AI, advanced analytics not part of most medical groups’ value-based care toolkit

Shifting from fee-for-service (FFS) to value-based care arrangements requires several changes within a medical practice, whether it is a small move to a shared savings program or a full embrace of capitation.

Recent MGMA research shows that more than seven in 10 (71%) of healthcare providers who made these changes added technological tools, such as data analytics/reporting platforms for quality metrics or population health management tools.

But as artificial intelligence (AI) applications have quickly matured and expanded for use across the healthcare industry, the most advanced tech tools have yet to reach most practices: An Aug. 13, 2024, MGMA Stat poll finds that three out of four (75%) medical groups do not use advanced analytics or AI tools to help with value-based care, while 18% do. Another 7% responded “unsure.” The poll had 292 applicable responses.

Where AI can help with value

While not all practices have embraced value-based care arrangements, half include some form of quality performance metrics in their physician compensation plans. As quality performance increasingly is tied to physician pay, having tools to measure and analyze performance in value-based arrangements will be crucial for medical groups.

Provider organizations that have already embraced more risk in their payments can benefit from several advanced analytics and AI tools in these five key areas:

1. Enhanced predictive capabilities for clinical decision-making and quality improvement

• Risk stratification: Identifying patients at high risk of developing chronic conditions or experiencing adverse health events allows for providers to intervene early and work to prevent costly treatments and hospitalizations. Through integration of multiple data sources, AI algorithms can aid in building accurate patient risk profiles.
• Predictive modeling and personalized risk prediction: AI-driven predictive models that forecast a patient’s risk of developing certain conditions or experiencing adverse outcomes allow healthcare providers to tailor interventions that improve patient health and reduce unnecessary costs.
• Personalized care plans: Analyzing individual patient data can help create personalized treatment plans that are more effective, leading to better patient outcomes and increased patient satisfaction.
• Continuous monitoring and measurement: Tools designed for continuous patient data monitoring can feed advanced analytics to measure and track outcomes, helping to alert providers about potential issues before they escalate, while also measuring performance metrics in value-based arrangements.

2. Operational efficiency and cost reduction

Automating tasks that once took staff and clinicians away from patient care is a popular area of AI implementation, reducing administrative burdens and helping to address burnout. But for most administrators, the biggest benefit is the time and resource savings:

• Process optimization: AI can streamline administrative tasks, such as scheduling, billing and patient follow-ups, reducing operational costs and freeing up time for healthcare providers to focus on patient care. In the realm of ambient AI (used for transcribing speech from patient visits and drafting notes), nearly three in 10 (28%) of medical groups have incorporated this technology into their workflows.
• Resource allocation: With fewer manual tasks requiring staff, equipment and (in some cases) facilities, resources can be reallocated and optimized to support value-based care.
• Waste reduction: Identifying inefficiencies and unnecessary procedures with AI and analytics tools can help reduce care costs without compromising quality.

3. Population health management

• Chronic disease management: AI tools can monitor and highlight chronic diseases across large patient populations, identifying trends and enabling proactive care management strategies.
• Social determinants of health (SDoH): Studies have shown that generative AI models can help identify SDoH in doctors’ notes. Incorporating SDoH into patient care gives providers the awareness to address factors that influence health outcomes, such as socioeconomic status, education and access to care.

4. Enhanced patient engagement

While value-based care often focuses on patients’ outcomes in clinical and quality measures, ensuring adherence to treatment plans starts with a strong patient-provider relationship, which can be nurtured with the help of AI tools.

• Personalized communication: AI can facilitate more personalized patient communication and education, increasing patient engagement and adherence to care plans.
• Patient portals and apps: Advanced analytics integrated into patient portals and mobile apps can provide patients with real-time feedback and guidance, encouraging proactive management of their health.

5. Contract optimization

Understanding your practice’s performance and communicating the value you offer to a payer can be an incredible asset when negotiating value-based care contracts. Analyzing cost and performance data can help your medical group achieve the desired financial and clinical outcomes, leading to better negotiated rates and, over time, reaching key outcome thresholds for incentive payments.

Have a success story in using AI or advanced analytics for your value-based arrangements? We’d love to hear it.

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