探索住院病人 EMR 算法在糖尿病识别方面的可靠性

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2023-12-01 DOI:10.1136/bmjhci-2023-100894
Seungwon Lee, Elliot A Martin, Jie Pan, Cathy A Eastwood, Danielle A Southern, David J T Campbell, Abdel Aziz Shaheen, Hude Quan, Sonia Butalia
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引用次数: 0

摘要

导言 在实时住院环境中准确识别医疗状况对医疗系统至关重要。目前的住院病人合并症算法依赖于整合各种来源的管理数据,但有时在获取和连接这些数据方面存在相当大的滞后性。我们的研究目标是开发基于电子病历(EMR)数据的住院病人糖尿病表型算法。材料和方法 完成了对 3040 人的病历审查,其中 583 人患有糖尿病。我们将这些患者的电子病历数据与国际疾病分类(ICD)管理数据库进行了链接。我们开发了以下基于 EMR 数据的糖尿病算法:(1) 实验室数据;(2) 药物数据;(3) 实验室和药物数据;(4) 糖尿病概念关键词;(5) 糖尿病自由文本算法。使用的组合算法或上述算法之间的语句。使用病历审查作为金标准来衡量算法性能。我们将灵敏度(SN)和阳性预测值(PPV)表现较高的算法确定为表现最佳的算法。结果 测试的算法普遍表现良好:ICD编码数据,SN为0.84,特异性(SP)为0.98,PPV为0.93,阴性预测值(NPV)为0.96;药物和实验室算法,SN为0.90,SP为0.95,PPV为0.80,NPV为0.97;所有文件类型算法,SN为0.95,SP为0.98,PPV为0.94,NPV为0.99。讨论 基于自由文本数据的糖尿病算法可产生与常用的 ICD 编码算法相当或更高的性能,可作为现有方法的补充。这类基于住院病人 EMR 的病例识别算法可能成为及时规划资源和提供护理的关键方法。数据可能来自第三方,不对外公开。这些数据的可用性受到限制。数据来自艾伯塔省卫生服务机构,经艾伯塔省卫生服务机构许可后提供。
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Exploring the reliability of inpatient EMR algorithms for diabetes identification
Introduction Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms. Materials and methods A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV). Results The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99. Discussion Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery. Data may be obtained from a third party and are not publicly available. Restrictions apply to the availability of these data. Data were obtained from Alberta Health Services and are available with the permission of Alberta Health Services.
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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