Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management

Abstract

Objective To characterize challenges and strategies related to algorithmic risk scoring for care management eligibility determinations. Materials and Methods Interviews with 19 administrators from 13 physician organizations representing over 2200 physician offices and 8800 physicians in Michigan. Post-implementation interviews were coded using thematic analysis. Results Utility of algorithmic risk scores was limited due to outdated claims or incomplete information about patients’ socially situated risks (eg, caregiver turnover, social isolation). Resulting challenges included lack of physician engagement and inefficient use of staff time reviewing eligibility determinations. To address these challenges, risk scores were supplemented with physician knowledge and clinical data. Discussion and Conclusion Current approaches to risk scoring based on claims data for payer-led programs struggle to gain physician acceptance and support because of data limitations. To respond to these limitations, physician input regarding socially situated risk and utilization of more timely data may improve eligibility determinations.

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study (Preprint)

“…Due to the gap between the predictive AI's accuracy and its lack of observed impact on health outcomes, many researchers have studied health professional perceptions of ML-based tools and related implementation challenges. 17,[22][23][24][25][26][27] However, patient perspectives of AI have been understudied. [28][29][30] While some predictive AI developers may not intend patients to view the AI's output on their own, it has become more likely that patients may have access to predictive AI output, due to recent advances in patient data ownership and access.…”