Yue Gao1, Robert S Nocon1, Kathryn E Gunter1, Ravi Sharma2, Quyen Ngo-Metzger3, Lawrence P Casalino4, Marshall H Chin5. 1. Section of General Internal Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA. 2. Bureau of Primary Health Care, Health Resources and Services Administration, U.S. Department of Health and Human Services, Rockville, MD, USA. 3. U.S. Preventive Services Task Force Program, Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services, Rockville, MD, USA. 4. Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA. 5. Section of General Internal Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA. mchin@medicine.bsd.uchicago.edu.
Abstract
BACKGROUND: The patient-centered medical home (PCMH) model is being implemented in health centers (HCs) that provide comprehensive primary care to vulnerable populations. OBJECTIVE: To identify characteristics associated with HCs' PCMH capability. DESIGN: Cross-sectional analysis of a national dataset of Federally Qualified Health Centers (FQHCs) in 2009. Data for PCMH capability, HC, patient, neighborhood, and regional characteristics were combined from multiple sources. PARTICIPANTS: A total of 706 (70 %) of 1014 FQHCs from the Health Resources and Services Administration Community Health Center Program, representing all 50 states and the District of Columbia. MAIN MEASURES: PCMH capability was scored via the Commonwealth Fund National Survey of FQHCs through the Safety Net Medical Home Scale (0 [worst] to 100 [best]). HC, patient, neighborhood, and regional characteristics (all analyzed at the HC level) were measured from the Commonwealth survey, Uniform Data System, American Community Survey, American Medical Association physician data, and National Academy for State Health Policy data. KEY RESULTS: Independent correlates of high PCMH capability included having an electronic health record (EHR) (11.7-point [95 % confidence interval, CI 10.2-13.3]), more types of financial performance incentives (0.7-point [95 % CI 0.2-1.1] higher total score per one additional type, maximum possible = 10), more types of hospital-HC affiliations (1.6-point [95 % CI 1.1-2.1] higher total score per one additional type, maximum possible = 6), and location in certain US census divisions. Among HCs with an EHR, location in a state with state-supported PCMH initiatives and PCMH payments was associated with high PCMH capability (2.8-point, 95 % CI 0.2-5.5). Other characteristics had small effect size based on the measure unit (e.g. 0.04-point [95 % CI 0-0.08] lower total score per one percentage point more minority patients), but the effects could be practically large at the extremes. CONCLUSIONS: EHR adoption likely played a role in HCs' improvement in PCMH capability. Factors that appear to hold promise for supporting PCMH capability include a greater number of types of financial performance incentives, more types of hospital-HC affiliations, and state-level support and payment for PCMH activities.
BACKGROUND: The patient-centered medical home (PCMH) model is being implemented in health centers (HCs) that provide comprehensive primary care to vulnerable populations. OBJECTIVE: To identify characteristics associated with HCs' PCMH capability. DESIGN: Cross-sectional analysis of a national dataset of Federally Qualified Health Centers (FQHCs) in 2009. Data for PCMH capability, HC, patient, neighborhood, and regional characteristics were combined from multiple sources. PARTICIPANTS: A total of 706 (70 %) of 1014 FQHCs from the Health Resources and Services Administration Community Health Center Program, representing all 50 states and the District of Columbia. MAIN MEASURES: PCMH capability was scored via the Commonwealth Fund National Survey of FQHCs through the Safety Net Medical Home Scale (0 [worst] to 100 [best]). HC, patient, neighborhood, and regional characteristics (all analyzed at the HC level) were measured from the Commonwealth survey, Uniform Data System, American Community Survey, American Medical Association physician data, and National Academy for State Health Policy data. KEY RESULTS: Independent correlates of high PCMH capability included having an electronic health record (EHR) (11.7-point [95 % confidence interval, CI 10.2-13.3]), more types of financial performance incentives (0.7-point [95 % CI 0.2-1.1] higher total score per one additional type, maximum possible = 10), more types of hospital-HC affiliations (1.6-point [95 % CI 1.1-2.1] higher total score per one additional type, maximum possible = 6), and location in certain US census divisions. Among HCs with an EHR, location in a state with state-supported PCMH initiatives and PCMH payments was associated with high PCMH capability (2.8-point, 95 % CI 0.2-5.5). Other characteristics had small effect size based on the measure unit (e.g. 0.04-point [95 % CI 0-0.08] lower total score per one percentage point more minority patients), but the effects could be practically large at the extremes. CONCLUSIONS: EHR adoption likely played a role in HCs' improvement in PCMH capability. Factors that appear to hold promise for supporting PCMH capability include a greater number of types of financial performance incentives, more types of hospital-HC affiliations, and state-level support and payment for PCMH activities.
Entities:
Keywords:
disparities; financial incentives; health center; medical home; vulnerable populations
Authors: Jonathan M Birnberg; Melinda L Drum; Elbert S Huang; Lawrence P Casalino; Sarah E Lewis; Anusha M Vable; Hui Tang; Michael T Quinn; Deborah L Burnet; Thomas Summerfelt; Marshall H Chin Journal: J Gen Intern Med Date: 2011-08-12 Impact factor: 5.128
Authors: Leif I Solberg; Logan H Stuck; A Lauren Crain; Juliana O Tillema; Thom J Flottemesch; Robin R Whitebird; Patricia L Fontaine Journal: Am J Med Qual Date: 2014-05-01 Impact factor: 1.852
Authors: Diane R Rittenhouse; Lawrence P Casalino; Stephen M Shortell; Sean R McClellan; Robin R Gillies; Jeffrey A Alexander; Melinda L Drum Journal: Health Aff (Millwood) Date: 2011-06-30 Impact factor: 6.301
Authors: Leiyu Shi; Diana C Lock; De-Chih Lee; Lydie A Lebrun-Harris; Marshall H Chin; Preeta Chidambaran; Robert S Nocon; Jinsheng Zhu; Alek Sripipatana Journal: Med Care Date: 2015-05 Impact factor: 2.983
Authors: Lawrence Casalino; Robin R Gillies; Stephen M Shortell; Julie A Schmittdiel; Thomas Bodenheimer; James C Robinson; Thomas Rundall; Nancy Oswald; Helen Schauffler; Margaret C Wang Journal: JAMA Date: 2003 Jan 22-29 Impact factor: 56.272
Authors: Karin M Nelson; Christian Helfrich; Haili Sun; Paul L Hebert; Chuan-Fen Liu; Emily Dolan; Leslie Taylor; Edwin Wong; Charles Maynard; Susan E Hernandez; William Sanders; Ian Randall; Idamay Curtis; Gordon Schectman; Richard Stark; Stephan D Fihn Journal: JAMA Intern Med Date: 2014-08 Impact factor: 21.873
Authors: Peter Mendel; Emily K Chen; Harold D Green; Courtney Armstrong; Justin W Timbie; Amii M Kress; Mark W Friedberg; Katherine L Kahn Journal: Health Serv Res Date: 2017-12-15 Impact factor: 3.402
Authors: Daniel Jung; Elbert S Huang; Eric Mayeda; Rachel Tobey; Eric Turer; James Maxwell; Allison Coleman; Jennifer Saber; Susan Petrie; Joshua Bolton; Daniel Duplantier; Hank Hoang; Alek Sripipatana; Robert Nocon Journal: Health Serv Res Date: 2022-03-21 Impact factor: 3.734
Authors: Janel L Jin; Joshua Bolton; Robert S Nocon; Elbert S Huang; Hank Hoang; Alek Sripipatana; Marshall H Chin Journal: Health Serv Res Date: 2022-05-02 Impact factor: 3.734