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Development of CancerLinQ, a Health Information Learning Platform From Multiple Electronic Health Record Systems to Support Improved Quality of Care.

Authors :
Potter, Danielle
Brothers, Raven
Kolacevski, Andrej
Koskimaki, Jacob E.
McNutt, Amy
Miller, Robert S.
Nagda, Jatin
Nair, Anil
Rubinstein, Wendy S.
Stewart, Andrew K.
Trieb, Iris J.
Komatsoulis, George A.
Source :
JCO Clinical Cancer Informatics. 10/26/2020, Vol. 4, p929-937. 9p.
Publication Year :
2020

Abstract

PURPOSE: ASCO, through its wholly owned subsidiary, CancerLinQ LLC, developed CancerLinQ, a learning health system for oncology. A learning health system is important for oncology patients because less than 5% of patients with cancer enroll in clinical trials, leaving evidence gaps for patient populations not enrolled in trials. In addition, clinical trial populations often differ from the overall cancer population with respect to age, race, performance status, and other clinical parameters. MATERIALS AND METHODS: Working with subscribing practices, CancerLinQ accepts data from electronic health records and transforms the local representation of a patient's care into a standardized representation on the basis of the Quality Data Model from the National Quality Forum. CancerLinQ provides this information back to the subscribing practice through a series of tools that support quality improvement. CancerLinQ also creates de-identified data sets for secondary research use. RESULTS: As of March 2020, CancerLinQ includes data from 63 organizations across the United States that use nine different electronic health records. The database includes 1,426,015 patients with a primary cancer diagnosis, of which 238,680 have had additional information abstracted from unstructured content. CONCLUSION: As CancerLinQ continues to onboard subscribing practices, the breadth of potential applications for a learning health care system widen. Future practice-facing tools could include real-world data visualization, recommendations for treatment of patients with actionable genetic variations, and identification of patients who may be eligible for clinical trials. Feeding these insights back into oncology practice ensures that we learn how to treat patients with cancer not just on the basis of the selective experience of the 5% that enroll in clinical trials, but from the real-world experience of the entire spectrum of patients with cancer in the United States. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
24734276
Volume :
4
Database :
Academic Search Index
Journal :
JCO Clinical Cancer Informatics
Publication Type :
Academic Journal
Accession number :
146635945
Full Text :
https://doi.org/10.1200/CCI.20.00064