As healthcare organizations move into 2026, quality and performance improvement programs are facing a convergence of pressure points, as well as opportunities with the rapidly expanding use of AI. Traditional, retrospective approaches to quality reporting and population health management are no longer sufficient to meet increasing performance targets or value-based reimbursement demands. At the same time, AI adoption is forcing health systems to address governance, trust, and operational integration challenges. The following five trends reflect where leading health systems are focusing to unlock ROI from quality initiatives. Together, they represent the shift from compliance-driven reporting to proactive, intelligence-driven quality programs.
1. Prospective, Predictive Quality Intelligence
Healthcare is shifting from retrospective reporting to near real-time and forward-looking insights. AI-enabled analytics platforms will increasingly predict at risk patients and care gaps before they occur enabling earlier interventions. Key capabilities such as healthcare-specific Large Language Models (LLMs) and Natural Language Processing (NLP) will transform how providers design and implement population health and value-based care programs.
2. Alignment with Evolving Regulatory & Value-Based Mandates
New frameworks like CMS TEAMS will push healthcare organizations to adopt technologies that deliver measurable impact. Quality improvement strategies will no longer be isolated from financial and regulatory performance — it will be central to enterprise success.
Our analysis of CMS and state-based population health initiatives indicates that these will be the key areas of focus to advance better care management at the population and patient level.
- Maternal Health and Perinatal Outcomes
Reducing maternal morbidity and mortality, improving pregnancy-related hypertension management, and addressing maternal mental health and substance use remain top federal and state priorities. - Chronic Disease Management
Hypertension, diabetes, cardiovascular disease, and related comorbidities continue to anchor population health programs due to their cost, prevalence, and impact on value-based reimbursement. - Behavioral Health and Substance Use Disorders
Mental health integration, substance use treatment, and overdose prevention are central to Medicaid, state public health funding, and equity-driven quality initiatives. - Preventive Care and Early Detection
Cancer screening (breast, colorectal), immunizations, and other preventive services remain in the core quality measures tied to population health performance and reimbursement. - Health Equity, SDOH, and Access Gaps
Quality and performance initiatives will increasingly incorporate social determinants of health (SDOH) and equity metrics to address disparities. Tools and workflows that integrate SDOH data with clinical quality programs will help health systems design interventions that improve outcomes across diverse and complex patient populations.
3. Full-Population Automation (Beyond Sampling)
Manual abstraction and sampling will give way to automated, full-population quality data capture, including chart abstraction allowing for more optimized ways to calculate quality measure performance. This enables more accurate performance measurement and reduces administrative burden on clinical teams.
4. CMS’s Goals for MIPS and MVPs
CMS’s 2026 focus for the Merit-based Incentive Payment System (MIPS) program focuses on participation stability, expanded MVP options, meaningful measures aligned to clinical practice, and a clear progression toward MVPs as the future of quality reporting. These changes are designed to help health systems shift from traditional MIPS to an MVP-centered reporting model.
5. AI Governance and Observability as a Standard
As generative and predictive AI tools continue to proliferate, strong AI governance frameworks and observability tooling will become essential. Organizations will invest in explainability, audit trails, model monitoring (for bias, drift, and safety), and compliance alignment (e.g., NIST AI RMF, ONC HTI), turning AI into a trusted enterprise asset rather than an unmonitored point solution.
I hope you have found these insights helpful and worth a few moments of your time. One of my goals for the new year is to share updates like this more regularly, and I’m looking forward to continuing the conversation throughout the year ahead. If you’d like to learn more about how Healthcare Innovations Solutions partners with health systems and physicians to turn quality programs into performance results, feel free to message me on LinkedIn.
About me
I am the Executive Director of Healthcare Innovation Solutions (HCIS), a subsidiary of the New Jersey Innovation Institute (NJII), where I lead value-based care, quality improvement, and population health technology initiatives across health systems and provider practices. With a clinical background as a registered nurse, I focus on applying data and AI-driven solutions to improve outcomes, streamline quality reporting, and support performance improvement programs. I focus primarily on state and federal quality improvement initiatives, helping clinical, quality and operational teams translate complex requirements into measurable results. My work centers on building practical, clinician-centered innovations that bridge healthcare delivery and technology to drive meaningful impact.