Tampa General Hospital · Patient flow / queue / Sepsis management

A Evidence level

Tampa General Hospital: Patient flow / queue / Sepsis management

Publisher: Palantir · Vendors ' official customer information · Direct sources at the case level

Claim origin: Public disclosure by manufacturer or customer · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Standard case | Useful reference with remaining gaps 9 / 12

Business context1 / 2

Transformation workflow2 / 2

Technical workflow2 / 2

Human roles & governance / 2

Measured outcomes2 / 2

Source traceability2 / 2

Remaining gaps: Business context, Human roles & governance

The key to this case is not the single-point use of AI, but the re-establishment of “patient flow/shifting/sepsis management” as an enforceable, verifiable production stream with boundaries of human responsibility.

Business problem

Hospital data are scattered and patient flow, staffing and clinical operations decisions require a single view in real time.

Solution

Consolidation of key data with Foundry Ontology for forecasting, staffing and patient flow optimization; expansion of 360° real-time patient/medical view within 24 hours of Hurricane Ian.

Technical architecture & production workflow

Step 01

EHR/Bed/Personnel/Operational Incident Data

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Step 02

Harmonization of patients with operating entities

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Step 03

Real-time status + prediction/rules/AI analysis

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Step 04

Recommendations by clinical/operational priority

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Step 05

Medical/operator identification and execution

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Step 06

Rewrite the results and monitor them on an ongoing basis

Key technology & infrastructure components

Medical data setOntology/Unified EntityProjections/rules/AIAudit of authorityClinical/operational workstations

Human roles & accountability

Clinical and operational teams make final arrangements based on recommendations.

FDE delivery actions

Common definition of issues and security boundaries with clinical/operational teams
Address measurable operational bottlenecks and avoid placing AI under the responsibility of diagnosing
Get data access and real-time updates.
Design of warning, recommendation and manual confirmation mechanisms
Receiving and inspection using operational indicators such as length of hospitalization, bed turnover, etc.

Reusable delivery patterns

It's usually easier to quantify medical AI's first entry from an operating closed loop.
Clinical responsibility must remain with human professionals.
Competence, audit, real-time and data quality are as important as models

Business outcomes & delivery results

Follow-up public information indicates that the length of hospitalization of Sepsis patients has been reduced by about 15 per cent.

Evidence boundaries & verification notes

Public information confirming business processes; technical components are abstract architecture based on public description
The source can be directly located in the case; the value remains disclosed by the source and does not represent an independent audit.

Primary source: Tampa General Hospital: Patient flow / queue / Sepsis management ↗