Every day, another "revolutionary" AI healthcare solution hits the market. In the vast sea of options, it's important to have a way to cut through the noise and compare options effectively. When reviewing all of your potential partners — and even your current ones — measurable ROI is the great equalizer.

The reality is your healthcare system needs a solid ROI strategy for evaluation and validation, built on four steps: clearly defined objectives, trackable metrics with a baseline for comparison, a system for tracking results, and methods for quantifying impact. This playbook walks through each.

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Step 1: Define clear objectives and metrics

As the foundation of a comprehensive ROI strategy, clearly defining objectives and metrics for success is no light matter. Objectives should cover multiple fronts — financial, clinical, and operational — and metrics need to be easily tracked with supporting data.

• Financial. Objective: improve revenue capture while reducing denials. Example metrics: increased revenue, reduction in claim denials, reduction in outsourced and third-party CDI queries, increased Elixhauser capture, and improvement of revenue cycle metrics and cost structures.
• Clinical. Objective: reduce administrative burden while improving patient outcomes. Example metrics: reduction in medical errors, reduced readmission rates, improved medical notes, reduction in time spent on administrative tasks, increased physician satisfaction, and improved patient safety.
• Operational. Objective: improve team efficiency and reduce the need for CDI queries. Example metrics: reduction in CDI queries, reduction in documentation rework, improved team collaboration and communication, and faster chart closes and final bill creation.

Step 2: Establish a baseline

Before introducing or reviewing any new healthcare tool, establishing a solid baseline is crucial for accurately measuring its potential impact. This involves meticulously gathering data on relevant metrics across current financial performance, existing clinical outcomes, and current operational efficiency. A few best practices as you build your benchmarks:

• Data integrity. Ensure your data's accuracy and consistency through rigorous validation and quality checks. Your benchmarks are only as reliable as the data they're built upon.
• Relevant timeframe. Use data from the past six to twelve months to capture recent performance and trends, providing a relevant picture for evaluation.
• Benchmark selection. To account for data anomalies without distorting overall trends, use the average of your dataset as your metric benchmark. This provides a balanced and realistic baseline.

Step 3: Track and measure results

A thorough system for tracking and measuring defined metrics ensures accurate ROI assessment and demonstrates the tool's contribution to strategic goals. Your system should incorporate both a structured review process and comprehensive data collection.

Review cadence

• Onboarding. During the critical onboarding phase, monthly check-ins are essential for the first three to four months. These allow for real-time adjustments and address immediate challenges, ensuring smooth technical integration and process implementation.
• Quarterly business reviews. Following onboarding, QBRs provide a broader perspective — a thorough assessment of progress against established goals and the identification of trends.
• Annual reviews. A comprehensive, year-long analysis evaluating the overall impact of the AI solution on key performance indicators.

Data gathering

• Software reporting. The software's built-in reporting provides detailed insight into performance metrics, including user behavior through financial capture.
• Internal systems. Systems that gather and house data, such as the EHR or data analytics tools, provide a broader perspective on the software's impact on overall operations.
• Qualitative feedback. Surveys and interviews offer invaluable insights into user experience, satisfaction, and perceived benefits that may not be seen through data alone.

Step 4: Quantify impact

After tracking and measuring defined metrics, quantifying results illuminates the true impact of any implemented AI solution. This methodology should be codeveloped with your partner for truly tailored results. Clearly delineating between the financial, clinical, and operational impacts is a great starting point:

• Financial: new revenue through deeper chart review and more accurate documentation, improvements to quality measures that impact payer negotiations, and revenue protected through reduced claim denials.
• Clinical: reduction in burnout, reduced medical errors, improvement in observed-over-expected length of stay, and reduced readmission rates.
• Operational: higher team efficiency through standardized notes for back-office teams, time savings on administrative tasks for clinical staff, and reduction in CDI queries.

This framework works. It's how health systems like Penn Highlands Healthcare independently validated a $7 million revenue impact from their AI investment — by defining metrics upfront, establishing baselines with control groups, and quantifying results with their own data.

Ready to put this strategy into practice?

Regard understands the challenges healthcare systems face in this dynamic environment, and we've built our platform with a focus on delivering measurable, impactful results. If you're ready to explore how Regard can help your organization achieve its financial goals and enhance patient care, schedule a tailored demonstration with one of our Regard experts.

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