1. Longitudinal Analysis of Electronic Health Record Adoption on Staffing Mix in Community Health Centers
ASHEcon 6th Biennial Conference
Philadelphia, PA
June 15, 2016
Bianca K. Frogner, PhD
Associate Professor, Department of Family Medicine
Director, Center for Health Workforce Studies
School of Medicine

2. Acknowledgements Co-authors: Xiaoli Wu, MS
Research Associate, GW Milken Institute School of Public Health
Jeongyoung Park, PhD
Assistant Professor, GW School of Nursing
Patricia Pittman, PhD
Associate Professor, GW Milken Institute School of Public Health
Funding:
Cooperative Agreement for a Regional Center for Health Workforce Studies (1 U81HP ) from the Health Resources and Services Administration awarded to The George Washington University (PI: Patricia Pittman)
Additional data provided by The George Washington University Geiger Gibson Program in Community Health Policy

3. Background
Community health centers (CHCs) have long operated with challenges in staffing recruitment and retention
Although electronic health records (EHRs) are slow to proliferate across primary care settings, CHCs have been leaders in the adoption of EHRs among primary care providers
However, some CHCs are faster adopters than others and many remain without EHRs
Limited understanding on whether there is something unique about the staffing in CHCs that lend themselves to adopt EHRs
May be useful for other ambulatory care settings to learn as they increasingly adopt EHRs – workforce is often a significant barrier
Add my cross sectional study results and Jeongyoung PCMH results

4. Related Studies
Community health centers (CHCs) have adaptable staffing patterns, and manage to maintain similar productivity levels across varying staffing models (Ku et al, 2015, Health Affairs)
CEOs of CHCs frequently consider environmental factors (e.g., local supply, scope of practice regulations, relative wages, and reimbursement arrangements) in hiring decisions; also consider personal attributes, clinical experience, quality of professional education, and likelihood of retention in determining distribution and roles (Pittman et al, under review)
CHC adoption of the Patient Centered Medical Home Model (PCMH) found to be associated with significant growth in use of advanced practice staff (Park et al, under review)
Productivity of physicians in CHCs significantly improved with more years of experience with EHRs, while nurses had a notable but not statistically significant negative impact with EHR adoption (Frogner et al, under review)

5. Data and Sample Study Question: Data: Sample
How does EHR adoption change staffing mix of CHCs over time?
Data:
2007 to 2013 Uniform Data System (UDS)
Survey responses from “Readiness for Meaningful Use and HIT and Patient Centered Medical Home Recognition Survey”
Sample
CHCs operating in the fifty states and DC that were operating for the entire study period of 2007 to 2013
CHCs with identifiable year of EHR adoption
N=5250 CHC-year observation (750 unique CHCs)

6. Analytic Approach
Fractional Multinomial Logit (fmlogit) to estimate how adoption of EHR impacts the change in proportion of provider types
Example: How the proportion of one type of provider category such as physicians, shifts the proportion of another type of provider category, such as nurses, keeping all else constant
Controls: CHC size, geographic location, presence of a patient-centered medical home model, and other local market conditions
quasi maximum likelihood
dependent variables that each must range between 0 and 1 and must always, for each observation, add up to 1

8. Baseline Community Health Center Profile by EHR Adoption Status, 2007
Never
Adopted EHRs
(N=314)
EHR Adopter
(N=311)
Always
had EHRs
(N=125)
Female
57.2%
58.5%
57.8%
Age
Age 18 and under
31.2%
34.8%
31.3%
Age 19-64
61.4%
73.2%
63.0%
Age 65 and over
7.7%
8.5%
7.9%
Race/Ethnicity
White
59.1%
54.2%
51.8%
Black
20.5%
22.9%
19.1%
Hispanic
24.5%
24.1%
Asian/Pacific Islander
2.4%
3.0%
4.3%
American Indian/Alaska Native
3.1%
0.8%
Other/Unknown
23.%
24.2%
30.1%
Insurance Type
Uninsured**
41.4%
46.1%
39.6%
Medicaid
30.2%
28.7%
31.7%
Medicare
9.4%
10.4%
9.5%
Other Public*
1.4%
1.9%
2.5%
Private
19.8%
17.9%
18.9%
Patients with limited English proficiency
19.0%
19.9%
20.0%
Patients at 100% or below poverty level
46.9%
50.0%
49.9%
Total number of patients*
13,200
14,212
18,002
Rural (Non-Metro)
53.0%
52.0%
59.0%
Located in Health Professional Shortage Area
(Measured by Whole County)
50.3%
51.4%
55.2%
Local Market Conditions
Total Active Physicians per 1,000 Population**
2.7
2.8
3.5
Total Nurse Practitioners w/ National Provider Identifier
per 1,000 Population*
0.3
0.4

9. Predicted Shares of Staffing Type by Adoption of EHRs in CHCs

10. Key Findings
Over the seven-year study period, 17% CHCs had EHRs for the entire period, 42% CHCs never adopted EHRs, and 41% adopted at some point over the study period
CHCs with EHRS had significantly lower share of physicians compared to CHCs without EHRs throughout the study period
CHCs with EHRs had significantly higher share of other medical staff among early adopters and late adopters
The trend in share of other medical staff suggests the need for adequate support staff to get an EHR system to successfully “go live”
2011 peak year of adoption

11. Conclusions and Policy Implications
CHCs with and without EHRs experienced similar trends over time in their staffing configuration
But CHCs with EHRs allocate their total medical staff differently than CHCs without EHRs.
CHCs with EHRs appear to elevate the use of other medical staff over all other staffing
Especially in the early years of adoption and again in later years.
The finding with regards to other medical staff appears to confirm early studies that suggest that adequate support staff is necessary to get an EHR system to successfully “go live”
Although this study does not confirm it, this findings provide support for the hypothesis that EHR adoption in CHCs allows for greater flexibility among staff types

12. QUESTIONS? CONTACT:
Bianca Frogner, PhD
CHWS Website
Twitter
@biancafrogner
@uwchws

13. Nurse Practitioners/Physician Assistants
Regression Coefficients on Key Variables Estimating Share of Staffing (N=3498)
Coefficient
Robust SE
P Value
Nurse Practitioners/Physician Assistants
EHR
0.104
0.220
0.635
PCMH
0.227
0.451
0.615
EHR * Year
0.004
0.022
0.864
PCMH * Year
-0.025
0.037
0502
EHR * PCMH
-0.085
0.089
0.340
Year
0.023
0.013
0.082
Nurses
0.079
0.207
0.703
-0.269
0.519
0.604
0.020
0.854
0.042
0.601
-0.150
0.087
0.086
-0.011
0.012
0.360
Other Medical Staff
0.239
0.199
0.229
-0.371
0.435
0.394
-0.017
0.379
0.026
0.035
0.464
0.116
0.084
0.168
0.063
0.000
Omitted category: physicians; Control vars: CHC size, urban geography, number of physicians and nurse practitioners per thousand capita in county