We’ve broken AI down into many pieces to analyze the many different aspects of AI and how it affects the different areas of healthcare organizations. One area we haven’t talked about enough yet is the return on investment of AI initiatives once they’ve been deployed in the real world. Today, we set out to fix this wrong and learn more not only about the financial ROI of AI initiatives but also the clinical and operational ROI.
We’ve reached out to our brilliant Healthcare IT Today Community to ask — how do you measure the clinical, operational, and financial return on investment of AI initiatives once they are deployed in real-world settings? The following are their answers.
Elevsis Delgadillo, SVP, Customer Success at KeenStack
There’s no need to reinvent reporting to measure AI ROI. Most organizations are already tracking the right metrics. In referral management, that might be leakage or time to schedule, and in clinical use cases, it could be outcomes like hospital-acquired sepsis rates. In the revenue cycle, it’s collections and denials. The key is enabling an AI workflow in a specific area and measuring what changes so the impact can be clearly attributed.
Shay Perera, Co-Founder & CTO at Navina
Meaningful ROI in healthcare AI must be assessed through a combination of clinical, operational, and financial metrics. Clinically, we measure factors like improved risk adjustment accuracy and care gap closure rates; operationally, reductions in chart review, documentation, and coding time, and administrative burden, alongside high clinician engagement with the solution; financially, enhanced documentation quality contributes to audit readiness and more predictable revenue under value-based contracts.
Jared Hamilton, Cyber Managing Director at Crowe LLP
Physician feedback is one of the most important measures of return on investment. In clinical settings, we look closely at whether AI tools are meaningfully reducing time spent on documentation and administrative tasks, such as manual charting, and allowing providers to spend more time interacting directly with patients.
That impact shows up not only in efficiency metrics, but also in the patient experience. Most of us have been patients ourselves, and we understand the difference between a provider who is fully engaged in the conversation versus one focused on a keyboard. When AI helps shift attention back to the patient, it delivers both clinical and experiential value, which ultimately supports provider satisfaction and long-term operational and financial returns.
Denis Whelan, CEO at Documo
ROI for AI initiatives is measured using the operational and financial metrics organizations already track. This includes reduced manual processing time, faster referrals and authorizations, fewer document errors, and lower cost per transaction. On the clinical side, teams focus on metrics like improved turnaround times, fewer delays in patient care, and improvements in population health metrics.
For example, one healthcare organization using AI to process inbound documents was able to cut handling time by 40-50% while redeploying staff to higher-value tasks – achieving measurable efficiency gains without adding headcount.
Joe Russolello PT, DPT, MBA, Senior Vice President, Growth at WebPT
ROI only becomes real when it shows up in the clinician’s experience first. Clinically, that means less burnout and higher satisfaction driven by reduced documentation burden.
Operationally, the most consistent metric is time. Often, one to two hours per clinician per week is reclaimed and significantly less after-hours charting. Financial gains follow when those efficiencies reduce denials, accelerate billing cycles, and shrink AR days, with many organizations reaching sustained ROI within the first year.
If AI doesn’t make clinicians’ lives meaningfully easier, the financial returns rarely last.
Deepak Prakash, Co-Founder & CTO at Sonio
Comparing key benchmarks of time, cost, and performance of initiatives prior to AI integration in a health system can show the stark differences in operational efficiencies due to its use, such as lessening clinician documentation burden, greater reimbursement rates, and faster diagnostic results. Yielding earlier-stage diagnostics from AI-powered software allows patients to make more informed decisions, and can be measured in defining the decreased time spent per appointment, higher patient engagement rates, and more personalized care journeys.
Lisa Israelovitch, Co-Founder & Chief Executive Officer at AssistIQ
Real-world settings such as hospitals and other care facilities often drive clear return on investment from integrating AI platforms in their networks, seen through tangible outcomes in areas such as reduced cost per case, lower inventory waste, and time saved per procedure. Setting recurrent assessments around operational efficiencies in the early stages of new AI initiatives creates a useful lens to compare strategy effectiveness with previous benchmarks.
Mohan Giridharadas, CEO at LeanTaaS
In the real world, the ROI of AI centers on whether it changes outcomes in a measurable, sustained way. We measure operational ROI by tracking flow and capacity metrics that reflect day-to-day performance: discharge processing time, ED boarding, transfer declines, length of stay, OR utilization, block utilization, and surgical throughput.
Clinical ROI is often indirect but real: when the system runs with less gridlock, patients get to the right care faster, and clinicians spend less time doing manual workarounds.
Financial ROI comes from unlocked capacity and avoided cost: more admissions and surgeries without building new beds or ORs. Specifically in the perioperative space, Rush University Medical Center increased primetime OR utilization by 4% and improved surgeon block utilization by 12%, enabling 1,705 additional surgeries over three years and delivering a 12x ROI in one recent year.
Patrick Sheehan, Vice President of Value-Based Care at Withings Health Solutions
Real-world ROI from AI in healthcare is realized when it improves both how care is delivered and the outcomes it produces. While operational AI is already delivering measurable efficiency gains, the next frontier of real ROI will come from clinical use cases that directly enable earlier, more confident intervention. Clinical AI is advancing rapidly, enabling earlier identification of patient deterioration and giving care teams the confidence to intervene proactively rather than react to symptoms.
This is especially impactful in heart failure, where disease progression patterns vary widely, and early signs of deterioration are difficult to detect, contributing to avoidable hospitalizations. By improving operational efficiency and enabling earlier intervention, AI helps health systems deliver high-quality, scalable care to complex populations and perform better under value-based care models that reward quality and affordability.
Ben Moore, Chief Innovation Officer at PerfectServe
Now that healthcare is deep into the AI hype cycle, the focus needs to shift from experimentation to solving specific, measurable problems based on the wealth of knowledge we’ve already compiled. When the use case is narrowly tailored, the expected results should be easier to anticipate and track. For example, we’ve done some research that suggests the average clinician may spend 30–40% of their time during a shift just trying to communicate with their colleagues to coordinate patient care. That kind of built-in friction is a perfect target for AI. Train an AI agent with all of the rules from our routing engine and deploy it to stem the number of errant or unnecessary communications that flow throughout a hospital. Deploy another agent to execute emergency shift swaps when a provider has a sudden family emergency and can’t cover a shift.
These applications remove toil and stress from important clinical workflows and give time and peace of mind back to clinicians. And because so many hospital processes are touched by communication and coordination, the opportunities for measurement are extensive. Measurement opportunities span call center efficiency metrics—more efficient patient transfers, higher volumes of urgent calls handled, pre/post analyses of calls misdirected to off-call providers, and engagement surveys tracking provider satisfaction with scheduling flexibility and autonomy. As these AI applications mature, the results will shift from promising to proven.
Greg Farnum, SVP GM, Federal and Strategic Advisory at Audacious Inquiry
Just as early time-and-motion studies made the invisible work of information exchange visible and quantifiable, customer-specific language models have the potential to illuminate ROI in ways generic LLMs cannot. While general AI tools can demonstrate time savings, curated SLMs that truly understand organizational workflows, terminology, and decision frameworks unlock a different level of measurement.
The ROI opportunity with customer-specific models lies in their ability to capture organizational friction that’s currently invisible: the cognitive load of context-switching, the emotional burden of repetitive administrative tasks, and the time lost to information retrieval. Like HIE before it, we need proxy measures first—time saved, burden reduced, experience improved—before we can connect these to hard financial metrics.
Ben Scharfe, EVP for AI at Altera Digital Health
Measuring ROI currently relies heavily on leading indicators that signal long-term financial health. While many measurements are currently soft, we focus on physician and patient satisfaction as primary markers. High satisfaction scores are direct predictors of reduced physician turnover and increased patient retention and referenceability, both of which have material financial impacts. Operationally, we still track chart closure times and clean claim rates. When ambient AI reduces the administrative burden, the return is found in the stability of the workforce and the improved integrity of the patient encounter.
So many great experiences here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.
How do you measure the clinical, operational, and financial return on investment of AI initiatives once they are deployed in real-world settings? Let us know over on social media, we’d love to hear from all of you!
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