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High-Risk Patient Identification: A Review of Current Methodologies | Biomedgrid

Journal: American Journal of Biomedical Science & Research (Vol.6, No. 1)

Publication Date:

Authors : ; ;

Page : 89-91

Keywords : High risk; High Cost High Need; Predictive modelling; Risk adjustment; Research priorities; AJBSR;

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Identifying High Cost High Need (HCHN) patients has been increasingly identified as important among healthcare stakeholders since the 1997 Balanced Budget Act prompted the Health Care Financing Administration (HCFA) to require risk-adjusted payment methodologies. High risk patient identification was originally derived primarily from claims data. This has since evolved to using multiple data sources, including clinical data from electronic health records, self-reported health measures etc. Importantly, the case definition for HCHN patients is evolving and needs further research. Various high risk identifications are used by payers and providers for a range of purposes ranging from targeted outreach for disease management, reducing readmissions, and strategies for greater care coordination through patient centered medical homes and Accountable care organizations (ACOs). However, the great diversity of case definitions, diversity among stakeholders, availability of source data, access to technology and analytical manpower, all complicate the refinement and use of a high-risk patient identification. With the ongoing momentum to leverage big data technologies, several solutions for HCHN patient identification are being developed. It is therefore imperative to understand the current practices and evidence from research prior to adopting a flexible and proactive predictive analytics approach to HCHN patients. The critical need for further research and funding is outlined

Last modified: 2019-12-04 13:49:53