ArticleBMC medical informatics and decision making2022
The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- A systematic review and comparative evaluation to develop and validate a comprehensive framework for cancer surveillance systems.Archives of public health = Archives belges de sante publique · 2025Pooled it
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Response to Bates.JNCI cancer spectrum · 2026Article
- Barriers to Health Care and Cancer Screening.JAMA network open · 2026Article
- Evaluating the association between upstream perceived individual and neighborhood determinants of health and intensity of breast cancer screening.American journal of epidemiology · 2026Article
- Role of Community-Clinical Partnerships to Promote Cancer Screening: Lessons Learned From the National Breast and Cervical Cancer Early Detection Program.Health promotion practice · 2026Article
- Small-Area Lung Cancer Incidence and Mortality: Cross-Sectional Population-Based Study Using Hospital Discharge and Death Registration Data.JMIR public health and surveillance · 2025Article
- Development of a Cervical Cancer Screening Program in Rural Guatemala.Global health, science and practice · 2025Article
- Delivering Guideline-Concordant Care for Patients With High-Risk HPV and Normal Cytologic Findings.JAMA network open · 2025Article
- Usage of the National Cancer Institute Cancer Research Data Commons by Researchers: A Scoping Review of the Literature.JCO clinical cancer informatics · 2024Article
- Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan.Healthcare informatics research · 2024Article
- Advancements in oncology nursing: Embracing technology-driven innovations.Asia-Pacific journal of oncology nursing · 2024Article
- Prostate-specific antigen testing rates in high-risk populations: results from the All of Us Research Program.Cancer causes & control : CCC · 2024Article
- Data Challenges in Identifying Patients Due for Colorectal Cancer Screening in Rural Clinics.Journal of the American Board of Family Medicine : JABFM · 2023Article
- A Collaborative Approach to Improving Breast Cancer Screening and Follow-Up Through Multicomponent Interventions and Process Improvements: The Links to Care Community Grants Project study protocol and baseline findings.Journal of primary care & community healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
introductionState cancer prevention and control programs rely on public health surveillance data to set objectives to improve cancer prevention and control, plan interventions, and evaluate state-level progress towards achieving those objectives. The goal of this project was to evaluate the validity of using electronic health records (EHRs) based on common data model variables to generate indicators for surveillance of cancer prevention and control for these public health programs.
methodsFollowing the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity.
resultsOf the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR.
conclusionCommon data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.