Pinpoint every patient who hasn’t received what your own clinical guidelines recommend.
Even for well-established guidelines, a large share of eligible patients go without the screening, monitoring, or therapy they should receive. Every missed gap lowers quality scores and raises downstream cost.
of adults 45-75 are not up to date on colorectal cancer screening; most were never screened.
of eligible atrial fibrillation patients receive no anticoagulant.
in added annual medical cost per patient non-adherent to blood pressure therapy.
The guidelines are not the problem. Visibility is. When care teams get an accurate, per-patient view of who is due, closure rates rise without new clinical programs.
Depression screening across 20 primary care practices rose from 78.4% to 91.1% once teams could systematically see which patients were missing it.
Uncredited care depresses HEDIS rates, Star Ratings, and value-based contract performance.
When a worklist systematically names patients who already had the test, nurses stop using it and fall back on slow, costly manual chart review.
A gap count without a traceable source is taken on trust. Here, every finding traces back to the document that justifies it.
Start from the guideline A medical LLM turns the cited guideline passage into readable criteria your quality team can check and correct before anything runs.
Evaluate the full record Coded data and note evidence are checked together, so documented care counts regardless of where it was written down. Same data, same answer, every run.
Review, then act Clinicians confirm each finding against the patient timeline before it drives outreach or counts against a metric. Confirmed gaps export to care management and quality reporting.
Gap rate = open gaps ÷ (eligible – documented exclusions)
Four HEDIS-aligned statuses — results feed quality reporting without translation.
Breast, colorectal, cervical, and lung cancer screening; diabetic eye and foot exams.
HbA1c, blood pressure, LDL, and kidney function monitoring.
Statins after a cardiac event, heart failure therapy, anticoagulation in AFib, asthma controllers.
Influenza, pneumococcal, and shingles vaccination; tobacco cessation; depression screening.
+ custom institutional rules
| Manual Chart Review | Claims & Code-Based Rules | General-Purpose LLM | Care Gap Analyzer | |
|---|---|---|---|---|
| Reads clinical notes | Yes, at human speed |
No | Partially, unvalidated |
Yes, with source traced |
| Clinical accuracy | Varies by reviewer |
Limited to what was coded |
Below healthcare- specific models |
96%+ extraction, 98% coding |
| Audit trail for quality submission | Manual documentation |
Partial | None | Guideline version to source document |
| Scales to full population | Limited by reviewer hours |
Yes | Cost scales per use |
Batch job on your infrastructure |
| Data stays in your environment | Yes | Varies by vendor | Often not | Yes, including air- gapped |
No. Care gaps analyzer runs inside your deployment with no external model in the detection path. Designed for HIPAA and GDPR data protection by design.
No. The LLM reads guidelines and writes reports; deterministic rules decide which patients have a gap. Same data, same answer.
Yes. Each finding carries its full lineage - guideline version, criteria, source documents, and reviewer decision - retained for audit.
Your clinicians. Nothing counts against a metric or triggers outreach until a reviewer confirms it; overrides are logged.
Role-based access at field level, with sensitive categories such as behavioral health, HIV, and substance use controlled separately.
On-premises, AWS, Azure, Databricks, Snowflake, or air-gapped. Open standards throughout: OMOP, FHIR, SNOMED CT, LOINC, RxNorm, etc. no proprietary formats.
Identify overdue patients, prioritize urgent cases, and send confirmed gaps to care teams or EHR queues.
Close HEDIS and Star Ratings gaps without manual chart reviews, with evidence from the same run.
Track gap closure and compare rates across sites, patient groups, and demographics.
Measure guideline adherence and use gap rates in real-world evidence studies.
A practice, panel, or payer population where incomplete data costs you most.
Codes only vs. the full record, side by side, in your environment.
See which gaps close, which open, and the evidence behind each.
Add measures, rerun on the same cohort, and watch closure rates move.
A 30-minute briefing for quality and value-based care leaders: how the side-by-side comparison works, what a pilot needs from your team, and how results feed your existing reporting.
An agent in Patient Journey Intelligence that finds patients with unmet care needs by comparing published clinical guidelines against the full patient record — including clinical notes and scanned documents — and assigns each gap one of four HEDIS-aligned statuses: Closed, Open – never done, Open – overdue, or Excluded.
Claims- and code-based reports only see care that was coded. Care delivered at an outside facility, in a scanned report, or described in a discharge summary is invisible to them, producing open gaps for patients who already received care. The analyzer credits documented care wherever it was written down.
No. A medical LLM reads the guideline and writes the report; the gap decision is made by deterministic rules against your data. Every intermediate step is inspectable, so a disagreement becomes a question about a stated criterion.
Yes — review is required, not optional. Every gap is confirmed by a clinician or quality reviewer before it counts against a metric or drives outreach, and overrides are recorded in the audit trail.
ACC/AHA, ADA Standards of Care, GOLD, NCCN, USPSTF, and CMS/NCQA HEDIS specifications, plus custom rules for institution- or program-specific measures.
No. The analyzer runs entirely inside your deployment — on-premises, AWS, Azure, Databricks, Snowflake, or air-gapped — with no external API call or PHI transfer in the detection path.
No. Results export with full provenance to care management worklists and quality program submissions, and it works with existing EHR integrations and an OMOP data model you may already maintain.
Rerun the same measures on the same cohort and compare gap rates over time, by site, panel, or demographic. The trend view shows whether interventions are closing gaps.
Demos
