Comparing ascertainment of chronic condition status with problem lists versus encounter diagnoses from electronic health records. (15th February 2022)

• Record Type: Journal Article
• Authors: Voss, Robert W; Schmidt, Teresa D; Weiskopf, Nicole; Marino, Miguel; Dorr, David A; Huguet, Nathalie; Warren, Nate; Valenzuela, Steele; O'Malley, Jean; Quiñones, Ana R
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 5(2022)
• Page Start: 770
• Page End: 778
• Publication Date: 2022-02-15
• Subjects: multimorbidity -- chronic disease ascertainment -- concordance -- community health centers (CHC) -- electronic health records (EHR); Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac016
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To assess and compare electronic health record (EHR) documentation of chronic disease in problem lists and encounter diagnosis records among Community Health Center (CHC) patients. Materials and methods: We assessed patient EHR data in a large clinical research network during 2012–2019. We included CHCs who provided outpatient, older adult primary care to patients age ≥45 years, with ≥2 office visits during the study. Our study sample included 1 180 290 patients from 545 CHCs across 22 states. We used diagnosis codes from 39 Chronic Condition Warehouse algorithms to identify chronic conditions from encounter diagnoses only and compared against problem list records. We measured correspondence including agreement, kappa, prevalence index, bias index, and prevalence-adjusted bias-adjusted kappa. Results: Overlap of encounter diagnosis and problem list ascertainment was 59.4% among chronic conditions identified, with 12.2% of conditions identified only in encounters and 28.4% identified only in problem lists. Rates of coidentification varied by condition from 7.1% to 84.4%. Greatest agreement was found in diabetes (84.4%), HIV (78.1%), and hypertension (74.7%). Sixteen conditions had <50% agreement, including cancers and substance use disorders. Overlap for mental health conditions ranged from 47.4% for anxiety to 59.8% for depression. Discussion: Agreement between the 2 sources varied substantially. Conditions requiring regular management in primary care settings may have a higher agreement than those diagnosed and treated in specialty care. Conclusion: Relying on EHR encounter data to identify chronic conditions without reference to patient problem lists may under-capture conditions among CHC patients in the United States.