Quality assurance · Production

New York Area Health System achieves 6x ROI and closes 4,000+ care gaps with Notable's AI chart scrubbing

The problem

A New York Area Health System faced a growing backlog of payer-submitted care gaps whose supporting clinical evidence was scattered across scanned documents and free-text notes in their EHR, making manual chart reviews unable to keep pace with volume and leaving patient care uncredited.

Workflow diagram · grounded in source
1
Payer care gaps arrive
trigger
“Every year, thousands of payer-submitted gaps arrived”
2
AI chart scrubbing
ai_action
“AI-powered chart scrubbing and Flow Builder automation that enabled system-wide, high-accuracy review of both structured and unstructured clinical data. With the ability to parse PDFs, scanned notes and non-standard formats, the solution…”
3
Line-by-line validation and audits
validation
“a line-by-line validation, staged scaling and ongoing monthly audits to maintain accuracy and compliance”
4
Care gap closures identified
output
“identifying more than 4,000 incremental gap closures that would otherwise have remained open”
Reported outcome

Since go-live, the health system reviewed over 50,000 charts, raised the care gap closure rate from 3–4% to 7%, identified more than 4,000 incremental gap closures, achieved documentation accuracy exceeding 95%, saved 4,000+ staff hours, and delivered $400,000 in ROI on $60,000 in production utilization — a 6x return.

Reported metrics
Charts reviewed system-wideover 50,000
Care gap closure rate3–4% to 7%
Incremental care gap closures identifiedmore than 4,000
Documentation identification accuracyexceeded 95%
Show all 9 reported metrics
charts reviewed system-wideover 50,000
care gap closure rate3–4% to 7%
incremental care gap closures identifiedmore than 4,000
documentation identification accuracyexceeded 95%
staff time saved4,000+ hours
projected annual staff time savedbeyond 10,000 annually
ROI delivered$400,000
production utilization cost$60,000
return on investment6x
Reported stack
NotableFlow BuilderEHR
Source
https://www.notablehealth.com/customer-stories/new-york-area-health-system-streamlines-clinical-evidence-with-notables-ai-boosting-care-quality-and-performance
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Since go-live, the health system reviewed over 50,000 charts, raised the care gap closure rate from 3–4% to 7%, identified more than 4,000 incremental gap closures, achieved documentation accuracy exceeding 95%, saved…

What tools did this team use?

Notable, Flow Builder, EHR.

What results were reported?

Charts reviewed system-wide: over 50,000; Care gap closure rate: 3–4% to 7%; Incremental care gap closures identified: more than 4,000; Documentation identification accuracy: exceeded 95% (source-reported, not independently verified).

How is this quality assurance AI workflow structured?

Payer care gaps arrive → AI chart scrubbing → Line-by-line validation and audits → Care gap closures identified.

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