Harnessing Data for Continuous Improvement of the Whole Health Index in Integrated Care Models

Jaishankar Inukonda

doi:10.32628/IJSERSET242433

Authors

Jaishankar Inukonda Healthcare Data Specialist, Richmond, USA Author

DOI:

https://doi.org/10.32628/IJSERSET242433

Keywords:

WHI, Whole Health Index, SDOH, Healthcare, Social Indicators, Data Integration, Data Analytics, Predictive analytics, AI/ML, Care models

Abstract

The Whole Health Index (WHI) is a multidimensional measure that assesses patient outcomes and care quality by integrating physical, mental, and social health indicators. In the context of integrated care models, the effective use of data is critical to continuously improving WHI metrics. This article explores how data-driven approaches enhance the WHI by leveraging advanced analytics, interoperability, and predictive modeling. It discusses key strategies for data collection, integration, and utilization to optimize care delivery and improve patient outcomes. Additionally, the article highlights challenges such as data privacy, standardization, and implementation and proposes actionable solutions for overcoming these barriers.

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