Patient Data Exploration AI Prompts

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.