Temporal Data Analysis of Encounter Patterns to Predict High-Risk Patients in Medicaid
Keywords:
Temporal data analysis, encounter patterns, Medicaid, predictive analyticsAbstract
The study looks at how the analysis of temporal data from patient interactions could help to forecast high-risk Medicaid patients, therefore enabling early intervention & their better allocation of resources. We examine over time patterns in patient visits, hospitalizations & also treatments using ML, statistical models & advanced data analysis techniques. Our approach picks up trends in their healthcare consumption, risk factors suggestive of probable health deterioration & also recurrent patterns. The findings highlight important markers of high-risk people, including frequent ER visits, inadequate preventive care & the progress in chronic illnesses. Medicaid administration & their healthcare professionals may effectively distribute their resources, change treatment plans & reduce avoidable hospitalizations by use of their predictive analytics. The study emphasizes how data-driven decision-making might increase Medicaid operations' efficiency, help to save expenses & raise their patient outcomes by means of their enhanced performance of Medicaid operations. By allowing quick interventions for at-risk populations & changing from reactive to proactive by treatment methods, the integration of predictive models into Medicaid systems can drastically change the provision of their healthcare services.
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References
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