Healthcare Data Analyst 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.

Create a comprehensive demographic profile of the patient population in this dataset. 1. Age distribution: histogram with 10-year age bands, mean, median, and IQR 2. Sex/gender breakdown: count and percentage 3. Race and ethnicity breakdown if available: count, percentage, and flag if >10% are recorded as 'Unknown' or 'Other' 4. Insurance/payer mix: breakdown by payer type (Medicare, Medicaid, Commercial, Self-pay, Other) 5. Geographic distribution: by zip code, county, or state if available — identify top 10 areas by patient volume 6. Socioeconomic indicators if present: area deprivation index, social determinants of health flags Compare this population to national or regional benchmarks where possible. Flag any demographic group that is underrepresented and may affect generalizability of findings.

Analyze the diagnosis codes (ICD-10-CM) in this dataset. 1. Count the total number of unique ICD-10 codes present 2. Show the top 20 most frequent primary diagnoses with code, description, count, and % of encounters 3. Group diagnoses by ICD-10 chapter (first 3 characters) — what are the top 5 disease categories? 4. Check coding quality: - What % of diagnoses use unspecified codes (codes ending in '9' or containing 'unspecified')? High rates suggest poor coding specificity. - Are there any invalid or non-existent ICD-10 codes? - Is there a mix of ICD-9 and ICD-10 codes? 5. Identify the top 10 comorbidity pairs — which two diagnoses most frequently appear together for the same patient? 6. Flag any patients with an unusually high number of diagnosis codes per encounter (>15 codes may indicate upcoding)

Analyze the distribution of laboratory values in this dataset. For each lab test column: 1. Compute: mean, median, std, min, max, and key percentiles (5th, 25th, 75th, 95th) 2. Show the reference range for each lab (normal range) and calculate: - % of values below normal range - % of values within normal range - % of values above normal range 3. Flag clinically critical values (panic values) — values so extreme they require immediate clinical attention: - e.g. potassium < 2.5 or > 6.5 mEq/L, glucose < 40 or > 500 mg/dL, sodium < 120 or > 160 mEq/L 4. Check for implausible values that are likely data entry errors (e.g. hemoglobin of 0 or 500) 5. Show missingness rate per lab — high missingness may indicate the test is only ordered for specific patient types Return a lab profile table and flag any lab with more than 30% critical or implausible values.

Give me a complete overview of this patient dataset. Include: - Total number of patients and total number of records (are there multiple records per patient?) - Key demographic columns: age distribution, sex breakdown, race/ethnicity if present - Date range of the data and what time period it covers - Clinical identifiers present: patient ID, encounter ID, admission ID - Key clinical columns and their data types: diagnoses, procedures, medications, lab values, vitals - Missing values per column (%) Flag any immediate data quality concerns specific to healthcare data: - Implausible clinical values (e.g. age > 120, heart rate = 0, negative lab values) - Patients with unusually high record counts that may indicate data duplication - Date inconsistencies (discharge before admission, future dates)

Explore the vital signs data in this dataset. For each vital sign (heart rate, blood pressure systolic/diastolic, respiratory rate, temperature, oxygen saturation, weight, BMI): 1. Distribution statistics: mean, median, std, 5th and 95th percentiles 2. Percentage of readings outside normal clinical range: - HR: normal 60–100 bpm - BP systolic: normal 90–140 mmHg - RR: normal 12–20 breaths/min - SpO2: normal ≥ 95% - Temp: normal 36.1–37.2°C (97–99°F) 3. Implausible values: HR = 0, SpO2 > 100%, negative values — flag as likely data errors 4. If multiple readings per patient exist: show the trend over time for the 5 most common vital signs 5. Correlate vital signs with diagnosis categories — do sepsis patients show expected patterns (high HR, high RR, low BP)? Return a vital signs summary table with a clinical interpretation note for any metric where more than 10% of readings fall outside normal range.

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.

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.

Analyze inpatient bed utilization and capacity in this dataset. 1. Calculate daily census for each unit or service line: total occupied beds per day 2. Calculate occupancy rate: (occupied beds / staffed beds) × 100 - Target range: 80–85% for most acute care units - Flag any unit consistently above 90% (capacity crisis) or below 70% (inefficiency) 3. Analyze bed turnover ratio: admissions / average daily census — higher is more efficient 4. Identify peak demand periods: - Hour of day with highest census - Day of week with highest occupancy - Seasonal patterns (flu season, summer vs winter) 5. Calculate boarding hours: time admitted patients spend in the ED waiting for an inpatient bed 6. Model: what occupancy rate reduction is needed to eliminate boarding waits of >4 hours? 7. Forecast: based on current admission trends, when will average occupancy exceed 90%? Return: occupancy dashboard by unit, peak demand heatmap, and capacity forecast chart.

Analyze discharge timing patterns and their operational impact. 1. Plot the distribution of actual discharge times by hour of day — when do most discharges happen? 2. Calculate the % of discharges that occur before noon vs after noon - Target: ≥30% of discharges before noon (industry best practice) 3. Analyze the relationship between discharge timing and: - ED boarding time (do early discharges reduce ED waits?) - Occupancy rate by hour (does morning discharge free capacity?) 4. Break down discharge timing by: - Service line / attending physician - Day of week - Discharge disposition (home discharges vs SNF vs other) 5. Identify which physicians or units have the best early discharge rates 6. Calculate the estimated impact: if early discharge rate improved from current to 30%, how many additional bed-hours would be freed per day? Return: discharge timing histogram, early discharge rate by service and physician, and capacity impact estimate.

Analyze Emergency Department throughput and flow in this dataset. 1. Calculate key ED flow metrics: - Door-to-triage time: arrival to first nursing assessment - Door-to-physician time: arrival to first physician contact - Door-to-disposition time: arrival to admit/discharge decision - Door-to-departure time: total ED LOS - Left without being seen (LWBS) rate - Left against medical advice (AMA) rate 2. Show 50th, 75th, 90th, and 95th percentile for each time metric 3. Compare to CMS and Joint Commission benchmarks: - Door-to-physician: target ≤ 60 minutes (median) - Admitted patient ED LOS: target ≤ 360 minutes 4. Break down all metrics by: - Hour of day and day of week (heatmap format) - ESI triage level (1–5) - Admit vs discharge patients 5. Identify the top 3 bottlenecks in the ED flow based on where time is most lost Return a throughput dashboard with benchmark comparisons and bottleneck analysis.

Step 1: Calculate nursing hours per patient day (NHPPD) for each unit by dividing total nursing hours worked by total patient days. Compare to target NHPPD by unit type (ICU: 12–24, Med/Surg: 6–8, Telemetry: 8–10). Step 2: Identify units with NHPPD significantly above or below target. Above target may indicate overstaffing or high patient acuity; below target may indicate understaffing risk. Step 3: Analyze overtime usage: what % of total nursing hours are overtime? High overtime (>5%) increases cost and may indicate staffing shortages. Step 4: Correlate staffing levels with patient outcomes: is there a statistically significant relationship between NHPPD and falls, pressure injuries, or 30-day readmissions on each unit? Step 5: Identify peak demand hours where actual staffing consistently falls below target nurse-to-patient ratios. Step 6: Model the cost impact: calculate the cost per patient day at current staffing vs optimized staffing, and the potential savings from better shift scheduling.

Write a clinical performance executive summary based on this dataset for a hospital leadership audience. The summary should cover exactly 4 sections: 1. Patient Volume & Mix (2–3 sentences) - Total admissions, ED visits, and outpatient encounters for the period - Key payer mix highlights and any significant shifts vs prior period 2. Quality & Safety Performance (2–3 sentences) - 30-day readmission rate vs target and national benchmark - Mortality rate and HAC rate vs benchmark - Highlight one quality win and one quality concern 3. Operational Efficiency (2–3 sentences) - Average LOS vs geometric mean benchmark - ED throughput metrics and any capacity concerns - Case mix index vs prior period 4. Priority Actions (3 bullet points) - Three specific, data-driven recommendations based on the analysis - Each bullet: what to do, why (cite a specific number), and who should own it Tone: concise, direct, evidence-based. No clinical jargon — write for a CFO and CMO audience. Maximum 300 words.

Step 1: Define the population — total attributed patients, demographics breakdown, payer mix, and geographic distribution. Step 2: Stratify by risk — use available risk scores (HCC RAF score, LACE, or a custom risk model) to classify patients into low, moderate, high, and very high risk tiers. Show size and cost of each tier. Step 3: Analyze utilization patterns — ED visit rate, hospitalization rate, and preventable admission rate (ACSC conditions) per 1,000 patients for each risk tier. Step 4: Identify care gaps — for chronic disease patients, what % are meeting evidence-based care standards? (e.g. HbA1c tested in last 12 months for diabetics, annual eye exam, statin prescribed for CAD patients) Step 5: Calculate total cost of care — PMPM (per member per month) cost by risk tier, broken down by inpatient, ED, outpatient, pharmacy. Step 6: Write a population health summary report: population profile, risk stratification results, top 5 care gaps with prevalence rates, cost drivers, and three priority interventions with estimated ROI.

Generate a quality measure performance report for {{measure_name}} for the period {{reporting_period}}. The report must include: 1. Measure definition - Full measure name and steward (e.g. CMS, TJC, NQF) - Numerator definition: {{numerator_definition}} - Denominator definition: {{denominator_definition}} - Exclusions: {{exclusions}} 2. Performance results - Numerator count, denominator count, and measure rate - Performance vs target: {{target_rate}} - Performance vs national benchmark (50th and 90th percentile) - Trend: rate for the current period vs the prior 4 periods 3. Stratification - Rate broken down by: service line, payer, age group, and race/ethnicity (for health equity analysis) - Flag any subgroup performing more than 10 percentage points below the overall rate 4. Root cause and action plan - Top 3 contributing factors to any gap from target - Specific improvement actions with owner and due date Format as a structured report suitable for submission to a quality committee.