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Clinical Operations · Clinical Documentation

Clinical Documentation Improvement Agent

Surfaces CDI query opportunities from notes and orders to improve documentation specificity, coding accuracy, and case mix index.

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The work it takes off your plate

Incomplete or non-specific documentation leaves secondary diagnoses, POA indicators, and severity of illness understated, eroding CMI and quality reporting. CDI specialists cannot manually review every chart in near real time, so query opportunities are missed until late in the stay or after coding. Clinicians receive poorly timed or low-value queries that create alert fatigue and slow response rates. The result is revenue leakage, denial exposure, and misaligned quality metrics that do not reflect true patient complexity.

How it works

The Clinical Documentation Improvement Agent analyzes progress notes, H&Ps, orders, labs, and problem lists throughout the encounter to detect documentation gaps against coding and clinical criteria. It prioritizes high-impact query opportunities with suggested wording and evidence snippets for CDI specialists and attending physicians. Closed-loop tracking measures query response, agreement rates, and CMI lift to continuously refine targeting.

  1. 1Continuous Chart SurveillanceA prioritized worklist of encounters with likely documentation improvement opportunities.
  2. 2Generate Evidence-Based Query OpportunitiesReady-to-send, AHIMA-compliant query drafts for CDI review and physician response.
  3. 3Route and Capture Physician ResponseFaster physician response cycles with transparent status for CDI and coding teams.
  4. 4Measure Impact and Feedback LearningSustained gains in documentation specificity, coding accuracy, and case mix index with audit trails.

  1. Step 1: Continuous Chart Surveillance

    • Ingest clinical notes, orders, labs, imaging, and problem lists
    • Map findings to potential CC/MCC and specificity gaps
    • Score charts by financial and quality impact

    Result: A prioritized worklist of encounters with likely documentation improvement opportunities.

  2. Step 2: Generate Evidence-Based Query Opportunities

    • Draft compliant CDI query language with supporting evidence
    • Link clinical indicators (e.g., labs, meds, vitals) to suggested diagnoses
    • Avoid duplicate or previously answered queries

    Result: Ready-to-send, AHIMA-compliant query drafts for CDI review and physician response.

  3. Step 3: Route and Capture Physician Response

    • Route queries to the responsible attending via EHR inbox or CDI workflow
    • Track open, agreed, disagreed, and clarified outcomes
    • Update working DRG and documentation status in near real time

    Result: Faster physician response cycles with transparent status for CDI and coding teams.

  4. Step 4: Measure Impact and Feedback Learning

    • Attribute CMI, SOI/ROM, and denial avoidance to accepted queries
    • Feed coder and CDI feedback into model refinements
    • Report specialty-level documentation trends

    Result: Sustained gains in documentation specificity, coding accuracy, and case mix index with audit trails.

Why use it

  • Higher Documentation Specificity. Captures CC/MCC and acuity details before discharge.
  • Improved CMI. Aligns coded severity with true clinical complexity.
  • Compliant Queries. Generates AHIMA-aligned query language with clinical evidence.
  • Reduced Chart Review Burden. Focuses CDI specialists on the highest-yield encounters automatically.