Evidence map›Paper›PMID 25992389›Full record

ArticleEGEMS (Washington, DC)2015

Linking electronic health records to better understand breast cancer patient pathways within and between two health systems.

Caroline A Thompson, Allison W Kurian, Harold S Luft

Abstract read
In one paragraph

Article in EGEMS (Washington, DC), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Article
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  8. Review
  9. Integrating Electronic Health Record, Cancer Registry, and Geospatial Data to Study Lung Cancer in Asian American, Native Hawaiian, and Pacific Islander Ethnic Groups.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021
    Article
  10. Observational
  11. Validation of Real-World Data-based Endpoint Measures of Cancer Treatment Outcomes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021
    Article
  12. 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 · 2020
    Article
  13. Leveraging Digital Data to Inform and Improve Quality Cancer Care.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Article
  14. Article
  15. Review
  16. A conceptual framework for evaluating data suitability for observational studies.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  17. Article
  18. Review
  19. Article
  20. Synergistic drug combinations from electronic health records and gene expression.Journal of the American Medical Informatics Association : JAMIA · 2017
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Caroline A ThompsonPalo Alto Medical Foundation Research Institute.
Allison W KurianStanford University School of Medicine.
Harold S LuftPalo Alto Medical Foundation Research Institute.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn a fragmented health care system, research can be challenging when one seeks to follow cancer patients as they seek care which can continue for months or years and may reflect many physician and patient decisions. Claims data track patients, but lack clinical detail. Linking routine electronic health record (EHR) data with clinical registry data allows one to gain a more complete picture of the patient journey through a cancer care episode. However, valid analytical approaches to examining care trajectories must be longitudinal and account for the dynamic nature of what is "seen" in the EHR.

methodsThe Oncoshare database combines clinical detail from the California Cancer Registry and EHR data from two large health care organizations in the same catchment area-a multisite community practice and an academic medical center-for all women treated in either organization for breast cancer from 2000 to 2012. We classified EHR encounters data according to typical periods of the cancer care episode (screening, diagnosis, treatment) and posttreatment surveillance, as well as by facility used to better characterize patterns of care for patients seen at both organizations.

findingsWe identified a "treated" cohort consisting of women receiving interventions for their initial cancer diagnosis, and classified their encounters over time across multiple dimensions (type of care, provider of care, and timing of care with respect to their cancer diagnosis). Forty-three percent of the patients were treated at the academic center only, 42 percent at the community center only, and 16 percent of the patients obtained care at both health care organizations. Compared to women seen at only one organization, the last group had similar-length initial care episodes, but more frequently had multiple episodes and longer observation periods. DISCUSSION: Linking EHR data from neighboring systems can enhance our information on care trajectories, but careful consideration of the complexity of the treatment process and data generating mechanisms is necessary to make valid inferences. CONCLUSION/NEXT STEPS: If analyzed as a timeline, and with careful characterization of diagnostic tests, surgical interventions, and type and frequency of physician encounters, the pathways taken by women through their breast cancer episode may lead to better understanding of patient decisions.

Indexed as

2014 EDM Forum SymposiumData Use and QualityMethods

Identifiers

PMID25992389
PMCPMC4435001

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LicenceCC BY-NC-ND
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Registered trials

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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.