High-Risk Case Identification for Use in Comprehensive Complex Care Management

Abstract

The current study aimed to develop a patient selection process for muiltimorbid care management that balances the needs to accurately identify patients who are at risk for future high costs and assures that those selected can clinically benefit from proactive care management. Six physicians were surveyed on characteristics of their current (2012) patients to elicit clinical considerations for high-risk patient identification. Data from 2010–2011 were extracted from Clalit Health Services' (Israel's largest managed care organization) comprehensive database to derive the Adjusted Clinical Groups (ACG) predictive model risk scores for risk of future high costs. Model discriminatory power was assessed using the c-statistic and positive predictive value (PPV), before and after application of the clinical exclusion criteria. Inclusion criteria were refined based on physician input from a survey on 375 patients. Recommended reasons for exclusion: active cancer, schizophrenia, dialysis, residence in nursing homes or long-term care facilities, and age 95 years or older. The high-risk group included 5341 patients (mean 50 patients per physician). The c-statistic of the ACG model before and after exclusions applied was 0.80 and 0.75, respectively. After exclusion, the PPV for the 6% highest risk patients was 40%. High-risk patients' age, number of chronic conditions, and utilization were substantially higher than those of all other patients. This study shows that a validated predictive modeling tool provides acceptable discriminatory power for selecting multimorbid patients for participation in proactive care management, even after some of the highest risk patients are excluded because of priori clinical considerations. (Population Health Management 2015;18:15–22)