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Cited by 57Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationMedical Coding and Health Information

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

Open access
Potter, Danielle, Brothers, Raven, Kolacevski, Andrej, Koskimaki, Jacob E., McNutt, Amy, Miller, Robert S., Nagda, Jatin, Nair, Anil +4 more·2020·JCO Clinical Cancer Informatics
DOI 10.1200/cci.20.00064PMC PMC7608629
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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.

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Crossref · 10.1200/cci.20.00064

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  • The American Society of Clinical Oncology, through its subsidiary CancerLinQ, developed a platform that enables researchers to utilize a wealth of data from patients with cancer to help guide optimal treatment and improve outcomes.

    “The American Society of Clinical Oncology, through its subsidiary called CancerLinQ, developed a platform that enables researchers to utilize a wealth of data from patients with cancer to help guide optimal treatment and improve outcomes 73 .”

    From Multimodal biomedical AI (2022) · cites this paper as [73]

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Authors
Potter, D., Brothers, R., Kolacevski, A., Koskimaki, J. E., McNutt, A., Miller, R. S., Nagda, J., Nair, A., Rubinstein, W. S., Stewart, A. K., Trieb, I. J., & Komatsoulis, G. A.
Affiliations
American Society of Clinical Oncology (US)
Corresponding author
Potter, Danielle
ORCID
Venue
JCO Clinical Cancer Informatics
Publisher
American Society of Clinical Oncology (ASCO)
Published
2020-11
Volume
4
Issue
4
Pages
929-937
ISSN
2473-4276 (ISSN-L)
DOI
10.1200/cci.20.00064
PMC
PMC7608629
OpenAlex
W3094583104
Cited by
57
References
13
Citations by year
2026: 4 · 2025: 9 · 2024: 14 · 2023: 10 · 2022: 11 · 2021: 9
FWCI
6.59
Type
journal article
Language
en
Open access
Locations
  • JCO Clinical Cancer Informatics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
Keywords
CancerLinQ, learning health systems, electronic health records, quality improvement, real-world data, inclusion and exclusion criteria, quality of cancer care
MeSH terms
Data Accuracy, Humans, Medical Oncology, Neoplasms, Societies, Medical, United States, Electronic Health Records

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