Clinical Outcomes Analysis AI Prompts

Identify and analyze hospital-acquired complications (HACs) and adverse events in this dataset. 1. Identify the following HAC categories using ICD-10 codes: - Hospital-acquired pressure injuries (POA flag = N for HAPI codes) - Catheter-associated urinary tract infections (CAUTI) - Central line-associated bloodstream infections (CLABSI) - Surgical site infections (SSI) - Falls with injury - Venous thromboembolism (DVT/PE) with POA = N 2. Calculate HAC rate per 1,000 patient days for each category 3. Compare to CMS national rates and flag any HAC above the 75th percentile nationally 4. Analyze HACs by: - Unit or department - Shift (if time data is available) - Patient risk factors (age, LOS, comorbidities) 5. Calculate the estimated financial impact: average CMS HAC payment reduction × number of HAC cases Return a HAC dashboard table with rates, benchmarks, and estimated financial impact per category.

Analyze inpatient length of stay (LOS) in this dataset. 1. Calculate overall LOS statistics: mean, median, std, 25th, 75th, 90th, 95th percentiles 2. Use median rather than mean as the primary metric — LOS is right-skewed and mean is sensitive to outliers 3. Break down median LOS by: - Primary diagnosis (top 15 conditions) - Service line or unit - Discharge disposition (home, SNF, rehab, AMA, death) - Payer type - ICU vs non-ICU stay 4. Identify geometric mean LOS per DRG and compare to CMS national geometric mean LOS benchmarks 5. Flag outlier stays: admissions with LOS > 3× the condition-specific median 6. Analyze LOS trends over time: is average LOS increasing or decreasing by quarter? 7. Estimate excess days: for outlier stays, how many total bed-days were consumed beyond the expected LOS? Return a LOS breakdown table and a trend chart by month or quarter.

Analyze inpatient mortality in this dataset. 1. Calculate crude in-hospital mortality rate: deaths / total admissions 2. Break down mortality rate by: - Primary diagnosis category - Age group (especially 65+, 75+, 85+) - ICU vs non-ICU admission - Elective vs emergency admission - Day of week of admission (weekend effect on mortality is well-documented) 3. Compute case mix index (CMI) adjusted mortality if DRG data is available 4. Compare condition-specific mortality rates to national benchmarks: - Sepsis: national mortality ~15–20% - AMI: national in-hospital mortality ~5–6% - Stroke: national in-hospital mortality ~5–8% 5. Analyze time to death distribution: what % of deaths occur within 24 hours, 48 hours, 7 days, and 30 days of admission? 6. Identify the top 5 conditions with mortality rates significantly above benchmark Return a mortality summary table with benchmark comparisons and flag any rate that exceeds 1.5× the national benchmark.

Step 1: Calculate observed rates for the top 5 clinical outcome metrics: 30-day readmission, in-hospital mortality, LOS, HAC rate, and discharge to home rate. Step 2: Risk-adjust each metric using available patient demographics and comorbidities (age, sex, Elixhauser or Charlson comorbidity index, admission type, payer). Calculate expected rates. Step 3: Compute the observed-to-expected (O/E) ratio for each metric. O/E > 1 indicates worse than expected performance; O/E < 1 indicates better. Step 4: Compare O/E ratios to CMS national benchmarks and rank the facility's performance percentile for each metric. Step 5: Identify the 3 metrics with the worst O/E ratios. For each, drill down to the top 3 contributing patient segments or conditions. Step 6: Write a performance summary report: overall standing, top achievements, priority improvement areas, and recommended next analytical steps.

Analyze 30-day hospital readmission rates in this dataset. 1. Define readmission: any inpatient admission within 30 days of a prior discharge for the same patient 2. Calculate: - Overall 30-day readmission rate - 7-day and 90-day readmission rates for comparison 3. Break down readmission rates by: - Primary diagnosis category (top 10 conditions) - Service line or department - Payer type - Age group (10-year bands) - Day of week of original discharge (are Friday/weekend discharges more likely to readmit?) 4. Identify the top 10 diagnosis pairs: original admission diagnosis vs readmission diagnosis 5. Compare your readmission rate to CMS national benchmarks for the top conditions (AMI, heart failure, pneumonia, COPD, hip/knee replacement, CABG) Flag any condition or patient segment with a readmission rate more than 2× the overall average.