Related Experiment Video
Updated: Jun 10, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Creating a Learning Health System in a Cancer Center: Generalizability of an Electronic Health Record Phenotype for
Anne M Walling1,2,3, Karl A Lorenz3,4,5, Anita Yuan1
1VA Greater Los Angeles Health System, Los Angeles, CA.
Purpose:
To test the generalizability of an electronic health record (EHR) phenotype for patients with advanced solid cancer, which was previously developed in a single cancer center.
Methods:
We compared an algorithm to identify patients with advanced solid cancer from a random sample of patients with active cancer in the Veterans Health Administration (VA) and an academic cancer center with a human-coded reference standard between January 1, 2016, and December 31, 2019.
Results:
Compared with the human-coded reference standard, the algorithm had high specificity (93%; 95% CI, 87 to 99 and 97%; 95% CI, 93 to 100) and reasonable sensitivity (85%; 95% CI, 76 to 94 and 87%; 95% CI, 77 to 97) in the VA and academic cancer center populations, respectively. Patients with advanced cancer (compared with those with active nonadvanced cancer) had higher mortality at the VA and academic cancer center (29.2% and 17.0% 6-month mortality v 6.8% and 3.5%), respectively.
Conclusion:
This EHR phenotype can be used to measure and improve the quality of palliative care for patients with advanced cancer within and across health care settings.
