Data Quality and Compliance AI Prompts

Audit the quality of this clinical dataset and return a structured quality report. Check each of the following dimensions: 1. Completeness: which required clinical fields are missing? - Critical fields (flag if >5% missing): patient_id, admission_date, discharge_date, primary_diagnosis, discharge_disposition - Important fields (flag if >15% missing): attending_physician, procedure_codes, payer, age, sex 2. Validity: are clinical values within plausible ranges? - Negative LOS (discharge before admission) - Age > 120 or < 0 - Invalid ICD-10 codes (not in official code list) - Discharge disposition codes that don't exist in standard NUBC taxonomy 3. Consistency: are related fields logically consistent? - Death as discharge disposition but no mortality flag - Pediatric patients with adult diagnoses (and vice versa) - Procedure dates outside the admission window 4. Timeliness: when was the data last updated? Are there records with suspiciously old last-modified dates? Return: quality scorecard with pass/fail per dimension, top 10 specific issues, and estimated % of records affected by each issue.

Analyze the accuracy and completeness of clinical coding in this dataset. 1. CC/MCC capture rate: - What % of cases have at least one Complication or Comorbidity (CC) or Major CC (MCC) coded? - Compare to expected national capture rates by DRG (most DRGs have 60–75% CC/MCC rates) - Low CC/MCC capture may indicate undercoding and lost revenue 2. Query rate analysis (if CDI query data is available): - What % of admissions triggered a Clinical Documentation Improvement query? - What is the agreement rate (physician accepted the suggested code)? 3. DRG optimization check: - For the top 20 DRGs by volume, calculate the case mix index (CMI) - Compare CMI to national geometric mean — significantly lower CMI may indicate undercoding 4. Specificity analysis: - What % of diagnoses use unspecified codes when a more specific code exists? - Flag the top 10 unspecified codes most frequently used and their more specific alternatives 5. Sequencing errors: - Identify cases where the principal diagnosis may be incorrectly sequenced (e.g. symptom coded as principal when the underlying condition is also coded) Return: coding quality scorecard, estimated revenue impact of undercoding, and top 5 coding improvement opportunities.

Verify that this dataset has been properly de-identified in compliance with HIPAA Safe Harbor or Expert Determination standards. Check for the presence of the 18 HIPAA identifiers: 1. Direct identifiers to scan for: - Names: scan all text columns for patterns matching full names - Geographic data: zip codes with <20,000 population, full street addresses, city+state combinations that identify small areas - Dates: scan for specific dates of birth, death, admission, or discharge that could identify individuals (dates should be shifted or replaced with age/year only) - Phone numbers, fax numbers, email addresses - Social Security Numbers (pattern: XXX-XX-XXXX) - Medical record numbers, health plan numbers, account numbers - Certificate/license numbers, vehicle identifiers, device serial numbers - URLs and IP addresses - Biometric identifiers - Full-face photographs 2. Quasi-identifiers: flag any combination of age + zip + sex + rare diagnosis that could re-identify a patient 3. For each identifier found: column name, number of affected rows, severity (direct identifier vs quasi-identifier) Return a de-identification gap report with recommended remediation for each finding.

Validate the Present on Admission (POA) flags in this dataset. POA flags indicate whether a diagnosis existed before the hospital admission. Correct POA coding is critical for quality reporting and HAC identification. 1. Check completeness: what % of secondary diagnoses have a POA flag? CMS requires POA for all diagnoses on inpatient claims. 2. Check value distribution: what % are Y (yes), N (no), U (unknown), W (clinically undetermined), 1 (exempt)? - Flag if >10% are U or W — this indicates documentation gaps 3. Validate HAC-relevant codes: for conditions that are CMS Hospital-Acquired Conditions (e.g. CAUTI, CLABSI, pressure injuries, DVT), verify that POA = N or W is correctly assigned 4. Check for impossible POA assignments: - Chronic diseases like diabetes, COPD, hypertension should almost never have POA = N - Flag any case where a common chronic condition has POA = N (likely a coding error) 5. Calculate the financial impact: how many cases have HAC conditions with POA = N, triggering potential CMS payment reductions? Return a POA validation report with error rates per condition category and estimated payment impact.