Facilitating the identification of patients hospitalized for acute myocardial infarction and heart failure and the assessment of their readmission risk through the Patient Navigator Program.

Document Type


Publication Date


Publication Title

American heart journal




BACKGROUND: Optimal transition care mitigates early hospital readmission risk. Given limited resources, hospitals need to identify patients with high readmission risk. This article examines whether a coordinated quality improvement campaign can help achieve this objective.

METHODS: The American College of Cardiology Patient Navigator Program, a 2-year quality improvement campaign, sought to assess the impact of transition care interventions on 30-day readmission rates for patients with acute myocardial infarction (AMI) or heart failure (HF) at 35 hospitals. This article examines the change in 2 of the 36 performance metrics the campaign tracked: the number of AMI and HF patients identified predischarge and those whose readmission risk was assessed.

RESULTS: The number of facilities identifying AMI and HF patients predischarge increased from 24 (68.6%) and 28 (80.0%), respectively, at baseline, to 34 (97.1%) (P = .0016) and 34 (97.1%) (P = .014), respectively, at 2 years. The number of facilities assessing the readmission risk of AMI and HF patients risk increased from 9 (25.7%) and 11 (31.4%), respectively, at baseline, to 32 (91.4%) (P < .0001) and 33 (94.5%) (P < .0001), respectively, at 2 years. Importantly, baseline reporting of performance for both metrics was poor, with >25% of the hospitals missing data.

CONCLUSIONS: Implementation of a coordinated quality improvement campaign may increase the number of facilities identifying AMI and HF patients predischarge and assessing their readmission risk. Further research is needed to determine if increased identification reduces 30-day readmission or facilitates improvement in other important clinical outcomes.

Clinical Institute

Cardiovascular (Heart)




Center for Cardiovascular Analytics, Research + Data Science (CARDS)