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

Paige Nong, Julia Adler-Milstein

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 Results: Utility of algorithmic risk scores was limited due to outdated claims or incomplete information about patients socially situated risks (eg, caregiver turnover, social isolation).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 ofmore timely datamay improve eligibility determinations.

Original languageEnglish (US)
Article numberooab076
JournalJAMIA Open
Volume4
Issue number3
DOIs
StatePublished - Jul 1 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Oxford University Press. All rights reserved.

Keywords

  • algorithms
  • patient care management
  • patient selection
  • risk assessment

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