# 2555 Ehr Data Considerations 2b564248 > EHR Data Considerations - Skill: `tools-only/2555-ehr-data-considerations-2b564248` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add tools-only/2555-ehr-data-considerations-2b564248` - Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/2555-ehr-data-considerations-2b564248/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tools-only (https://skillmd.com/u/tools-only) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/tools-only/2555-ehr-data-considerations-2b564248 --- # EHR Data Considerations ## Overview Electronic Health Record (EHR) data from MIMIC, eICU, and similar databases present unique methodological challenges. This reference covers pitfalls specific to clinical database research. --- ## Selection Bias ### ICU Selection Bias (Survivorship Bias) **Problem:** Patients in the ICU are a highly selected population. - They survived long enough to be admitted to the ICU - Someone decided they needed ICU care - They had access to ICU beds **Implications:** - Findings may not generalize to non-ICU patients - Comparing ICU patients to general population is problematic - "Sicker" patients in ICU may actually be more salvageable (otherwise why admit?) **Mitigation:** - Clearly define target population - Don't generalize beyond ICU patients - Consider severity adjustment (APACHE, SOFA) --- ### Single-Center Bias (MIMIC) **Problem:** MIMIC is from Beth Israel Deaconess Medical Center in Boston. - Academic tertiary care center - Specific patient demographics (Boston area) - Specific practice patterns - May not represent other hospitals **Implications:** - External validity is unknown - Validation on other datasets essential before clinical deployment **Mitigation:** - Validate on eICU (multi-center) or other databases - Report center characteristics - Acknowledge generalizability limitations --- ### Temporal Selection **Problem:** Who gets included depends on data availability period. - MIMIC-IV: 2008-2019 - Practice patterns changed over this period - ICD-9 → ICD-10 transition (Oct 2015) - Patients who died early vs. late in the study period differ **Mitigation:** - Consider temporal validation (train early, test late) - Check for secular trends - Stratify by era if appropriate --- ## Time-Related Biases ### Immortal Time Bias **Problem:** Time between cohort entry and treatment initiation during which the outcome cannot occur. **Classic example:** "Patients who received Drug X had lower mortality" - But to receive Drug X, you had to survive long enough to get it - Deaths in the "immortal time" are misclassified **Detection:** - Is there a gap between time-zero and treatment? - Could outcomes occur during this gap? **Mitigation:** - Align time-zero with treatment decision point - Landmark analysis (start follow-up at fixed time) - Time-varying exposure models --- ### Time-Zero Definition **Problem:** When does follow-up start? | Choice | Issue | |--------|-------| | Hospital admission | May precede ICU; different risk profile | | ICU admission | Most common; but which admission for readmits? | | Treatment initiation | Immortal time if not everyone treated immediately | | Diagnosis time | Variable latency to diagnosis | **Best practice:** - Time-zero should be the point of decision/eligibility - All patients should be "at risk" for outcome at time-zero - Document and justify clearly --- ### Lead Time Bias **Problem:** Earlier detection ≠ longer survival. If an exposure is associated with earlier testing/diagnosis: - Patients appear to survive longer - But they just knew about their condition longer **Mitigation:** - Use clinically meaningful endpoints - Landmark analysis - Consider time from symptom onset, not diagnosis --- ## Missing Data ### Missingness Is Informative in EHR **Key insight:** In clinical data, missingness is rarely random. | Pattern | Likely Meaning | |---------|----------------| | Lab value missing | Clinician didn't think it was needed | | Vital sign gap | Patient stable (or recorder busy) | | Med never given | Not indicated (or contraindicated) | | Note not written | Nothing concerning to document | **Implications:** - MCAR (missing completely at random) assumption usually wrong - Complete case analysis may be biased - Imputation must be done thoughtfully --- ### Handling Missing Data | Approach | When Appropriate | Caution | |----------|------------------|---------| | Complete case | Truly MCAR (rare) | Loses power, may bias | | Single imputation (mean, median) | Quick exploration | Underestimates variance | | Multiple imputation | MAR plausible | Requires careful modeling | | Indicator method | Missingness is meaningful | Only if theory supports | | Model-based (mice, etc.) | MAR, complex patterns | Computationally intensive | **For prediction models:** - Missingness indicators can be features (missingness is information) - Tree-based models can handle missing values natively **For inference:** - Multiple imputation preferred - Sensitivity analysis under MNAR scenarios --- ### Common Missing Data Patterns in MIMIC/eICU | Data Type | Typical Pattern | Notes | |-----------|-----------------|-------| | Labs | Ordered when clinically indicated | More missing = likely more stable | | Vitals | Charted periodically | Gaps during transport, procedures | | Medications | Documented when given | Absence ≈ not indicated | | Notes | Written when events occur | Sparse = uneventful | | Diagnoses (ICD) | Coded at discharge | Retrospective, billing-driven | --- ## Coding and Definition Issues ### ICD Code Transition **Problem:** ICD-9-CM → ICD-10-CM on October 1, 2015. - Codes are not 1:1 mappable - Specificity changed (ICD-10 more granular) - Same condition may be coded differently pre/post **Mitigation:** - Use validated crosswalk mappings - Check consistency across eras - Consider ICD-agnostic definitions (lab-based, clinical criteria) --- ### Diagnosis Timing **Problem:** ICD codes are assigned at discharge, not in real-time. - Codes reflect billing, not clinical reasoning during stay - Cannot use discharge diagnoses to define who "had" a condition at admission - Present-on-admission (POA) flag helps but is imperfect **Implications for prediction:** - Don't use discharge diagnoses as predictors at admission - Use admission diagnoses (often limited) or prior history --- ### Medication Data Complexity | Issue | Description | |-------|-------------| | Order vs. administration | Order ≠ given; use MAR for actual receipt | | PRN medications | May be ordered but rarely given | | Infusions | Rates change; need to model over time | | Generic vs. brand | Same drug, different names | | Units | mcg vs mg; mL/hr vs dose | --- ### Lab Value Interpretation | Issue | Description | |-------|-------------| | Reference ranges | Vary by lab; not always provided | | Critical values | May trigger repeated testing | | Timing | First value? Worst value? Closest to event? | | Multiple samples | Same timepoint may have multiple results | | Units | Some values in different units (e.g., glucose mg/dL vs mmol/L) | --- ## Confounding in Observational Data ### Confounding by Indication **Problem:** Treatments are given for reasons related to outcome. Example: "Vasopressors associated with higher mortality" - But vasopressors are given to sicker patients - The indication (shock) causes both treatment and outcome **Mitigation:** - Careful confounder adjustment - Propensity methods - Consider indication directly - Acknowledge limitations --- ### Severity Scores as Confounders Common severity scores in ICU data: - APACHE II/III/IV - SOFA (Sequential Organ Failure Assessment) - SAPS II/III - Charlson/Elixhauser comorbidity indices **Considerations:** - Calculated at different timepoints (admission vs. first 24h) - May include components on causal pathway (mediators) - Adjust for, but don't over-adjust --- ## eICU-Specific Considerations ### Multi-Center Structure **eICU:** 208 hospitals across the US, 2014-2015. **Strengths:** - Geographic diversity - Allows site-level validation - More generalizable than single-center **Challenges:** - Clustering by hospital (must account for) - Variable data quality by site - Different practice patterns **Analysis considerations:** - Mixed models or GEE with hospital as cluster - Site-level random effects - Consider site as stratification variable for validation --- ### Telehealth Context eICU patients were monitored by telehealth ICU system. - May differ from non-telehealth ICUs - Additional monitoring may affect documentation patterns --- ## Reproducibility Considerations ### Version Control MIMIC and eICU have multiple versions: - MIMIC-III vs MIMIC-IV (different schemas, patients) - Derived tables may change between releases **Best practice:** - Document exact version used - Pin to specific derived table versions if possible - Include data extraction date --- ### Cohort Definition Documentation Always document: - Inclusion criteria (with counts) - Exclusion criteria (with counts at each step) - Time period - Unit of analysis - Handling of multiple admissions per patient --- ## Checklist: EHR Data Quality Assessment Before analysis, check: - [ ] **Cohort:** Appropriate inclusion/exclusion applied? - [ ] **Time-zero:** Clearly defined, appropriate for question? - [ ] **Outcome:** Defined, measured consistently? - [ ] **Predictors:** Available at prediction time? No leakage? - [ ] **Missing data:** Patterns assessed? Handling strategy justified? - [ ] **Confounding:** Key confounders identified? Adjustment plan? - [ ] **Coding:** ICD version consistent? Definitions validated? - [ ] **Clustering:** Patient/hospital clustering accounted for? - [ ] **Temporality:** Secular trends checked? ICD transition handled? - [ ] **Generalizability:** Limitations acknowledged?