Making Healthcare Data AI-Ready: Unified, Reformatted, and Useful at the Point of Care
Kevin Ritter of Altera Digital Health points to a simple truth: AI only helps clinicians if the data feeding it is unified and clean. His team's focus is taking clinical, claims, and patient-generated data, then reformating it so analytics and AI models can deliver practical decision support.
If you lead clinical operations or informatics, this isn't a future project. It's table stakes for safer care, lower admin waste, and faster insight.
What "unify and reformat" actually means
- Normalize data to shared standards (e.g., HL7 FHIR, SNOMED CT, LOINC, RxNorm, ICD-10, CPT) so models see the same concepts across sources.
- Resolve patient identity across systems, collapse duplicates, and build longitudinal timelines that track encounters, meds, labs, images, and claims.
- Map unstructured notes into structured signals using NLP, with provenance and confidence stored alongside the output.
- Engineer features that models actually use: problem lists that persist, current med lists, lab trends, care gaps, risk scores, and utilization patterns.
- Add rigorous data quality checks (completeness, timeliness, plausibility) and versioning so results are auditable.
Why clinicians should care
- Cleaner alerts with fewer false positives because inputs are consistent and current.
- Faster chart review: reconciled meds, recent labs, and risk signals surfaced in one place.
- Closed care gaps at scale-vaccinations, screenings, chronic disease follow-ups.
- Smoother prior auth and utilization review when clinical context and claims history line up.
Interoperability requirements that make this work
- Standards-based exchange (FHIR APIs, eventing) and clear write-back rules to avoid stale decision support.
- Latency aligned to the use case: real-time for bedside alerts, near-real-time or daily for population health.
- Consent management, data minimization, and controls for sensitive categories (e.g., 42 CFR Part 2).
- Security and governance: role-based access, PHI auditing, and clear model oversight to reduce bias and drift.
Implementation playbook for health systems
- Start with one high-value use case (e.g., sepsis early signal, readmission risk, care gap closure) and define 3-5 outcome metrics up front.
- Inventory your data sources, standards coverage, and quality gaps. Close the largest gaps before model deployment.
- Pilot in one unit or clinic, compare against baseline, and iterate on alert thresholds with clinician feedback.
- Operationalize: embed into workflows, set up monitoring, and publish weekly metrics to clinical leaders.
Smart questions to ask your platform vendor
- Which code systems and note types are mapped end-to-end? How often are mappings updated?
- How is identity resolution done, and what's the match precision/recall?
- What guardrails exist for PHI, de-identification, and access logging? Certifications (e.g., SOC 2, HITRUST)?
- Can we inspect model inputs/outputs and track provenance for every prediction?
- What's the typical data latency and uptime SLA? How are downtimes handled in the EHR workflow?
Metrics that prove value
- Precision/recall of alerts and resulting clinical actions taken.
- Clinician adoption and alert acknowledgment rates over time.
- Changes in documentation time, LOS, readmissions, and avoidable ED visits.
- Prior authorization turnaround time and denial rate reductions.
- Total cost to integrate and maintain per use case versus savings delivered.
Bottom line
Ritter's point is practical: unify and reformat data first, and AI becomes useful instead of noisy. Do that well, and decision support stops being a separate screen and starts becoming a reliable part of care.
If you're building team capability for AI in clinical operations and analytics, see curated options by role at Complete AI Training - Courses by Job.
Your membership also unlocks:
