Clinical Data Warehouse & Reporting: README and Pipeline Checklist

Welcome — Why this README matters

Analytics teams need dependable data flows and consistent reporting. This guide helps CDW teams turn fragmented pipelines into trustworthy, auditable reporting services by documenting the pipeline, validating each handoff, clarifying roles, protecting PHI, and standardizing SLAs. Use it as the project README, an onboarding document for new data stewards and analysts, and a pre-production acceptance checklist.

When to use this playbook

• Starting a new CDW or analytics project.
• Onboarding a new data source or ETL process.
• Preparing reporting pipelines for production or audit.
• Investigating inconsistent KPIs, stale reports, or trust issues with data consumers.

Quick contents

• Data lineage map template
• ETL validation checklist (concrete checks)
• Roles & responsibilities (practical RACI-style list)
• Reporting SLA template (metrics & examples)
• Example data dictionary entry
• PHI protection and compliance guidance
• Reproducible, auditable report practices
• Pipeline production readiness checklist

Data lineage map template (what to capture)

Map the flow from source system to final report. A simple diagram plus a short table is ideal. Include these columns for each artifact:

• Artifact name: source table / view / staged table / mart / report
• System: EHR, LIS, scheduling, claims, etc.
• Fields/columns: key fields with mapping to downstream names
• Extraction logic: CDC vs full extract, query or export used
• Transformations: join rules, lookups, derived fields, business rules
• Frequency / latency: how often it refreshes and SLA for freshness
• Owner: data owner and data steward contact
• PHI flag: whether artifact contains PHI and protection needs
• Downstream consumers: which reports, dashboards, or teams use it

Keep a living diagram (Visio/DrawIO) with links to the lineage table and code repositories.

ETL validation checklist — practical checks to run

Run these checks at each ETL checkpoint (source→stage, stage→warehouse, warehouse→mart):

1. Row-count reconciliation: compare source rows captured vs rows loaded; verify within an agreed tolerance.
2. Checksum / hash validation: compute row-level checksums for critical tables to ensure identical payloads after transfer.
3. Null and completeness rates: monitor mandatory key fields (MRN, encounter_id, event_ts) for unexpected nulls; set thresholds and alerts.
4. Referential integrity: confirm foreign keys (patient id, encounter id) map to expected master tables; log orphaned rows for review.
5. Value-range/checklist validation: validate vitals/lab values against clinical plausibility ranges; flag outliers for clinical review.
6. Schema drift detection: detect added/removed columns, type changes, and mismatches against the expected schema.
7. Duplicate detection: find and explain duplicate keys, especially after deduplication logic runs.
8. Timestamp monotonicity and freshness: ensure incremental loads advance expected watermark timestamps; report delays.
9. Business-rule regression tests: automated unit tests for core transformations (e.g., admission/discharge logic, derived risk scores) with example inputs and expected outputs.
10. Audit trail verification: confirm logs capture who ran pipelines, job IDs, run times, and any exception messages.

Record results and tolerances as part of the pipeline run metadata so reports can link back to the validation status.

Roles & responsibilities (practical)

Assign clear owners for each part of the pipeline. Suggested role descriptions:

• Data Owner: clinical or business owner accountable for the source data's meaning and use; approves definitions and access.
• Data Steward: maintains data definitions, dictionary entries, and resolves semantic questions; triages data-quality issues.
• ETL Engineer / Data Engineer: builds and operates extraction/transformation pipelines; implements automated checks and alerting.
• Analytics Lead / Report Owner: owns the KPI definition, report logic, and validates that reports meet business needs.
• Security / Privacy Lead: ensures PHI handling, masking, encryption, and access controls meet policy and regulation.
• Release / Ops Coordinator: manages deployment windows, change control, and communicates scheduled changes to stakeholders.

Use a simple RACI (Responsible, Accountable, Consulted, Informed) per artifact to avoid ambiguity.

Reporting SLA template — what to promise

Suggested SLA elements to include with examples:

• Freshness: e.g., critical operational dashboards refresh hourly; executive dashboards refresh daily.
• Completeness: column-level acceptable null rate (e.g., <0.5% for MRN); daily completeness check passed in 95% of runs.
• Accuracy / reconciliation: reconciliation job must pass within defined tolerance or an incident is opened.
• Latency / availability: report availability 99% during business hours; data job success rate 99% per week.
• Issue response: acknowledge incidents within 1 business hour, action plan within 1 business day for P1 issues.
• Change notice: stakeholders notified of planned changes at least 3 business days ahead.

Embed these SLAs in the README and publish a lightweight runbook describing escalation steps when thresholds fail.

Example data dictionary entry

Use a one-row-per-field structure with the following attributes. Example shown as a bullet list for a hypothetical encounter table field:

• Field name: encounter_id
• Display name: Encounter ID
• Description: Unique identifier for a patient encounter; assigned by EHR at admission
• Source system: EHR (ADT module)
• Data type: string / GUID
• PI/PHI: PHI: No (identifier tied to MRN which is PHI)
• Allowed values / constraints: non-null, unique per encounter
• Transformation notes: trimmed, normalized, mapped to master encounter table
• Owner / steward: Admissions team / Data Steward

Keep the data dictionary in a searchable location (wiki, spreadsheet, or catalog) and link fields back to lineage entries.

Protecting PHI — practical controls

PHI protection must be explicit in the README. Essential controls include:

• Classification: mark artifacts that contain PHI. Prefer separate PHI staging and de-identified marts.
• Least privilege: role-based access; review access quarterly.
• Masking / tokenization: store direct identifiers in a protected vault or token service; use tokens in analytics marts.
• Encryption: encrypt data at rest and in transit per organizational policy.
• Audit logging: log all access to PHI-containing artifacts and retain logs per policy.
• Data minimization: only extract required fields; avoid staging large PHI payloads when not needed.
• QA and synthetic test data: use de-identified or synthetic datasets for development and testing whenever possible.

Document compliance owners and required approvals for PHI access in the README.

Reproducible, auditable reports

Make reports repeatable and traceable to source data and transformation code:

• Version control: store ETL scripts, transformation notebooks, and report SQL/queries in Git. Tag releases used in production.
• Parameterized builds: avoid hard-coded dates; use parameters so reports can be re-run for a specific snapshot.
• Snapshotting: for critical KPIs, snapshot source indicators (counts, denominators) used to compute the KPI at the time of report generation.
• Automated tests: include unit tests for transformations and regression tests for KPI values after code changes.
• Run metadata: each scheduled pipeline should write a run record with job id, git commit id, data ranges, validation results, and user who triggered a manual run.
• Documented assumptions: every derived measure should have a short explanation and link to the business definition in the wiki or dictionary.

Pipeline production readiness checklist

Use this short checklist before declaring a pipeline production-ready:

1. Lineage diagram and data dictionary entries created for new artifacts.
2. ETL jobs scheduled and run successfully in a staging environment for at least 3 consecutive runs.
3. All ETL validation checks pass within tolerances; exceptions documented and triaged.
4. Access controls and PHI protections in place and validated with the security team.
5. Monitoring and alerting configured (job failures, freshness breaches, quality thresholds).
6. Reconciliation jobs implemented for key tables and scheduled to run automatically.
7. Run metadata captured and linked to report artifacts.
8. Stakeholders have reviewed report logic, signed off, and understand the SLA and incident process.
9. Rollback and remediation procedures documented and tested.

Monitoring & operational notes

Operationalize the pipeline with simple dashboards that show:

• Latest job run status and latency
• Data quality KPIs (null rates, row-count diffs, checksum pass rate)
• Freshness by artifact
• Open incidents and recent changes (commits deployed)

Establish a lightweight cadence: weekly data-ops huddle to review incidents and upcoming changes; monthly stakeholder review for SLA adherence.

Next steps and living improvements

Start by filling the lineage template for the highest-priority sources and implementing automated ETL validation checks for one critical pipeline. Over time, incrementally add more checks, publish the data dictionary, and convert manual checklist tasks into automated validations and alerts.

Templates & attachments (suggested)

• Lineage CSV/Spreadsheet template (columns listed above)
• ETL validation checklist (machine-readable for automation)
• Data dictionary template (CSV or catalog ingest format)
• Reporting SLA template (editable document)
• Run metadata schema (job_id, commit_id, start_ts, end_ts, validation_status, notes)

Attach or link these templates in the project repo or the team wiki.

Keep improving

This README should be a living document. Treat the artifacts, checks, and SLAs as configurable: tighten thresholds as trust grows, and add monitoring for the most impactful defects you uncover. The goal is consistent, auditable reports that stakeholders trust.