Where AI Actually Helps Hospital Quality Improvement

What does AI actually do in hospital quality improvement?

AI in healthcare quality improvement can help teams find relevant information in clinical records, identify patterns in quality data, and prioritize areas for review. But AI is only one part of the process. Validated measure logic, reliable data, and human oversight remain essential to accurate reporting and meaningful quality improvement.

The key is knowing where AI is genuinely useful and where other technology or human expertise should take over.

Let's look at four stages of the quality improvement cycle: chart review and abstraction, identifying changes in measure performance, determining measure results, and improvement planning.

Stage 1: Chart review and abstraction

Chart abstraction requires reviewing a patient's clinical record to identify the data elements required for a specific measure or registry. Depending on the measure, that could include diagnoses, procedures, medications, timing of interventions, test results, or documentation of specific care processes.

This is an area where AI can take on some of the most time-consuming work.

AI-assisted abstraction can analyze unstructured clinical documentation, identify potentially relevant information, surface the supporting passage in the medical record, and recommend an answer for the abstractor to review.

The distinction between recommendation and validation matters.

Rather than asking an abstractor to manually search through an entire record for every required data element, AI can help bring the relevant information to them. The abstractor can then review the source documentation and accept or correct the recommendation.

Human review remains important when documentation is incomplete, contradictory, or clinically ambiguous. It also provides an important safeguard before abstracted data move further into the quality reporting process.

For quality teams facing large abstraction workloads, the goal isn't simply to "use AI." It's to use AI where it can reduce manual searching while keeping the people who understand the clinical and regulatory context in control.

Learn more: Explore how AI-assisted chart abstraction can support clinical data abstraction workflows.

Stage 2: Identifying changes in measure performance

Quality improvement becomes more difficult when teams don't recognize a performance issue until the end of a reporting period.

Ongoing monitoring can help teams identify when performance begins moving away from an organization's established goals or historical performance. But it's important to distinguish the role of traditional analytics and automation from AI.

Dashboards, automated calculations, and threshold-based alerts can continuously monitor measure performance and notify teams when results meet predefined criteria.

AI may add another layer by helping teams examine larger or more complex datasets, surface patterns, and prioritize measures or cases that may warrant additional investigation.

Neither one replaces the quality professional.

A change in performance could reflect a clinical issue, documentation gap, coding problem, data-quality issue, workflow change, or another factor entirely. Technology can tell the team where to look. Determining why the change occurred and what to do about it still requires investigation and expertise.

That distinction helps quality teams use technology appropriately: automate the monitoring, use AI to help surface meaningful patterns, and keep people responsible for interpreting what those patterns mean.

Stage 3: Determining measure results

This is also where precision matters.

For many quality measures, the numerator represents the process, condition, event, or outcome that satisfies the measure's defined criteria. The denominator identifies the population being evaluated. Depending on the measure, there may also be exclusions, exceptions, or additional populations defined in the specifications.

These determinations should follow the applicable measure specifications and calculation logic.

AI can support the process by finding relevant evidence in the clinical record, extracting or recommending values for required data elements, and flagging information that may need closer review.

Once those data are validated, the applicable measure logic determines how the case is evaluated.

That's an important boundary.

AI should not be treated as a substitute for official measure specifications or validated calculation logic. And when the underlying clinical documentation is incomplete, conflicting, or unclear, human review may still be necessary before the data used for measure calculation can be considered reliable.

Data quality matters too. AI can only work with the information available to it. Quality teams evaluating AI-assisted tools should understand which data sources the technology can access, how it handles missing or inconsistent information, and whether users can trace an AI-generated recommendation back to its source.

For a deeper look at how measure populations and logic work, read our guide to understanding eCQM specifications.

Can AI replace clinical abstractors?

AI can reduce the manual work involved in clinical abstraction, but human review remains an important part of Medisolv's AI-assisted abstraction approach. The technology can surface supporting evidence and recommend data-element answers, while experienced abstractors validate those recommendations and resolve cases where the documentation requires additional interpretation.

That's an important distinction for quality leaders evaluating AI.

The question shouldn't simply be whether an AI model can make a mistake. Any technology can produce an incorrect or incomplete result.

A more useful question is: What happens when it does?

Quality teams should be able to see where an AI-generated answer came from, review the supporting clinical documentation, and correct the result before it becomes part of downstream quality reporting.

That visibility makes AI assistance more useful without asking quality teams to blindly trust a black box.

Stage 4: Improvement planning

Collecting accurate quality data is only part of the job. The larger goal is using that information to improve performance and patient care.

Analytics and AI can help quality teams explore where performance gaps are concentrated. For example, teams may be able to examine whether cases requiring attention cluster around a particular care setting, workflow, timeframe, or documentation step.

Those patterns can help teams focus their investigation.

But identifying a pattern isn't the same as identifying its root cause.

Quality professionals still need to determine what is driving the issue, decide whether a clinical or operational change is appropriate, work with clinicians and staff, implement the intervention, and monitor whether it actually improves performance.

This is also why the entire quality data lifecycle matters. Faster abstraction alone doesn't create quality improvement. Data must still be validated, evaluated according to the appropriate measure requirements, monitored over time, and ultimately translated into action.

For organizations that need additional support moving from performance data to an improvement strategy, Medisolv's Advanced Quality Improvement services provide ongoing support for identifying opportunities and advancing quality initiatives.

Questions to ask AI vendors making accuracy or automation claims

Not every AI solution approaches healthcare quality data the same way. Before introducing AI into a quality workflow, ask:

• How was your accuracy measured? What was tested, and was human review part of the workflow?
• What happens when the AI is wrong or uncertain? Can users review and correct a recommendation before it moves downstream?
• Can every recommendation be traced to its source? Reviewers should be able to see the evidence behind an extracted data element.
• What data does the technology use? Understand which clinical data sources are available to the system and how it handles missing or inconsistent information.
• Which measures and registry programs are supported today? Ask about current capabilities rather than relying on roadmap plans.
• How is the technology maintained and monitored? Ask how performance is evaluated over time and how changes to applicable measure specifications are addressed.
• How is patient data protected? Understand how protected health information is secured and how your organization's data may be used.
• Where does human review occur? Know who is responsible for validating AI-generated information before it is used in reporting or quality improvement decisions.

These questions can help separate meaningful workflow improvements from broad claims about what AI can automate.

For more guidance, explore our resource on evaluating healthcare quality improvement vendors.

AI should support the quality cycle, not replace it

The most valuable role for AI in hospital quality improvement isn't taking the entire process out of human hands.

It's helping quality teams spend less time searching through records and sorting through data so they can spend more time investigating performance, improving processes, and applying the clinical and regulatory expertise that technology cannot replace.

Used appropriately, AI becomes another tool within a larger quality infrastructure: one that combines reliable data, validated measure logic, effective analytics, and experienced people.

Sources:

• Centers for Medicare & Medicaid Services, Electronic Clinical Quality Measure Logic and Implementation Guidance for 2026 Reporting/Performance
• Centers for Medicare & Medicaid Services, Guide for Reading Electronic Clinical Quality Measures (eCQMs)
• Office of the Assistant Secretary for Technology Policy, Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023–2024
• Medisolv, Medisolv Acquires Health Elements AI to Reinvent How Healthcare Organizations Capture and Use Quality Data