mimic-patient-analysis

name

mimic-patient-analysis

description

Comprehensive patient analysis using the MIMIC-IV clinical database. Use this skill whenever asked to analyze, summarize, or investigate a patient's medical history, hospital admissions, diagnoses, medications, procedures, or clinical course from a MIMIC-IV SQLite database. Triggers on prompts like "Analyze patient [ID]", "summarize patient history", "what happened to patient X", or any request to explore patient-level EHR data from MIMIC-IV tables.

MIMIC-IV Patient Analysis

Perform a comprehensive, systematic analysis of a patient's complete clinical record from the MIMIC-IV database by querying the SQLite database directly and efficiently.

Database Structure

The database has 27 tables. Key tables and their primary columns:

Core patient tables:

• hosp_patients — demographics: subject_id, gender, anchor_age, anchor_year, anchor_year_group, dod
• hosp_admissions — hospital stays: subject_id, hadm_id, admittime, dischtime, deathtime, admission_type, admission_location, discharge_location, insurance, language, marital_status, race, edregtime, edouttime, hospital_expire_flag

Clinical data (per admission):

• hosp_diagnoses_icd — ICD diagnoses: subject_id, hadm_id, seq_num, icd_code, icd_version
• hosp_d_icd_diagnoses — diagnosis dictionary: icd_code, icd_version, long_title
• hosp_procedures_icd — ICD procedures: subject_id, hadm_id, seq_num, chartdate, icd_code, icd_version
• hosp_d_icd_procedures — procedure dictionary: icd_code, icd_version, long_title
• hosp_drgcodes — DRG billing: subject_id, hadm_id, drg_type, drg_code, description, drg_severity, drg_mortality
• hosp_services — clinical service: subject_id, hadm_id, transfertime, prev_service, curr_service
• hosp_transfers — unit movements: subject_id, hadm_id, transfer_id, eventtype, careunit, intime, outtime

Medications:

• hosp_prescriptions — prescribed drugs: subject_id, hadm_id, starttime, stoptime, drug, drug_type, dose_val_rx, dose_unit_rx, route
• hosp_emar — administration record: subject_id, hadm_id, emar_id, charttime, medication, event_txt, scheduletime
• hosp_pharmacy — pharmacy fills: subject_id, hadm_id, pharmacy_id, drug, starttime, stoptime

Diagnostics:

• hosp_microbiologyevents — cultures: subject_id, hadm_id, charttime, spec_type_desc, test_name, org_name, interpretation, comments
• hosp_omr — vitals/anthropometrics: subject_id, chartdate, seq_num, result_name, result_value
• hosp_hcpcsevents — billing codes: subject_id, hadm_id, chartdate, hcpcs_cd, short_description
• hosp_d_hcpcs — HCPCS dictionary: code, category, long_description, short_description

Orders:

• hosp_poe — provider orders: subject_id, hadm_id, poe_id, ordertime, order_type, order_subtype, transaction_type, order_status

ICU tables (only present if patient had ICU stay):

• icu_icustays — ICU episodes: subject_id, hadm_id, stay_id, first_careunit, last_careunit, intime, outtime, los
• icu_inputevents — IV fluids/medications: stay_id, starttime, endtime, itemid, amount, amountuom, ordercategoryname
• icu_outputevents — urine/drainage: stay_id, charttime, itemid, value, valueuom
• icu_procedureevents — ICU procedures: stay_id, starttime, endtime, itemid, value, valueuom, ordercategoryname
• icu_d_items — ICU item dictionary: itemid, label, category

Critical Column Name Pitfalls

Avoid these common errors that cause query failures:

Table	WRONG	CORRECT
hosp_transfers	transfertime	intime (sort by intime)
hosp_poe	order_time	ordertime
hosp_omr	charttime	chartdate
hosp_hcpcsevents JOIN hosp_d_hcpcs	ON h.hcpcs_cd = d.hcpcs_cd	ON h.hcpcs_cd = d.code
hosp_procedures_icd JOIN hosp_d_icd_procedures	alias mismatch	ensure alias used in JOIN matches the one defined

Analysis Workflow

Start with get_database_info to confirm table availability, then query directly — do not call describe_table before each query; use the column names listed above.

Step 1 — Patient demographics

SELECT * FROM hosp_patients WHERE subject_id = <patient_id>

Note: anchor_age is age in anchor_year (dates are shifted for privacy). If dod is not null, the patient died.

Step 2 — All hospital admissions

SELECT * FROM hosp_admissions WHERE subject_id = <patient_id> ORDER BY admittime

For each hadm_id, note: admission/discharge times, type, source, destination, insurance, hospital_expire_flag.

Track discharge destination progression across admissions (HOME → HOME HEALTH CARE → SNF → LTACH → died in hospital) as it signals functional decline trajectory.

Step 3 — ICU stays

SELECT * FROM icu_icustays WHERE subject_id = <patient_id> ORDER BY intime

Empty result = no ICU. If ICU present, note care units and length of stay (los).

Step 3.5 — ICU deep dive (when ICU stays exist)

For each stay_id, query ICU event tables for critical clinical detail:

-- Procedures (ventilation, dialysis, arterial lines, etc.)
SELECT pe.starttime, pe.endtime, d.label, d.category, pe.value, pe.valueuom
FROM icu_procedureevents pe
JOIN icu_d_items d ON pe.itemid = d.itemid
WHERE pe.stay_id = <stay_id>
ORDER BY pe.starttime

-- Key inputs (fluids, vasopressors, medications)
SELECT ie.starttime, d.label, d.category, ie.amount, ie.amountuom, ie.ordercategoryname
FROM icu_inputevents ie
JOIN icu_d_items d ON ie.itemid = d.itemid
WHERE ie.stay_id = <stay_id>
ORDER BY ie.starttime LIMIT 50

-- Outputs (urine, drainage)
SELECT oe.charttime, d.label, oe.value, oe.valueuom
FROM icu_outputevents oe
JOIN icu_d_items d ON oe.itemid = d.itemid
WHERE oe.stay_id = <stay_id>
ORDER BY oe.charttime LIMIT 30

From ICU events, capture: mechanical ventilation duration, vasopressor use, fluid balance (total inputs vs outputs), dialysis/CRRT, invasive monitoring (arterial line, central line).

Step 4 — Diagnoses (with human-readable names)

SELECT d.icd_code, d.icd_version, d.long_title, diag.hadm_id, diag.seq_num
FROM hosp_diagnoses_icd diag
JOIN hosp_d_icd_diagnoses d ON diag.icd_code = d.icd_code AND diag.icd_version = d.icd_version
WHERE diag.subject_id = <patient_id>
ORDER BY diag.hadm_id, diag.seq_num

seq_num=1 is the primary diagnosis. Note special ICD codes:

• Z88x = drug allergy documentation (e.g., Z880 = penicillin allergy)
• Z66 = do not resuscitate (DNR) order
• Z515 = encounter for palliative care
• Z79x = long-term medication use (e.g., Z7901 = anticoagulants)

For patients with 4+ admissions, also run an aggregate query to identify recurring diagnoses:

SELECT d.long_title, COUNT(*) as admission_count
FROM hosp_diagnoses_icd diag
JOIN hosp_d_icd_diagnoses d ON diag.icd_code = d.icd_code AND diag.icd_version = d.icd_version
WHERE diag.subject_id = <patient_id> AND diag.seq_num <= 5
GROUP BY d.long_title
ORDER BY admission_count DESC
LIMIT 20

Step 5 — Procedures

SELECT p.hadm_id, p.seq_num, p.chartdate, p.icd_code, proc.long_title
FROM hosp_procedures_icd p
JOIN hosp_d_icd_procedures proc ON p.icd_code = proc.icd_code AND p.icd_version = proc.icd_version
WHERE p.subject_id = <patient_id>
ORDER BY p.hadm_id, p.seq_num

Step 6 — Medications prescribed

SELECT hadm_id, drug, starttime, stoptime, dose_val_rx, dose_unit_rx, route
FROM hosp_prescriptions
WHERE subject_id = <patient_id>
ORDER BY hadm_id, starttime

For patients with 4+ admissions, rank medications by frequency:

SELECT drug, COUNT(*) as prescription_count
FROM hosp_prescriptions
WHERE subject_id = <patient_id>
GROUP BY drug
ORDER BY prescription_count DESC
LIMIT 20

Step 7 — Physical measurements (BMI, weight, height, BP)

SELECT chartdate, result_name, result_value
FROM hosp_omr
WHERE subject_id = <patient_id>
ORDER BY chartdate

For longitudinal patients, separately track weight and blood pressure trends:

SELECT chartdate, result_value FROM hosp_omr
WHERE subject_id = <patient_id> AND result_name = 'Weight (Lbs)'
ORDER BY chartdate

SELECT chartdate, result_value FROM hosp_omr
WHERE subject_id = <patient_id> AND result_name = 'Blood Pressure'
ORDER BY chartdate

Weight loss ≥5% from baseline is clinically significant; ≥10% suggests disease-related cachexia or malnutrition.

Step 8 — Microbiology cultures

SELECT chartdate, spec_type_desc, test_name, org_name, interpretation, comments
FROM hosp_microbiologyevents
WHERE subject_id = <patient_id>
ORDER BY chartdate

org_name null with a comment like "< 10,000 CFU/mL" = negative culture. For positive cultures, record the organism name, specimen type, and interpretation.

Step 9 — Clinical service and transfers

-- Service
SELECT * FROM hosp_services WHERE subject_id = <patient_id> ORDER BY transfertime

-- Physical location movements (per hadm_id)
SELECT * FROM hosp_transfers WHERE hadm_id = <hadm_id> ORDER BY intime

Step 10 — DRG billing codes

SELECT * FROM hosp_drgcodes WHERE subject_id = <patient_id>

APR-DRG has severity (1-4) and mortality (1-4) scores. Severity 3-4 or mortality 3-4 indicates a high-complexity/high-risk admission.

Step 11 — Provider orders and eMAR (targeted)

-- Provider orders overview
SELECT ordertime, order_type, order_subtype, transaction_type, order_status
FROM hosp_poe WHERE subject_id = <patient_id> AND hadm_id = <hadm_id>
ORDER BY ordertime LIMIT 30

-- Medication administration (check "Not Given" vs "Administered")
SELECT charttime, medication, event_txt, scheduletime
FROM hosp_emar WHERE subject_id = <patient_id> AND hadm_id = <hadm_id>
ORDER BY charttime LIMIT 50

The hosp_emar event_txt field distinguishes "Administered" from "Not Given" — this reveals medication compliance and route changes (PO/NG route = nasogastric feeding, suggesting dysphagia).

Synthesizing the Analysis

After gathering data, produce a structured summary covering:

1. Demographics — age, sex, race, insurance, vital status (alive/deceased + date if known)
2. Admission summary — number of admissions, date range, types, sources, discharge destinations; explicitly note trajectory (e.g., progressive shift to institutional discharge = functional decline)
3. ICU course — whether ICU was needed, which units, total duration, key interventions (ventilation, vasopressors, fluid balance if queried)
4. Primary diagnoses by admission — primary condition per hadm_id; use a table format for multi-admission patients
5. Comorbidities — significant secondary diagnoses across admissions; for multi-admission patients, note which conditions appear across how many admissions
6. Procedures — surgical and therapeutic interventions with dates
7. Medications — key drug classes, notable transitions or polypharmacy; for multi-admission patients, list top prescriptions by frequency
8. Diagnostics — positive culture results (organism + specimen), physical measurement trends (weight trajectory, BP range)
9. Clinical service trajectory — services and care unit progression across admissions
10. Key clinical insights — clinically meaningful patterns with explanations:
    • Discharge to rehab/SNF → functional impairment
    • Multiple laxatives (Senna + Bisacodyl + Docusate) → immobility or opioid use
    • PO/NG drug routes → nasogastric feeding (likely dysphagia)
    • Sequential anticoagulant changes → treatment optimization
    • Z88x codes → drug allergies
    • Z66/Z515 codes → DNR/palliative care goals
    • Weight loss ≥10% → cachexia or disease progression
    • Insurance transition Private→Medicare → age 65 crossed during observation period
    • Discharge destinations: HOME → HOME HEALTH → SNF → LTACH → hospital death = functional decline

Include evidence anchors: specific ICD codes, exact dates, drug names with doses, organism names, DRG severity/mortality scores, and weight values. These factual anchors make the analysis verifiable and clinically useful.

End the analysis with FINISH: followed by the full summary.

Efficiency Tips

• Query all admissions first, then drill into individual hadm_id values for detailed data
• For patients with multiple admissions, use subject_id-level queries before hadm_id-level ones
• If a query fails with a column error, correct the column name immediately using the pitfalls table above — do not call describe_table
• Use LIMIT when exploring eMAR/POE (large tables); paginate with OFFSET if needed
• For ICU patients, prioritize icu_procedureevents (procedures are most clinically discriminating) over exhaustive input/output enumeration
• Skip ICU event tables entirely when icu_icustays returns empty