Cargando…
Using cancer risk algorithms to improve risk estimates and referral decisions
BACKGROUND: Cancer risk algorithms were introduced to clinical practice in the last decade, but they remain underused. We investigated whether General Practitioners (GPs) change their referral decisions in response to an unnamed algorithm, if decisions improve, and if changing decisions depends on h...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053195/ https://www.ncbi.nlm.nih.gov/pubmed/35603307 http://dx.doi.org/10.1038/s43856-021-00069-1 |
_version_ | 1784696945472700416 |
---|---|
author | Kostopoulou, Olga Arora, Kavleen Pálfi, Bence |
author_facet | Kostopoulou, Olga Arora, Kavleen Pálfi, Bence |
author_sort | Kostopoulou, Olga |
collection | PubMed |
description | BACKGROUND: Cancer risk algorithms were introduced to clinical practice in the last decade, but they remain underused. We investigated whether General Practitioners (GPs) change their referral decisions in response to an unnamed algorithm, if decisions improve, and if changing decisions depends on having information about the algorithm and on whether GPs overestimated or underestimated risk. METHODS: 157 UK GPs were presented with 20 vignettes describing patients with possible colorectal cancer symptoms. GPs gave their risk estimates and inclination to refer. They then saw the risk score of an unnamed algorithm and could update their responses. Half of the sample was given information about the algorithm’s derivation, validation, and accuracy. At the end, we measured their algorithm disposition. We analysed the data using multilevel regressions with random intercepts by GP and vignette. RESULTS: We find that, after receiving the algorithm’s estimate, GPs’ inclination to refer changes 26% of the time and their decisions switch entirely 3% of the time. Decisions become more consistent with the NICE 3% referral threshold (OR 1.45 [1.27, 1.65], p < .001). The algorithm’s impact is greatest when GPs have underestimated risk. Information about the algorithm does not have a discernible effect on decisions but it results in a more positive GP disposition towards the algorithm. GPs’ risk estimates become better calibrated over time, i.e., move closer to the algorithm. CONCLUSIONS: Cancer risk algorithms have the potential to improve cancer referral decisions. Their use as learning tools to improve risk estimates is promising and should be further investigated. |
format | Online Article Text |
id | pubmed-9053195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90531952022-05-20 Using cancer risk algorithms to improve risk estimates and referral decisions Kostopoulou, Olga Arora, Kavleen Pálfi, Bence Commun Med (Lond) Article BACKGROUND: Cancer risk algorithms were introduced to clinical practice in the last decade, but they remain underused. We investigated whether General Practitioners (GPs) change their referral decisions in response to an unnamed algorithm, if decisions improve, and if changing decisions depends on having information about the algorithm and on whether GPs overestimated or underestimated risk. METHODS: 157 UK GPs were presented with 20 vignettes describing patients with possible colorectal cancer symptoms. GPs gave their risk estimates and inclination to refer. They then saw the risk score of an unnamed algorithm and could update their responses. Half of the sample was given information about the algorithm’s derivation, validation, and accuracy. At the end, we measured their algorithm disposition. We analysed the data using multilevel regressions with random intercepts by GP and vignette. RESULTS: We find that, after receiving the algorithm’s estimate, GPs’ inclination to refer changes 26% of the time and their decisions switch entirely 3% of the time. Decisions become more consistent with the NICE 3% referral threshold (OR 1.45 [1.27, 1.65], p < .001). The algorithm’s impact is greatest when GPs have underestimated risk. Information about the algorithm does not have a discernible effect on decisions but it results in a more positive GP disposition towards the algorithm. GPs’ risk estimates become better calibrated over time, i.e., move closer to the algorithm. CONCLUSIONS: Cancer risk algorithms have the potential to improve cancer referral decisions. Their use as learning tools to improve risk estimates is promising and should be further investigated. Nature Publishing Group UK 2022-01-10 /pmc/articles/PMC9053195/ /pubmed/35603307 http://dx.doi.org/10.1038/s43856-021-00069-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kostopoulou, Olga Arora, Kavleen Pálfi, Bence Using cancer risk algorithms to improve risk estimates and referral decisions |
title | Using cancer risk algorithms to improve risk estimates and referral decisions |
title_full | Using cancer risk algorithms to improve risk estimates and referral decisions |
title_fullStr | Using cancer risk algorithms to improve risk estimates and referral decisions |
title_full_unstemmed | Using cancer risk algorithms to improve risk estimates and referral decisions |
title_short | Using cancer risk algorithms to improve risk estimates and referral decisions |
title_sort | using cancer risk algorithms to improve risk estimates and referral decisions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053195/ https://www.ncbi.nlm.nih.gov/pubmed/35603307 http://dx.doi.org/10.1038/s43856-021-00069-1 |
work_keys_str_mv | AT kostopoulouolga usingcancerriskalgorithmstoimproveriskestimatesandreferraldecisions AT arorakavleen usingcancerriskalgorithmstoimproveriskestimatesandreferraldecisions AT palfibence usingcancerriskalgorithmstoimproveriskestimatesandreferraldecisions |