ArticleEGEMS (Washington, DC)2016
Developing an Algorithm to Identify History of Cancer Using Electronic Medical Records.
Article in EGEMS (Washington, DC), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Developing an algorithm across integrated healthcare systems to identify a history of cancer using electronic medical records.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2021Article
- Data Integration to Improve Real-world Health Outcomes Research for Non-Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration.JMIR cancer · 2021Article
- Gap between real-world data and clinical research within hospitals in China: a qualitative study.BMJ open · 2020Article
- Utilization of the Cancer Medications Enquiry Database (CanMED)-National Drug Codes (NDC): Assessment of Systemic Breast Cancer Treatment Patterns.Journal of the National Cancer Institute. Monographs · 2020Article
- Development and Utility of the Observational Research in Oncology Toolbox: Cancer Medications Enquiry Database-Healthcare Common Procedure Coding System (HCPCS).Journal of the National Cancer Institute. Monographs · 2020Article
- Population-Based Registry Linkages to Improve Validity of Electronic Health Record-Based Cancer Research.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- Structured Approach for Evaluating Strategies for Cancer Ascertainment Using Large-Scale Electronic Health Record Data.JCO clinical cancer informatics · 2018Article
- Early Post-Therapy Prescription Drug Usage among Childhood and Adolescent Cancer Survivors.The Journal of pediatrics · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION/
objectiveThe objective of this study was to develop an algorithm to identify Kaiser Permanente Colorado (KPCO) members with a history of cancer.
backgroundTumor registries are used with high precision to identify incident cancer, but are not designed to capture prevalent cancer within a population. We sought to identify a cohort of adults with no history of cancer, and thus, we could not rely solely on the tumor registry.
methodsWe included all KPCO members between the ages of 40-75 years who were continuously enrolled during 2013 (N=201,787). Data from the tumor registry, chemotherapy files, inpatient and outpatient claims were used to create an algorithm to identify members with a high likelihood of cancer. We validated the algorithm using chart review and calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for occurrence of cancer.
findingsThe final version of the algorithm achieved a sensitivity of 100 percent and specificity of 84.6 percent for identifying cancer. If we relied on the tumor registry alone, 47 percent of those with a history of cancer would have been missed. DISCUSSION: Using the tumor registry alone to identify a cohort of patients with prior cancer is not sufficient. In the final version of the algorithm, the sensitivity and PPV were improved when a diagnosis code for cancer was required to accompany oncology visits or chemotherapy administration.
conclusionElectronic medical record (EMR) data can be used effectively in combination with data from the tumor registry to identify health plan members with a history of cancer.
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