Why Value-Based Care Still Struggles: Data, Incentives & AI in Healthcare Operations
Guest Spotlight:
Mahrukh Saif is the Principal Consultant | Value-Based Care Strategy at HQI Strategiesand a healthcare consultant specializing in the operational realities of value-based care. With expertise in healthcare analytics, data science, and quality improvement, she helps healthcare organizations bridge the gap between value-based care strategy and day-to-day clinical operations. Her work focuses on care gap management, quality incentive programs, payer-provider alignment, population health, workflow optimization, and the growing role of artificial intelligence in healthcare. Mahrukh collaborates with physician practices, healthcare organizations, private equity firms, and investment groups to improve operational performance, strengthen quality outcomes, and navigate the complexities of value-based reimbursement models.
Recognized for her analytical approach and thought leadership, Mahrukh regularly shares insights on healthcare operations, payer incentives, data transparency, and emerging technologies shaping the future of value-based care. Through her work, she is committed to helping healthcare organizations build more efficient, data-driven systems that improve patient outcomes while supporting long-term financial sustainability.
Here are the 3 Key Takeaways from our Conversation:
1. Value-Based Care Often Fails Before Clinical Care Begins
One of the strongest insights from the conversation was that many value-based care challenges are not clinical failures, they are data and operational failures.
Providers may receive inaccurate or outdated care gap lists, fragmented reporting from multiple health plans, and conflicting quality requirements. This can lead to wasted appointments, unnecessary outreach, and frustrated clinicians, even when patients are receiving appropriate care.
2. Incentive Design Shapes Provider Behavior
Mahrukh emphasized that quality incentive programs do more than reward performance—they influence what clinics prioritize.
Measures tied to larger bonuses or tighter deadlines naturally receive more attention than measures with smaller incentives or longer timelines. This can unintentionally push important issues such as depression screening, anxiety screening, and social determinants of health (SDOH) lower on the operational priority list.
3. AI Can Help but It Won't Fix a Broken Operating System
The discussion challenged the common belief that AI will automatically solve value-based care problems.
AI can improve risk stratification, patient prioritization, and decision support. However, it struggles with payer-specific contract rules, attribution logic, documentation requirements, and fragmented data systems.
As Mahrukh put it, automation may simply help organizations move through fragmentation faster, while AI may help them analyze fragmentation more intelligently.
Listen to the full episode of When Health Freezes Over now!
👉 YouTube - https://www.youtube.com/channel/UC7CR1wzokjtVdyyWHLziUJQ/
👉 Spotify - https://open.spotify.com/show/3bRobaBZlM3IbCJ5334PJV?si=41aa6416371e4bc2
👉 Apple Podcast -https://podcasts.apple.com/us/podcast/when-health-freezes-over/id1887501951
#WhenHealthFreezesOver
