An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand
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 exam...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2015-10-01
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| Series: | Academic Pathology |
| Online Access: | https://doi.org/10.1177/2374289515606730 |
| _version_ | 1797936036013146112 |
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| author | Saurabh Gupta BPharm W. Stephen Black-Schaffer MD James M. Crawford MD, PhD David Gross PhD Donald S. Karcher MD Jill Kaufman MBA Doug Knapman MBA Michael B. Prystowsky MD, PhD Thomas M. Wheeler MD Sarah Bean MD Paramhans Kumar MBA Raghav Sharma B Tech Vaibhav Chamoli MBA Vikrant Ghai B Pharm, MBA Vineet Gogia BDS, MBA Sally Weintraub Michael B. Cohen MD Stanley J. Robboy MD |
| author_facet | Saurabh Gupta BPharm W. Stephen Black-Schaffer MD James M. Crawford MD, PhD David Gross PhD Donald S. Karcher MD Jill Kaufman MBA Doug Knapman MBA Michael B. Prystowsky MD, PhD Thomas M. Wheeler MD Sarah Bean MD Paramhans Kumar MBA Raghav Sharma B Tech Vaibhav Chamoli MBA Vikrant Ghai B Pharm, MBA Vineet Gogia BDS, MBA Sally Weintraub Michael B. Cohen MD Stanley J. Robboy MD |
| author_sort | Saurabh Gupta BPharm |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T18:24:28Z |
| format | Article |
| id | doaj.art-5cb5cc4d175c48f69341bb1e8852118e |
| institution | Directory Open Access Journal |
| issn | 2374-2895 |
| language | English |
| last_indexed | 2024-04-10T18:24:28Z |
| publishDate | 2015-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Academic Pathology |
| spelling | doaj.art-5cb5cc4d175c48f69341bb1e8852118e2023-02-02T06:21:45ZengElsevierAcademic Pathology2374-28952015-10-01210.1177/237428951560673010.1177_2374289515606730An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and DemandSaurabh Gupta BPharm0W. Stephen Black-Schaffer MD1James M. Crawford MD, PhD2David Gross PhD3Donald S. Karcher MD4Jill Kaufman MBA5Doug Knapman MBA6Michael B. Prystowsky MD, PhD7Thomas M. Wheeler MD8Sarah Bean MD9Paramhans Kumar MBA10Raghav Sharma B Tech11Vaibhav Chamoli MBA12Vikrant Ghai B Pharm, MBA13Vineet Gogia BDS, MBA14Sally Weintraub15Michael B. Cohen MD16Stanley J. Robboy MD17 Contify, New Delhi, Delhi, India Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Department of Pathology, Hofstra North Shore-LIJ School of Medicine, North Shore-LIJ Health System, Manhasset, NY, USA College of American Pathologists, Washington DC, WA, USA Department of Pathology, George Washington University, Washington, DC College of American Pathologists, Washington DC, WA, USA College of American Pathologists, Washington DC, WA, USA Department of Pathology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA Department of Pathology, Baylor College of Medicine, Houston, TX, USA Department of Pathology, Duke University Medical Center, Durham, NC, USA Integron, New Delhi, Delhi, India Integron, New Delhi, Delhi, India XLRI-Xavier School of Management, Jamshedpur, Jharkhand, India Integron, New Delhi, Delhi, India Rhondium Limited, Katikati, New Zealand College of American Pathologists, Washington DC, WA, USA Department of Pathology, University of Utah, Salt Lake City, UT, USA Department of Pathology, Duke University Medical Center, Durham, NC, USAEffective 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.https://doi.org/10.1177/2374289515606730 |
| spellingShingle | Saurabh Gupta BPharm W. Stephen Black-Schaffer MD James M. Crawford MD, PhD David Gross PhD Donald S. Karcher MD Jill Kaufman MBA Doug Knapman MBA Michael B. Prystowsky MD, PhD Thomas M. Wheeler MD Sarah Bean MD Paramhans Kumar MBA Raghav Sharma B Tech Vaibhav Chamoli MBA Vikrant Ghai B Pharm, MBA Vineet Gogia BDS, MBA Sally Weintraub Michael B. Cohen MD Stanley J. Robboy MD An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand Academic Pathology |
| title | An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand |
| title_full | An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand |
| title_fullStr | An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand |
| title_full_unstemmed | An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand |
| title_short | An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand |
| title_sort | innovative interactive modeling tool to analyze scenario based physician workforce supply and demand |
| url | https://doi.org/10.1177/2374289515606730 |
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