The aim of the present study is to use the ACG (Adjusted Clinical Groups) System to create an impactibility model by identifying homogeneous clinical subgroups of patients with high risk of an adverse health outcome in a population of heart failure patients with complex health care needs (PCHCN). This method will allow policy makers to target and prioritize services for the highest risk PCHCN in the context of limited health care resources, by identifying relatively homogeneous groups of patients with similar comorbidities. Subjects classified in 2012 as PCHCN in a local health unit by the ACG System were linked with hospital discharge records in 2013. The authors applied the Apriori algorithm to identify the most common sets of the most predictive diseases for the following outcomes of interest: at least 1 admission and at least 1 preventable admission in the year. Predictive performance for the former outcome was compared between the impactability model with the available ACG's individual risk score. The Apriori algorithm also was applied to predict the latter outcome as an example of an event that a policy maker would be able to prevent. Evidence showed no statistically significant difference between the 2 methods. The present model also displayed evidence of good calibration. The Apriori algorithm was applied as an impactibility model, built based on the ACG System, that allowed the authors to obtain an "ACG-based group risk score" and use it to identify clinically homogeneous subgroups of PCHCN. This will help policy makers develop "tool kits" for homogeneous groups of patients that improve health outcomes.