OCHIN, Inc, Portland, Oregon, USA.
Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
J Am Med Inform Assoc. 2022 Apr 13;29(5):770-778. doi: 10.1093/jamia/ocac016.
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.
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.
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.
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.
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.
评估和比较社区医疗中心(CHC)患者的电子健康记录(EHR)中的慢性病问题列表和就诊记录中的文档。
我们在 2012 年至 2019 年期间评估了大型临床研究网络中的患者 EHR 数据。我们纳入了提供门诊、45 岁以上老年初级保健服务、研究期间有≥2 次就诊的 CHC。我们的研究样本包括来自 22 个州的 545 家 CHC 的 1180290 名患者。我们使用 39 个慢性疾病仓库算法的诊断代码仅从就诊诊断中识别慢性病,并与问题列表记录进行比较。我们测量了一致性,包括一致性、kappa、患病率指数、偏差指数和患病率调整偏差调整的 kappa。
在确定的慢性病中,就诊诊断和问题列表的重叠率为 59.4%,仅在就诊中确定的疾病有 12.2%,仅在问题列表中确定的疾病有 28.4%。各种疾病的共同识别率从 7.1%到 84.4%不等。糖尿病(84.4%)、HIV(78.1%)和高血压(74.7%)的一致性最高。16 种疾病的一致性低于 50%,包括癌症和物质使用障碍。心理健康状况的重叠率从焦虑症的 47.4%到抑郁症的 59.8%不等。
这两种来源的一致性差异很大。在初级保健环境中需要定期管理的疾病可能比在专科护理中诊断和治疗的疾病有更高的一致性。
在美国,仅依靠 EHR 就诊数据来识别慢性病而不参考患者问题列表,可能会导致 CHC 患者的慢性病漏报。