AI Playbook for Sepsis Readmission Reduction

A hospital's 30-day sepsis readmission rate is 22%, against a national benchmark of 16%. CMS readmission penalties are in effect. Analysis shows that 60% of readmissions occur within 10 days of discharge, and the most common readmission diagnoses are recurrent infection, fluid and electrolyte imbalance, and AKI.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A hospital's 30-day sepsis readmission rate is 22%, against a national benchmark of 16%.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Sepsis Readmission Reduction".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Sepsis readmission data (past 12 months: readmission date, primary diagnosis, LOS at initial admission, discharge disposition)
• Discharge summary review for 50 readmitted cases
• Transition of care protocol documentation
• Home health and SNF discharge utilization
• CMS readmission penalty calculation and projected penalty amount

Attachments: Documents (Documents)

You are a quality improvement specialist developing a sepsis readmission reduction plan for a hospital facing CMS penalties. I am attaching:

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

Produce:
1. Identify the root causes of readmission for the top 3 readmission diagnoses: what in the original hospitalization or discharge process led to each readmission pattern?
2. Assess the discharge planning for the 50 reviewed cases: were follow-up appointments scheduled, were medications reconciled, was adequate home support arranged?
3. Identify the highest-risk patients at discharge: what clinical and social factors at the time of discharge are most predictive of 10-day readmission?
4. Develop the post-discharge intervention: what level of follow-up (phone call, home health, rapid follow-up clinic) reduces readmission risk for high-risk patients?
5. Tell me the projected readmission rate reduction from each intervention and the net financial impact after accounting for the cost of the interventions.

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

What to expect

• Readmission root cause by diagnosis
• Discharge planning gap analysis for 50 cases
• High-risk patient identification criteria
• Post-discharge intervention recommendations by risk level
• Readmission rate reduction projection and net financial impact

Review before you act

• Validate this output against source files before relying on it: Identify the root causes of readmission for the top 3 readmission diagnoses: what in the original hospitalization or discharge process led to each readmission pattern?.
• Validate this output against source files before relying on it: Assess the discharge planning for the 50 reviewed cases: were follow-up appointments scheduled, were medications reconciled, was adequate home support arranged?.
• Validate this output against source files before relying on it: Identify the highest-risk patients at discharge: what clinical and social factors at the time of discharge are most predictive of 10-day readmission?.
• Validate this output against source files before relying on it: Develop the post-discharge intervention: what level of follow-up (phone call, home health, rapid follow-up clinic) reduces readmission risk for high-risk patients?.
• 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.