DSM) IV.17
Table 3
Median pre-post difference in health utilization
Pre Post Difference p value
Number of hospitalizations 1 (0 to 2) 1 (0 to 2) 0 (−1 to 1) 0.17

Hospital LOS (days) 0.5 (−1.7 to 2.7) 0.3 (−1.2 to 1.8) 0 (−1.5
to 1.5) 0.21
Number of ED visits 2 (1 to 3) 1 (0 to 2) −1 (−2 to 0) G0.001*
ED LOS (hours) 15.4 (2.3 to 28.5) 2.6 (−10.6 to 15.8) −9.3
(−36.5 to 17.9) G0.001*
BPre^ indicates the 6-month period before enrollment date;
BPost^ indicates the 6-month period after
enrollment date. Results are from paired t tests (pre-post)
*Indicates statistical significance with p value G0.05
October 2018
were medically complex. This would be consistent with Billings
et al.’s finding that high utilizer
patients have high rates of complex medical comorbidities.15
In addition, since hospitalization is more of a reflection on the
severity of illness in a patient than
is ED use, it is possible that care coordination is more
successful at redirecting ED utilization to
community and ambulatory resources and less effective at
improving overall patient burden of
disease. A more intensive, community-based intervention may
be more effective at impacting
burden of disease since this intervention is limited to only
providing services when the patient
interacted with the ED.
Limitations
There were several limitations to this study.
The program evaluation began after the intervention had taken

place and the intervention was
implemented as a new standard of care; therefore, patients were
not separated into control or
intervention groups as part of the intervention design. Because
patients were used as their own
controls, it is pertinent that we consider regression to the mean
as a possible explanation for our
results. Regression to the mean is a commonly described
phenomenon whereby extreme cases
represent a brief period of atypical activity that then eventually
normalize. Studies looking at
patterns of ED use have observed regression to the mean,
making it more difficult to be certain of
the validity of positive results.16 Our results would therefore be
strengthened with the inclusion of a
comparable control group, most likely via propensity score
methodologies, since it would be
ethically difficult to consent patients into intervention and
control groups while in active
psychiatric crisis and since the care coordinator has been
established as a standard of care.
This study was restricted in looking at provider-centered
outcomes as opposed to patient-
centered outcomes, such as quality of life, patient satisfaction,
and severity of disease. Decreasing
rates of ED use and hospitalizations imply improved health;
however, it would be valuable to
include direct patient-centered metrics in future analyses.
Also, as a retrospective cohort study, we were unable to control
for other potential confounders
and we cannot definitively relate the introduction of the care
coordinator with increased EHR
access to the reduction in ED utilization.

Future Directions
It was initially intended to incorporate community and
ambulatory level data into this study;
however, the ability to share data was delayed due to legal
review and contractual obligation. Due
to this limitation, we were unable to include community and
ambulatory level data as possible
metrics of continuity of care and impact on ambulatory service
utilization. Such results would be
useful in gaining better insights into the scope of effect of the
intervention. Further research is also
required to better understand the lack of impact on
hospitalizations.
A qualitative component of the study would also provide a
deeper understanding on the impact
of the addition of the care coordinator on the ED psychiatric
team. Outcomes to consider in a
qualitative study include impact on ED workflow, provider
satisfaction, and patient satisfaction.
Improved integration of the community and hospital EHRs may
include getting more ED providers
access to the Alliance EHR. Ultimately, the idea solution would
be the bilateral exchange of information
seamless between the two EHR platforms so that providers can
efficiently work in one space.
Implications for Behavioral Health
Health care services have traditionally been siloed, requiring
tremendous effort in order to share
information or work collaboratively. Institutional barriers
perpetuate this and can be a significant

detriment to patient care. The care coordinator intervention
sought to cross these boundaries and
work towards a more cohesive model of care for entities that
collectively serve the same group of
patients. Patients with behavioral health issues are a
particularly vulnerable group that may struggle
more in navigating a fragmented system, leading them to rely on
acute settings such as the
emergency department when they fall into the gaps. The
implementation of integrated services may
prove beneficial in closing these gaps and improving the quality
of care these patients receive. This
study suggests that there is potential for such interventions,
particularly for reducing ED utilization.
More research and implementation of innovative integration
strategies are needed to better assess
this potential.
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no
conflict of interest.
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Journal of Behavioral Health Services & Research is a copyright
of Springer, 2018. All Rights
Reserved.
Evaluating the Effectiveness of Community and Hospital
Medical Record Integration on Management of Behavioral
Health in the Emergency
DepartmentAbstractIntroductionBackgroundMethodsStudy
Setting and Program DescriptionStudy
DesignPopulationMeasures and OutcomesData
AnalysisResultsDiscussionLimitationsFuture
DirectionsImplications for Behavioral HealthCompliance with
Ethical StandardsReferences

Evaluating the Effectiveness of Communityand Hospital Medica

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