Evidence map›Paper›PMID 38081564›Full record

ArticleJournal of biomedical informatics2024

Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own.

Richard L Bradshaw, Kensaku Kawamoto, Jemar R Bather, Melody S Goodman, Wendy K Kohlmann, Daniel Chavez-Yenter, Molly Volkmar, Rachel Monahan, Kimberly A Kaphingst, Guilherme Del Fiol

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Article in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Richard L BradshawDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; University of Utah Health, Salt Lake City, UT, USA.
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; University of Utah Health, Salt Lake City, UT, USA.
Jemar R BatherDepartment of Biostatistics, New York University School of Global Public Health, New York, NY, USA; Center for Anti-racism, Social Justice, & Public Health, New York University School of Global Public Health, New York, NY, USA.
Melody S GoodmanDepartment of Biostatistics, New York University School of Global Public Health, New York, NY, USA; Center for Anti-racism, Social Justice, & Public Health, New York University School of Global Public Health, New York, NY, USA.
Wendy K KohlmannUniversity of Utah Health, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Daniel Chavez-YenterHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA; Department of Communication, University of Utah, Salt Lake City, UT, USA.
Molly VolkmarHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Rachel MonahanNew York University Langone Health, New York, NY, USA.
Kimberly A KaphingstHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA; Department of Communication, University of Utah, Salt Lake City, UT, USA.
Guilherme Del FiolDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; University of Utah Health, Salt Lake City, UT, USA. Electronic address: guilherme.delfiol@utah.edu.

Funding

Leveraging an electronic medical record infrastructure to identify primary care patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery modelsU01CA232826 · NCI · UNIVERSITY OF UTAH · PI KAPHINGST, KIMBERLY A, SIGIREDDI, MEENAKSHI · 2018 to 2022
$5.5M
Scalable Clinical Decision Support for Individualized Cancer Risk ManagementU24CA204800 · NCI · UNIVERSITY OF UTAH · PI DEL FIOL, GUILHERME, KAWAMOTO, KENSAKU · 2017 to 2021
$3.7M
GARDE: Scalable Clinical Decision Support for Individualized Cancer Risk ManagementU24CA274582 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI GUILHERME DEL FIOL, Kensaku Kawamoto · 2023 to 2026
$3.3M
NCI NIH HHS U01 CA232826NCI NIH HHS U24 CA204800NCI NIH HHS U24 CA274582
6 · The paper itself

Abstract

objectiveThis study aimed to 1) investigate algorithm enhancements for identifying patients eligible for genetic testing of hereditary cancer syndromes using family history data from electronic health records (EHRs); and 2) assess their impact on relative differences across sex, race, ethnicity, and language preference. MATERIALS AND

methodsThe study used EHR data from a tertiary academic medical center. A baseline rule-base algorithm, relying on structured family history data (structured data; SD), was enhanced using a natural language processing (NLP) component and a relaxed criteria algorithm (partial match [PM]). The identification rates and differences were analyzed considering sex, race, ethnicity, and language preference.

resultsAmong 120,007 patients aged 25-60, detection rate differences were found across all groups using the SD (all P < 0.001). Both enhancements increased identification rates; NLP led to a 1.9 % increase and the relaxed criteria algorithm (PM) led to an 18.5 % increase (both P < 0.001). Combining SD with NLP and PM yielded a 20.4 % increase (P < 0.001). Similar increases were observed within subgroups. Relative differences persisted across most categories for the enhanced algorithms, with disproportionately higher identification of patients who are White, Female, non-Hispanic, and whose preferred language is English.

conclusionAlgorithm enhancements increased identification rates for patients eligible for genetic testing of hereditary cancer syndromes, regardless of sex, race, ethnicity, and language preference. However, differences in identification rates persisted, emphasizing the need for additional strategies to reduce disparities such as addressing underlying biases in EHR family health information and selectively applying algorithm enhancements for disadvantaged populations. Systematic assessment of differences in algorithm performance across population subgroups should be incorporated into algorithm development processes.

Indexed as

AlgorithmsNeoplastic Syndromes, HereditaryElectronic Health RecordsFemaleGenetic TestingHumansNatural Language ProcessingAlgorithm developmentElectronic health recordsGenetic testingHealthcare disparitiesHereditary cancer syndromes

Identifiers

PMID38081564
PMCPMC10842777

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.