Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study.
Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study.
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DOI：
10.2196/ijmr.4022
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发表时间：
2015-01-13
影响因子：
2
通讯作者：
Ling XB
中科院分区：
文献类型：
--
作者：
Hu Z;Jin B;Shin AY;Zhu C;Zhao Y;Hao S;Zheng L;Fu C;Wen Q;Ji J;Li Z;Wang Y;Zheng X;Dai D;Culver DS;Alfreds ST;Rogow T;Stearns F;Sylvester KG;Widen E;Ling XB
一个易于访问的基于网络的实时实用程序可评估未来急诊科 (ED) 就诊的患者风险，可以帮助医疗保健提供者指导资源分配，以更好地管理高风险患者群体，从而减少不必要的急诊室使用。我们的主要目标是在缅因州开发一个基于健康信息交换的未来 6 个月的 ED 风险监测系统。缅因州健康信息交换中心 HealthInfoNet (HIN) 整合的电子病历 (EMR) 数据被用来开发基于网络的监测系统，用于人口 ED 未来 6 个月的风险预测。为了建立模型，使用了 2012 年 1 月 1 日至 12 月 31 日期间具有全面临床史的 829,641 名患者的回顾性队列进行训练，然后使用 2012 年 7 月 1 日至 2013 年 6 月 30 日期间的 875,979 名患者的前瞻性队列进行测试。多变量统计分析确定了 101 个预测未来 6 个月急诊就诊风险的变量：4 个年龄组、 8 种不同的就诊类型、17 种主要诊断和 8 种继发诊断史、8 种特定慢性疾病、28 项实验室检查结果、3 项放射检查史以及 25 种门诊处方药史。回顾性和前瞻性队列的 c 统计量分别为 0.739 和 0.732。将我们的方法实时集成到 HIN 安全全州数据系统中，前瞻性地验证了其性能。回顾性和前瞻性分析中的聚类分析揭示了高风险患者的离散亚群，围绕多个“锚定”人口统计数据和慢性病进行分组。通过基于网络的人口风险监测企业仪表板，主动病例发现算法的有效性已得到缅因州临床医生和护理人员的验证。主动病例发现模型和相关的基于网络的实时应用程序旨在以纵向方式跟踪所有付费者、所有疾病和所有年龄组的急诊就诊的总人口风险的演变。因此，提供者可以对具有相似临床病史模式的患者亚组实施有针对性的护理管理策略，从而推动提供更高效、更有效的医疗保健干预措施。据我们所知，这个经过前瞻性验证的基于电子病历、基于网络的工具是第一个允许对全州急诊就诊进行实时总人口风险评估的工具。
An easily accessible real-time Web-based utility to assess patient risks of future emergency department (ED) visits can help the health care provider guide the allocation of resources to better manage higher-risk patient populations and thereby reduce unnecessary use of EDs. Our main objective was to develop a Health Information Exchange-based, next 6-month ED risk surveillance system in the state of Maine. Data on electronic medical record (EMR) encounters integrated by HealthInfoNet (HIN), Maine’s Health Information Exchange, were used to develop the Web-based surveillance system for a population ED future 6-month risk prediction. To model, a retrospective cohort of 829,641 patients with comprehensive clinical histories from January 1 to December 31, 2012 was used for training and then tested with a prospective cohort of 875,979 patients from July 1, 2012, to June 30, 2013. The multivariate statistical analysis identified 101 variables predictive of future defined 6-month risk of ED visit: 4 age groups, history of 8 different encounter types, history of 17 primary and 8 secondary diagnoses, 8 specific chronic diseases, 28 laboratory test results, history of 3 radiographic tests, and history of 25 outpatient prescription medications. The c-statistics for the retrospective and prospective cohorts were 0.739 and 0.732 respectively. Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. Cluster analysis in both the retrospective and prospective analyses revealed discrete subpopulations of high-risk patients, grouped around multiple “anchoring” demographics and chronic conditions. With the Web-based population risk-monitoring enterprise dashboards, the effectiveness of the active case finding algorithm has been validated by clinicians and caregivers in Maine. The active case finding model and associated real-time Web-based app were designed to track the evolving nature of total population risk, in a longitudinal manner, for ED visits across all payers, all diseases, and all age groups. Therefore, providers can implement targeted care management strategies to the patient subgroups with similar patterns of clinical histories, driving the delivery of more efficient and effective health care interventions. To the best of our knowledge, this prospectively validated EMR-based, Web-based tool is the first one to allow real-time total population risk assessment for statewide ED visits.