MIPS Performance Improvement: How AI-Powered Data Analysis Drives Results

Introduction: The MIPS Challenge in Modern Healthcare

The Merit-based Incentive Payment System (MIPS) has become a defining feature of U.S. physician reimbursement under CMS value-based payment programs. As a core pathway of the Quality Payment Program (QPP), MIPS directly ties Medicare Part B payment adjustments to measurable performance in quality reporting, interoperability, improvement work, and cost efficiency. For healthcare organizations—particularly physician groups, multi-specialty practices, and health systems billing under clinician-level identifiers—MIPS is no longer a compliance exercise. It is a revenue and reputation lever.

Yet MIPS performance improvement remains difficult in day-to-day clinical operations. Many organizations struggle to translate measure specifications into reliable workflows, reconcile disparate data sources, and maintain performance tracking that is actionable before submission deadlines. Too often, teams discover late in the performance year that a high-priority measure was under-captured, a denominator was misinterpreted, or documentation did not support the intended quality action. The result can be lost points, increased administrative effort, and avoidable negative payment adjustments—particularly painful in an environment of rising costs and tight staffing.

The financial stakes are clear: CMS applies positive, neutral, or negative payment adjustments based on MIPS final scores, and those adjustments affect Medicare reimbursements. In competitive markets, performance also influences payer contracting narratives and broader quality positioning. As CMS continues to expand its emphasis on outcomes, equity, and digital quality measurement, the organizations that can operationalize accurate, timely analytics will be best positioned to thrive.

Intelligent data analysis—particularly AI-assisted measurement, chart review, and performance forecasting—offers a pragmatic path forward. By improving data completeness, surfacing gaps early, and enabling continuous performance improvement, AI-powered approaches can help organizations align clinical practice with MIPS requirements while reducing the manual burden traditionally associated with quality reporting.

Understanding MIPS Performance Categories and Data Requirements

MIPS is scored across four performance categories. Each category has distinct data inputs, operational dependencies, and common failure modes. Performance improvement requires understanding not only what CMS expects, but also how local documentation and data pipelines influence whether credit is earned.

The Four MIPS Performance Categories

Quality
Clinicians report on a set of quality measures (often eCQMs, MIPS CQMs, or specialty measures) and are scored based on performance rates compared against benchmarks. Quality is frequently the most visible category and can be the most operationally challenging because it depends on accurate denominators, numerator capture, exclusions, and measure-specific documentation.
Promoting Interoperability (PI)
PI focuses on meaningful use of certified EHR technology (CEHRT), including e-prescribing, health information exchange, and patient electronic access. PI performance is highly dependent on EHR configuration and workflow adherence—and can be undermined by incomplete interoperability events, inconsistent portal utilization, or configuration gaps after system upgrades.
Improvement Activities (IA)
IA evaluates participation in activities that improve care processes, patient engagement, and safety (e.g., care coordination, behavioral health integration, patient safety initiatives). IA is often seen as “easier” to score well on, but organizations may lose points due to insufficient documentation, unclear evidence retention, or misalignment between selected activities and operational reality.
Cost
Cost is calculated by CMS from claims and does not require direct submission. However, organizations often underestimate its impact because it can pull down overall scores even when quality reporting is strong. Understanding attribution, episode-based measures, and practice pattern variation is central to improving cost performance over time.

Why MIPS Data Collection Becomes Complex

MIPS quality reporting is not simply “pull a report from the EHR.” It requires mapping measure logic to real-world care delivery, including:

Measure specifications: denominator eligibility, numerator actions, exclusions, exceptions (when allowed), performance period definitions, and required fields.
Documentation dependencies: whether structured data fields are used (problem lists, lab results, vitals, medications), whether the EHR captures the correct codes, and whether clinician documentation aligns with measure intent.
Multi-system inputs: EHRs, practice management systems, clearinghouses, registry submissions, claims feeds, referral platforms, and patient engagement tools may all contribute to the data needed for accurate measurement.

Even in organizations with robust reporting teams, complexity grows when multiple specialties, locations, and EHR templates are involved. A single variation in workflow—such as documenting smoking status in free text rather than a structured field—can materially affect a quality measure’s performance rate.

Fragmented Systems Create Reporting Gaps and Missed Opportunities

Fragmentation is a common root cause of underperformance. Organizations may have:

Multiple EHR instances following mergers or acquisitions
Separate tools for care management and patient engagement
Claims data that is delayed, incomplete, or difficult to reconcile with clinical encounters
Inconsistent use of problem lists, diagnosis coding, and procedure documentation across clinicians

These gaps can lead to:

Under-counting numerators (the care was delivered but not captured in the right field)
Over-counting denominators (patients included incorrectly due to coding or attribution issues)
Late discovery of missing data (often after the performance period ends)

Benchmarking and Timely Submission: Why Accuracy Matters

CMS benchmarks convert raw performance rates into category points. Small differences in measure performance can produce meaningful point changes depending on the benchmark distribution, topped-out measures, and specialty-specific patterns. Inaccurate submissions can yield:

Lost points from incorrect measure logic
Risk of audit exposure if documentation does not support reported results
Missed opportunities to select measures with better scoring potential given local practice patterns

Accurate, timely submission is not merely administrative. It is a strategic requirement in CMS value-based payment models.

How Intelligent Data Analysis Transforms Quality Reporting

Intelligent data analysis changes the operating model from retrospective reporting to continuous performance management. Rather than relying on end-of-year extraction and manual reconciliation, AI-supported approaches can continuously harmonize data, detect gaps, and generate actionable insights for clinicians and quality teams.

Automated Data Aggregation Across Systems

One of the most practical advantages of AI-enabled analytics is the ability to aggregate and normalize data from multiple sources, including:

EHR clinical data (structured fields and, when appropriate, extracted insights from unstructured notes)
Practice management and scheduling data
Billing and claims data (including attribution signals and utilization)
Laboratory interfaces and imaging orders/results
Care management platforms and patient outreach tools

When these datasets are connected, organizations can build a more complete picture of measure eligibility and performance. This reduces the common “blind spots” that occur when quality reporting depends solely on one system’s reporting module.

Real-Time Dashboards That Surface Measure Gaps Early

Traditional workflows often identify issues late—after months of performance drift. Real-time or near-real-time dashboards enable:

Monitoring of measure performance by clinician, location, payer segment, and patient cohort
Identification of underperforming measures before deadlines
Drill-down into patient lists for outreach (e.g., overdue screenings, uncontrolled chronic conditions)
Visibility into documentation shortfalls (e.g., numerator actions performed but not credited)

This is critical because most MIPS performance improvement is operational: care gaps must be closed while there is still time in the performance year.

Predictive Analytics for Optimal Measure Selection

Measure selection is not just a clinical choice; it is a scoring strategy that should reflect practice patterns and feasibility. Predictive analytics can help organizations:

Evaluate which measures have strong baseline performance and adequate denominators
Estimate scoring potential under current workflows and patient mix
Identify measures likely to be “topped out” or benchmark-disadvantaged
Model tradeoffs between effort and points, including data capture burden

When organizations choose measures misaligned with their workflow realities, they often pay later in manual workarounds or low-scoring submissions.

Reducing Administrative Burden While Improving Accuracy

AI-assisted data analysis can reduce the manual tasks that overwhelm quality teams, such as:

Manual chart review for numerator confirmation
Reconciliation of conflicting data across systems
Spreadsheet-based tracking of measure gaps
Ad hoc clinician outreach near deadlines

Importantly, reducing burden does not mean “automating away” clinical judgment. The best programs combine automation with governance—using AI to surface issues and recommend actions, while clinicians and compliance leaders validate decisions and ensure documentation integrity.

Case Example: Identifying Underperforming Measures Before It’s Too Late

Consider a multi-site primary care group tracking a diabetes control measure (e.g., HbA1c control). Mid-year dashboards reveal that one location’s performance rate is significantly lower than peers. A deeper analysis shows:

A subset of HbA1c lab results are arriving via external labs and being scanned as PDFs rather than interfaced as discrete results.
The labs are clinically available to providers but not counted in the measure numerator due to lack of structured data mapping.
In addition, a template change led some clinicians to document diabetes status inconsistently on the problem list, affecting denominator accuracy.

An intelligent data analysis approach flags the discrepancy early, prompting:

Interface optimization or structured data extraction for external lab results
Template and workflow standardization for problem list maintenance
Targeted education for clinicians in the underperforming site
Patient outreach workflows for truly uncontrolled patients

By addressing both data capture and clinical care gaps, the organization improves quality reporting accuracy and patient outcomes—without relying on a last-minute reporting scramble.

Practical Strategies for MIPS Performance Improvement

Sustained performance improvement requires operational discipline, clinical alignment, and measurement infrastructure. The following strategies are consistently associated with stronger MIPS outcomes and lower reporting burden.

1) Implement Continuous Monitoring, Not End-of-Year Scrambles

Organizations improve MIPS performance when they treat measurement as a year-round process:

Establish monthly or quarterly checkpoints for each MIPS category
Track measure denominators early to ensure sufficient case volume
Create “exception reports” for missing data elements (e.g., required screenings not documented)
Use trend lines to detect performance drift before it becomes irrecoverable

This approach also supports better clinician engagement—providers can improve in real time rather than reacting to retrospective scorecards.

2) Use AI Recommendations to Close Care Gaps and Improve Outcomes

AI-supported analytics can convert performance tracking into clinical action. Examples include:

Identifying patients overdue for preventive screenings (colorectal cancer screening, breast cancer screening, immunizations)
Detecting chronic disease care gaps (uncontrolled hypertension, diabetes monitoring, medication adherence)
Prioritizing outreach based on risk and likelihood of completion before period end
Highlighting documentation omissions that prevent measure credit

When used responsibly, these tools support the underlying goal of MIPS: improving patient outcomes while creating a more consistent standard of care.

3) Integrate Workflows at the Point of Care

Reporting cannot be “bolted on” after the visit. High-performing organizations embed measurement needs into clinical workflow:

Visit templates that capture required structured fields
Pre-visit planning that flags measure-related actions
In-visit prompts that are clinically appropriate and not alert-fatiguing
Post-visit workflows for referrals, labs, and follow-up

Operationally, the focus should be on making the “right action” the “easy action.” When clinicians must remember complex measure logic, performance becomes inconsistent.

4) Benchmark Against Peers to Set Realistic Improvement Targets

Peer benchmarking helps organizations understand whether a measure is realistically improvable given patient mix and specialty patterns. Benchmarking can be used to:

Identify which measures are most likely to generate points
Set achievable targets by clinician and site
Separate “true care gaps” from “documentation/data capture gaps”
Inform decisions about whether to retire low-yield measures in future years

Benchmarking also supports governance conversations with clinical leaders by grounding decisions in external comparators rather than anecdote.

5) Optimize Documentation to Ensure Proper Credit

Many MIPS points are lost not because care was inadequate, but because documentation did not meet measure specifications. Documentation optimization should focus on:

Increasing structured documentation where required (e.g., vitals, smoking status, lab results)
Standardizing problem list and diagnosis coding practices
Ensuring referral orders and results are captured discretely, not only in narrative text
Training staff on measure-specific “must capture” elements

Documentation optimization should be approached carefully. The goal is accurate representation of care delivered, not “documentation for documentation’s sake.” Governance and compliance oversight remain essential to avoid inappropriate documentation practices.

Practical Takeaways

Establish continuous MIPS monitoring with monthly performance reviews to prevent late-year surprises.
Prioritize data integrity work: structured data capture, interface reliability, and consistent templates across sites.
Use gap lists and targeted outreach workflows to close care gaps while there is still time in the performance year.
Apply predictive analytics to select measures aligned with actual practice patterns and achievable benchmark performance.
Distinguish documentation gaps from true clinical gaps; address both with tailored interventions.
Create clinician-facing dashboards that are actionable (patient lists, next steps), not merely retrospective scorecards.
Integrate measure actions into pre-visit planning and point-of-care workflows to reduce reliance on memory and manual tracking.
Maintain audit-ready evidence for Improvement Activities and ensure PI configurations remain stable after EHR updates.
Incorporate cost awareness initiatives (appropriate utilization, care coordination) even though the Cost category is claims-based.
Build a governance structure that includes clinical leadership, quality, compliance, IT, and revenue cycle for aligned decision-making.

Future Outlook: The Future of Value-Based Payment and AI Integration

CMS’s long-term trajectory continues toward quality metrics, digital measurement, and outcomes-based reimbursement. MIPS remains a central pathway, but organizations should expect requirements to evolve—especially as alternative payment models expand and quality measurement becomes more data-driven.

Increasing Emphasis on Outcomes and Digital Quality Measurement

CMS has steadily signaled interest in:

More meaningful outcome measures (not only process measures)
Greater reliance on electronic clinical quality measures (eCQMs) and digital reporting
Stronger interoperability expectations as a foundation for measurement and care coordination

As measurement shifts toward digital-first approaches, organizations will need reliable structured data capture and well-governed analytics pipelines. Practices that still depend heavily on manual abstraction or fragmented reporting tools may face increasing burden.

Emerging AI Capabilities in Population Health and Risk Stratification

AI in healthcare is advancing rapidly in areas relevant to MIPS performance improvement:

Risk stratification to identify patients most likely to benefit from outreach or care management
Population health analytics that link clinical data and utilization patterns to targeted interventions
Chart review acceleration to validate measure compliance and close documentation gaps responsibly
Workflow intelligence to reduce administrative load and ensure consistent capture of required elements

These capabilities can support both MIPS and broader quality strategies—particularly when aligned with clinical governance and evidence-based care pathways.

From Reactive Reporting to Proactive Performance Optimization

Historically, MIPS has encouraged a “reporting mindset.” The next phase will favor organizations that operationalize proactive optimization:

Identifying performance drift early
Predicting measure outcomes before submission
Taking corrective action through clinical workflow changes, not just reporting adjustments
Aligning quality improvement and financial incentives across stakeholders

This is where intelligent data analysis becomes strategic: it turns a compliance obligation into a continuous improvement engine.

Preparing for Evolving MIPS Requirements and Alternative Models

Healthcare leaders can prepare by investing in:

Data governance and interoperability foundations
Standardized documentation and structured data capture
Scalable analytics that can adapt to new measures and specifications
Cross-functional teams that link clinical operations, quality, IT, and finance

As organizations mature, the same infrastructure can support participation in advanced APMs, payer quality programs, and enterprise population health initiatives.

In this landscape, solutions like Arkangel AI can serve as part of a broader operating model—helping organizations extract actionable insights from complex clinical data while maintaining the oversight and accountability required in regulated environments.

Conclusion: Taking Action on Data-Driven MIPS Success

MIPS is a high-stakes component of CMS value-based payment, and performance improvement requires more than annual reporting. High-performing organizations treat quality reporting as a continuous operational discipline—supported by accurate data, actionable analytics, and workflow integration.

Intelligent data analysis is increasingly central to this work. By aggregating data across systems, surfacing measure gaps early, enabling predictive measure strategy, and reducing manual burden, AI-powered approaches help organizations improve both reporting accuracy and patient care. The most durable gains come from pairing technology with governance: standardized documentation practices, clinician engagement, audit-ready evidence, and cross-functional accountability.

For healthcare leaders, the near-term opportunity is practical and measurable: build the infrastructure and workflows that make MIPS performance visible, manageable, and improvable throughout the year—not only at submission time. Organizations that adopt these capabilities early will be better positioned not only to maximize MIPS scores, but to compete effectively as CMS continues to accelerate toward outcomes-based reimbursement. Arkangel AI works with healthcare organizations in this direction by enabling more scalable chart review and performance insights that support sustained quality improvement.