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Zuckerberg San Francisco General Hospital cuts heart failure readmission rate from 34% to 19% with Epic predictive model and decision support

“Zuckerberg San Francisco General Hospital cuts heart failure readmission rate from 34% to 19% with Epic predictive model and decision support” documents a Clinical Decision Support deployment in Hospital & Health System at Zuckerberg San Francisco General Hospital. www.hcinnovationgroup.com reports readmission rate reduction: 34% → 19% (>13 percentage points); this directory has not independently verified that result.

Maintained by Peter Korpak, Founder & Chief AnalystHow evidence is checked

Evidence at a glance

Evidence status:
Automated evidence gate passed
Deployment timeframe:
Not reported by source
Reported outcome metrics:
3 cited below
Directory entry published:
Source link checked:

The source-link check confirms reachability, not independent re-verification of every claim.

34% → 19% (>13 percentage points)Readmission Rate Reduction
$8M saved on $1M investment (~8x ROI)Cost Savings vs. Investment
6%Heart Failure Mortality Reduction

Source-reported figures — cited source: www.hcinnovationgroup.com

Zuckerberg San Francisco General Hospital
Metric Before After Impact
Readmission Rate 34% 19% 13 percentage point reduction
Cost Savings $1M $8M ~8x ROI
Heart Failure Mortality 6% reduction 6% reduction
Readmission Equity Gap Existing Eliminated Fully eliminated by 2022

The Challenge

As of 2016, ZSFG had some of the worst heart failure readmission rates in California among safety net hospitals, with a peak readmission rate of ~34% and elevated mortality rates. The hospital faced ~$1.2M in at-risk funding tied to readmission performance metrics under the Hospital Readmission Reduction Program. Significant equity gaps existed, with Black/African-American patients experiencing worse outcomes than the general heart failure population.

The Solution

ZSFG localized Epic's readmission risk model to their population and built a decision support ecosystem on top of it using Epic's logic-block tooling. The system surfaced personalized, guideline-based recommendations to providers at the point of care — including medication guidance and substance use referral triggers. A population health dashboard enabled care teams to proactively identify and outreach high-risk patients in the community before readmissions occurred, and high-risk patients received prioritized cardiology referral queue placement.

Results

From 2019 to 2024, readmission rates dropped from ~34% to ~19% — an over 13-percentage-point reduction — moving ZSFG from among the worst to one of the best-performing safety net hospitals in California. Mortality among heart failure patients fell by 6%, and by 2022 the readmission equity gap between Black/African-American patients and the general population was fully eliminated. The program cost approximately $1M to develop but generated close to $8M in savings.

Key Takeaways

  • Pairing a predictive risk model with embedded clinical decision support — rather than just surfacing a risk score — is critical to driving provider action and measurable outcomes.
  • Safety net hospitals can achieve outsized ROI from AI-driven care programs; a $1M investment yielded ~$8M in savings while also reducing mortality and eliminating equity gaps.
  • Sharing AI innovations across a collaboratory of safety net hospitals can democratize access to costly tools that individual resource-limited systems could not build independently.

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Details

Company Size
Enterprise
Evidence status
Automated evidence gate passed
Deployment timeframe
Not reported by source
Directory entry published
Source link checked

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