Evidence map›Paper›PMID 28725751›Full record

ArticleAcademic pathology

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand.

Saurabh Gupta, W Stephen Black-Schaffer, James M Crawford, David Gross, Donald S Karcher, Jill Kaufman, Doug Knapman, Michael B Prystowsky, Thomas M Wheeler, Sarah Bean and 8 more

Abstract read
In one paragraph

Article in Academic pathology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

18 authors.

Saurabh GuptaContify, New Delhi, Delhi, India.
W Stephen Black-SchafferDepartment of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
James M CrawfordDepartment of Pathology, Hofstra North Shore-LIJ School of Medicine, North Shore-LIJ Health System, Manhasset, NY, USA.
David GrossCollege of American Pathologists, Washington DC, WA, USA.
Donald S KarcherDepartment of Pathology, George Washington University, Washington, DC.
Jill KaufmanCollege of American Pathologists, Washington DC, WA, USA.
Doug KnapmanCollege of American Pathologists, Washington DC, WA, USA.
Michael B PrystowskyDepartment of Pathology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA.
Thomas M WheelerDepartment of Pathology, Baylor College of Medicine, Houston, TX, USA.
Sarah BeanDepartment of Pathology, Duke University Medical Center, Durham, NC, USA.
Paramhans KumarIntegron, New Delhi, Delhi, India.
Raghav SharmaIntegron, New Delhi, Delhi, India.
Vaibhav ChamoliXLRI-Xavier School of Management, Jamshedpur, Jharkhand, India.
Vikrant GhaiIntegron, New Delhi, Delhi, India.
Vineet GogiaRhondium Limited, Katikati, New Zealand.
Sally WeintraubCollege of American Pathologists, Washington DC, WA, USA.
Michael B CohenDepartment of Pathology, University of Utah, Salt Lake City, UT, USA.
Stanley J RobboyDepartment of Pathology, Duke University Medical Center, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models.

Indexed as

pathologistphysician workforce supply and demandsoftware modeling tool

Identifiers

PMID28725751
PMCPMC5479464

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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.