Comparing Prescribing and Dispensing Data of the PCORnet Common Data Model Within PCORnet Antibiotics and Childhood Growth Study.
Comparing Prescribing and Dispensing Data of the PCORnet Common Data Model Within PCORnet Antibiotics and Childhood Growth Study.
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DOI：
10.5334/egems.274
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发表时间：
2019-04-12
期刊：
影响因子：
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通讯作者：
Block, Jason P
中科院分区：
文献类型：
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作者：
Lin, Pi-I D;Daley, Matthew F;Block, Jason P
研究人员经常使用电子健康记录 (EHR) 中的处方数据或药物或医疗索赔中的配药数据来确定药物使用情况。然而，这两个来源都没有有关药物使用的完整信息。我们比较了国家以患者为中心的临床研究网络 (PCORnet) 抗生素和儿童生长研究中 200,395 名患者的抗生素处方和配药记录。我们按输送系统类型[封闭集成 (cIDS) 和非 cIDS] 进行分层分析；在 cIDS 和非 cIDS 中，90.5% 和 39.4% 的处方记录具有匹配的配药记录，92.7% 和 64.0% 的配药记录具有匹配的处方记录。大多数没有匹配处方的配药并没有在 EHR 中当天遇到，这表明它们是在提供数据的机构之外给予的药物，例如来自紧急护理或零售诊所的药物。使用配药作为黄金标准，EHR 中处方的敏感性对于 cIDS 和非 cIDS 分别为 99.1% 和 89.9%。在 cIDS 和非 cIDS 患者中，分别只有 0.7% 和 6.1% 的患者被归类为假阴性，即在实际配药时完全未接触抗生素。这些患者更有可能患有复杂的慢性病或哮喘。总体而言，处方记录可以很好地识别抗生素的暴露情况。 EHR 数据（例如 PCORnet 中提供的数据）是临床研究的独特且重要的资源。通过了解为什么无法捕获处方来缩小数据差距可以改善此类数据，使其更适合观察研究。
Researchers often use prescribing data from electronic health records (EHR) or dispensing data from medication or medical claims to determine medication utilization. However, neither source has complete information on medication use. We compared antibiotic prescribing and dispensing records for 200,395 patients in the National Patient-Centered Clinical Research Network (PCORnet) Antibiotics and Childhood Growth Study. We stratified analyses by delivery system type [closed integrated (cIDS) and non-cIDS]; 90.5 percent and 39.4 percent of prescribing records had matching dispensing records, and 92.7 percent and 64.0 percent of dispensing records had matching prescribing records at cIDS and non-cIDS, respectively. Most of the dispensings without a matching prescription did not have same-day encounters in the EHR, suggesting they were medications given outside the institution providing data, such as those from urgent care or retail clinics. The sensitivity of prescriptions in the EHR, using dispensings as a gold standard, was 99.1 percent and 89.9 percent for cIDS and non-cIDS, respectively. Only 0.7 percent and 6.1 percent of patients at cIDS and non-cIDS, respectively, were classified as false-negative, i.e. entirely unexposed to antibiotics when they in fact had dispensings. These patients were more likely to have a complex chronic condition or asthma. Overall, prescription records worked well to identify exposure to antibiotics. EHR data, such as the data available in PCORnet, is a unique and vital resource for clinical research. Closing data gaps by understanding why prescriptions may not be captured can improve this type of data, making it more robust for observational research.