%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 6
%N 10
%P e32666
%T Bolstering the Business Case for Adoption of Shared Decision-Making Systems in Primary Care: Randomized Controlled Trial
%A Sperl-Hillen,JoAnn M
%A Anderson,Jeffrey P
%A Margolis,Karen L
%A Rossom,Rebecca C
%A Kopski,Kristen M
%A Averbeck,Beth M
%A Rosner,Jeanine A
%A Ekstrom,Heidi L
%A Dehmer,Steven P
%A O’Connor,Patrick J
%+ Research Department, HealthPartners Center for Chronic Care Innovation, 8170 33rd Ave S, Bloomington, MN, 55425, United States, 1 952 967 5009, JoAnn.M.SperlHillen@HealthPartners.Com
%K clinical decision support
%K primary care
%K ICD-10 diagnostic coding
%K CPT levels of service
%K shared decision-making
%D 2022
%7 6.10.2022
%9 Original Paper
%J JMIR Form Res
%G English
%X Background: Limited budgets may often constrain the ability of health care delivery systems to adopt shared decision-making (SDM) systems designed to improve clinical encounters with patients and quality of care. Objective: This study aimed to assess the impact of an SDM system shown to improve diabetes and cardiovascular patient outcomes on factors affecting revenue generation in primary care clinics. Methods: As part of a large multisite clinic randomized controlled trial (RCT), we explored the differences in 1 care system between clinics randomized to use an SDM intervention (n=8) versus control clinics (n=9) regarding the (1) likelihood of diagnostic coding for cardiometabolic conditions using the 10th Revision of the International Classification of Diseases (ICD-10) and (2) current procedural terminology (CPT) billing codes. Results: At all 24,138 encounters with care gaps targeted by the SDM system, the proportion assigned high-complexity CPT codes for level of service 5 was significantly higher at the intervention clinics (6.1%) compared to that in the control clinics (2.9%), with P<.001 and adjusted odds ratio (OR) 1.64 (95% CI 1.02-2.61). This was consistently observed across the following specific care gaps: diabetes with glycated hemoglobin A1c (HbA1c)>8% (n=8463), 7.2% vs 3.4%, P<.001, and adjusted OR 1.93 (95% CI 1.01-3.67); blood pressure above goal (n=8515), 6.5% vs 3.7%, P<.001, and adjusted OR 1.42 (95% CI 0.72-2.79); suboptimal statin management (n=17,765), 5.8% vs 3%, P<.001, and adjusted OR 1.41 (95% CI 0.76-2.61); tobacco dependency (n=7449), 7.5% vs. 3.4%, P<.001, and adjusted OR 2.14 (95% CI 1.31-3.51); BMI >30 kg/m2 (n=19,838), 6.2% vs 2.9%, P<.001, and adjusted OR 1.45 (95% CI 0.75-2.8). Compared to control clinics, intervention clinics assigned ICD-10 diagnosis codes more often for observed cardiometabolic conditions with care gaps, although the difference did not reach statistical significance. Conclusions: In this randomized study, use of a clinically effective SDM system at encounters with care gaps significantly increased the proportion of encounters assigned high-complexity (level 5) CPT codes, and it was associated with a nonsignificant increase in assigning ICD-10 codes for observed cardiometabolic conditions. Trial Registration: ClinicalTrials.gov NCT 02451670; https://clinicaltrials.gov/ct2/show/NCT 02451670 
%M 36201392
%R 10.2196/32666
%U https://formative.jmir.org/2022/10/e32666
%U https://doi.org/10.2196/32666
%U http://www.ncbi.nlm.nih.gov/pubmed/36201392