Rackspace Technology uses Amazon AI to speed healthcare insights
Rackspace Technology is working with Infocare Health to put Amazon's analytics to work for clinicians. The team tested Amazon QuickSight alongside FAIR data practices to cut manual queries and surface insights faster. The goal is simple: less time pulling data, more time making clinical decisions.
What this means for your team
- Fewer ad-hoc SQL pulls: Standard dashboards and governed datasets reduce back-and-forth requests.
- Consistent data logic: FAIR principles (findable, accessible, interoperable, reusable) help break silos and reduce conflicting metrics.
- Faster time-to-answer: BI features, including natural-language querying, can shorten the path from question to chart.
- Better allocation of clinical time: Nurses, physicians, and care managers spend less effort wrangling data and more on patient care.
How it can work in practice
Data from EHR, RPM, claims, and quality systems is modeled once, governed, and published to analytics workspaces. QuickSight provides dashboards for operational, quality, and financial views, with row-level security for PHI. Clinicians and service-line leaders get curated views, while analysts keep deeper exploration rights.
Expected outcomes
- Shorter cycles from question to insight for throughput, LOS, readmissions, and utilization.
- Higher confidence in metrics by using shared definitions and audit trails.
- Reclaimed hours previously spent on manual extracts and one-off spreadsheets.
Governance and safety checks
- HIPAA-first design: Enforce least-privilege access, encryption, and PHI masking in analytics layers.
- Data quality gates: Validate feeds at ingestion; alert on schema drift and missing values.
- Clinician oversight: Pair analytics teams with clinical leaders to vet measures and avoid misleading proxies.
- Auditability: Track source, transform, and version for each metric to support reviews and accreditation needs.
Quick start checklist
- Pick 2-3 high-impact use cases (e.g., ED throughput, care gap closure, denials).
- Map data to FAIR practices and define metric owners and refresh cadences.
- Stand up a governed dataset and pilot QuickSight dashboards with one department.
- Set SLAs for data freshness and incident response; publish a change-log for metric updates.
- Train a small group of super-users and schedule 30-minute weekly office hours for feedback.
Why this aligns with broader industry movement
Healthcare is shifting to data-led operations, and the AWS stack has matured for secure analytics at scale. Rackspace's focus at AWS re:Invent signals more investment in AI-assisted reporting, faster BI cycles, and cleaner data plumbing that clinicians can trust.
Further reading
Upskill your team
If you're formalizing AI analytics skills across clinical ops and quality teams, consider a focused training path. Start here: AI courses by job role.
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