ON THE ROAD TO MEANINGFUL USE OF ELECTRONIC HEALTH RECORDS: EXAMINING IMPLEMENTATION IN FEDERALLY QUALIFIED COMMUNITY HEALTH CENTERS

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1 ON THE ROAD TO MEANINGFUL USE OF ELECTRONIC HEALTH RECORDS: EXAMINING IMPLEMENTATION IN FEDERALLY QUALIFIED COMMUNITY HEALTH CENTERS 2014 Academy Health Annual Research Meeting, San Diego, CA Panel: HIT Adoption after the Tipping Point: Successes and Challenges June 8, 2014 Jenna T. Sirkin, MA, PhD Candidate Donald Shepard, PhD Deborah Garnick, ScD Cindy Parks Thomas, PhD The Heller School, Brandeis University

2 Overview Research hquestions Background Methods National Data Sets Study Sample Meaningful Use Indices Case Study Component Quantitative Results Conclusions Policy Implications

3 Research Questions I. What are the organizational and contextual factors associated with Meaningful Use (MU) progress among a national sample of federally qualified community health centers (CHCs)? II. How are CHC providers and staff using electronic health records (EHRs) to facilitate health h information i exchange, chronic disease management, and quality measurement? 3

4 Meaningful Use?

5 Meaningful Use Medicaid Incentive Program Eligibility and Incentives Every state runs own program; Program runs from Eligible Professionals Practice Predominantly in FQHC Timeline for Medicaid Year 1 of participation, providers can receive incentive payment for Adopting, p Implementing, OR Upgrading a certified EHR Maximum incentive: $63,750 (across 6 Remaining 5 years: Demonstrate MU years of program participation); No penalties for Medicaid Program

6 Study Setting: Federally Qualified Community Health Centers Non-profit, community-based primary care providers To qualify for Section 330 grant funding from Bureau of Primary Health lthcare and enhanced Medicaid id reimbursement CHCs must meet the following criteria: Located in high-need area Governed by a community board Provide comprehensive primary care to all, regardless of ability to pay Provide high-quality, cost effective care and reduce health care disparities Source: National Association of Community Health Centers [NACHC]. United State's Health Center Fact Sheet, Bethesda, MD, 2014 Available at:

7 Study Setting: Federally Qualified Community Health Centers, ,198 CHCs operate over 8,912 service delivery sites; provided care to 21 million patients nationally CHC Patient t Characteristics ti 72% at or below 100% FPL (20% U.S. Population) 93% at or below 200% FPL (40%US U.S. Population) 36% Uninsured (15% U.S. Population) 40% Medicaid (16% U.S. Population) Three times more likely than the general population to seek care for chronic conditions CHC patients more likely to experience referral difficulties Source: National Association of Community Health Centers [NACHC]. United State's Health Center Fact Sheet, Bethesda, MD, 2014 Available at: Bureau of Primary Health Care, HRSA, DHHS, 2012 Uniform Data System (UDS).

8 EHR Diffusion & Practice Integration Barriers in CHCs Diffusion in safety net historically slow due to limited resources and more complex patient needs. Resources Constraints High start-up costs (direct); high on-going costs Need training/ support for implementation & practice integration Provider attitudes; need to train clinical champions Misaligned costs and benefits (lack of incentives) Interface and interoperability constraints CHC regulatory and reporting requirements Lack of EHR system flexibility for highly-complex patient needs Shields AE, Shin P, Leu MG, et al. Adoption of health information technology in community health centers: Results of a national survey. Health Aff (Millwood). 2007;26(5): ; McAlearney AS, Robbins J, Hirsch A, Jorina M, Harrop JP. Perceived efficiency impacts following electronic health record implementation: an exploratory study of an urban community health center network. International journalof medical informatics. 2010;79(12):807-16; Miller RH, West CE. The value of electronic health records in community health centers: policy implications. Health Aff (Millwood). 2007;26(1): ; National Opinion Research Center (NORC). Understanding the impact of health it in underserved communities and those with health disparities. Bethesda MD: Presented to: The U.S. Department of Health and Human Services, 2012

9 Methods: Data Sources and Sample George Washington University and NACHC, Readiness for Meaningful Use Survey, % response rate (n=714) For each MU element: Yes, now (Current MU Readiness) Yes, by 2012 (Future MU Readiness) No/ Unsure Bureau of Primary Health Care, Uniform Data System (UDS), 2011 Universe of federally qualified CHCs Patient and organizational characteristics Data Sources: Cunningham M Lara A Shin P Results from the Readiness for MeaningfulUse of HIT and Patient Centered Medical Data Sources: Cunningham M, Lara A, Shin P. Results from the Readiness for Meaningful Use of HIT and Patient Centered Medical Home Recognition Survey. Washington, D.C.: Geiger Gibson/ RCHN Community Health Foundation Research Collaborative, George Washington University; 2011 Nov. 3. Report No.: Policy Research Brief #27 Uniform data systems: 2011 national health center data [database on the Internet]. Bureau of Primary Health Care Available from:

10 100.0% Wide Variation in Meaningful Use Measures, % 82.1% 75.9%75.2%75.0% 73.1% 80.0% % CHCs Ye es, Now 70.0% 60.0% 61 6% 61.6% 52.3% 51.9% 50.0% 40.0% 30.0% 20.0% 10 0% 10.0% 0.0% Source: Cunningham M, Lara A, Shin P, % 61.5% 63.5% 59.3% 53.9% 43.8% 49.9% 42.4% 34.5% 26.3% 38.4% 41.3% 17.4% 40.3% 17.2% 39.5% 34.5%

11 Characteristics of CHC Study Sample: Combined Survey & UDS, 2011 (n=679) Select Patient Characteristics %Female 58.4% % Patients 100% FPL* 72.7% % Uninsured 35.5% % Medicaid 40.3% % Minority 45.7% % Patients Best Served in Foreign Language 24.2% Select CHC Characteristics Average Number of Patients 19,796 Average Total Revenue ($, in millions) $5.7 Participation in Regional Extension Center 48.3% Participation in Clinical Data Warehouse 44.0% Notes: Final study sample is 679 CHCs. *Income relative to FPL is based on the 77% of patients that reported income in UDS 2011.

12 Methods: Indices for Evaluating Stage 1 MU MU-1B Expansive Index MU-1A Narrow Index 24-point denominator 19-point denominator 14 core & 10 menu Index Mean: 16.3 (5.4 Std. Dev.) 14 core & 5[of 10] menu Index Mean: 11.4 (6.4 Std. Dev.) Future Credit: Partial Future Credit: None Yes, Now & Yes, by 2012 Yes, Now only Frequency of Index Sco ore Frequency of Index Sco ore 12 MU-1B Expansive Index (24) MU-1A Narrow Index (19)

13 Results: CHC Factors Associated with MU Progress Multivariate i t Analysis (OLS) Models MU-1B (24) Expansive Index Effect MU-1A (19) Narrow Index Effect Participating in a REC Participating in a CDW 0.078*** 0.120*** Total revenue (natural log) 0.021* Prop. of Patients 100% FPL * ** Prop. of patients prefer foreign lang * Prop. of uninsured patients Prop. of minority patients Prop. of female patients CHC location (urban) a CHC location (both urban & rural) a The final sample for the analysis was 679 CHCs; a Reference Category is Rural. Significance: * p<0.05, ** p<0.01, *** p<0.001

14 Summary of Quantitative Findings CHCs participating in local or regional clinical data warehouse projects were more likely (p<.001) to have met a higher proportion of MU objectives CHCs Serving a higher percentage of patients below 100% of the FPL and patients best served in a foreign language were behind (p<.05) in achieving MU Greater total revenue was positively associated (p<.05) with MU progress

15 Study Limitations Cross-sectional sectional nature limits causal inference Mixed methods approach Rigorous site selection approach (dimensions from quantitative) Survey data self reported Measure limitations Innovative MU Indices Leadership team and provider perspective on MU Understand barriers and what works well

16 Overview: Case Study Component Selection criteria Early vs. later adopters (MU Attestation) Type of EHR network Regional Extension Center (state IOO) Clinical Data Warehouse (Azara DRVS) Site visits and semi-structured provider & staff interviews (n=37)

17 Conclusions What Facilitates Meaningful EHR Use? Clinical Data Warehouse (economy of scale) Local EHR Support Networks (REC, Primary Care Associations, Hospital) CHC revenue and slack resources CHC leadership involvement Vision, planning & implementation strategy Clinical training, buy-in, &longterm long-term support e.g., clinical health IT champion, CIO, hospital network Barriers and Challenges Economic/ social disadvantage CHC patients Inadequate planning and resources, including provider and staff time Increasing complexity of reporting and organizational demands Technical and logistic interoperability challenges data sharing, referrals, labs, imaging EHR adaption and integration with behavioral health records

18 Policy Implications Evolving role of Clinical Data Warehouse Networks that address safety-net needs Behavioral health: Funding, record integration, provider buy-in, updated EHRs Interoperability: Gov t &/ pvt. sector incentives Standardize and streamline reporting demands Sustainability considerations and economies of scale

19 Dissertation Support Agency for Health Care Research and Quality (AHRQ) PhD Training Fellowship The Massachusetts Health Data Consortium The Heller School for Social Policy and Management Alumni Fund Office of the Provost, Brandeis University

20 Ak Acknowledgements ld Dissertation Committee Cindy Parks Thomas, PhD, Chairperson Nina Kammerer, PhD, MPH Deborah Garnick, ScD Donald Shepard, PhD Heller School, Brandeis University John D. Halamka, MD, MS Beth Israel Deaconess Medical Center & Harvard Medical School Peter Shin, PhD Thank you! George Washington University, it Geiger Gibson/ RCHN Research Collaborative Deborah Gurewich, PhD UMass Center for Health Policy & Research Craig Schneider, PhD Mathematica Policy Research Ellen Hafer, Executive VP MA League of CHCs Shane Hickey, HIT Director Michelle Proser, Research Director National Association of CHCs

21 EXTRAS

22 Rates of EHR Adoption and MU Demonstration by RECenrolled practice types 9% (1,723) of REC providers in HRSAfunded FQHCs and FQHC Look-alikes are demonstrating meaningful use of certified EHR technology.

23 Factors Associated w CHC s MU Progress: Multivariate i t OLS Regression Analysis MU-1B (24) Expansive Index MU-1A (19) Narrow Index Effect [95% CI] Effect [95% CI] Participating in a REC.027 [-0.01,0.06].026 [-0.02,0.08] Participating in a CDW 0.078*** [0.05,0.11] 0.120*** [0.07,0.17] Total revenue (natural log) 0.021* [0.00,0.04] [-0.01,0.05] Prop. of Patients 100% FPL * [-0.23,-0.02] ** [-0.39,-0.07] Prop. of patients prefer foreign lang * [-0.15,-0.01] [-0.17,0.05] Prop. of uninsured patients [-0.12,0.07] 0.04 [-0.10,0.18] Prop. of minority patients [-0.08,0.05] [-0.11,0.09] Prop. of female patients [ ,0.46] [ ,0.57] 0 CHC location (urban) a [-0.01,0.07] [-0.03,0.10] CHC location (both urban & rural) a [-0.06,0.04] [-0.10,0.05] The final sample for the analysis was 679 CHCs; a Reference Category is Rural. * p<0.05, ** p<0.01, *** p<0.001

24 Case Study Site Selection Matrix Comparative Case Study: Site Selection Matrix MU Stage 1 Attestation Type of EHR Support Network Earlier Adopters Later adopters Massachusetts League of Site I: Site II: Community Health Centers Mass League-early Mass League-late Other Massachusetts EHR Support Network Site III: Other-early Site IV: Other-late

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