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@ShahidNShah
@ShahidNShah
Exploring the Role of AI in Healthcare Risk Stratification
Artificial intelligence is taking the healthcare industry by storm, and its applications in risk stratification have significant potential to improve outcomes.In the era of value-based care, preventing or mitigating the impact of adverse patient outcomes before they happen could significantly improve care delivery and reduce costs. However, preventing outcomes requires healthcare stakeholders to possess a vast array of data points and the ability to analyze them effectively.In recent years, the rise of electronic health records (EHRs) and risk scores has proven invaluable to this process, enabling health systems to begin stratifying their patient populations based on risk.Risk stratification plays a key role in care coordination and chronic disease management, and the advent of predictive analytics has, in many ways, enhanced these efforts.
Medigy Insights
The National Association of Community Health Centers (NACHC) conceptualizes risk stratification as “the process of assigning a risk status to patients, then using this information to direct care and improve overall health outcomes.”NACHC notes that this process aims to segment patients into groups based on complexity and care needs. To do so, healthcare organizations must consider patients on individual and population levels to efficiently identify them and provide the right level of care and services across patient subgroups.Assigning a patient’s risk category — low, medium or high — at the individual level marks the first step in implementing a personalized care plan. When patients are stratified across these risk categories at the population level, care models can be tailored to the needs of each subgroup.
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