Udoka Obinwa1, Adriana Pérez1, Ildiko Lingvay2,3, Luigi Meneghini2,4, Ethan A Halm2,3, Michael E Bowen5,3. 1. School of Public Health, University of Texas Health Science Center at Houston, Houston, TX. 2. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX. 3. Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX. 4. Parkland Health & Hospital System, Dallas, TX. 5. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX michael.bowen@utsouthwestern.edu.
Abstract
OBJECTIVE: Variation in diabetes screening in clinical practice is poorly described. We examined the interplay of patient, provider, and clinic factors explaining variation in diabetes screening within an integrated health care system in the U.S. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study of primary care patients aged 18-64 years with two or more outpatient visits between 2010 and 2015 and no diagnosis of diabetes according to electronic health record (EHR) data. Hierarchical three-level models were used to evaluate multilevel variation in screening at the patient, provider, and clinic levels across 12 clinics. Diabetes screening was defined by a resulted gold standard screening test. RESULTS: Of 56,818 patients, 70% completed diabetes screening with a nearly twofold variation across clinics (51-92%; P < 0.001). Of those meeting American Diabetes Association (ADA) (69%) and U.S. Preventive Services Task Force (USPSTF) (36%) screening criteria, three-quarters were screened with a nearly twofold variation across clinics (ADA 53-92%; USPSTF 49-93%). The yield of ADA and USPSTF screening was similar for diabetes (11% vs. 9%) and prediabetes (38% vs. 36%). Nearly 70% of patients not eligible for guideline-based screening were also tested. The USPSTF guideline missed more cases of diabetes (6% vs. 3%) and prediabetes (26% vs. 19%) than the ADA guideline. After adjustment for patient, provider, and clinic factors and accounting for clustering, twofold variation in screening by provider and clinic remained (median odds ratio 1.97; intraclass correlation 0.13). CONCLUSIONS: Screening practices vary widely and are only partially explained by patient, provider, and clinic factors available in the EHR. Clinical decision support and system-level interventions are needed to optimize screening practices.
OBJECTIVE: Variation in diabetes screening in clinical practice is poorly described. We examined the interplay of patient, provider, and clinic factors explaining variation in diabetes screening within an integrated health care system in the U.S. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study of primary care patients aged 18-64 years with two or more outpatient visits between 2010 and 2015 and no diagnosis of diabetes according to electronic health record (EHR) data. Hierarchical three-level models were used to evaluate multilevel variation in screening at the patient, provider, and clinic levels across 12 clinics. Diabetes screening was defined by a resulted gold standard screening test. RESULTS: Of 56,818 patients, 70% completed diabetes screening with a nearly twofold variation across clinics (51-92%; P < 0.001). Of those meeting American Diabetes Association (ADA) (69%) and U.S. Preventive Services Task Force (USPSTF) (36%) screening criteria, three-quarters were screened with a nearly twofold variation across clinics (ADA 53-92%; USPSTF 49-93%). The yield of ADA and USPSTF screening was similar for diabetes (11% vs. 9%) and prediabetes (38% vs. 36%). Nearly 70% of patients not eligible for guideline-based screening were also tested. The USPSTF guideline missed more cases of diabetes (6% vs. 3%) and prediabetes (26% vs. 19%) than the ADA guideline. After adjustment for patient, provider, and clinic factors and accounting for clustering, twofold variation in screening by provider and clinic remained (median odds ratio 1.97; intraclass correlation 0.13). CONCLUSIONS: Screening practices vary widely and are only partially explained by patient, provider, and clinic factors available in the EHR. Clinical decision support and system-level interventions are needed to optimize screening practices.
Authors: Michael E Bowen; Julie A Schmittdiel; Jeffrey T Kullgren; Ronald T Ackermann; Matthew J O'Brien Journal: Curr Diab Rep Date: 2018-09-19 Impact factor: 4.810
Authors: Kai McKeever Bullard; Mohammed K Ali; Giuseppina Imperatore; Linda S Geiss; Sharon H Saydah; Jeanine B Albu; Catherine C Cowie; Nancy Sohler; Ann Albright; Edward W Gregg Journal: PLoS One Date: 2015-04-30 Impact factor: 3.240
Authors: Matthew J O'Brien; Ji Young Lee; Mercedes R Carnethon; Ronald T Ackermann; Maria C Vargas; Andrew Hamilton; Nivedita Mohanty; Sarah S Rittner; Jessica N Park; Amro Hassan; David R Buchanan; Lei Liu; Joseph Feinglass Journal: PLoS Med Date: 2016-07-12 Impact factor: 11.069