Why Data Management in Healthcare Must Evolve in 2026

Hospital Quality and Safety leaders face simultaneous pressures from multiple directions, and some legacy data systems can’t keep pace. Many payers are moving toward more frequent review cycles, with some conducting near real-time assessments that demand accurate data without manual steps. Payers increasingly want earlier insight into readmissions, complications, length of stay, and documentation patterns that affect reimbursement. At the same time, regulatory requirements are expanding both the volume and complexity of data hospitals must manage.

Major regulatory changes take effect in 2026, including CMS’s Interoperability and Prior Authorization Final Rule, requiring standardized data exchange for prior authorization and patient access, and the ONC HTI-1 Final Rule mandating expanded data classes and transparency requirements for clinical algorithms through USCDI v3. These requirements may reveal how disconnected some hospital data environments actually are because meeting them requires hospitals to aggregate and exchange data across multiple systems. When hospitals can track and analyze safety events, complaints, and quality measures through structured data capture and interactive dashboards, patterns can become visible that fragmented systems obscure. Older platforms often require leaders to reconstruct events manually, a limitation that becomes more apparent as compliance demands and operational needs grow.

The stakes rise further as hospitals continue to explore AI applications. AI tools require complete, consistent, and reliable data to perform effectively. Some healthcare organizations are exploring AI applications for early detection of clinical changes, patient deterioration risk assessment, and admission prediction. Hospitals are applying similar approaches to infection prevention through analysis of lab trends and temperature patterns. These applications all depend on accurate, connected information across multiple data sources, including lab systems, clinical documentation, medication records, and vital signs.

The year ahead will require data models that support proactive safety oversight alongside regulatory compliance.

Four Data Challenges That Limit Healthcare Quality and Safety

This convergence of pressures surfaces four weaknesses in legacy data environments: fragmented testing and diagnostics, weak governance, delayed detection, and limited analytics readiness.

A. Disconnected Data Limits Visibility Into Relationships

Separate systems and reviews prevent leaders from seeing how events influence each other. When data is spread across different locations, teams often analyze issues in isolation, hiding patterns that affect safety.

AHRQ emphasizes that safety investigations need a unified factual record; fragmented systems make causal chains harder to reconstruct and recurring events harder to prevent.

B. Hospitals React To Problems After The Fact

Some reviews only begin when someone notices a problem. Legacy systems reinforce this pattern because they can’t provide real-time insights. Staff must revisit records, gather information from various sources, and then reconstruct what happened. By the time the reconstruction is finished, the early window for action is gone.

The ONC Standards Bulletin 2025-2 highlights this challenge by noting that modern safety work depends on standardized and current clinical data. The bulletin describes new data elements in USCDI v6 that support patient care, including care plans, device identification, and dates associated with clinical events. Legacy systems may struggle to support these updates, making it harder to work with current clinical information.

C. Artificial Intelligence Exposes Blind Spots in Legacy Systems

AI tools need clean, consistent data for reliable results. When information is incomplete or poorly connected, outputs become unreliable. This limitation becomes visible the moment hospitals use advanced analytics in routine work.

AHRQ’s Digital Healthcare Research program shows how timely, well-connected data improves patient triage and helps identify risk earlier. Fragmented systems can’t provide this early signal because information is often scattered.

D. Healthcare Data Governance Is Unclear And Inconsistent

Beyond technical limitations, poor organizational structure creates problems. When no one is accountable for a dataset’s accuracy, the information can’t be trusted. Metrics shift depending on who pulls them because teams use different definitions. These disagreements can pull attention away from underlying safety issues. Important details stay hidden until someone searches for them manually, forcing Quality leaders to spend more time resolving data conflicts instead of addressing safety concerns.

AHRQ points out that Clinical Decision Support Systems depend on large, well-organized datasets. Legacy systems with unclear governance can’t provide that structure, limiting hospitals’ ability to leverage decision support tools. Poor governance also perpetuates health disparities, as incomplete or inconsistent data can disproportionately affect care for marginalized populations.

Each challenge reinforces the others, making it more difficult for hospitals to understand risk early, respond efficiently, or build a reliable picture of patient care.

Six Priorities for Strengthening Hospital Data Management

These four challenges have implications for Quality and Safety leaders managing cross-department coordination, regulatory interpretation, and data reconciliation. The path forward requires addressing both technical infrastructure and organizational processes (but not all at once).

The gap between good and poor data management is now more visible and consequential, yet modernization requires investment at a time when healthcare leaders are already managing regulatory requirements, preventable adverse events, and value-based care arrangements. As Dartmouth Health Chief Strategy Officer Stephen J. LeBlanc noted at the 2025 Value-Based Payment Summit, value-based payment goals align with hospital missions, but “it’s the execution that’s the challenge, it’s the investment in the infrastructure.”

Quality and Safety leaders essentially face a sequencing challenge: governance without consolidated data creates consistent definitions for scattered information; analytics without standardization amplifies existing inconsistencies; workflow integration without governance automates flawed processes. The priorities below outline what must be in place and a suggested order, but organizations should determine which foundational elements come first based on their current capabilities and 2026 compliance deadlines.

1. Establish Clear Data Governance Structures

Hospitals benefit from structures that clarify who owns the accuracy and meaning of key data. Quality and Safety leaders often oversee this work because reliability influences patient safety.

Why this comes first: Without governance, every subsequent priority becomes a negotiation about definitions, ownership, and accountability. Organizations that skip governance find themselves building analytics on shifting foundations, investing in dashboards that show different numbers depending on who pulls the report.

Effective governance includes cross-functional councils, standard data-quality controls, and integrated safety-reporting workflows that enforce data standards. Structured data capture and standardized categorization support both transparency and regulatory compliance while establishing governance patterns applicable across quality programs. For example, structured complaints and grievances management systems demonstrate how governance principles apply across safety and quality workflows.

Clear governance also reduces time spent reconciling conflicting reports across departments.

2. Consolidate Data for Better Visibility

Once governance clarifies data ownership, the next priority is bringing information together. Some hospitals operate with data scattered across systems. This fragmentation delays decisions and obscures patterns.

The tension: Consolidation projects can be expensive and time-consuming, but 2026 regulatory requirements essentially mandate it. The CMS and ONC rules require data exchanges that fragmented systems can’t support without extensive manual work. Organizations must weigh the cost of infrastructure investment against the ongoing cost of manual workarounds that grow more expensive as regulatory and payer demands increase.

Consolidated views of clinical, operational, and administrative information give teams faster access for investigations and trend analysis. Research on real-time analytics for patient safety demonstrates that hospitals using unified surveillance systems detect adverse events and predict patient deterioration more effectively through continuous monitoring rather than periodic retrospective review.

AHRQ estimates that hospital-acquired condition reductions saved approximately $19.9 billion in healthcare costs between 2010 and 2014. These reductions resulted from multiple factors, including improved use of electronic health records and investments in data and measures to track change, demonstrating the value of data infrastructure in patient safety improvement.

3. Standardize Definitions and Data Elements

Definitions must be consistent across teams. With USCDI v3 becoming the certification baseline in January 2026, hospitals need a shared language for measures that influence safety work. When Quality defines “hospital-acquired infection” differently than Infection Prevention, or when Patient Safety calculates fall rates using different numerators than Risk Management, teams waste time reconciling numbers instead of addressing root causes.

The practical challenge: Standardization requires difficult conversations about whose definitions prevail. Departments have legitimate reasons for their current approaches; Infection Prevention may align with CDC definitions, while Quality aligns with Joint Commission measures. The 2026 imperative is choosing a primary standard (often the regulatory requirement) and mapping variations to it rather than attempting perfect uniformity.

Standard definitions eliminate conflicts and allow teams to focus on underlying safety concerns.

4. Reduce Manual Work Through Workflow Integration

Safety reviews progress more efficiently when information flows directly into established workflows. Integrated approaches provide reviewers with needed details at the point of review, supporting faster cycles and earlier pattern recognition.

Where organizations stumble: Workflow integration projects often fail because they automate existing inefficient processes rather than redesigning workflows around integrated data. Without governance to ensure accurate data capture and standardization to ensure consistency, integration simply moves bad data faster through flawed processes.

Beyond workflow integration, two complementary approaches address manual work: automating structured data capture where possible, and outsourcing data abstraction for registries and quality measures to free internal staff for performance improvement. Patient safety event reporting applications automate workflow integration with role-based task assignments and real-time notifications, maintaining audit-ready documentation while reducing administrative burden. For activities such as registry reporting and quality measure abstraction, clinical data abstraction services can redirect 50-70% of staff time from data collection to performance improvement initiatives, enabling hospitals to address systemic care gaps while maintaining required reporting.

5. Build Readiness for Advanced Analytics

With governance, consolidation, and standardization in place, organizations can leverage analytics capabilities to gain earlier visibility into emerging risk patterns.

The capability gap: Some organizations purchase analytics tools before their data infrastructure can support them, then blame the tools when outputs prove unreliable. Analytics readiness isn’t just about buying software; it’s about ensuring the preceding four priorities create a foundation for meaningful analysis. Without that foundation, analytics investments may produce dashboards that raise more questions than they answer.

As CMS-0057-F and USCDI v3 requirements take effect in 2026, the technical foundation for these capabilities becomes a regulatory baseline. Organizations should consider implementing a data infrastructure that supports real-time dashboards, trend visualization, and case-level drill-down for both daily operations and strategic quality improvement initiatives.

6. Treat Data Management as a Safety Standard

The shift from viewing data as an administrative burden to recognizing it as a clinical tool changes how organizations allocate resources and prioritize infrastructure investments.

The strategic implication: This shift isn’t just about Quality and Safety departments. It requires executive commitment to data infrastructure investment with the same urgency as clinical equipment or facility upgrades. Organizations that treat data management as “back office IT work” might struggle with meeting the 2026 requirements, while those that frame it as patient safety infrastructure can make a stronger business case for necessary investment.

When hospitals treat information reliability as integral to patient safety, they can identify concerns sooner and respond more effectively to regulatory and operational demands. This approach ensures decisions rely on information that supports both immediate safety oversight and long-term quality improvement.

The Path Forward for Data Management in Healthcare

The effect of regulatory, payer, and technology changes in 2026 creates both pressure and opportunity. Systems built primarily for documentation and billing now require enhancement to support the real-time oversight and analytics that increasingly define healthcare quality programs.

The organizations that will succeed in this environment won’t be those with the largest IT budgets. They’ll be the ones that treat data infrastructure as patient safety infrastructure. They’ll establish governance before rushing to analytics, standardize definitions before automating workflows, and understand the difference between buying tools and building capability.

Quality and Safety leaders who address these six priorities systematically can also position their organizations to identify risk earlier, respond to regulatory requirements more efficiently, and demonstrate outcomes more effectively. As value-based care arrangements continue to expand, this foundation becomes not just operationally advantageous but strategically essential.