Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.
Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.
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DOI：
10.13063/2327-9214.1181
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发表时间：
2015-01-01
期刊：
影响因子：
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通讯作者：
Batal, Holly
中科院分区：
文献类型：
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作者：
Johnson, Tracy L;Brewer, Daniel;Batal, Holly
背景：医疗保险和医疗补助创新中心（CMMI）授予丹佛健康（DH）综合安全网医疗保健系统1980万美元，用于在初级保健中实施“人口健康”方法。这种主要的实践转变建立在以病人为中心的医疗之家（PCMH）和瓦格纳的慢性护理模式（CCM）的基础上，以实现“三重目标”：改善人口健康，照顾个人，降低人均成本。案例描述：本文提出了一个案例研究，介绍了卫生署如何整合已发表的预测模型和一线临床判断，以实现临床可操作的患者风险分层。这种人口细分方法用于部署增强的护理团队工作人员资源，并根据患者的需要量身定制护理管理服务，特别是针对可避免住院的高风险患者。为患者风险分层开发、实施并获得临床对健康信息技术（HIT）解决方案的接受是赠款的主要目标。研究发现：除了描述信息技术（IT）解决方案本身，我们还关注了促进其多学科发展和持续迭代改进的领导和组织过程，包括以下内容：团队组成、目标人群定义、算法规则开发、绩效评估和临床工作流程优化。我们提供了一些例子，说明动态商业智能工具如何通过支持从人群角度到患者特定变量的实时数据视图，促进临床对程序设计决策的可访问性。结论：我们得出的结论是，将临床观点与预测模型结果相结合的人群分割方法可以更好地识别出适合家庭医疗、强化护理团队干预的高危患者。
CONTEXT: The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health's (DH) integrated, safety net health care system $19.8 million to implement a "population health" approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner's Chronic Care Model (CCM) to achieve the "Triple Aim": improved health for populations, care to individuals, and lower per capita costs.CASE DESCRIPTION: This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective.FINDINGS: In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables.CONCLUSIONS: We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.