JACM-D-13-00014proof

LWW/JACM JAC200233 May 3, 2013 21:58
J Ambulatory Care Manage
Vol. 00, No. 00, pp. 1–9
Copyright C 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins
Early Assessment of Health
Care Utilization Among a
Workforce Population With
Access to Primary Care
Practices With Electronic
Health Records
Samantha F. De Leon, PhD; Lucas Pauls, MPA;
Sarah C. Shih, MPH; Thomas Cannell, MA;
Jason J. Wang, PhD
Abstract: This study assesses the health care costs and utilization among labor union members
from 2008 to 2010 and compares whether members accessing primary care providers participat-
ing in a public health city program, the Primary Care Information Project (PCIP), had different
health care usage or cost patterns. Using claims data, the number of hospital inpatient services
utilized decreased by 16 per 100 members among those with chronic conditions accessing PCIP
providers, whereas members seeing non-PCIP providers increased by 15 per 100 members. Access
to providers participating in a population health initiative was associated with lower utilization
of inpatient services and overall costs. Key words: ambulatory care, electronic health record,
health care costs, health information technology, health promotion, health care reform, health
services research, preventive health services, primary health care
ACCORDING TO the Centers for Disease
[AQ1]
Control and Prevention, almost half of
the population in the United States has at
least one chronic disease and the management
Author Affiliations: Primary Care Information
Project, NYC Department of Health & Mental
Hygiene, Long Island City, New York (Drs De Leon
and Wang, Ms Shih, and Mr Cannell); and 32BJ
Health Fund, New York (Mr Pauls).
This research was funded by New York City tax levy.
The authors declare that there are no financial or cor-
porate relationships, patent holdings, or other conflicts
of interest related to this article.
Correspondence: Samantha F. De Leon, PhD, Primary
Care Information Project, NYC Department of Health &
Mental Hygiene, 42-09 28th St, CN-52, Long Island City,
NY, 11101 (sdeleon@health.nyc.gov).
DOI: 10.1097/JAC.0b013e31829741e0
and treatment of chronic diseases account
for more than 75% of annual medical costs
(Anderson, 2010). Some studies suggest that
better management and prevention of chronic
diseases by improving the capacity of primary
care and the delivery of preventive services
can reduce costs due to a reduction in com-
plications or hospitalizations (Bodenheimer &
Fernandez, 2005; Denberg et al., 2008; Farley
et al., 2010; Maciosek et al., 2010; Mays &
Smith, 2011; Richard et al., 2012; Weintraub
et al., 2011).
The mission of the Primary Care Informa-
tion Project (PCIP) (Frieden & Mostashari,
2008; Mostashari et al., 2009), a bureau of
the New York City Department of Health and
Mental Hygiene, is to transform primary care
to be more efficient and effective at deliver-
ing clinical preventive care and to improve
Copyright © 2013 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

LWW/JACM JAC200233 May 3, 2013 21:58
2 JOURNAL OF AMBULATORY CARE MANAGEMENT/JULY–SEPTEMBER 2013
population health by the use of information
technology. Many of the practices partici-
pating in the program are located in lower-
income communities, serving traditionally
medically underserved patient populations
with a higher proportion of Medicaid and
uninsured patients. The PCIP assists medical
practices through the following: (i) electronic
health record (EHR) adoption services to facil-
itate the implementation of EHRs with built-in
clinical decision support system (CDSS) for
health promotion and disease prevention in
the areas of chronic disease management
such as for diabetes and hypertension,
smoking cessation and cancer screening; (ii)
on-site consulting and training services with
Quality Improvement Specialists to improve
workflows, improve documentation in the
EHR, and optimize utilization of the CDSS for
health promotion and disease prevention; (iii)
attainment of patient-centered medical home
recognition—an assessment of practices that
are coordinating care to enhance access
and continuity of care; and (iv) supporting
participation in pay-for-performance pro-
grams that reward EHR-enabled practices
for preventive care given in the areas of
cardiovascular health and smoking cessation,
particularly among underserved popula-
tions such as those with Medicaid or the
uninsured.
The objective of this study was to de-
termine whether a program focused on
preventive care and the utilization of a pop-
ulation health-oriented EHR among primary
care providers in lower-income communities
has an impact on health care costs and
utilization of services among an ethnically
and racially diverse workforce employed in
building services. Union members whose
primary care providers were affiliated with
the PCIP, and members whose primary
care providers were unaffiliated with the
PCIP were compared in terms of health
services utilization and total costs (inpatient,
outpatient, and all services combined) in
2008 (before most of the PCIP practices
implemented the EHR) and 2010 (when most
of the PCIP practices had implemented the
EHR).
MATERIALS AND METHODS
32BJ Health Fund
The 32BJ Health Fund manages the health
benefits for members of the 32BJ SEIU labor
union and their dependents. 32BJ has more
than 120 000 members in 8 states, with about
64 000 in New York City, working in build-
ing services, mainly as doormen, porters, and
maintenance workers, in a variety of locations
such as residential buildings, commercial of-
fices, and schools. The median household in-
come of members is $35 000, and roughly half
are immigrants for whom English is not their
first language.
Data source
Paid claims data were restricted to mem-
bers who had at least one outpatient primary
care visit within the 5 boroughs of New York
City from 2008 to 2010 with a primary care
physician (ie, family practice, geriatrics, gen-
eral practice, internal medicine, nurse practi-
tioner, obstetrics & gynecology, and preven-
tive medicine). Claims for adult members of
the 32BJ union were considered; all depen-
dents, regardless of age, were excluded from
the study.
Attribution of members to practices
To clearly separate members into the PCIP
and non-PCIP groups, members who received
100% of their outpatient primary care with
any provider enrolled in the PCIP were at-
tributed to the PCIP group, and members
were attributed to the non-PCIP group if 100%
of their primary care visits were with any
provider not enrolled in the PCIP. About 6000
members were assigned to the PCIP group
and about 22 000 members were assigned to
the non-PCIP group, with approximately 9000
unassigned members.
Health care service utilization and costs
Once members were assigned to the PCIP
or non-PCIP primary care groups, total volume
of health care services were calculated per
patient and calendar year across all providers
who rendered health services to the patient.

LWW/JACM JAC200233 May 3, 2013 21:58
Health Care Utilization With EHR-Enabled Primary Care Practices 3
This would include specialty care, hospital
outpatient, hospital inpatient, and emergency
department services regardless of whether
[AQ2]
services were rendered in New York City or
elsewhere in New York State. Hospital inpa-
tient services include all surgical procedures
and treatments, with the potential for mul-
tiple claims per inpatient visit. Total health
care costs were calculated as the total amount
reimbursed to the provider(s) by the 32BJ
Health Fund.
Total volume of health care services and
costs per patient were stratified into yearly
time periods to assess the impact of the pro-
gram over time. Most providers participating
in the PCIP went live on the EHR between
2008 (∼18%) and 2009 (∼75%); by 2010, ap-
proximately 90% were live on the EHR. Health
care costs and utilization trends were com-
pared in 2008 (before many of the PCIP prac-
tices implemented the EHR) and 2010 (when
most were live on the EHR).
To assess how the presence of major
chronic diseases targeted by the PCIP qual-
ity improvement initiative, such as diabetes
and hypertension, impacts cost and utiliza-
tion of health care services, the volume of
health care services and costs for members
with and without major chronic diseases were
compared for the PCIP and non-PCIP groups.
Cancer was considered as a disease category
not specifically targeted by the PCIP quality
improvement initiative.
Statistical analysis
Patient demographics and provider charac-
teristics for the PCIP and non-PCIP groups
were compared using the χ2
test for cate-
gorical variables and the t test for continuous
variables. Multivariate linear regression anal-
yses were conducted to compare the PCIP
and non-PCIP patient populations in terms
of total health care costs and utilization ad-
justed for potential confounders such as pa-
tient sex, age, presence of major chronic[AQ3]
diseases, and county of residence. Members
were considered to have a major chronic
disease when any of the following diagno-
sis codes, as classified by the International
Classification of Diseases, 9th Revision, Clin-
ical Modification, ever appeared on a claim:
diabetes (codes 250.xx), hypertension (codes
401.xx-405.xx), cerebrovascular disease (CV)
(codes 430.xx-438.xx), and cancer (codes
140.xx-210.xx). For all multivariate analyses,
data were restricted to members who had
data in 2008 and 2010 to ensure that the
same members were being compared in the
baseline year (2008) and at the end of study
(2010). To assess whether providers’ partici-
pation in the PCIP affected health care costs
and utilization over time, time and interaction
of time and PCIP status terms were also in-
cluded in the regression model. P values less
than .05 were considered statistically signifi-
cant. All analyses were conducted using SAS
statistical software version 9.2 (SAS, Inc, Cary,
North Carolina).
Claims data were obtained with approval
through the 32BJ Health Fund. This study was
reviewed and approved by the institutional re-
view board (IRB#: 10-085) as research on in-
dividual or group characteristics or behavior.
RESULTS
Patient and provider demographics
Although there were statistically significant
differences between the members accessing
PCIP and non-PCIP practices, the observed
magnitude was small (Table 1). For example, [T1]
members accessing non-PCIP practices were
older, more likely to be female, and more
likely to have a diagnosis of hypertension or
CV, compared with members accessing PCIP
practices.
During the study period, approximately
1100 PCIP and 3300 non-PCIP primary care
providers were identified in the claims data,
using New York State license numbers of the
individual physicians. When the data were
limited to outpatient primary care services,
patient volume (number of patients) and num-
ber of claims per provider for the PCIP and
non-PCIP primary care providers were not sig-
nificantly different (Table 1).
Health care service utilization and costs
Across all members, with or without a
chronic disease, between approximately 5%
and 7% of the population had at least one

LWW/JACM JAC200233 May 3, 2013 21:58
Table 1. Study Population Demographics
(2008-2010)
Non-
PCIP PCIP
Patient population 22 056 6 074
Sex,a
% male 82.2 88.0
Mean age,b
y 49.3 48.1
Presence of major
chronic diseases
% Diabetesa
14.8 12.8
% Hypertensiona
39.0 31.9
% Cerebrovascular
diseasea
5.0 2.5
% Cancera
3.5 2.7
Total no. claims
per patientb
9.6 7.0
Primary care
physicians
3 539 1 030
Patient volume
No. patients per
provider
19.3 25.5
No. claims per
provider
99.7 97.0
Average primary
care costs per
providerc
$9 331.00 $8 056.00
Abbreviation: PCIP, Primary Care Information Project.
aP values less than .05 for χ2 tests comparing categorical
variables.
bP values less than .05 for t tests comparing continuous
variables.
cTotal primary care (outpatient) costs were tabulated
for each provider, across all members assigned to that
provider.
claim for a hospital inpatient service such as
[AQ4]
a surgical procedure or treatment in a given
year (Table 2). The proportion ranged from[T2]
approximately 9% to 11% among those with
a diagnosis of diabetes or hypertension to ap-
proximately 22% to 23% among those with
cancer.
For both PCIP and non-PCIP members
(Figure 1), the unadjusted number of inpatient[F1]
services utilized was lower for those with dia-
betes and hypertension (∼1- 1.5 per member
per year at baseline) than for those with can-
cer (∼2- 4 per member per year at baseline).
From the beginning of the study (2008) to the
end of study (2010), for members with hyper-
tension (the most prevalent chronic disease in
this population), the unadjusted number of in-
patient services utilized per patient decreased
significantly for the PCIP group but not for
the non-PCIP group. Although nonsignificant,
members treated at a PCIP practice also had
larger decreases in the unadjusted number of
inpatient services used for those with diabetes
and CV than for members accessing non-PCIP
practices, and both groups of members had
decreased utilization of hospital inpatient ser-
vices among those with a diagnosis of cancer.
At baseline, hospital inpatient costs tended
to be higher for PCIP patients. In 2008, the
average hospital inpatient cost for patients
with hypertension was $22 785 for non-PCIP
patients and $24 580 for PCIP patients.
Similarly, for CV, costs were $24 687 for non-
PCIP patients and 33 417 for PCIP patients,
whereas for diabetes, costs were comparable,
$27 115 for non-PCIP patients and $27 068
for PCIP patients. Although nonsignificant,
hospital inpatient costs per patient from the
beginning of the study to the end of study,
decreased by $1240 for diabetes, $2890
for hypertension, and $3719 for CV for
members accessing PCIP practices, whereas
for members accessing non-PCIP practices,
costs increased by $2421, $4247, and $9440,
respectively (Figure 2). Both groups had de- [F2]
creased hospital inpatient costs for members
with cancer, although the decreases were
larger for members accessing PCIP practices.
Among all members, the number of spe-
cialty care visits increased from 2008 to 2010,
with greater utilization of specialty care ser-
vices among those with a chronic disease. For
members with hypertension and diabetes, the
2 most commonly diagnosed chronic diseases
in this population, increases in specialty care
utilization were higher for members accessing
PCIP practices (Figure 3) (ie, increase of + [F3]
0.81 specialty care visits for diabetes and +
0.6 for hypertension per member per year,
compared with + 0.24 and + 0.25 for the
non-PCIP group). Utilization of specialty care
services increased over time among patients
diagnosed with cancer or CV for members ac-
cessing PCIP or non-PCIP practices, although
these differences were not significant.

LWW/JACM JAC200233 May 3, 2013 21:58
Table 2. Unadjusted Yearly Utilization of Hospital Inpatient Services Among Members
Non-PCIP PCIP
% Using Hospital
Inpatient Services Members,a
n
% Using Hospital
Inpatient Services Members,a
n
Diabetes
2008 13 3 266 10 775
2010 11 3 598 8 826
Hypertension
2008 10 8 593 9 1 938
2010 9 8 844 8 1 888
Cerebrovascular disease
2008 16 1 093 22 152
2010 16 1 152 15 183
Cancer
2008 26 780 23 166
2010 22 779 15 182
All membersb
2008 7 22 056 5 6 074
2010 6 22 474 5 6 186
aTotal number of members with a chronic disease per year.
bTotal number of members with or without a chronic disease per year.
To assess trend in utilization of inpatient
[AQ5]
[AQ6]
services and specialty care visits, a multi-
variate regression analysis was conducted,
including calendar year and only members
with claims in both 2008 and 2010. After
adjusting for member demographics such
as age, sex, and the presence of chronic
diseases, the results suggested that there
was a signiﬁcant positive trend over time for
both number of inpatient services ( + 0.15
per member or an increase of 15 inpatient
services per 100 members) and number of
Figure 1. Unadjusted number of hospital inpatient services utilized per member stratiﬁed by comorbidity
status. a
Exclude cases where comorbidity status is missing. b
P < .05 for 2-sample t test taking into account
unequal sample variances. CV indicates cerebrovascular disease; CY, . . . ; PCIP, Primary Care Information
Project.

LWW/JACM JAC200233 May 3, 2013 21:58
Figure 2. Inpatient costs per member (2008-
2010). a
P < .05 for 2-sample t test taking into
account unequal sample variance. CV indicates
cerebrovascular disease; PCIP, Primary Care Infor-
mation Project.
specialty care visits ( + 0.19 per member or
an increase of 19 inpatient services per 100
members) for members accessing non-PCIP
practices (Table 3). The interaction terms[T3]
were not significant, but the coefficients
suggest that PCIP patients had a decreasing
trend in the utilization of inpatient services
over time ( − 0.16 per member) whereas
they had an increasing trend in the number
of specialty care visits over time ( + 0.15 per
member)
The number of emergency department vis-
its among members with access to PCIP and
non-PCIP practices was also compared (re-
sults not shown); however, there were no
differences in trend over time for any of the
major chronic disease categories considered.
DISCUSSION
From 2008 to 2010, members who had a
major chronic disease and whose primary
care providers participated in the PCIP had
decreased utilization of potentially costly
inpatient services. However, both groups
showed increased utilization of specialty
care services over time. The PCIP targeted
providers in medically underserved commu-
nities with health information technology
(IT) and quality improvement guidance to
increase the delivery of primary care and
preventive services and potentially decrease
health disparities. There is some evidence in
the literature to suggest that involvement in
quality improvement initiatives is linked to
improved patient care and decreased costs
(Flottemesch et al., 2011; Willits et al., 2012).
Studies have shown that the EHR with
CDSS can be used to promote compliance
with recommended guidelines of preventive
care (De Leon & Shih, 2011; Friedberg et al.,
2009; Welch et al., 2007) and is expected
Figure 3. Unadjusted number of specialty care visits per member stratified by comorbidity status. a
Exclude
cases where comorbidity status is missing. b
P < .05 for 2-sample t test taking into account unequal sample
variance. CV indicates cerebrovascular disease; CY, . . . ; PCIP, Primary Care Information Project.

LWW/JACM JAC200233 May 3, 2013 21:58
Table 3. Multivariate Regression Results Comparing Number of Hospital Inpatient Services and Number
of Specialty Care Visits per Member Among PCIP and Non-PCIP Practices, Adjusting for Member
Demographicsa
No. Hospital Inpatient
Services per Member
No. Specialty Care Visits
per Member
Unadjusted estimates
PCIP
Baseline (2008) 0.44 3.05
End of study 0.38 3.35
Difference over time (2010-2008) − 0.06 +0.3
Non-PCIP
Baseline (2008) 0.62 4.03
End of study 0.64 4.15
Difference over time (2010-2008) +0.02 +0.12
Adjusted estimates
Difference over time (2010-2008)
PCIP − 0.16 0.15
Non-PCIP 0.15c
0.19c
aOnly members with claims in 2008 and 2010 (paired data) were included in the analyses to ensure that the same
members are being compared in the baseline year (2008) and at the end of study (2010).
bMultivariate regression results adjusted for patient demographics and common comorbidities. All predictor variables
were dichotomized to create binary (0/1) variables such that PCIP status (yes) = 1; age ≥50 years = 1; male = 1;
presence of diabetes = 1; presence of hypertension = 1; presence of cerebrovascular disease = 1; and presence of
cancer = 1.
cP < .05.
to improve the efficiency of the health care
[AQ7]
[AQ8]
system by potentially decreasing costs due to
redundant care and poor case management
or care coordination (Bates et al., 1999; Fryer
et al., 2011; Gilfillan et al., 2010). While
PCIP providers were equipped with the EHR
with CDSS that promotes population health
and disease prevention, they also had access
to a wide range of services that promote
population health such as on-site clinical
quality-of-care consulting services; assistance
attaining patient-centered medical home
recognition; and panel management and
pay-for-performance programs for high-risk
patients with chronic diseases. We did not
specifically test for the effect of the EHR
and do not have EHR information on the
non-PCIP providers. Differences observed in
this study between the PCIP and non-PCIP
groups cannot be fully attributed to the EHR.
This study has several limitations. First, the
study focused on a population of working
adults with few members older than 65 years
and no children. Second, the study is based
on claims paid by the 32BJ Health Fund, in-
cluding all procedures, hospitalizations, and
office visits, but does not capture utilization
of other services that may have been bundled
or not paid by 32BJ Health Fund. We were
not able to address the issue of access to
care, but since this population has access to a
private network of providers, we would not
expect this to affect utilization patterns. As
this study was based on available utilization
data from a payer, additional provider and
practice characteristics were not available for
comparison with the non-PCIP practices (eg,
clinical quality data, organizational size).
With the growing burden of chronic dis-
eases in the US population (Anderson, 2010),
utilization and costs of health care services are
expected to increase over time (Anderson &
Horvath, 2004; Gilmer & Kronick, 2011;
Thorpe et al., 2004). While access to quality
improvement initiatives that utilize health IT
has the potential for improving the quality of

LWW/JACM JAC200233 May 3, 2013 21:58
care and reducing costs, reform of payment
for services is an integral part of containing
increasing health care costs (Averill et al.,
2010; Quinn, 2010).
In working to improve the health of their
member population while reducing the rise
of health care costs, the 32BJ Health Fund
is piloting a payment reform model among
selected practices that would receive a
management fee, in addition to the regular
fee-for-service claims, for each member with
a chronic disease (such as diabetes) enrolled
in the practice. To be eligible for these
payments, the practice must demonstrate the
ability to deliver patient self-management ed-
ucation and care coordination and use health
IT both to focus on health promotion and
disease prevention and to report on health
outcomes. As health care costs continue to
increase across the country, further study
is needed to understand how the use of
health IT, payment reform methods, patient
quality-of-care initiatives, and other types of
interventions can sustain decreases in health
care costs and utilization without adversely
affecting patient quality of care.
As part of the American Recovery and
Reinvestment Act of 2009, the federal govern-
ment will be providing ﬁnancial incentives
for providers with substantial Medicaid and
Medicare patient populations to meaningfully
use EHRs in order to improve patient care,
increase care coordination through health
information exchange, and increase the
collection and tracking of structured data ele-
ments on patient care and population health.
As physicians across the United States comply
with the meaningful use requirements, in
settings ranging from solo provider to large
multisite practices, there is great potential for
improving population health and potentially
decreasing health care costs, particularly
when providers have the additional support
of health IT or quality improvement initiatives
to help them transition to electronic health
information systems.
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on quality and cost of care. Journal of the American
Medical Informatics Association, 14(3), 320–328.
Willits, K. A., Nies, M. A., Racine, E. F., Troutman-Jordan,
M. L., Platonova, E., & Harris, H. L. (2012, July–
September). Medical home and emergency depart-
ment utilization among children with special health
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LWW/JACM JAC200233 May 3, 2013 21:58
Queries to Author
Title: Early Assessment of Health Care Utilization Among a Workforce Population With Access
to Primary Care Practices With Electronic Health Records
Author: Samantha F. De Leon, Lucas Pauls, Sarah C. Shih, Thomas Cannell, Jason J. Wang
[AQ1]: Please verify whether afﬁliations are OK.
[AQ2]: Please verify whether the short title for the article is OK.
[AQ3]: Note that “gender” has been changed to “sex” throughout the text. Please verify.
[AQ4]: Please verify whether the layout of Table 1 is OK.
[AQ5]: Please cite footnote “a” in the artwork of Figures 1-3.
[AQ6]: Please provide the expansion of “CY.”
[AQ7]: Please verify whether the symbol (minus sign) for the empty box in Table 3 has been
identiﬁed correctly.
[AQ8]: Please provide the citation of footnote b in Table 3.

JACM-D-13-00014proof

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