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首页> 外文期刊>BMC Medical Informatics and Decision Making >Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records
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Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records

机译：结合来自临床数据仓库和药品数据库的信息以生成检测电子病历中合并症的框架

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Medical coding is used for a variety of activities, from observational studies to hospital billing. However, comorbidities tend to be under-reported by medical coders. The aim of this study was to develop an algorithm to detect comorbidities in electronic health records (EHR) by using a clinical data warehouse (CDW) and a knowledge database. We enriched the Theriaque pharmaceutical database with the French national Comorbidities List to identify drugs associated with at least one major comorbid condition and diagnoses associated with a drug indication. Then, we compared the drug indications in the Theriaque database with the ICD-10 billing codes in EHR to detect potentially missing comorbidities based on drug prescriptions. Finally, we improved comorbidity detection by matching drug prescriptions and laboratory test results. We tested the obtained algorithm by using two retrospective datasets extracted from the Rennes University Hospital (RUH) CDW. The first dataset included all adult patients hospitalized in the ear, nose, throat (ENT) surgical ward between October and December 2014 (ENT dataset). The second included all adult patients hospitalized at RUH between January and February 2015 (general dataset). We reviewed medical records to find written evidence of the suggested comorbidities in current or past stays. Among the 22,132 Common Units of Dispensation (CUD) codes present in the Theriaque database, 19,970 drugs (90.2%) were associated with one or several ICD-10 diagnoses, based on their indication, and 11,162 (50.4%) with at least one of the 4878 comorbidities from the comorbidity list. Among the 122 patients of the ENT dataset, 75.4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 44.6% of the cases. Among the 4312 patients of the general dataset, 68.4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 20.3% of reviewed cases. This simple algorithm based on combining accessible and immediately reusable data from knowledge databases, drug prescriptions and laboratory test results can detect comorbidities.
机译：医学编码用于从观察研究到医院计费的各种活动。但是，合并症往往被医疗编码员少报。这项研究的目的是开发一种通过使用临床数据仓库（CDW）和知识数据库来检测电子健康记录（EHR）中合并症的算法。我们用法国国家合并症清单丰富了Theriaque制药数据库，以鉴定与至少一种主要合并症相关的药物并诊断与药物适应症有关的诊断。然后，我们将Theriaque数据库中的药物适应症与EHR中的ICD-10计费代码进行了比较，以根据药物处方检测出可能遗漏的合并症。最后，我们通过匹配药物处方和实验室测试结果来改善合并症检测。我们使用从雷恩大学医院（RUH）CDW提取的两个回顾性数据集测试了获得的算法。第一个数据集包括2014年10月至2014年12月之间在耳，鼻，喉（ENT）外科病房住院的所有成年患者（ENT数据集）。第二个样本包括2015年1月至2月之间在RUH住院的所有成年患者（一般数据集）。我们查看了病历，以查找当前或过去住院期间建议合并症的书面证据。在Theriaque数据库中存在的22,132个通用分配单位（CUD）代码中，根据其适应症，有19,970种药物（90.2％）与一项或多项ICD-10诊断相关，而有11,162种药物与至少一种诊断相关联。合并症清单中的4878合并症。在ENT数据集的122位患者中，有75.4％的患者至少有一张处方药没有相应的ICD-10代码。该算法建议的合并症诊断在44.6％的病例中得到证实。在一般数据集的4312名患者中，有68.4％的患者至少有一种药物处方没有相应的ICD-10代码。该算法建议的合并症诊断在20.3％的回顾病例中得到了证实。这个简单的算法基于将知识数据库，药物处方和实验室测试结果中的可访问数据和可立即重用的数据相结合，可以检测合并症。

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