Cohort Analysis AI Prompts

Build and profile a chronic disease patient cohort from this dataset. Target disease: {{disease}} (e.g. Type 2 Diabetes, Heart Failure, COPD, CKD) 1. Identify cohort inclusion criteria using ICD-10 codes for {{disease}} — list the specific codes used 2. Apply inclusion and exclusion criteria: - Include: patients with ≥2 diagnoses of {{disease}} at least 30 days apart (to confirm chronic status) - Exclude: patients with only a rule-out or screening code 3. Profile the cohort: - Size: how many patients qualify? - Demographics: age, sex, payer mix - Top comorbidities and their prevalence rates - Average number of hospitalizations, ED visits, and outpatient encounters per year 4. Compute disease severity distribution if a severity classification exists (e.g. HbA1c ranges for diabetes, NYHA class for heart failure) 5. Compare cohort demographics and utilization to the non-{{disease}} patient population Return a cohort definition table and a summary profile comparing cohort vs non-cohort patients.

Calculate and analyze the comorbidity burden of patients in this dataset. 1. Calculate the Charlson Comorbidity Index (CCI) for each patient using their ICD-10 diagnosis codes: - Map each ICD-10 code to its CCI weight - Sum weights per patient - Classify: CCI 0 (no comorbidity), 1–2 (low), 3–4 (moderate), ≥5 (severe) 2. Calculate the Elixhauser Comorbidity Score as an alternative measure 3. Show distribution of CCI scores across the patient population 4. Analyze relationship between CCI and outcomes: - Mean LOS by CCI category - 30-day readmission rate by CCI category - In-hospital mortality rate by CCI category 5. Identify the 10 most common comorbidity combinations (top comorbidity pairs and triples) 6. Map comorbidity burden by age group — show how CCI increases with age Return a comorbidity burden table, CCI distribution chart, and outcomes by CCI category.

Identify and profile high utilizer patients — those consuming a disproportionate share of healthcare resources. 1. Define high utilizers using these thresholds (adjust based on data): - ≥4 ED visits in the past 12 months, OR - ≥2 inpatient admissions in the past 12 months, OR - Top 5% of patients by total cost of care 2. Calculate what percentage of total visits, bed days, and costs are consumed by high utilizers 3. Profile high utilizers vs the general patient population: - Demographics (age, sex, payer mix) - Top 10 primary diagnoses - Prevalence of behavioral health diagnoses (depression, substance use disorder, anxiety) - Prevalence of social determinants of health flags (housing instability, food insecurity) 4. Calculate the average cost per high utilizer vs average patient 5. Identify the top 20 individual patients by total encounters — these are candidates for care management programs Return a high utilizer profile and the potential savings if average utilization were reduced by 20% for this group.

Step 1: Define the index admission cohort — all inpatient discharges in the study period, excluding deaths, AMA discharges, and transfers to other acute facilities. Step 2: Calculate the LACE score for each patient (Length of stay, Acuity of admission, Charlson Comorbidity Index, ED visits in past 6 months). Classify: low risk (0–4), moderate (5–9), high (≥10). Step 3: Validate LACE score performance against actual 30-day readmissions in the dataset: compute AUC-ROC, sensitivity, and specificity at each risk threshold. Step 4: Profile the high-risk cohort (LACE ≥10): size, top diagnoses, demographics, payer mix, and social risk factors. Step 5: Identify which high-risk patients were not readmitted — what interventions or patient factors may have protected them? Step 6: Prioritize the top 50 patients by readmission risk for care management outreach. Return a ranked list with LACE score, primary diagnosis, payer, and suggested intervention type.