AI Playbook for Pediatric Sepsis Protocol Development
A children's hospital has adult sepsis protocols but no pediatric-specific protocol. In the past year, 3 pediatric patients experienced delayed sepsis recognition and 1 died. Pediatric sepsis recognition is more complex because normal vital sign ranges differ by age, and children compensate longer before decompensating.
When to use this playbook
- Use this playbook when the decision looks like the situation above: A children's hospital has adult sepsis protocols but no pediatric-specific protocol.
- It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Pediatric Sepsis Protocol Development".
- Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.
What you'll need
- The 3 delayed recognition cases (de-identified): timelines, vital signs, lab results, clinical notes
- Current adult sepsis protocol and alert thresholds
- PALS and Surviving Sepsis Campaign pediatric guidelines
- Age-stratified normal vital sign ranges (neonate, infant, toddler, school-age, adolescent)
- ED and inpatient nursing workflow documentation
Attachments: Documents (Documents)
The Prompt
You are a pediatric clinical quality specialist developing a sepsis recognition protocol for a children's hospital. I am attaching: Work only from the attached source files. If a conclusion is not supported, say so. Produce: 1. Analyze the 3 delayed recognition cases: at what point did the patient meet pediatric sepsis criteria, and what was the delay to recognition and treatment? 2. Define the pediatric-specific SIRS and sepsis criteria using age-stratified vital sign thresholds that map to current evidence. 3. Design the alert algorithm: what vital sign and lab combinations should trigger a sepsis screen for each age group, and what is the recommended positive predictive value threshold? 4. Develop the clinical workflow: who gets alerted, what is the initial assessment checklist, and when does the rapid response team activate? 5. Tell me what physician and nursing education is needed and how to measure protocol effectiveness at 6 and 12 months. Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.
What to expect
- Delayed recognition analysis for 3 cases
- Pediatric sepsis criteria by age group
- Alert algorithm design with PPV target
- Clinical workflow and rapid response activation criteria
- Education plan and effectiveness measurement framework
Review before you act
- Validate this output against source files before relying on it: Analyze the 3 delayed recognition cases: at what point did the patient meet pediatric sepsis criteria, and what was the delay to recognition and treatment?.
- Validate this output against source files before relying on it: Define the pediatric-specific SIRS and sepsis criteria using age-stratified vital sign thresholds that map to current evidence.
- Validate this output against source files before relying on it: Design the alert algorithm: what vital sign and lab combinations should trigger a sepsis screen for each age group, and what is the recommended positive predictive value threshold?.
- Validate this output against source files before relying on it: Develop the clinical workflow: who gets alerted, what is the initial assessment checklist, and when does the rapid response team activate?.
- Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
- Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
- Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.
Why compare models on this
For Pediatric Sepsis Protocol Development, running the same attachments across independent models is useful because the hard part is classification and completeness, not fluency. The workflow is already designed to surface delayed recognition analysis for 3 cases; pediatric sepsis criteria by age group; alert algorithm design with ppv target; clinical workflow and rapid response activation criteria. Those are comparison artifacts — they only exist if more than one model runs. Models disagree on whether an alert is noise, whether a death was sepsis-attributable, and whether a risk model is calibrated. Those disagreements belong in a morbidity-and-mortality style review, not an auto-implemented rule.
See governed multi-model AI on your own prompt
Compare GPT-5, Claude, and Gemini side by side, with human review and a decision record built in.

