Trial of Electronic Medical Record Integrated Next-Generation Sequencing Ordering in Veterans Affairs Cancer Care

Author:

Stoeckle James H.1ORCID,Poland Sarah G.2ORCID,Maynard Hannah2,Roman Stefanie D.3ORCID,Mettman Daniel24ORCID,Makarov Danil V.24,Sherman Scott24ORCID,Becker Daniel J.24ORCID

Affiliation:

1. Dana-Farber Cancer Institute, Boston, MA

2. New York University School of Medicine, NYU Langone Health, New York, NY

3. TrialSpark, New York, NY

4. VA NY Harbor Healthcare System, New York, NY

Abstract

PURPOSE Previous studies document underuse of next-generation sequencing (NGS). We examined the impact to oncology care for veterans of incorporating NGS ordering into the Veterans Affairs (VA) electronic medical record (EMR) at two New York City VA Medical Centers. METHODS We identified patients with non–small cell lung cancer and prostate cancer with oncology clinic visits and NGS testing indications between January and December 2021. Patients were divided into external ordering (EO) with visits before we implemented an EMR ordering system for NGS in July 2021, and internal ordering (IO) with visits after this date. The primary outcome was proportion of NGS testing performed in EO versus IO groups. Secondary outcomes were time between metastatic disease diagnosis to receipt of test by vendor, time of metastatic diagnosis to result, and proportion of testing by race. RESULTS A total of 168 patients were identified, 116 EO and 52 IO patients. Between IO and EO periods, testing significantly increased from 52% to 87% ( P ≤ .01); it was conducted more quickly, with time from metastatic diagnosis to sample receipt by the NGS vendor improving to median 37 days from 299 days ( P = .03); and the time from documented metastatic disease to a test result improved to median 56 days from 309 days ( P = .03). The proportion of tissue received by the vendor was not significantly different between the two groups. There were no significant differences in testing according to self-reported race. CONCLUSION Integration of NGS ordering in the EMR led to increased proportion and speed of testing for a vulnerable patient population served by the country's largest health system.

Publisher

American Society of Clinical Oncology (ASCO)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimizing Cancer Patient Classification Forecasting With Bayesian Pattern Recognition;International Journal of Healthcare Information Systems and Informatics;2024-08-14

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