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通过非侵入式获取入院诊断和医疗服务提供者计费代码来实现基于索赔的决策支持。

Enabling claims-based decision support through non-interruptive capture of admission diagnoses and provider billing codes.

作者信息

Walsh Colin G, Vawdrey David K, Stetson Peter D, Fred Matthew R, Hripcsak George

机构信息

Department of Biomedical Informatics, Columbia University, New York, NY ; Department of Medicine, Columbia University, New York, NY.

Department of Biomedical Informatics, Columbia University, New York, NY ; Department of Information Technology, NewYork-Presbyterian Hospital, New York, NY.

出版信息

AMIA Annu Symp Proc. 2014 Nov 14;2014:1950-9. eCollection 2014.

Abstract

The patient problem list, like administrative claims data, has become an important source of data for decision support, patient cohort identification, and alerting systems. A two-fold intervention to increase capture of problems on the problem list automatically - with minimal disruption to admitting and provider billing workflows - is described. For new patients with no prior data in the electronic health record, the intervention resulted in a statistically significant increase in the number of problems recorded to the problem list (3.8 vs 2.9 problems post-and pre-intervention respectively, p value 2×10(-16)). The majority of problems were recorded in the first 24 hours of admission. The proportion of patients with at least one problem coded to the problem list within the first 24 hours increased from 94% to 98% before and after intervention (chi square 344, p value 2×10(-16)). ICD9 "V codes" connoting circumstances beyond disease were captured at a higher rate post intervention than before. Deyo/Charlson comorbidities derived from problem list data were more similar to those derived from claims data after the intervention than before (Jaccard similarity 0.3 post- vs 0.21 pre-intervention, p value 2×10(-16)). A workflow-sensitive, non-interruptive means of capturing provider-entered codes early in admission can improve both the quantity and content of problems on the patient problem list.

摘要

患者问题清单与行政索赔数据一样,已成为决策支持、患者队列识别和警报系统的重要数据来源。本文描述了一种双重干预措施,旨在自动增加问题清单上问题的捕获量,同时对入院流程和供应商计费工作流程的干扰降至最低。对于电子健康记录中无既往数据的新患者,干预措施使记录在问题清单上的问题数量在统计学上显著增加(干预后为3.8个问题,干预前为2.9个问题,p值为2×10(-16))。大多数问题是在入院后的头24小时内记录的。干预前后,在头24小时内至少有一个问题被编码到问题清单的患者比例从94%增加到98%(卡方检验值为344,p值为2×10(-16))。干预后,表示疾病以外情况的ICD9“V编码”的捕获率高于干预前。与干预前相比,干预后从问题清单数据得出的Deyo/Charlson合并症与从索赔数据得出的合并症更为相似(干预后Jaccard相似系数为0.3,干预前为0.21,p值为2×10(-16))。一种对工作流程敏感且不干扰的方法,可在入院早期捕获医生输入的编码,这既能提高患者问题清单上问题的数量,又能改善其内容。

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