AI Sepsis Alert Fatigue Analysis Playbook

A 600-bed academic medical center's sepsis alert system fired 14,200 alerts in Q3. Nursing compliance with the 1-hour bundle was 61%. A mortality review found that 8 of 14 sepsis deaths in Q3 had documented alerts that were dismissed or delayed. The CMO has asked for a root cause analysis before the next quality committee meeting.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A 600-bed academic medical center's sepsis alert system fired 14,200 alerts in Q3.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Sepsis Alert Fatigue Analysis".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Q3 sepsis alert log (14,200 alerts: time, patient, unit, alert type, nurse response time)
• 1-hour bundle compliance data by unit and shift
• Mortality review summaries for the 8 cases
• Alert threshold parameters for the current sepsis algorithm
• Nurse staffing ratios by unit and shift

Attachments: Documents (Documents)

You are a clinical quality analyst investigating sepsis alert fatigue at a 600-bed academic medical center. I am attaching:

Work only from the attached source files. If a conclusion is not supported, say so.

Produce:
1. Calculate the true positive rate of the sepsis alerts in Q3: what percentage of alerts resulted in confirmed sepsis diagnosis? Identify the alert burden per nurse per shift.
2. For the 8 mortality cases, reconstruct the alert-to-response timeline: how long did each alert sit before action, and was the delay correlated with shift, unit, or staffing ratio?
3. Identify the alert parameters most associated with false positives—the specific vital sign or lab combinations that fire alerts on non-septic patients.
4. Recommend algorithm modifications to reduce false-positive alerts without increasing missed sepsis events.
5. Tell me what to present to the CMO and the quality committee and what CMS Sepsis Core Measure implications exist.

Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.

What to expect

• Alert true positive rate and per-nurse alert burden
• Mortality case timeline analysis
• False positive parameter identification
• Algorithm modification recommendations
• CMO briefing language and CMS core measure implications

Review before you act

• Validate this output against source files before relying on it: Calculate the true positive rate of the sepsis alerts in Q3: what percentage of alerts resulted in confirmed sepsis diagnosis? Identify the alert burden per nurse per shift.
• Validate this output against source files before relying on it: For the 8 mortality cases, reconstruct the alert-to-response timeline: how long did each alert sit before action, and was the delay correlated with shift, unit, or staffing ratio?.
• Validate this output against source files before relying on it: Identify the alert parameters most associated with false positives—the specific vital sign or lab combinations that fire alerts on non-septic patients.
• Validate this output against source files before relying on it: Recommend algorithm modifications to reduce false-positive alerts without increasing missed sepsis events.
• 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.