AI Playbook for Sepsis-Related Length of Stay Reduction

A hospital's average LOS for sepsis patients is 9.4 days, compared to a peer benchmark of 7.1 days. The excess LOS costs approximately $4,200 per patient. The hospital treats 820 sepsis patients annually, resulting in an estimated $1.8M in excess costs. The CFO and CMO jointly want a reduction plan.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A hospital's average LOS for sepsis patients is 9.4 days, compared to a peer benchmark of 7.1 days.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Sepsis-Related Length of Stay Reduction".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Sepsis patient LOS data (past 12 months, by case, by unit, by severity)
• Discharge disposition data (home, SNF, rehab, LTACH)
• Care coordination and case management workflow documentation
• Peer benchmark LOS by severity (APACHE II or equivalent)
• Readmission rates for sepsis discharges (30-day)

Attachments: Documents (Documents)

You are a clinical operations specialist developing a sepsis LOS reduction plan for a joint CMO/CFO initiative. I am attaching:

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

Produce:
1. Decompose the LOS variance: how much is explained by patient severity (sicker patients = longer stays) vs. discharge planning delays vs. clinical pathway variation?
2. Identify the LOS outliers (cases >14 days) and their common characteristics: co-morbidities, complications, disposition barriers.
3. Calculate the achievable LOS reduction if discharge planning delays were eliminated, and the associated cost savings.
4. Develop the clinical pathway and care coordination changes: what specific interventions (daily goals, early PT, discharge planning at admission) have the strongest evidence base.
5. Tell me the 30-day readmission risk of aggressive LOS reduction and how to monitor for harm.

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

What to expect

• LOS variance decomposition by cause
• Outlier case analysis
• Discharge planning delay cost savings estimate
• Evidence-based intervention recommendations
• Readmission risk monitoring plan

Review before you act

• Validate this output against source files before relying on it: Decompose the LOS variance: how much is explained by patient severity (sicker patients = longer stays) vs. discharge planning delays vs. clinical pathway variation?.
• Validate this output against source files before relying on it: Identify the LOS outliers (cases >14 days) and their common characteristics: co-morbidities, complications, disposition barriers.
• Validate this output against source files before relying on it: Calculate the achievable LOS reduction if discharge planning delays were eliminated, and the associated cost savings.
• Validate this output against source files before relying on it: Develop the clinical pathway and care coordination changes: what specific interventions (daily goals, early PT, discharge planning at admission) have the strongest evidence base.
• 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.