Evidence map›Paper›PMID 32412071›Full record

ArticleJournal of the National Cancer Institute. Monographs2020

Sensitivity of Medicare Data to Identify Oncologists.

Joan L Warren, Michael J Barrett, Dolly P White, Robert Banks, Susannah Cafardi, Lindsey Enewold

Abstract read
In one paragraph

Article in Journal of the National Cancer Institute. Monographs, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Urban-rural differences in the age of US physicians.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025
    Article
  2. Article
  3. Organization of Cancer Specialists in US Physician Practices and Health Systems.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2023
    Article
  4. Article
  5. Healthcare Access Domains Mediate Racial Disparities in Ovarian Cancer Treatment Quality in a US Patient Cohort: A Structural Equation Modelling Analysis.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
    Article
  6. Article
  7. Associations of Healthcare Affordability, Availability, and Accessibility with Quality Treatment Metrics in Patients with Ovarian Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
    Article
  8. Article
  9. Article
  10. Updated Overview of the SEER-Medicare Data: Enhanced Content and Applications.Journal of the National Cancer Institute. Monographs · 2020
    Review
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

6 authors.

Joan L WarrenNational Cancer Institute, Division of Cancer Control and Population Science, Bethesda, MD.
Michael J BarrettInformation Management Services, Calverton, MD.
Dolly P WhiteNational Cancer Institute, Division of Cancer Control and Population Science, Bethesda, MD.
Robert BanksInformation Management Services, Calverton, MD.
Susannah CafardiCenter for Medicare and Medicaid Services, Baltimore, MD.
Lindsey EnewoldNational Cancer Institute, Division of Cancer Control and Population Science, Bethesda, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth services researchers have studied how care from oncologists impacts treatment and outcomes for cancer patients. These studies frequently identify physician specialty using files from the Center for Medicare and Medicaid Services (CMS) or the American Medical Association (AMA). The completeness of the CMS data resources, individually or combined, to identify oncologists is unknown. This study assessed the sensitivity of CMS data to capture oncologists included in the AMA Physician Masterfile.

methodsOncologists were identified from three CMS data resources: physician claims, the National Plan and Provider Enumeration System Registry, and the Medicare Data on Provider Practice and Specialty file. CMS files and AMA data were linked using a unique physician identifier. Sensitivity to identify any oncologists, radiation oncologists (ROs), surgical oncologists (SOs), and medical oncologists (MOs) was calculated for individual and combined CMS files. For oncologists in the AMA data not identified as oncologists in the CMS data, their CMS specialty was assessed.

resultsIndividual CMS files each captured approximately 83% of the 17 934 oncologists in the AMA Masterfile; combined CMS files captured 90.4%. By specialty, combined CMS data captured 98.2% of ROs, 89.3% of MOs, and 70.1% of SOs. For ROs and SOs in the AMA data not identified as oncologists in the CMS data, their CMS specialty was usually similar to the AMA subspecialty; ROs were radiologists and SOs were surgeons.

conclusionUsing combined files from CMS identified most ROs and MOs found in the AMA, but not most SOs. Determining whether to use the AMA data or CMS files for a particular research project will depend on the specific research question and the type of oncologist included in the study.

Indexed as

MedicareSpecializationAmerican Medical AssociationHumansOncologistsUnited States

Identifiers

PMID32412071
PMCPMC7868031

What OpenQuestion holds

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Registered trials

None linked

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.