AI in Healthcare: What to Expect in 2026
Evidence-backed predictions on how artificial intelligence will reshape care delivery, operations, and data strategy in the year ahead.
Few forces have gained momentum in healthcare as quickly as artificial intelligence. In 2025, health systems, payers, and digital health innovators moved from exploratory pilots to real operational deployments. These deployments have been simple and in areas of low risk, but are approaching more exciting impactful applications rapidly. At the same time, industry research revealed a critical truth: AI can only be as effective as the data it’s trained on and the context available to it.
Looking ahead to 2026, AI will shift from experimental to essential. The combination of workforce pressure, rising costs, and maturing interoperability creates conditions where AI adoption is no longer optional. This isn’t a movement to fear; it’s a movement to embrace, to prepare for, and to ride toward new levels of healthcare outcomes.
Here’s what the evidence tells us to expect.
- AI-Driven Administrative Automation Will Surge — Out of Necessity
Healthcare’s workforce crisis is not slowing. The American Hospital Association reported in 2025 that labor accounts for 56% of hospital operating expenses, and shortages persist across nursing, care management, and revenue cycle roles. Reimbursement hasn’t kept pace: between 2022 and 2024, inflation rose 14.1% while Medicare inpatient payment rates increased only 5.1%.
These numbers make one thing clear:
Organizations can no longer hire their way out of administrative burden.
In 2026, just as we’ve seen in the tech sector, healthcare will strive for smaller teams with higher leverage. To achieve this, leaders will expand AI rapidly across:
- prior authorization triage and documentation extraction
- HCC and risk adjustment support
- quality measure abstraction
- claims editing assistance
- care-management routing
- clinical note summarization
AI adoption will be driven not by novelty, but by economic survival.
- AI Will Become the Central Engine of Care Management and Population Health
Significant findings were realized in 2025 from research linking environmental and social exposures (the “exposome”) to clinical outcomes. SDoH-EHR linkage datasets showed that integrating social, behavioral, environmental, and clinical factors substantially improves prediction of utilization, disease progression, and life expectancy.
Generative AI and large language models (LLMs) excel at synthesizing this kind of multi-modal data. We’ve also seen the big AI players begin to slow down investments in better models and shift towards maturing Agentic AI technology. The result? In 2026 we’ll see Care Management and Population Health teams rely on AI Agent specialists working 24/7/365 behind the scenes to help care managers and pop health leaders:
- distill complex history into short, actionable profiles
- prioritize gaps with the highest impact on outcomes
- understand behavioral, social and environmental patterns
- personalize outreach messages
- predict risk trajectories
2026 will mark the shift from broad population health strategies to precision-guided, AI-augmented interventions.
AI won’t replace care managers — but it will finally give them the tools they’ve always needed.
- AI Will Become a Compliance and Regulatory Infrastructure
Regulatory complexity continues to grow. In 2025:
- Medicare announced new digital-first chronic care management pilots
- health plans faced expanded transparency requirements
- documentation demands associated with Stars, MA risk adjustment, and quality programs increased
- audits and appeals workloads continued to rise
These pressures are prompting organizations to explore AI as a compliance partner, not just an operational tool.
In 2026, AI will be used to:
- generate and summarize medical-necessity documentation
- prepare audit packages
- support Stars and HEDIS reporting
- review encounter data for accuracy
- detect coding anomalies or missing justifications
- ensure data quality for CMS interoperability mandates
Compliance — historically one of the most manual domains — will become one of AI’s fastest-growing use cases.
- Generative AI Will Reshape Clinical Workflows — But OnlyWithGuardrails
2025 proved that LLMs can summarize charts, draft notes, and assist with decision support. But it also revealed risks: hallucinations, outdated training data, and inconsistent performance across unstructured documents.
Health systems learned that deploying AI safely requires:
- transparent data lineage
- human-in-the-loop oversight
- complete and accurate, longitudinal records
- standardized terminology and coding
- well-designed clinical workflows
- careful governance and risk assessment
Prediction for 2026:
LLMs and agents will become more deeply embedded in EHRs and care-management tools, but with stronger guardrails and using emerging standards around governance. AI won’t replace clinicians but it will become their cognitive co-pilot.
- Only Organizations With Strong Data Foundations Will Find Success With AI
In survey after survey, health systems cited data readiness as the top barrier to meaningful AI deployment. KLAS reported in 2025 that organizations are “more dependent than ever on real-time and predictive information,” but most still struggle with fragmented, unstandardized data.
At the same time, tools such as the Spezi Data Pipeline and FHIRconnect mapping engine gaining broader attention in 2025 demonstrated that FHIR-based systems are moving beyond data transport to supporting advanced semantic interoperability.
Organizations who report higher reliability and efficiency from AI models invested in:
- normalized longitudinal patient records
- real-time event feeds
- standardized coding (FHIR, SNOMED, LOINC)
- clean claims + clinical integration
- mature data governance
The implication for 2026:
AI will deepen the urgency for high-quality, normalized, longitudinal data.
Organizations with clean, integrated records will capture outsized value from AI.
Those with siloed or inconsistent data will find AI unreliable, unscalable, or unusable.
Interoperability and AI will no longer be separate initiatives.
AI will make interoperability a business imperative.
- AI Will Push the Industry Toward Platform-Based Data Strategies
As AI adoption scales, organizations will abandon fragmented data systems in favor of unified platforms capable of:
- aggregating multi-source data
- normalizing records
- supporting real-time updates
- delivering features through APIs
- running ML/LLM workloads securely
- producing audit-ready logs
The market will shift from “point solutions with AI features” to platforms where AI is native.
This shift mirrors what we’ve seen in finance and retail: when intelligence becomes core to the business, infrastructure must evolve to support it.
The Bottom Line for 2026:
AI is reshaping healthcare but data interoperability and quality will determine who benefits.
The evidence is clear:
- Labor and cost pressures are accelerating AI adoption.
- Interoperability standards like FHIR are maturing.
- SDoH and exposome research is expanding what data matters.
- Regulatory complexity is increasing the need for automation.
- Health systems and payers overwhelmingly cite data readiness as the limiting factor.
In 2026, organizations with clean, connected, real-time data infrastructures will unlock extraordinary benefits from AI.
Those without it will struggle.
AI is not the future of healthcare —
AI + interoperability + high-quality data is.
Opala is positioned at the center of that intersection. Through our work with leading standards groups such as Da Vinci and CHAI, Opala partners with healthcare organizations on innovations and outcomes. Call us today if you are ready to get the most out your AI strategy and drive tangible benefits in 2026.
