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Not all phenotypes are created equal: covariates of success in e-phenotype specification
BACKGROUND: Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phen...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846689/ https://www.ncbi.nlm.nih.gov/pubmed/36069977 http://dx.doi.org/10.1093/jamia/ocac157 |
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author | Hamidi, Bashir Flume, Patrick A Simpson, Kit N Alekseyenko, Alexander V |
author_facet | Hamidi, Bashir Flume, Patrick A Simpson, Kit N Alekseyenko, Alexander V |
author_sort | Hamidi, Bashir |
collection | PubMed |
description | BACKGROUND: Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results. MATERIALS AND METHODS: Noninformaticist experts (n = 21) were recruited to produce expert-mediated e-phenotypes using i2b2 assisted by a honest data-broker and a project coordinator. Patient- and visit-sets were reidentified and a random sample of 20 charts matching each e-phenotype was returned to experts for chart-validation. Attributes of the queries and expert characteristics were captured and related to chart-validation rates using generalized linear regression models. RESULTS: E-phenotype validation rates varied according to experts’ domains and query characteristics (mean = 61%, range 20–100%). Clinical domains that performed better included infectious, rheumatic, neonatal, and cancers, whereas other domains performed worse (psychiatric, GI, skin, and pulmonary). Match-rate was negatively impacted when specification of temporal constraints was required. In general, the increase in e-phenotype specificity contributed positively to match-rate. DISCUSSIONS AND CONCLUSIONS: Clinical experts and informaticists experience a variety of challenges when building e-phenotypes, including the inability to differentiate clinical events from patient characteristics or appropriately configure temporal constraints; a lack of access to available and quality data; and difficulty in specifying routes of medication administration. Biomedical query mediation by informaticists and honest data-brokers in designing e-phenotypes cannot be overstated. Although tools such as i2b2 may be widely available to noninformaticists, successful utilization depends not on users’ confidence, but rather on creating highly specific e-phenotypes. |
format | Online Article Text |
id | pubmed-9846689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98466892023-01-20 Not all phenotypes are created equal: covariates of success in e-phenotype specification Hamidi, Bashir Flume, Patrick A Simpson, Kit N Alekseyenko, Alexander V J Am Med Inform Assoc Research and Applications BACKGROUND: Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results. MATERIALS AND METHODS: Noninformaticist experts (n = 21) were recruited to produce expert-mediated e-phenotypes using i2b2 assisted by a honest data-broker and a project coordinator. Patient- and visit-sets were reidentified and a random sample of 20 charts matching each e-phenotype was returned to experts for chart-validation. Attributes of the queries and expert characteristics were captured and related to chart-validation rates using generalized linear regression models. RESULTS: E-phenotype validation rates varied according to experts’ domains and query characteristics (mean = 61%, range 20–100%). Clinical domains that performed better included infectious, rheumatic, neonatal, and cancers, whereas other domains performed worse (psychiatric, GI, skin, and pulmonary). Match-rate was negatively impacted when specification of temporal constraints was required. In general, the increase in e-phenotype specificity contributed positively to match-rate. DISCUSSIONS AND CONCLUSIONS: Clinical experts and informaticists experience a variety of challenges when building e-phenotypes, including the inability to differentiate clinical events from patient characteristics or appropriately configure temporal constraints; a lack of access to available and quality data; and difficulty in specifying routes of medication administration. Biomedical query mediation by informaticists and honest data-brokers in designing e-phenotypes cannot be overstated. Although tools such as i2b2 may be widely available to noninformaticists, successful utilization depends not on users’ confidence, but rather on creating highly specific e-phenotypes. Oxford University Press 2022-09-07 /pmc/articles/PMC9846689/ /pubmed/36069977 http://dx.doi.org/10.1093/jamia/ocac157 Text en © The Author(s) 2022. 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-NonCommercial 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 | Research and Applications Hamidi, Bashir Flume, Patrick A Simpson, Kit N Alekseyenko, Alexander V Not all phenotypes are created equal: covariates of success in e-phenotype specification |
title | Not all phenotypes are created equal: covariates of success in e-phenotype specification |
title_full | Not all phenotypes are created equal: covariates of success in e-phenotype specification |
title_fullStr | Not all phenotypes are created equal: covariates of success in e-phenotype specification |
title_full_unstemmed | Not all phenotypes are created equal: covariates of success in e-phenotype specification |
title_short | Not all phenotypes are created equal: covariates of success in e-phenotype specification |
title_sort | not all phenotypes are created equal: covariates of success in e-phenotype specification |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846689/ https://www.ncbi.nlm.nih.gov/pubmed/36069977 http://dx.doi.org/10.1093/jamia/ocac157 |
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