Cargando…
Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management
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....
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433423/ https://www.ncbi.nlm.nih.gov/pubmed/34522847 http://dx.doi.org/10.1093/jamiaopen/ooab076 |
_version_ | 1783751375174238208 |
---|---|
author | Nong, Paige Adler-Milstein, Julia |
author_facet | Nong, Paige Adler-Milstein, Julia |
author_sort | Nong, Paige |
collection | PubMed |
description | 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 RESULTS: Utility of algorithmic risk scores was limited due to outdated claims or incomplete information about patients’ socially situated risks (eg, caregiver turnover, social isolation).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. |
format | Online Article Text |
id | pubmed-8433423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84334232021-09-13 Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management Nong, Paige Adler-Milstein, Julia JAMIA Open Case Report 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. Oxford University Press 2021-09-11 /pmc/articles/PMC8433423/ /pubmed/34522847 http://dx.doi.org/10.1093/jamiaopen/ooab076 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Case Report Nong, Paige Adler-Milstein, Julia Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management |
title | Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management |
title_full | Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management |
title_fullStr | Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management |
title_full_unstemmed | Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management |
title_short | Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management |
title_sort | socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management |
topic | Case Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433423/ https://www.ncbi.nlm.nih.gov/pubmed/34522847 http://dx.doi.org/10.1093/jamiaopen/ooab076 |
work_keys_str_mv | AT nongpaige sociallysituatedriskchallengesandstrategiesforimplementingalgorithmicriskscoringforcaremanagement AT adlermilsteinjulia sociallysituatedriskchallengesandstrategiesforimplementingalgorithmicriskscoringforcaremanagement |