The pressure from CMS for health plans to shift to value-based care and risk contracts has escalated the need to improve efficiency, accuracy and the financial performance of risk-based outcomes.
Health plans need greater transparency and and access to medical and behavioral health conditions, medications and social determinants of health data in order to create precision member risk profiles.
Patient data, both structured and unstructured, is growing rapidly, with over 80% of data residing in silos and much of it in text formats (clinical notes). Internal coding teams reviewing claims and other data cannot keep up with demand while data analytics teams are unable to glean timely insights to design effective risk programs.
This is where AI/ML and automation play a critical role in the future (which is now) of value-based care and risk programs. AI/ML not only brings speed and efficiency into the mix, but also provides timely insights that help inform risk-based strategy and planning.
AI/ML solutions, leveraging NLP and deep learning, can improve the quality of care and maximize outcomes from risk adjustment programs. Components of AI/ML solutions should include:
1️⃣ Digitize clinical records such as medical record notes, prescription orders, imaging, etc.
2️⃣ Utilize high-precision NLP algorithms to identify relevant coding (ICD-10, NDC, CPT, etc.).
3️⃣ Develop comprehensive member-level profiles that include HRA survey results, SDoH and self-reported data.
4️⃣ Advanced analytics to identify gaps in care related to STARS and HEDIS measures.
5️⃣ Leverage ML insights to design and customize member engagement outreach.
6️⃣ Utilize NLP-based coding to develop patient risk score and cost predictors.
Harness data to provide cost-effective solutions and key insights. The complexities of healthcare data makes that goal an ever-evolving challenge.
However, health plans, providers and government health agencies are implementing more and more automation and introducing powerful tools such as AI/ML to support value-based care programs and ultimately improve patient care and reduce health care costs.
Should your risk-adjustment strategy include AI/ML tools?
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Tell us, what has your experience been using AI/ML in your approach to value-based programs? 📝