Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach

Jianlin Shi; Keaton L Morgan; Richard L Bradshaw; Se-Hee Jung; Wendy Kohlmann; Kimberly A Kaphingst; Kensaku Kawamoto; Guilherme Del Fiol

doi:10.2196/37842

Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach

JMIR Med Inform. 2022 Aug 11;10(8):e37842. doi: 10.2196/37842.

Authors

Jianlin Shi^{1

2

3}, Keaton L Morgan^{3

4}, Richard L Bradshaw³, Se-Hee Jung^{3

5}, Wendy Kohlmann^{6

7}, Kimberly A Kaphingst^{7

8}, Kensaku Kawamoto³, Guilherme Del Fiol³

Affiliations

¹ Veterans Affairs Informatics and Computing Infrastructure, Department of Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, United States.
² Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States.
³ Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
⁴ Department of Emergency Medicine, University of Utah, Salt Lake City, UT, United States.
⁵ College of Nursing, University of Utah, Salt Lake City, UT, United States.
⁶ Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States.
⁷ Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.
⁸ Department of Communication, University of Utah, Salt Lake City, UT, United States.

PMID: 35969459
PMCID: PMC9412758
DOI: 10.2196/37842

Abstract

Background: Family health history has been recognized as an essential factor for cancer risk assessment and is an integral part of many cancer screening guidelines, including genetic testing for personalized clinical management strategies. However, manually identifying eligible candidates for genetic testing is labor intensive.

Objective: The aim of this study was to develop a natural language processing (NLP) pipeline and assess its contribution to identifying patients who meet genetic testing criteria for hereditary cancers based on family health history data in the electronic health record (EHR). We compared an algorithm that uses structured data alone with structured data augmented using NLP.

Methods: Algorithms were developed based on the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing for hereditary breast, ovarian, pancreatic, and colorectal cancers. The NLP-augmented algorithm uses both structured family health history data and the associated unstructured free-text comments. The algorithms were compared with a reference standard of 100 patients with a family health history in the EHR.

Results: Regarding identifying the reference standard patients meeting the NCCN criteria, the NLP-augmented algorithm compared with the structured data algorithm yielded a significantly higher recall of 0.95 (95% CI 0.9-0.99) versus 0.29 (95% CI 0.19-0.40) and a precision of 0.99 (95% CI 0.96-1.00) versus 0.81 (95% CI 0.65-0.95). On the whole data set, the NLP-augmented algorithm extracted 33.6% more entities, resulting in 53.8% more patients meeting the NCCN criteria.

Conclusions: Compared with the structured data algorithm, the NLP-augmented algorithm based on both structured and unstructured family health history data in the EHR increased the number of patients identified as meeting the NCCN criteria for genetic testing for hereditary breast or ovarian and colorectal cancers.

Keywords: clinical natural language processing; cohort identification; family health history extraction; genetic testing of hereditary cancers.

©Jianlin Shi, Keaton L Morgan, Richard L Bradshaw, Se-Hee Jung, Wendy Kohlmann, Kimberly A Kaphingst, Kensaku Kawamoto, Guilherme Del Fiol. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 11.08.2022.

Abstract

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