playbook

Clinical Data Warehouse & Reporting Starter Kit

A practical playbook to design, build, validate, govern, and operationalize a clinical data warehouse for reliable reporting and analytics. Includes prioritized use-case guidance, example clinical data mappings (encounters, meds, labs), an ETL quality checklist, sample SQL for common KPIs, dashboard deployment and testing checklist, and governance role definitions.

Welcome

This starter kit helps analytics teams and clinical leaders stand up a dependable Clinical Data Warehouse (CDW) focused on operational reporting and trusted clinical KPIs. It emphasizes start-small, prove-value, and build reusable views and governance so dashboards remain accurate and trusted.

Who this helps

  • Analytics teams who need dependable data flows and reporting standards.
  • Clinical and quality leaders who need trusted KPIs for care and operations.
  • Engineers and data stewards building pipelines and governance.

Core hunger

Analytics teams need practical starter artifacts to stand up reliable clinical reporting and accelerate insights while avoiding fragmented pipelines that produce inconsistent KPIs and wasted effort.

Scope & approach

Focus on a small set of high-value use-cases (e.g., readmissions, length of stay, medication safety, lab turnaround) and design a CDW that supports them. Build canonical, reusable views that implement business logic once and expose consistent metrics to every dashboard and report.

Recommended process (high-level)

  1. Prioritize 2–4 initial use-cases with clear owners and acceptance criteria.
  2. Define canonical definitions for key entities (patient, encounter, medication order, lab result).
  3. Select base clinical model (practical options: FHIR-derived canonical views, OMOP, or simplified local canonical model).
  4. Map source systems to canonical tables and build ETL pipelines with built-in QA checks.
  5. Implement a semantic layer: curated, documented views that encode business rules.
  6. Validate metrics with clinicians and operations owners; iterate until trusted.
  7. Deploy dashboards with a checklist for performance, accuracy, and monitoring.
  8. Establish governance, change management, and ongoing monitoring of data quality.

Example CDW artifacts included (and what they do)

  • Table templates — canonical table templates for encounters, medications, and labs with suggested fields and keys.
  • ETL quality checklist — automated and manual checks to run during and after loads.
  • Sample SQL queries — ready-to-adapt queries for common KPIs and health system metrics.
  • Dashboard deployment checklist — tests and review steps before publishing reports.
  • Governance roles & RACI — definitions for data steward, analytics product owner, data engineer, clinical SME.

Example canonical table mappings (short)

Design canonical tables to hide source complexity. Examples below are illustrative; adapt names to your environment.

  • encounter: encounter_id, patient_id, encounter_start, encounter_end, facility_id, encounter_type, primary_diagnosis_code, attending_provider_id
  • medication_order: med_order_id, encounter_id, patient_id, order_datetime, drug_concept_id, dose, route, frequency, status
  • lab_result: lab_result_id, encounter_id, patient_id, specimen_datetime, loinc_code, result_value_numeric, result_value_text, units, reference_range

ETL quality checks (recommended set)

  1. Row-count reconciliation: source row counts vs. target after transform (daily/weekly).
  2. Schema validation: required columns present and type consistency.
  3. Null and completeness checks: critical keys (patient_id, encounter_id) must not be null.
  4. Referential integrity: encounter_id and patient_id reference master tables or expected domains.
  5. Range and plausibility checks: e.g., DOB not in future, lab values within clinical plausible bounds.
  6. Duplicate detection: identify duplicates by natural keys and timestamps.
  7. Code mapping success rates: % of source codes successfully mapped to canonical codes (LOINC, RxNorm, ICD).
  8. Latency checks: time from event in source to availability in CDW (SLA targets).

Sample KPI SQL snippets (illustrative)

Adapt to your schema and SQL dialect. These are patterns rather than final production queries.

30-day readmission rate (per discharge):

SELECT discharge_date, COUNT(*) AS discharges, SUM(CASE WHEN readmit_within_30=1 THEN 1 ELSE 0 END) AS readmissions, ROUND(SUM(CASE WHEN readmit_within_30=1 THEN 1 ELSE 0 END)::numeric/COUNT(*),4) AS readmit_rate FROM ( select e.patient_id, e.encounter_id as index_enc, e.discharge_date, CASE WHEN exists( select 1 from encounter e2 where e2.patient_id=e.patient_id and e2.encounter_start <= e.discharge_date + interval '30 day' and e2.encounter_start > e.discharge_date ) THEN 1 ELSE 0 END as readmit_within_30 from encounter e where e.discharge_date is not null ) sub GROUP BY discharge_date;

Average length of stay (LOS):

SELECT AVG(EXTRACT(epoch FROM (encounter_end - encounter_start))/86400) as avg_los_days FROM encounter WHERE encounter_end IS NOT NULL;

Governance: roles & responsibilities

  • Data Steward: clinical SME responsible for canonical definitions, code mappings, and approval of metric logic.
  • Analytics Product Owner: owns use-case acceptance criteria, validates results with stakeholders, prioritizes features.
  • Data Engineer: builds and maintains ETL pipelines, implements automated checks, monitors data latency.
  • Quality Lead/Test Owner: designs data verification tests and runs periodic audits of KPIs.
  • Platform/DevOps: ensures environment stability, backups, and performance monitoring.

Dashboard deployment checklist

  1. Metric verification: compare dashboard KPI with vetted SQL result for a recent sample period.
  2. Edge-case validation: confirm handling of transfers, same-day discharges, and duplicate encounters.
  3. Performance test: dashboard queries return within agreed SLA for expected concurrency.
  4. Documentation: metric definitions, data lineage, refresh cadence, known limitations published with the dashboard.
  5. Owner sign-off: analytics product owner and clinical steward approve release.
  6. Monitoring: alerts configured for ETL failures, unusual KPI deviations, or increases in code-mapping failure rates.

Operationalizing & scaling

  • Enforce semantic layer discipline: dashboards query curated views, never raw source tables directly.
  • Version control for mapping tables and transformation logic; maintain a change log and migration scripts.
  • Automate QA and publish daily status dashboards for ETL jobs and data quality metrics.
  • Schedule periodic metric reviews with clinical owners to catch drift in definitions or clinical workflows.

Common pitfalls to avoid

  • Trying to ingest everything at once — leads to delays and unclear priorities.
  • Letting reports query different source systems directly — causes inconsistent KPIs.
  • Missing code mappings or failing to track mapping coverage over time.
  • No clear ownership of metric definitions and change approvals.

Next steps & recommended experiments

  1. Choose 2 pilot use-cases and define acceptance tests with clinical owners.
  2. Implement canonical views and a minimal ETL pipeline for those cases with automated QA checks.
  3. Run side-by-side validation against legacy reports for one month, collect discrepancies, and iterate.
  4. After stabilization, publish a dashboard and use the deployment checklist and governance process for rollout.

Templates and artifacts provided

  • Canonical table templates for encounters, meds, labs
  • ETL quality checklist (suitable for automation and manual review)
  • Sample SQL patterns for KPIs
  • Dashboard deployment checklist and governance RACI

Closing note

This playbook is intentionally practical and incremental: build trust by delivering a few high-value, well-governed metrics first. Treat the CDW as a living product—continuously improve mappings, tests, and documentation so every dashboard becomes a reliable decision tool.

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