Work Intelligence in Healthcare: Choosing the Highest ROI AI Tools

Healthcare organizations are deploying AI tools at scale, but lack the infrastructure to measure actual ROI. Medical scribes promise to save clinicians two hours per day. Leadership tracks billable hours and adoption rates. The fundamental question remains unanswered: Did this AI tool actually improve outcomes?

A large healthcare system faced exactly this challenge.

After deploying AI medical scribes across 200 clinicians, traditional metrics showed success: 85% adoption, billable hours up 12%, clinician satisfaction 4.2 out of 5. But ops and transformation leadership needed to know: Were clinicians actually spending less time on documentation? Did saved time redirect to patient care? Was documentation quality maintained?

They deployed Fluency to find out.

The Challenge

Before Fluency, the healthcare system measured AI scribe success through standard metrics: billable hours, adoption rates, clinician satisfaction scores, vendor-provided time savings estimates.

These metrics couldn't answer the questions leadership needed answered.

Traditional metrics showed 85% adoption and billable hours up 12%. But this revealed nothing about actual workflow impact:

• Were clinicians spending less time on documentation, or did the AI just change when documentation happened?
• Did saved time redirect to patient care or shift to other administrative tasks?
• Did documentation quality match pre-AI standards?
• Which clinicians used scribes effectively and which struggled?

The metrics showed uniform "success" while masking what leadership suspected: significant performance variance across the same tool.

Billable hours proved particularly unreliable. They measure revenue activity, not workflow efficiency. Hours can increase for dozens of reasons unrelated to AI: patient volume changes, payer mix shifts, seasonal variation.

More critically, billable hours couldn't determine if the AI saved time or simply redistributed it across digital tasks.

What Fluency Revealed

Fluency captured how clinicians actually worked across all systems and tasks, in real time and continuously. This provided what traditional metrics couldn't: visibility into complete workflows, not isolated activities.

Actual time savings at the task level.

Documentation that previously required 25 minutes now required 8 minutes. Not estimated, measured. Not self-reported, observed. Not based on billable hours with confounding variables, but direct workflow analysis.

Where digital work time actually went.

Fluency tracked on-device activity across all systems. Clinicians who previously spent 25 minutes per encounter on documentation now spent 8 minutes. The remaining 17 minutes no longer appeared in EHR documentation workflows, inbox management, or other digital administrative tasks.

The time reduction was real and sustained, creating capacity for what matters most: human-to-human patient interaction that happens off the device.

Documentation quality maintained.

Fluency tracked error rates, completeness metrics, and revision frequency. AI-generated notes showed no increase in follow-up clarifications compared to pre-AI documentation. The speed gain didn't compromise quality.

Performance variance across clinicians.

Some physicians achieved two hours saved daily, others saved 20 minutes. Fluency surfaced which integration patterns worked, enabling the healthcare system to standardize effective practices instead of accepting wide variance.

Clear ROI attribution.

Rather than inferring value from billable hours, the healthcare system could state precisely: AI scribes reduced documentation time by 17 minutes per patient encounter across 200 clinicians, creating 56 hours of daily capacity redirected to patient care, with documentation quality maintained.

Decisions Enabled by Workflow Data

With Fluency's workflow visibility, the healthcare system could answer questions that were previously speculation:

Should we scale this AI tool or cut it? Fluency showed the tool saved significant time for certain specialties but created extra work for others. Decision: scale selectively, redesign workflows for specialties where it was failing.

Which clinicians need additional training? Performance variance wasn't random. Fluency revealed specific integration patterns that high-performers used. The healthcare system standardized these practices through targeted training.

Why did some departments show better results? Fluency revealed that departments with dedicated EHR integration specialists achieved better outcomes. This informed resource allocation decisions for the broader rollout.

Where should next year's AI budget go? Rather than evaluating vendor promises, leadership could compare actual workflow impact across different AI tools, allocating budget based on measured ROI.

The Real ROI Picture

Traditional metrics told one story: 85% adoption, 12% increase in billable hours, high satisfaction scores. Leadership would have declared success and moved on.

Fluency revealed the actual impact:

• 17 minutes saved per encounter across 200 clinicians
• 56 hours of daily capacity created for patient care
• Documentation quality maintained
• Specific workflow patterns identified for standardization

This is the difference between activity metrics and outcome measurement. Billable hours measure revenue. Adoption rates measure usage. Satisfaction scores measure perception. Fluency measured whether the AI tool actually improved how work happens.

For healthcare specifically, that meant confirming time saved redirected to patient care, quality was maintained, workflows worked for their clinicians in their systems, and improvements could be standardized across the organization.

Beyond This Deployment

This healthcare system now has continuous visibility into clinical and administrative workflows. When they evaluate new AI tools, they can measure actual impact from day one. When workflows change, they see whether improvements are real or imagined.

Every major healthcare capability improvement emerged from better data.

EHRs captured patient records. Population health platforms aggregated outcomes. Revenue cycle systems tracked financial flows. Fluency captures execution: how clinicians actually work, where AI tools create value versus activity, which workflows improve and which don't.

The competitive advantage isn't deploying AI tools first. It's deploying the infrastructure to measure which AI tools actually work, then standardizing the workflows and processes that deliver real outcomes.

This healthcare system built that capability. Their AI investments are now evidence-based, not promise-based.