CLIF Implementation Guide
Table-by-Table ETL Implementation Resource
Transform your electronic health record data into the standardized CLIF format with our comprehensive table-specific resources, validation rules, and ETL guidance.
Implementation Overview
18 Core Tables
Complete ETL guidance for all CLIF data tables
Validation Rules
Reference ranges and outlier detection thresholds
Best Practices
Proven implementation strategies and common solutions
CLIF Tables Implementation Guide
Patient
Core patient demographics
Hospitalization
Hospital stays and encounters
ADT
Admission, discharge, transfer
Vitals
Vital signs monitoring
Labs
Laboratory results
Continuous Medications
IV drips and infusions
Intermittent Medications
Scheduled and PRN doses
Respiratory Support
Ventilator and oxygen therapy
Microbiology Culture
Culture results and organisms
Microbiology Non-Culture
PCR, antigen, and other tests
Susceptibility Testing
Antibiotic sensitivity results
Patient Procedures
Medical procedures and interventions
Hospital Diagnosis
ICD codes and diagnoses
Patient Assessments
Clinical scales and scores
Position
Patient positioning data
Code Status
Resuscitation preferences
CRRT Therapy
Continuous renal replacement
ECMO/MCS
Extracorporeal support
patient ⚡ BETA
The foundation table containing core patient demographics and identifiers. This table establishes the primary key relationships for all other CLIF tables.
🔑 Key ETL Considerations
- • Establish patient_id as primary identifier across all tables
- • Implement robust patient matching algorithms
- • Handle demographic data standardization
- • Manage patient merges and identifier changes
📋 Required Fields
- •
patient_id- Primary key - •
sex- Patient gender - •
race- Race/ethnicity information - •
birth_date- Date of birth
vitals ⚡ BETA
ETL Implementation Guide
- High Volume Processing: Optimize for millions of vital sign records
- Real-time Integration: Handle continuous monitoring data streams
- Unit Standardization: Convert to standardized units (°C, mmHg, etc.)
- Outlier Detection: Implement validation rules for physiologically impossible values
- Temporal Accuracy: Ensure precise datetime stamps for trending
Validation & Quality Checks
Adult Vitals Outlier Thresholds
| Vital Category | Lower Limit | Upper Limit |
|---|
labs ⚡ BETA
ETL Implementation Guide
- LOINC Mapping: Map proprietary lab codes to standard LOINC codes
- Unit Standardization: Convert all units to CLIF specifications
- Reference Ranges: Include normal ranges for clinical context
- Result Processing: Handle numeric, categorical, and text results
- Delta Checks: Validate against previous results for quality
Validation & Quality Checks
Laboratory Reference Ranges
| Lab Category | Typical Min | Typical Max | Units |
|---|
Laboratory Outlier Thresholds
| Lab Category | Lower Limit | Upper Limit |
|---|
medication_admin_continuous ⚡ BETA
ETL Implementation Guide
- RxNorm Mapping: Standardize medication codes to RxNorm
- Dose Calculations: Convert to weight-based dosing where appropriate
- Infusion Rates: Handle rate changes and titrations over time
- Start/Stop Times: Precise timing for medication duration
- Concentration Tracking: Capture dilution and concentration data
Validation & Quality Checks
Continuous Medication Dosage Ranges
| Medication | Typical Min | Typical Max | Units |
|---|
respiratory_support ⚡ BETA
ETL Implementation Guide
- Ventilator Integration: Extract data from ventilator systems
- Mode Classification: Standardize ventilator modes across manufacturers
- Setting vs Measured: Distinguish between set parameters and observed values
- Oxygen Delivery: Capture FiO2, PEEP, and support levels
- Weaning Protocols: Track changes in respiratory support over time
Validation & Quality Checks
Respiratory Support Outlier Thresholds
| Parameter | Lower Limit | Upper Limit |
|---|
microbiology_culture 💡 CONCEPT
ETL Implementation Guide
- Include Negative Cultures: Essential for epidemiological research
- Multiple Organisms: Create separate rows for each organism in polymicrobial cultures
- Specimen Source: Standardize anatomical collection sites
- Growth Quantification: Capture colony counts and growth patterns
- Temporal Accuracy: Link collection, processing, and result times
Special Considerations
Polymicrobial Culture Handling
When multiple organisms grow from a single culture:
| Patient ID | Culture ID | Specimen | Organism |
|---|---|---|---|
| 12345 | BC001 | Blood | E. coli |
| 12345 | BC001 | Blood | Enterococcus |
Implementation Resources
Ready to Implement CLIF?
Start with the core tables (Patient, Hospitalization, Vitals, Labs) and expand your implementation systematically.