医疗理赔程序化检索对冠心病住院发生的影响。

James R Nestor, Janet G Knecht
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引用次数: 2

摘要

进行这项研究是为了检验这样一种主张,即医疗索赔记录在电子检索时可以可靠地用于定位个人、特定疾病的住院情况。在这项研究中,由雇主资助的慢性疾病管理(DM)服务接受者自我报告的冠状动脉疾病(CAD)入院情况,与医生编制的医疗记录进行了验证。然后,根据对所包括证据类型的各种条件要求,对相应的医疗索赔记录进行电子搜索。在最高灵敏度(92.6%)下,搜索算法确定了136个经过验证的录取中的126个,而错误地识别了另外1025个。在最高特异性(98.7%)下,该算法在136例中正确识别了55例,错误识别了13例。真假比最大值为4.47。通过要求诊断强度(具有cad相关诊断代码的事件相关索赔的比例)的最小值为0.20来获得最大约登指数值。该研究的结论是,应用于典型商业医疗索赔的入院搜索算法产生的结果对于确定CAD人群的入院发生率并不令人满意。虽然这些方法可能是合理的,但它们无法克服搜索数据的弱点。
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On the efficacy of programmatic searching of medical claims for the occurrence of hospital admissions for coronary artery disease.

This study was conducted in order to test the proposition that medical claim records, when searched electronically, can be reliably used to locate individual, disease-specific hospital admissions. For the study, admissions for coronary artery disease (CAD), self-reported by employer-sponsored recipients of chronic disease management (DM) services, were verified against physician-compiled medical records. Confirmed events were then subjected to electronic searching of the corresponding medical claim records using a variety of conditional requirements for included types of evidence. At maximum sensitivity (92.6%), the search algorithm positively identified 126 of 136 verified admissions while falsely identifying 1,025 others. At maximum specificity (98.7%), the algorithm positively identified 55 of 136 while falsely identifying 13. The maximum value of the true positive to false positive ratio was 4.47. The maximum Youden index value was obtained by requiring that the diagnostic intensity (proportion of event-related claims having a CAD-related diagnosis code) have a minimum value of 0.20. The study concluded that an admission search algorithm applied to typical commercial medical claims generated results that are unsatisfactory for the determination of admission incidence in the CAD population. While the methods may be sound, they fail to overcome the weaknesses of the searched data.

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