Forecasts of Physician Supply and

Requirements

April 1980

NTIS order #PB80-181670
Library of Congress Catalog Card Number 80-600063

For sale by the Superintendent of Documents, U.S. Government Printing Office

Washington, D.C. 20402 Stock No. 052-003 -00746-1
Foreword

Undertaken at the request of the Senate Committee on Labor and Human Resources, this report evaluates the assumptions, methods, and results of the two current

models used to forecast the number and kinds of physicians the country is likely to

need and have. Congress must rely heavily on such forecasts in shaping Federal policy

and programs for aiding education in the health professions and for providing health

resources and services.

This report examines the two most important physician forecasting efforts—

those of the Bureau of Health Manpower of the Department of Health and Human

Services (DHHS) and those of the DHHS-chartered Graduate Medical Education National Advisory Committee. These two efforts together are generally representative of

the kinds of techniques that are used to forecast physician and other health personnel

supplies and requirements.

The report points out that projections of physician supply and requirements depend on historical data to predict future events, but even recent historical data reflect

past policies, not current ones. The limits of forecasts must be fully understood if they

are to serve as effective tools in the shaping of Federal medical policy. Those limits

could be made clearer by explicitly describing the assumptions behind any forecasts,

by making alternative forecasts based on different sets of assumptions, and by expanding the forecasting process to include policy makers as well as technicians.

This analysis was prepared by OTA staff. Drafts of the report were reviewed by

an advisory panel convened for the study, by the Health Program Advisory Committee, and by various individuals associated with the forecasting activities analyzed.

JOHN H. GIBBONS

Director

iiz
OTA Health Program Advisory Committee

Frederick C. Robbins, Chairman

Dean, School of Medicine, Case Western Reserve University

Stuart H. Altman

Dean

Florence Heller School

Brandeis University

Robert M. Ball

Senior Scholar

Institute of Medicine

National Academy of Sciences

Lewis H. Butler

Adjunct Professor of Health Policy

Health Policy Program

School of Medicine

University of California, San Francisco

Kurt Deuschle

Professor of Community Medicine

Mount Sinai School of Medicine

Zita Fearon

Research Associate

Consumer Commission on the Accreditation

of Health Services, Inc.

Rashi Fein

Professor of the Economics of Medicine

Center for Community Health and

Medical Care

Harvard Medical School

Melvin A. Glasser

Director

Social Security Department

United Auto Workers

Patricia King

Professor

Georgetown Law Center

Sidney S. Lee

Associate Dean

Community Medicine

McGill University

Mark Lepper

Vice President for ]nter-institutional Affairs

Rush-Presbyterian—St. Luke's Medical

Center

C. Frederick Mosteller

Professor and Chairman

Department of Biostatistics

Harvard University

Beverlee Myers

Director

Department of Health Services

State of California

Mitchell Rabkin

General Director

Beth Israel Hospital

Boston, Mass.

Kerr L. White

Deputy Director of Health Sciences

Rockefeller Foundation
Forecasts of Physician Supply and Requirements

OTA Health Program Staff

Joyce C. Lashof, Assistant Director-, OTA

Health and Life Sciences Division

H. David Banta, Health Program Manager

Lawrence Mike, Project Director

Pamela Doty, Congressional/ Fellow

Nancy Kenney, Administration

OTA Publishing Staff

John C. Holmes, Publislzing Officer

Kathie S. Boss Debra M. Datcher Joanne Heming

Advisory Panel Members

E. Harvey Estes, Jr., Chairman

Chairman, Department of Community and Family Medicine

Duke University School of Medicine

E. B. Campbell

Executive Vice President

Lane College

Jack Hadley

The Urban Institute

John Hatch

School for Biomedical Education

City College of New York

Lauren LeRoy

Health Policy Program

School of Medicine

University of California, San Francisco

Charles Lewis

Department of Medicine

School of Medicine

University of California, Los Angeles

Ted Phillips

Associate Dean for Academic Affairs

School of Medicine

Unitiersity of Washington

Jane Record

Health Services Research Center

Kaiser Foundation

Alvin Tarlov

Department of Medicine

School of Medicine

University of Chicago

John Wennberg

Department of Community Medicine

Dartmouth College
List of Acronyms

AMA — American Medical Association

AOA — American Osteopathic Association

BCHS — Bureau of Community Health Services

BHM — Bureau of Health Manpower

BLS — Bureau of Labor Statistics

CMG — Canadian medical graduate

CPI — Consumer Price Index

DHHS — Department of Health and Human Services

DO – doctor of osteopathy

FMG — foreign medical graduate

FTE — full-time equivalent

GMENAC — Graduate Medical Education National Advisory Committee

GNP — gross national product

GP — general practitioner

HIS — Health Interview Survey

HMOS — health maintenance organizations

HMSA — Health Manpower Shortage Area

HSA — Health Service Area

MD — doctor of medicine

MUA — Medically Underserved Area

NHI – national health insurance

NHSC — National Health Service Corps

vi
Contents

Chapter

I. SUMMARY AND CONCLUSIONS

Introduction. . . . . . . . . . . . . . . . . . .

Current Activities . . . . . . . . . . . . . .

Findings and Conclusions. . . . . . . . .

supply . . . . . . . . . . . . . . . . . . .

Requirements . . . . . . . . . . . . . .

2. SUPPLY . . . . . . . . . . . . . . . . . . . . . .

Aggregate Supply. . . . . . . . . . . . . . .

Specialty Supply . . . . . . . . . . . . . . .

Locational Distribution . . . . . . . . . .

Summary. . . . . . . . . . . . . . . . . . . . .

3. REQUIREMENTS . . . . . . . . . . . . . .

Introduction. . . . . . . . . . . . . . . . . . .

EconomicModels. . . . . . . . . . . . . . .

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The Bureau ofLabor Statistics Model . . . . . . . .

Bureau ofHealthManpower ModeI . . . . . . . . .

TheFramework. . . . . . . . . . . . . . . . . . . . .

The Baseline Configuration . . . . . . . . . . . .

Contingency Modeling . . . . . . . . . . . . . . .

Productivity . . . . . . . . . . . . . . . . . . . . . . .

The Graduate Medical Education National Advisory

Comparison oftheBHMand GMENACModels . . .

Productivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Locational Requirements . . . . . . . . . . . . . . . . . . . . .

BIBLIOGRAPHY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of Tables

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Committee Model . . .

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Table No.

1. Derivation of Male and Female MD Retirement Rates and Death Rates by 5-YearAge

Cohort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2. MD First-Year Enrollment Projections Using 1977 First-Year Enrollment as Base,

to 1987 ., . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3. DO First-Year Enrollment Projections Using 1976 First-Year Enrollment as Base,

to 1987 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4. First-Year Enrollments in Medical and Osteopathic Schools Projected Under the Basic

Assumption; 1978-79 Through 1987-88. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5. U.S.-Trained Physicians, Graduates; Projected 1978-79 Through 1989-90. . . . . . . . . .

6. U.S,-Trained Physicians, Graduates; Projected for 1980 and 1990. . . . . . . . . . . . . . . .

7. Supply of Active Foreign-Trained Physicians, Using Basic Methodology,

Projected 1975-9(1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8. Basic, High, and Low Projections of the FMG Active Supply . . . . . . . . . . . . . . . . . . .

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Contents—continued

Table No. Page

9. Supply of Active Physicians by Country of Medical Education Using Basic

Methodology: 1974 and Projected 1975-90 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

10. Supply of Active Physicians by Country of Medical Education Using Basic

Methodology: Actual 1974, 1975; Projected 1980-90 . . . . . . . . . . . . . . . . . . . . . . . . . . 22

11. Data Sources on Physician Specialty Supply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

12. Internship and Residency Data Sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

13. First-Year Residency Distribution With Subspecialty Adjustment: Sept. 1, 1974 . . . . . 28

14, Percent Distribution of U. S./CMG First-Year Residency Projections Using Simple

Linear Regressions , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., . . . . . . . . 30

15. Percent Distribution of FMG First-Year Residency Projections Using Simple Linear

Regressions ..........,..,........,.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

16. Active Physicians, by Major Specialty Group: Actual 1974; Projected 1980-9(3 . . . . . . 32

17. Supply of Active Physicians, by Specialty :Actual 1974; Projected 1980-90). . . . . . . . . 33

18. Patient Care MDs by Selected Specialties and for High and Low States, 1976 . . . . . . . 34

19. Non-Federal Physicians Providing Patient Care in Metropolitan and Nonmetropolitan

Areas, 1963-75 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

20. New Physicians Entering Practice, 1973 to 1987 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

21. Percent of New Physicians Expected To Enter Primary Care ...,.... . . . . . . . . . . . . 37

22. Projected County Classes of Newly Practicing Physicians. . . . . . . . . . . . . . . . . . . . . . 37

23. Usual Source of Care for Urban Underserved Areas . . . . . . . . . . . . . . . . . . . . . . . . . . 38

24. Estimated Principal-Provider Patient Loads of General Practitioners, Family

Practitioners, and General Internists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

25. Comparisons of Supply With Requirements Using Different Models . . . . . . . . . . . . . . 46

26. 1960’s Projections of Physician Requirements in 1975 . . . . . . . . . . . . . . . . . . . . . . . . . 47

27. Bureau of Labor Statistics Projections of Physician Supply and Requirements, 1985. . 48

28. Population Matrix Used in the BHM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

29. Health Care Categories Used in the BHM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

30. Projected Shifts in Age and Income Distribution, 1970-90 . . . . . . . . . . . . . . . . . . . . . . 52

31. Projected Utilization Growth Factors, 1975-90 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

32. Prevalence of Selected Chronic Conditions Reported in Health [nterviews,

by Family Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . 54

33. Number of Disability Days per Person per Year by Family Income, 1973. . . . . . . . . . . 54

34. Number of Physician Visits per Year by Poor and Not Poor Status and for Whites

and Others, 1964 and 1973 . . . . . . . . . . . , . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

35. Number of Discharges From and Average Length of Stay in Short-Stay Hospitals,

by Income Status and Color, 1964and 1973 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

36. Estimated Growth in Per Capita Utilization, Four Forms of Health Care . . . . . . . . . . . 56

37. Increase in Demand From Population and Per Capita Utilization Changes,

1975 to 1990, BHM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

38. Dependence of Trend Projections on Alternative Starting Dates in the Baseline Data . 64

39. Comparison of Linear Versus Logarithmic Extrapolation of Utilization Data. . . . . . . . 65

40. Allocation of Physicians by Type and Setting of Care for the 1975 Base Year,

BHM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

41. Illustrative Computation of Manpower Requirements. . . . . . . . . . . . . . . . . . . . . . . . . 67

42. Specialty Areas and Subspecialties for Which Requirements Estimates Are Being

Planned or Considered by GMENAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

43. The Average Practice Profile of General Surgeons. . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

44. Proportion of Persons Whose Experience With Physician Visits Is Beyond the

Critical Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

45. Percent of Ethnic Groups Dissatisfied With Aspects of the Medical Care System . . , . . 72

46. Regional Differences in Certain Health-Care Statistics, United States, 1969-70 . . . . . . 79

47. Shortage, Adequate, and Surplus Levels of Primary Care Physicians . . . . . . . . . . . . . 80

48. Need for Primary Care Physicians and Psychiatrists in 1990 .., . . . . . . . . . . . . . . . . . 81
Contents—continued

Table No.

49. Criteria for Unmet Need Calculation by Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

50. Physician Encounters per Physician and Physician Encounters per Physician Hour by

Selected Cohorts, National Health Service Corps, 1976. . . . . . . . . . . . . . . . . . . . . . . .

List of Figures

Figure No.

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8.

9,

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14%

15,

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Diagram of Projection of Supply of Active Physicians Through 1990 . . . . . . . . . . . . .

Trend Data on Number of First-Year Residents: Total, Primary, Nonprimary Care

Specialties; 1960, 1968, 1974, and 1976 ........, . . . . . . . . . . . . . . . . . . . . . . . . . . .

Trend in Number of First-Year Allopathic Residents in Four Selected Specialties . . . . .

Projection of Active Physicians by Specialty. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Frequency Distribution of Physician Availability Indexes—Primary Care Physicians

and Surgeons for the 204 HSAs. . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . .

Per Capita Utilization of Physician office Services, 1966-76 . . . . . . . . . . . . . . . . . . . .

Per Capita Utilization of Short-Term Hospital Services, 1966-76 . . . . . . . . . . . . . . . . .

Per Capita Utilization of Dental office Services, 1966-76 . . . . . . . . . . . . . . . . . . . . . .

Per Capita Utilization of Community Pharmacy Services, 1966-76 . . . . . . . . . . . . . . .

Non-Price-Related Per Capita Utilization Trends, Physician Office Services, 1966-76.

Non-Price-Related Per Capita Utilization Trends, Short-Term Hospital Services,

1966 -76. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Non-Price-Related Per Capita Utilization Trendsr Dental Office Services, 1968-76 . . .

Non-Price-Related Per Capita Utilization Trends, Community Pharmacy Services,

1966 -76. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Illustrative Procedure for Arriving at Adjusted Needs Estimates . . . . . . . . . . . . . . . . .

GMENAC Model for Estimating Physician Requirements . . . . . . . . . . . . . . . . . . . . .

Consumer Satisfaction With Physician Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Consumer Satisfaction With Physician Services, by Nature of the Experience . . . . . . .

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ix
1.

Summary
1.

Summary

INTRODUCTION

Reauthorization of the Health Professions

Educational Assistance Act (Public Law 94-484)

is scheduled for 1980. Essentially, the Act reflects Congress’ policies toward medical and

other health professions educational support

and toward identifying and addressing the problems of medically underserved areas and populations.

The request for this assessment originated

with the Senate Committee on Labor and Human Resources, supported by the House Committee on Interstate and Foreign Commerce.

The Senate Committee’s letter pointed out that

there have been wide variations in the numbers

and types of physicians “required,” and that as

Congress begins to deal with the more difficult

issues of specialty and geographic maldistribution, legislative policy will have to rely on

such forecasting results and related forecasting

technologies for estimating the adequacy of specialty and geographic distribution. It would

therefore be helpful to Congress that an analysis

be undertaken of the assumptions underlying

the different forecasts, as well as the methods

and conclusions of the forecasts themselves, in

order to determine which forecasting technologies are most reasonable.

Projections of physician supply and requirements have influenced Federal policy toward

and legislation on health professions education

and the problem of medically underserved

areas, and play an important role in existing

Federal programs whose purposes are to build

up area medical resources or to provide medical

services directly.

Until the 1976 Act, Federal policy was to increase the supply of physicians and other health

professionals, because the perception was that

of acute shortages. Although the expiring legislation contains incentives to continue to accelerate the supply of physicians, the general consensus now is that the aggregate supply of physicians is at least adequate and perhaps even in

excess. Hence, attention has turned toward the

problems of specialty and geographical, or locational, maldistribution.

Efforts at correcting specialty maldistribution

have concentrated on the primary care specialties, which are usually identifed as general practitioners, family practitioners, general internists, and general pediatricians. All osteopathic

physicians are also included, although this profession is becoming more specialized (about 40

percent are now specialists). Psychiatrists, obstetrician-gynecologists, and general surgeons

have sometimes been included.

Definitional problems are obvious, and they

are important in determining the requirements

for primary care physicians. For example, primary care physicians may include only those

categories identified as primary care; i.e., different combinations of the categories identified

above. The underlying rationale is that the way

in which medical care is provided is crucial.

This approach sees primary care as requiring a

change in attitude toward patient care, a holistic

approach to patients and their families, and as

providing the appropriate entry point into the

medical care system. Others may concentrate

on office-based ambulatory care regardless of

the specialty designation of the physician providing such services and estimate requirements

on that basis.

In addition to definitional problems, approaches toward primary care have been reminiscent of past approaches to aggregate physician supply; the emphasis has been on simply

increasing the supply rather than simultaneously being concerned over what is an appropriate supply. Usually, this has meant that primary

care objectives have been phrased in terms of

the percent of the aggregate physician supply

that should be in primary care. Such objectives

3
4 . Forecasts of Physician Supply and Requirements

would be inappropriate if aggregate supply were

excessive.

Geographical or locational maldistribution is

generally a problem where health personnel and

services are found inadequate, by some defined

standard, to meet the health needs of the population of the identified communities, areas, or

institutional settings. Locational maldistribution is by definition a relative concept, where

some of our people are determined to be at a disadvantage relative to the rest of the United

States. Once these are identified, then the gap

between health personnel and services and that

population’s needs for them is quantified to determine: 1) how many personnel are needed to

bridge the gap, and 2) of the identified deficiency, how much of it will be addressed through a

specific program.

Quantifying locational maldistribution serves

two purposes. First, it is used as part of the eligibility criteria for the Health Manpower Shortage Area (HMSA) designation for: 1) National

CURRENT ACTIVITIES

Under the Health Professions Educational

Assistance Act of 1976, the Department of

Health and Human Services (DHHS) is required

to provide annual reports to the President and

Congress on the status of health personnel in the

United States. Estimating the present and future

supply of and requirements for physicians and

other health professions is the responsibility of

the Health Resources Administration through

its Manpower Analysis Branch of the Bureau of

Health Manpower (BHM). DHHS has produced

its first report (dated August 1978 and reprinted

in March 1979) and is in the final stages of review for its next report.

In addition, DHHS chartered a Graduate

Medical Education National Advisory Committee (GMENAC) on April 20, 1976, to make recommendations in 3 years to the Secretary on the

present and future supply of and requirements

for physicians, their specialty and geographic

distribution, and methods for financing graduHealth Service Corps (NHSC) sites; 2) designation as service areas in which students who borrow money under health professions student

loan programs can practice in lieu of repaying

the loans in money; 3) grants for various health

manpower training programs; 4) eligibility or

preference for grant funds for several Bureau of

Community Health Services programs, such as

the urban and rural health initiatives; and

5) certification of rural health clinics for nurse

practitioner’s and physicians’ assistant’s services

reimbursement through Medicare and Medicaid.

Second, these methods to quantify locational

maldistribution are used to plan for the future

size of NHSC. That is, given the estimated universe of existing and future HMSAS, plans must

be made for determining how many of those

medical manpower shortage areas will be

staffed by NHSC physicians. Currently, the major source for those future NHSC positions are

students who will be obligated

change for scholarship support.

to NHSC in exate medical education. Its most immediate impact will come from its recommendations on

how graduate medical education (residency programs) should (could) be changed to meet these

stated goals. GMENAC was given a l-year requested extension of its charter to April 20,

1980, at which time its final report must be submitted. An interim report was published in

April 1979.

Finally, the Bureau of Labor Statistics of the

U.S. Department of Labor includes physicians

and other health occupations in its projections

of occupational requirements and training

needs. These projections relate manpower to

projected economic demand (expenditures) as

provided by the Bureau’s model of the future

economy, which projects the future gross national product (GNP) and its components—

consumer expenditures, business investment,

governmental expenditures, and net exports; industrial output and productivity; the labor
Ch. 1—Summary Ž 5

force; average weekly

ployment for detailed

cupations.

hours of work; and emindustry groups and ocThe Bureau of Labor Statistics considers the

BHM’s modeling efforts to be a more sophisticated effort than its own, and in its forthcoming revision of its estimates, will adopt the midpoint of the range of projections from the BHM

FINDINGS AND CONCLUSIONS

supply

Forecasts of the future supply of physicians

consist of:

. current Supply, adjusted for attrition from

deaths and retirements, and

. additions to supply from:

—graduates of U.S. medical and osteopathic schools and

—immigration of physicians educated in

other countries plus U.S. citizens educated in foreign medical schools.

The supply of active physicians is projected to

be approximately 450,000 in 1980, 525,000 in

1985, and 600,000 in 1990. Compared to a 1975

supply of 378,000, the net increase will average

75,000 every 5 years.

BHM estimates of additions to supply from

graduates of U.S. medical and osteopathic

schools take first-year enrollment projections,

adjusted for attrition, to arrive at the number of

graduates per year. Estimates of first-year enrollments are based on trends in: 1) Federal

cavitation support, 2) Federal construction

grants activity, 3) new schools already planned,

and 4) potential State and local support of new

schools.

Estimates of additions to supply from immigration of physicians educated in other countries are currently based on the presumed impact of the Health Professions Educational Assistance Act of 1976, which was designed to

sharply curtail the immigration of physicians

into the United States.

model for its physician demand projections.

Thus, there are essentially two major efforts

currently underway, which will have immediate

impacts on Federal health manpower policy; the

sustained modeling activities of BHM and the

nearly completed deliberations of DHHS’S

GMENAC. These two activities also illustrate

well the different approaches through which

physician supply and requirements projections

can be made.

GMENAC’S approach to estimating supply

(not yet completed) uses a different way of disaggregating the U.S. medical school graduate

source. They will project graduates for each

school, based on information provided by the

Association of American Medical Colleges.

Although predictions of the future supply

have been consistent in the aggregate over the

past 5 years, the additions—domestic and foreign graduates—have changed considerably.

Current projections may overestimate the number of future domestic graduates because of the

assumption of full cavitation funding. In contrast, the addition to supply from foreign medical graduates, projected to be 1,000 to 2,000 in

the 1980’s, could be unrealistically low. U.S.

students studying abroad (currently under study

by the General Accounting Office) may not be

adequately accounted for and could double the

1,000 to 2,000 additions per year from foreign

medical schools in the 1980’s.

The net effect of overestimating domestic

sources and underestimating foreign sources

could “wash” each other out.

Supply projections leave the impression that

600,000 physicians in 1990 is a fixed number.

But the assumptions currently in use explicitly

recognize the influence of policy on supply. Estimates based on different sets of assumptions

could provide better indications of the variability of the projected supply and of the influence of

deliberate policy decisions on the ultimate numbers.
6 . Forecasts of Physician Supply and Requirements

For foreign graduates, the presumed full impact of Public Law 94-484 is deliberately factored into the model. For domestic sources, full

cavitation and continued development of new

medical schools in the 1980’s are also assumed.

The latter also reflects a presumed full impact of

existing Federal law, but past experience and

current consensus would deny the real possibility of ever gaining authorized cavitation levels,

although private medical schools continue to be

developed. And the impact of Public Law

94-484 on dampening foreign medical graduate

sources may be circumvented by the increasing

number of U.S. citizens studying medicine

abroad and eventually returning to the United

States to practice.

The specialty distribution of the projected

supply is estimated by taking the number of active practitioners by (self-designated) specialty,

adjusted for death and retirement, and distributing graduates among the specialties through

projections of first-year residency trends.

Trends in first-year residency positions are

used to predict future specialty distribution

because of lack of data on final-year residency

positions. However, first-year residency positions are often used for general clinical experience prior to concentration in a particular

subspecialty or in another specialty and therefore do not necessarily represent final specialty

choices; i.e., first-year residency counts are

duplicative for particular specialties in that a

proportion move on to subspecialization or to

another specialty altogether. BHM’s current

projections assume that the first-year residency

distribution trends for 1968, 1970-74, and 1976,

also apply through 1980-81. After 1980-81, the

residency distribution is held constant for the

statistical reason that the base years chosen to

establish the trend cover 6 years, so BHM has

chosen not to extend the extrapolation beyond 6

years. Downward adjustments are made to

minimize double-counting; the greatest adjustments occur in general surgery (62 percent) and

internal medicine (32 percent).

As a percent of the total projected supply,

physicians in general practice, family practice,

internal medicine, and pediatrics (those usually

counted as primary care specialties) are projected to comprise 39 percent in 1980,41 percent

in 1985, and 42 percent in 1990. The largest specialty among these, as well as among all the specialties, will be internal medicine, which will

have more than twice “as many physicians than

any one of the other specialties.

The locational distribution of the projected

supply, by specialty, is estimated by similar

methods as for aggregate and specialty supply;

i.e., current supply plus additions. These locational projections can be disaggregate in a variety of ways; e.g., by geographic criteria such as

by States, counties, Census-Defined State Economic Areas, or Health Service Areas, or by

special populations such as institutional care

(mental hospitals, prisons), the indigent, and

Native Americans.

Locational projections are used to identify

those locations with the least number of physicians for programs which intend to place physicians (e.g., NHSC) or for which shortage designation is necessary to qualify for Government

funds.

The process of designating and staffing

HMSAS presently includes estimating the future

supply of physicians for: 1) rural counties; 2) urban areas; 3) Federal, State, and local prisons;

4) State mental hospitals and community mental

health centers; and S) the Indian Health Service.

Projections of specialty and locational supply

depend on the standard method of relying on

historical data to predict future events, and in

particular, on most recent experience to predict

the most immediate future. This can be seen in

the use of mid- to late 1960’s to mid-1970’s data

to predict 1980-90 patterns. Aside from the inevitable finding of “inadequate data” which, for

one of the most important marker specialties

(internal medicine), contains an error factor of

at least 32 and perhaps as high as 62 percent in

the first-year residency count, the use of historical data has two other limitations in these

projections of specialty and locational distribution. The late 1960’s and 1970’s have witnessed:

1) Medicare and Medicaid and greater thirdparty private insurance coverage, 2) unprecedented increases in medical school enrollments

and a large influx of foreign medical graduates,
Ch. 1.–Summary ● 7

and 3) major changes in graduate medical education, including abolition of the free-standing

internship and its selective replacement by the

first year of some residency programs. Second,

legislation in this area has purposely tried to affect physician specialty and location choices,

and, given the lag time between physician education and eventual practice, late 1960’s and

early to mid-1970’s data reflect past policies,

current ones.

Requirements

Estimates of the numbers of physicians

quired in the future are derived by dividing

not

rethe

amount of services that it is anticipated physicians will or should provide a given population

in a given year, by physician productivity. Estimates of a population’s economic demand for

services measure the capacity of the population

to use physician services and are not limited to

physician care that is essential to the patient’s

health. In general, physician productivity is assumed to remain constant. Thus, the difference

between forecasting models is essentially one of

differences in the estimates of use.

Although productivity is generally assumed

constant, the particular measure chosen will

directly influence the estimates of physician requirements. For example, GMENAC’S workbook for estimating general surgeon requirements lists alternative estimates of average

weekly office visits that could be used as productivity measures as 77.2,58,51, and 43.

BHM’s estimates of economic demand for

physician services in 1990 are derived first from

current per capita use rates projected onto the

1990 population. These figures are then adjusted

for what the Bureau identifies as a long-term

trend toward rising use of services, based on

analysis of historical changes in per capita utilization during the period 1968-76. Thus, projections of future use can be separated into:

1) effects due simply to population growth and

changes in the population’s age, sex, and income

distribution; and 2) effects due to a projected

long-term trend toward increased per capita use

apart from demographic considerations.

The BHM model projects the U.S. population

by age, sex, and income subgroups, and use

rates for each of these (40) subgroups are

estimated for 20 types of health services settings. The historical trend in per capita use is

separated into price- and non-price-related components. The price-related component interprets

the effects of trends in out-of-pocket costs to

consumers on changes in use. Projections of increased demand for physician services in 1990

calculated on the basis of a presumed trend

toward rising per capita use of services are,

however, highly sensitive to the particular start

date chosen for the trend analysis. Stated

another way, the assumption that there is a currently ongoing strong historical trend toward

rising per capita use that can be projected to

continue to 1990 is highly dependent on using

the particular historical period 1968-76 as the

basis for calculating the trend factor. If a more

recent period were used to calculate the trend,

the projected growth rate in per capita use

would be considerably more moderate.

The BHM model assumes that supply and demand were in balance in 1975. This is a mathematical convenience to provide a constant base

against which the relative magnitude of projected future changes can be referenced. However, prior estimates on aggregate demand have

generally reached this conclusion (see table below). Using current use rates, demographic

Comparisons of Aggregate Physician (MD)

Supply With Requirements Using Different Models

Rate/100,000 Target year Total supply

HMO. . . . . . . . . . . . . . . . . 153.6 1972 321,000 333,000

Need-based. . . . . . . . . . . 167.8 1974 355,600 351,000

Professional judgment. . 187.3 1975 400,000 366,000

Demand/productivity . . . 182.8 1980 407,000 430,000

(projected)

SOURCE: See text.
8 ● Forecasts of Physician Supply and Requirements

changes (population increases plus changes in

age, sex, and income distribution) are projected

to lead to a lo-percent increase by 1990 over

1975 demand, or 415,000 physicians in 1990

versus 378,000 in 1975.

Using a trend factor of increasing use based

on 1968-76 data, an additional increase of

185,000 in physician demand is projected.

Thus, the total projected demand for physicians in 1990 is 600,000 (415,000 PIUS 185,000).a

Increases in demand attributable to a historical trend toward increased per capita use are

overestimated, particularly for office services.

The period 1968-76 is used to establish the

trend, but whereas a start date of 1968 yields a

distinctly upward trend for physician office

services, a start date of 1971 yields a downward

trend (see figure on p. 9).

Based on the BHM model, an alternative ap

proximation of the demand for physician services in 1990, adjusting only for demographic

changes, and assuming no long-term trend

toward increases in per capita use, would be

415,00 physicians, an increase of 37,000 from

378,000 in 1975. But use could change, as could

productivity. To some extent, these are policy

choices to be made. If it is considered desirable

for use to rise, for physicians to spend a few extra minutes with each patient, or for physicians

to have shorter workweeks, much of the projected supply of 600,000 physicians in 1990

could be appropriate.

As supply is estimated to be 600,000 in 1990,

there is a difference of 185,000 physicians between predicted supply and estimated demand

in a static situation.

Some flexibility in the model is necessary, for

several reasons. The enactment of national

health insurance should lead to some increase in

the demand for physician services. Second, physicians currently average longer workweeks

than most of the rest of the labor force. Current

projections are based on the assumption that

physician productivity will remain constant to

1990, which, in specific terms, means that it is

assumed that general surgeons will continue to

average 52-hour patient care workweeks, pediatricians so hours, etc. If physicians continued to

see patients at the same rate but shortened their

workweek, this would have the effect of raising

the number of physicians required to meet a specific level of demand for physician services.

Alternatively, physicians might work the same

number of hours, but see fewer patients and

spend more time with each one. This would also

raise the number of physicians required to meet

a specific level of demand for services. According to the National Center for Health Statistics, almost half of all office visits to physicians in 1973 and 1977 lasted 10 minutes or less.

With smaller patient loads, physicians might be

able to use the additional time to provide patients with more information, education, and

counseling and lead to greater patient satisfaction with the quality of medical care.

It is therefore necessary to decide how much

of these changes are desirable at the cost that

will be borne by the society.

The GMENAC normative, medical opinion

model estimates all diseases and conditions (on

demographic bases such as age and sex) that

should be treated by physicians and the amount

of physician services, on a disease-by-disease or

condition-by-condition basis, that should be

provided.

The theoretical level of use is usually adjusted

downwards to real-world estimates through

consensus formation techniques. Instead of

quantifying use by health care setting, these estimates quantify use on a specialty-by-specialty

basis.

Unlike the BHM model, which can project demand year to year (projections now exist up to

ZOO(I), GMENAC’S current future target is 1990,

although its model is capable of providing yearto-year projections. GMENAC’S modeling effort, because its ultimate aim is to provide

recommendations on graduate medical education, professes to be less concerned with aggregate requirements. When addressed, aggregate requirements will be more of a byproduct

of the parent GMENAC panel’s consolidating

the work of the individual specialty panels.
Ch.l—Summary ● 9

Non-Price-Related per Capita Utilization Trens, Physician Office Services, 1968-76

Non-price-related

per capita utilization

0

-- -

0 —.—

Legend

High-elasticity series-observed 1966-76

High series-linear trend 1966-76

High series-linear trend 1966-76

High series-iinear trend 1971-76

3.25 [ I 1 I I i I ! I I

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE: JWK International, Inc., Evaluation of Project SOAR (Supply,

HRA-232-78-0140, 1979.

On the other hand, the BHM model, as presently constructed, can only provide aggregate,

and not specialty-specific physician requirements, because demand is grouped by health

care setting, not by specialty care.

The normative, medical opinion model is

thus, better capable of estimating specialty-byspecialty requirements but could overestimate

aggregate physician requirements because of the

difficulty of reconciling overlapping patient care

responsibilities. This task is to be undertaken by

the GMENAC panel after the work of its specialty panels is completed.

An unresolved issue, however, is the requirements for the primary care specialties. There are

basic differences on what is primary care, disagreement over what specialties constitute the

primary care ones, and the pragmatic problem

that other specialists will continue to provide

Output, and Requirements), draft report, DHEW contract No.

similar services even if there were agreement on

what primary care is. The models cannot be expected to resolve these issues. Resolution of

these issues is a precondition to projecting the

requirements for primary care specialties.

The BHM trend projection model and

GMENAC’S medical opinion, goal-driven model are complementary, and not competing, models of estimating future physician requirements.

As such, each model’s results can aid in the interpretation of the other. Comparison of the

models can shed some light on the relationship

between medical need for physician services and

trends in the actual use of those services. Ideally, the medical opinion model could be used to

estimate the distribution of physicians by specialty within the aggregate requirements estimates provided by the BHM model.
10 . Forecasts of Physician Supply and Requirements

The GMENAC model focuses on translating a

normative definition of medical need into appropriate rates of use of medical services, while

the BHM model looks on medical care as a “consumer good” and treats empirical trends in the

use of medical services as a proxy for economic

demand. If the BHM demand estimates should

prove significantly greater than the GMENAC

estimates, this would suggest that there are

powerful factors at work that are pushing the

use of medical services beyond the level medically necessary and appropriate for “good”

care. This would then raise the policy question

of what percentage of the projected future economic demand for medical services over and

above the professional judgment-based estimates of medical need should be considered legitimate. Conversely, if the BHM demand estimates should prove significantly less than the

GMENAC estimates, this would suggest that

there remains and will remain in the near future

significant barriers to obtaining medically necessary care for large segments of the American

population rather than for a few discrete areas

and populations. Presumably, these barriers

could be financial, geographic, cultural, or involve ignorance about when to seek care—most

likely some mixture of these variables that

would need to be investigated. Finally, if the

BHM and GMENAC estimates prove to be in

rough parity-what could be viewed as the most

desirable outcome—this would suggest that the

economic demand for services is more or less in

line with professional estimates of the medical

need for physician services.

As the GMENAC model has yet to generate

any numbers, we cannot say which of these

three alternatives will prove to be the case. We

can say, however, that the most likely occurrence would appear to be rough parity or a

BHM demand estimate that is significantly

greater than the GMENAC aggregate estimate.

The major reason for anticipating that the BHM

estimate will most likely prove greater than or

at least equal to the GMENAC estimate is that

one of the major variables in the BHM model is

a projected trend toward rising per capita use of

medical services, independent of demographic

changes and projected changes in price. In contrast, the GMENAC model assumes no major

changes in medical need apart from changes in

medical need induced by demographic shifts

(e.g., an aging population) between now and

1990; hence, no medical rationale for large per

capita increases in the use of physician services.

Estimates of locational requirements are used

to address different problems than aggregate

and specialty estimates. Such estimates are used

in operating programs designed to provide physicians and other medical care resources to targeted populations. Thus, locational requirements are based not only on assumptions about

what are appropriate types and quantities of

medical services, but also on: 1) how medical

services should be redistributed, and 2) the

amount of care that the Federal Government

should provide or finance compared to other

public and private sources.

These additional assumptions are clearly reflected in the designation and staffing ratios that

were used to estimate the numbers of additional

primary care physicians “needed” in shortage

areas, and which, with additional criteria, provide the basis by which specific areas qualify as

HMSAS.

Designation ratio. —The actual minimum

ratio of active, non-Federal, patient care

physicians engaged in primary care to the

civilian population of an area below which

an area is considered to have a shortage of

health manpower sufficient to justify its

being counted as a shortage area.

Staffing ratio. —The theoretical maximum

ratio if active non-Federal, patient care

physicians engaged in primary care to the

civilian population of an area used as a

standard above which an area is considered

to have adequate health manpower so that

additional Federal intervention with NHSC

staffing is no longer necessary.

The designation ratio reflects that quarter of

the United States having the least number of

primary care physicians. It has been set at

1:3,500. The staffing ratio establishes a limitation on the extent of Federal involvement by

specifying an “appropriate” relationship between the service demands of the population

and the primary care physicians available to

provide these services. It has been set at 1:2,000.
Ch. 1–Summary ● 1 1

Estimates of shortage areas in 1990 must be

considered weak for a number of reasons. First,

data on patterns of distribution of physicians

aged 32 to 40 in 1974 are used as the base from

which projections are made. These data are currently the most recent available. They reflect,

however, the conditions and policies of the

1960’s. To assume that physicians will continue

to follow the same distributional patterns in

1990 is to discount the large increases in aggregate physician supply and deliberate policy

efforts to increase the physician supply in shortage areas that have occurred since the 1960’s.

Second, future estimates are based almost entirely on county physician-to-population ratios,

again due to limitations in available national

data. Actual HMSA designation, however,

often involves smaller areas that have lower

physician-to-population ratios than the county

as a whole. Thus, methods for estimating future

urban shortages are especially weak.

In such estimates, potential use divided by expected productivity (ultimately expressed in

physician-to-population ratios) is an inadequate

indicator of the targeted population’s use of

physician services, because average use and productivity calculated on a national basis can be

expected to deviate from a specific population’s

use of specific physician services, and access

problems (physical, financial, social) aIso determine whether use and productivity estimates are

realized.

Thus, physician-to-population ratios comprise only part of the eligibility criteria that

must be met to be designated an HMSA. Additional criteria include meeting specific definitions of “a rational area for the delivery of primary care services, ” and when “primary medical care manpower in contiguous areas are overutilized, excessively distant, or inaccessible to

the population of the area under consideration.”

Consequently, even if national aggregate and

specialty requirements were satisfied, it would

be unlikely that physicians would be evenly distributed in all geographic areas or equally accessible to all population groups. Thus, some areas

would always be underserved as measured

against the average national physician-to-population ratio.

Projections of supply and requirements depend on historical data to predict future events,

but legislation in this area has purposely tried to

affect physician specialty and location choices.

Given the lag time between medical education

and eventual practice, even recent historical

data reflect past policies, not current ones.

As currently published, the projections of aggregate requirements from BHM give no indication of the very different results that could be

obtained by simply shifting the first years of the

historical period used to establish the trend in

per capita use from 1968 to 1971. Assumptions

such as these are now hidden in the methodology, yet it is clear that they are crucial to the

results.

Second, these estimates may be given in

basic, high, and low projections or encompass a

range of numbers, but they all revolve around

the same set of assumptions. They are techniques that represent the degree of statistical

confidence the methodologists have in their

calculations, which is an entirely different question from projecting alternate estimates based

on fundamentally different sets of assumptions

about the factors that influence future supply

and requirements.

The final and most important observation is

that the forecasting process has remained too

technical a process, where statistical techniques,

economic knowledge, and medical expertise

greatly influence the process. Yet, more often

than not, the basic assumptions adopted in the

methodologies are policy ones. This is particularly true for projections of the future supply of

physicians and decisions on specialty distribution requirements. Further, policies that have

been made and are under consideration directly

impact on the projections, yet the reliance on

historical data can systematically underestimate

the effects of such policies. Methodologists

themselves, in the absence of specific policy

direction, are having to make decisions on

which policies will most directly influence their
— .

12Ž Forecasts of Physician Supply and Requirements

projections. The result is that current forecasting techniques may influence policy decisions to

a greater extent than called for.

Greater awareness of the limits of forecasting

and less preoccupation with a particular set of

numbers would be possible if the assumptions

underlying the projections are made more explicit; alternative forecasts are projected, based

on different sets of assumptions; and participation in the forecasting process is expanded to include policymakers as well as technicians.
2 m

supply
2

Supply

This chapter summarizes supply projections for physicians—doctors of medicine

(MD) and doctors of osteopathy (DO)-in the aggregate, by specialty, and by location. The elements to be covered are: 1) assumptions, 2) data sources, and 3) projections.

AGGREGATE SUPPLY

The future aggregate supply of physicians is

based on assumptions of the following factors

(USDHEW, 1979a):

● physicians currently active in practice,

. new graduates of U.S. medical and osteopathic schools, and

. immigration of physicians (including U.S.

citizens studying abroad) educated in other

countries.

The estimates based on these production factors assume that supply will not be affected by

the demand for physicians; i.e., there is an inelastic relationship between physician production and demand.

Data on currently active physicians are obtained from the American Medical Association

(AMA) and the American Osteopathic Association (AOA). Both AMA and AOA data rely on

the physician’s self-designation of specialty, so

the published data are based on this self-identification of primary specialty and activity and

provide no information on activities in other

specialty areas nor on the proportion of time

spent in actual patient care.

An additional factor is that the “not classified” category in the AMA data, introduced in

1971, has grown from 300 in 1971 to over

30,000 in 1976, plus approximately 8,800 physicians whose addresses were unknown. Seventy

percent of this “not classified” category is below

age 35 and most likely in active practice. In the

trend analysis for estimating specialty distribution, this “not classified” category is not included. However, “not classified” is included in

the aggregate projections, with the assumption

that its specialty distribution is identical to

physicians in residency programs.

For physicians currently active in practice,

the starting point (base year) is 1974. Data for

MDs include age, specialty, and country of

medical education. DO data for 1974 start with

1971 AOA data and add new DOS and subtract

retirements and deaths between 1971 and 1974.

Mortality and retirement rates for MDs are

computed by age and sex as derived from

studies on the physician population, not the

general population. 1967 data on retirement

rates are used, and mortality rates use an article

published in 1975. These rates are also applied

to osteopathic physicians. Both retirement and

mortality data, therefore, do not reflect trends

that might be occurring. Table 1 summarizes

these estimates.

Trends in new graduates of U.S. medical and

osteopathic schools start with estimates of firstyear enrollments to arrive at the number of

graduates per year after adjustments for attrition. 1974 data were the original starting point,

but data from the latest academic year, 1977-78,

are now used.

Estimates of first-year enrollments are based

on trends in: 1) Federal cavitation support, 2)

Federal construction grants activity, 3) new

schools already planned, and 4) potential State

and local support of new schools. Separate computations are made for first-year enrollments in

15
16 ● Forecasts of Physician Supply and Requirements

Table 1 .—Derivation of Male and Female MD Retirement Rates and Death Rates by 5-Year Age Cohort

Age Total MDs Number inactive Percent inactive Retirement rate Death rate Separation rate

Male MDs

Less than 30. . . . . . . .

31-34 . . . . . . . . . . . . . .

35-39 . . . . . . . . . . . . . .

40-44 . . . . . . . . . . . . . .

45-49 . . . . . . . . . . . . . .

50-54 . . . . . . . . . . . . . .

55-59 . . . . . . . . . . . . . .

60-64 . . . . . . . . . . . . . .

65-69 . . . . . . . . . . . . . .

70-74 . . . . . . . . . . . . . .

75 and over. . . . . . . . .

31,047

39,470

38,562

37,501

32,989

27,319

25,100

19,452

13,368

8,941

11,817

64

64

88

107

156

188

370

708

1,483

2,034

5,186

.0020

.0016

.0023

.0029

.0047

.0069

.0147

.0410

.1109

.2275

.4389

—

.0000

.0001

.0001

.0004

.0004

.0016

.0053

.0140

.0233

.0423

.0007

.0007

.0014

.0022

.0043

.0066

.0111

.0188

.0294

.0465

.1243

.0007

.0007

.0015

.0023

.0047

.0070

.0127

.0241

.0434

.0698

.1665

Female MDs

Less than 30. . . . . . . . 3,568 70 .0196 — .0005 .0005

31-34 . . . . . . . . . . . . . . 2,929 157 .0536 .0007 .0008 .0015

35-39 . . . . . . . . . . . . . . 2,617 166 .0634 .0020 .0013 .0033

40-44 . . . . . . . . . . . . . . 2,894 226 .0781 .0029 .0023 .0052

45-49 . . . . . . . . . . . . . . 2,313 163 .0705 .0015 .0028 .0013

50-54 . . . . . . . . . . . . . . 1,832 151 .0824 .0024 .0043 .0067

55-59 . . . . . . . . . . . . . . 1,410 126 .0894 .0014 .0064 .0078

60-64 . . . . . . . . . . . . . . 1,105 139 .1258 .0073 .0098 .0171

65-69 . . . . . . . . . . . . . . 993 242 .2437 .0236 .0152 .0388

70-74 . . . . . . . . . . . . . . 779 290 .3723 .0257 .0250 .0507

75 and over. . . . . . . . . 964 630 .6535 .0562 .0916 .1478

Based on:1)Amerlcan Medical Association, Department of Survey Research, Selected Characteristics of the Physician population, 1963 and 1967 (Chicago, 1978),

table 21, p.182; and2)R. Hendrickson, ’’Specialists Outlive Generalists:’ Prism, December 1975.

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, Education, and Welfare, Washington,

D. C.: Health Resources Administration, DHEW publication No. (HRA) 79-833, p. 119.

3-year programs because of different attrition

rates. Transferees into U.S. medical schools are

also estimated.

High, low, and basic projections are calculated for these first-year enrollments. Basic projections assume that full funding of cavitation

grants and moderate funding of construction

grants will be achieved by 1981, that seven new

medical and osteopathic schools will be established after the 1977-78 school year, and that

there will be some limited further State, local,

and private support for additional enrollment

growth. The low-level projections assume full

funding of cavitation grants, but minimum

funding of construction grants by 1981, the establishment of four new schools after 1977-78,

and no additional growth in enrollments arising

from State, local, or other support beyond

1977-78. The high-level projections assume full

funding of both cavitation and construction

grants by 1981, the establishment of 10 new

schools after 1977-78, and additional growth in

enrollments arising from State, local, or other

support beyond 1977-78 at half the annual rate

exhibited by the years 1953-54 through 1964-65

(before Federal programs had an impact).

Tables 2, 3, and 4 summarize these estimates for

MD and DO first-year students.

Attrition rates are based on historical trends

for 3- and 4-year MD programs, for osteopathic

programs, and for foreign-trained U.S. medical

students who transfer to U.S. medical schools.

Tables summarizes actual and projected graduates for 1978-79 to 1989-90, based on the foregoing assumptions. Table 6 summarizes similar

projections made at about the same time for the

Department of Health, Education, and Welfare’s

(HEW) (now the Department of Health and Human Services (DHHS)) annual report to the

President and Congress (USDHEW, 1979b). The

two tables show different projections for 1980

and 1990; for 1980, 16,375 v. 17,155; for 1990,

19,289 v. 19,987. The lower estimates are based

on the foregoing assumptions.

There are also discrepancies between the firstyear enrollment assumptions and the projected
Table 2.—MD First”Year Enrollment Projections Using 1977 First-Year Enrollment as Base, to 1987

1977-78 1978-79 1979-80 1980-81 1981-82 1982-83 1983-84 1984-85 1985-86. 1986-87 1987-88

Low series

Total . . . . . . . . . . . . . . . 16,136 16,486 16,908 16,921 16,931 16,936 16,938 16,940 16,942 16,944 16,944

Base year . . . . . . . . . . . . . . . 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136

Construction commitments — 300 700 700 700 700 700 700 700 700 700

New schools. . . . . . . . . . . . . — 50 72 85 95 100 102 104 106 108 108

Basic series

Total . . . . . . . . . . . . . . . 16,136 16,725 17,350 17,525 17,612 17,690 17,765 17,838 17,909 17,980 18,047

Base year . . . . . . . . . . . . . . . 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136

Construction commitments — 450 950 1,025 1,025 1,025 1,025 1,025 1,025 1,025 1,025

New schools . . . . . . . . . . . . . — 74 134 169 191 204 214 222 228 234 236

Other . . . . . . . . . . . . . . . . . . . — 65 130 195 260 325 390 455 520 585 650

High series

Total . . . . . . . . . . . . . . . 16,136 17,013 17,748 18,019 18,188 18,340 18,485 18,628 18,769 18,906 19,037

Base year . . . . . . . . . . . . . . . 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16,136 16.136 16.136

Construction commitments — 650 1,150 1,225 1,225 1,225 1,225 1,225 1,225 1,225 1,225

New schools . . . . . . . . . . . . . — 98 204 271 311 334 350 364 376 384 386

Other . . . . . . . . . . . . . . . . . . . — 129 258 387 516 645 774 903 1,032 1,161 1,290

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, Education, and welfare, Washington, D. C.: Health Resources Administration,

DHEW publication No. (HRA) 19-633, P.135.

Table 3.—First-Year Enrollment Projections Using 1976 First-Year Enrollment as Base, to 1987

1976-1977 1977-78 1978-79 1979-80 1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88

Low series

Total . . . . . . . . . . . . . . . 1,068 1,218 1,309 1,354 1,411 1,429 1,447 1,464 1,481 1,498 1,515 1,532

Base year . . . . . . . . . . . . . . . 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068

Construction commitments — 90 154 184 214 214 214 214 214 214 214 214

New schools. . . . . . . . . . . . . — 60 87 102 129 147 165 182 199 216 233 250

Basic series

Total . . . . . . . . . . . . . . . 1,068 1,258 1,364 1,437 1,522 1,562 1,603 1,643 1,682 1,722 1,762 1,801

Base Year . . . . . . . . . . . . . . . 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068

Construction commitments — 95 164 199 234 234 234 234 234 234 234 234

New schools. . . . . . . . . . . . . — 84 111 138 177 207 237 266 295 324 353 382

Other. . . . . . . . . . . . . . . . . . . — 11 21 32 43 53 64 75 85 96 107 117

High series

Total . . . . . . . . . . . . . . . 1,068 1,273 1,432 1,534 1,658 1,721 1,782 1,843 1,903 1,963 2,024 2,084

Base year . . . . . . . . . . . . . . . 1,068 1,068 1,068 1,068 1,068 1,068 1,068 1,068 –

1,068 1,068 1,068 1,068

Construction commitments — 100 214 264 264 264 264 264 264 264 264 264

New schools. . . . . . . . . . . . . — 84 188 241 282 322 361 361 400 439 478 517

Other. . . . . . . . . . . . . . . . . . . — 21 64 85 107 128 10 150 171 192 214 235

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, .Education and Welfare, Washington D. C.. Health Resources Administration,

●

DHEW publication No. (HRA) 19-633, P.136
18 ● Forecasts of Physician Supply and Requirements

Table 4.—First.Year Enrollments in Medical and Osteopathic Schools

Projected Under the Basic Assumption; 1978-79 Through 1987-88

Total MD and DO MD first-year DO first-year

Academic year first-year enrollments enrollments enrollments

1978-79 . . . . . . . . . . . . . 18,089 16,725 1,364

1979-80 . . . . . . . . . . . . . 18,787 17,350 1,437

1980-81 . . . . . . . . . . . . . 19,047 17,525 1,522

1981 -82 . . . . . . . . . . . . . 19,174 17,612 1,562

1982-83 . . . . . . . . . . . . . 19,293 17,690 1,603

1983-84 . . . . . . . . . . . . . 19,408 17,765 1,643

1984-85 . . . . . . . . . . . . . 19,520 17,838 1,682

1985-86 . . . . . . . . . . . . . 19,631 17,909 1,722

1986-87 . . . . . . . . . . . . . 19,742 17,890 1,762

1987-88 . . . . . . . . . . . . . 19,848 18,047 1,801

SOURCE: Interim Report o! the Graduate Medical Education National Advisory Committee to the Secretary, Department of

Health, Education, and We/fare, Washington, D. C.: Health Resources Administration, DHEW publication No. (HRA)

19-633, p.146.

Table 5.—U.S.-Trained Physicians, Graduates

(MD and DO); Projected 1978-79 Through 1989.90

Academic year Total graduates MD graduates DO graduates

1978-79 . . . . . . . . . . . . . 16,044 15,048 996

1979-80 . . . . . . . . . . . . . 16,375 15,346 1,029

1980-81 . . . . . . . . . . . . . 16,997 15,789 1,208

1981 -82 . . . . . . . . . . . . . 17,662 16,354 1,308

1982-83 . . . . . . . . . . . . . 18,333 16,956 1,377

1983-84 . . . . . . . . . . . . . 18,699 17,241 1,458

1984-85 . . . . . . . . . . . . . 18,818 17,322 1,496

1985-86 . . . . . . . . . . . . . 18,928 17,394 1,534

1986-87 . . . . . . . . . . . . . 19,036 17,464 1,572

1987-88 . . . . . . . . . . . . . 19,142 17,532 1,610

1988-89 . . . . . . . . . . . . . 19,201 17,554 1,647

1989-90 . . . . . . . . . . . . . 19,289 17,604 1,685

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of

Health, Education, and We/fare, Washington, D. C.: Health Resources Administration, DHEW publication No, (HRA)

19-633, p.147.

Table 6.—U.S.-Trained Physicians, Graduates

(MD and DO); Projected for 1980 and 1990

MD DO

Year Schools Graduates Schools Graduates Total graduates

1960 . . . . . . . . . . . . . . 86 7,081 6 427

1970 . . . . . . . . . . . . . . 103 8,367 432

1975 . . . . . . . . . . . . . . 114 12,714 9 698 1 3,412

1980 (projected). . . . . 121 16,086 13 1,069 17,155

1990 (projected). . . . . 121 18,318 13 1,069 19,987

SOURCE: A Report to the President and Congress on the Status of Health Professions Personnel in the United States,

Washington, D. C.: Bureau of Health Manpower, Health Resources Administration, DHEW publication No. (HRA)

79-93, p,ll-29,

numbers of graduates. The AMA’s annual re- ited or provisionally accredited for the first 2

port, Medical Education in the United States years of the MD program whose first-year en-

(AMA, 1978), lists 122 medical schools accred- rollments apparently were not included in the

i ted or provisionally accredited and 16,134 first- 1977-78 total of 16,134. And the Association of

year students in 1977-78, plus 2 schools accred- American Medical Colleges identified 2 addi-
Ch. 2–Supply ● 1 9

tional medical schools in 1979, for a total of 126

(American Medical News, 1979). The projections of first-year enrollments for 1977-78 match

the AMA’s estimates of the number of first-year

enrollees in medical school (16,136 v. 16,134).

But the projections to 1980 and 1990 (table 6)

state that there will be 121 medical schools and

13 osteopathic schools, compared to 114 medical schools and 9 osteopathic schools in 1975.

Thus, it is not clear whether the alternative

estimates of 7, 4, or 10 new medical and osteopathic schools include some of the 122 medical

schools already in existence, or whether they

represent additional schools, as the explanation

of the methodology seems to say.

In addition, the assumption of full cavitation

funding by 1981 also is unrealistic, and the projections also seem to indicate that full cavitation

is expected to be maintained after 1981, Currently, the issue with cavitation is whether it

will continue at all, not whether fully authorized levels will be appropriated.

Immigration of graduates of foreign medical

schools are calculated separately for Canadian

medical graduates (CMGS) and other foreign

medical graduates (FMGs). The Canadian addition is currently estimated to equal losses from

death, retirement, and emigration because the

recent historical growth has leveled off.

Additions from the rest of FMGs are particularly uncertain at this time because of the curtailing legislation in the Health Professions Educational Assistance Act of 1976 (Public Law

94-484). Since historical trends will not be predictive of future additions to supply by FMGs,

the 1974-76 period has been used, with major

adjustments that essentially try to guess at the

impact of the legislative changes. Temporaryvisa FMGs are assumed to equal the number of

graduate medical positions available to them

through regulations that implement Public Law

94-484, which require a stepwise reduction in

positions until available positions to FMGs

reach zero by 1990. The addition of permanentvisa FMGs to the supply is based on estimates of

the number of FMGs passing the National Board

of Medical Examiners’ Visa Qualifying Examination. This exam was begun in September

1977, so only 1 or 2 years of data are available.

Permanent-visa FMGs and the proportion of

temporary-visa FMGs estimated to establish

permanent status through marriage (based on

actual trends) are assumed to have the same

death and retirement rates as U.S.-educated

physicians.

Of crucial importance is the apparent lack of

analysis of the contribution from U.S. citizens

studying medicine abroad, a situation currently

under study by the General Accounting Office.

The projections do account for students returning to the United States to complete their

medical education in the United States, but they

comprise only a small part of the pool of U.S.

citizens studying medicine abroad.

Basic, high, and low projections are calculated for the FMG addition to supply. The basic

projection is summarized in table 7, with the

results of the alternative estimates of the active

FMG supply from the basic, high, and low estimates summarized in table 8.

These supply projections are prepared in two

matrices. The first matrix projects year-by-year

future MD graduates and attrition from the active work force by country of medical education. The second matrix distributes these future

graduates and attrition of active practitioners

by specialty, each by country of medical education. The first matrix projects graduates and

foreign additions utilizing estimates of first-year

enrollments, student attrition, other medicalschool-related trends, and the model of FMG

(including Canadians) immigration. The second

matrix distributes the graduates among medical

specialties through projections of first-year

residency trends, and computes deaths and retirements of active practitioners among the specialties, using the mortality and retirement rates

described earlier. Comparable disaggregation of

the data on DOS has not been developed,

although estimates of total DO supply have

been made.

The method is summarized in figure 1. Table

9 summarizes the projected supply of physicians

through 1990. For comparative purposes, table

10 summarizes estimates made in early 1978

(USDHEW, 1979b). The estimates are approximately equal. It should be noted that the
20 ● Forecasts of Physician Supply and Requirements

Table 7.–Supply of Active Foreign”Trained Physicians, Using Basic Methodology, Projected 1975-90

New entry supply Losses Active supply

Death and J-visa –

Year Total Permanent Temporary Total retirement emigrants FMG CMG

1974 . . . . . . . . . . . .

1975 . . . . . . . . . . . .

1976 . . . . . . . . . . . .

1977 . . . . . . . . . . . .

1978 . . . . . . . . . . . .

1979 . . . . . . . . . . . .

1980 . . . . . . . . . . . .

1981 . . . . . . . . . . . .

1982. . . . . . . . . . . .

1983. . . . . . . . . . . .

1984. . . . . . . . . . . .

1985. . . . . . . . . . . .

1986. . . . . . . . . . . .

1987. . . . . . . . . . . .

1988. . . . . . . . . . . .

1989. . . . . . . . . . . .

1990. . . . . . . . . . . .

—

7,316

6,609

6,596

4,150

4,857

3,847

4,591

3,581

4,325

3,315

4,059

3,049

3,793

3,023

3,287

3,023

—

3,898

3,399

3,399

1,152

2,521

2,521

2,521

2,521

2,521

2,521

2,521

2,521

2,251

2,521

2,521

2,521

—

3,418

3,210

3,197

2,042

2,336

1,326

2,070

1,060

1,804

794

1,538

528

1,272

502

766

502

—

2,166

2,569

2,626

2,680

2,737

2,107

2,371

1,751

2,355

1,735

2,349

1,739

2,353

1,923

2,227

2,153

—

764

815

872

917

983

1,047

1,109

1,184

1,276

1,351

1,453

1,538

1,640

1,740

1,862

1,971

—

1,402

1,754

1,754

1,763

1,754

1,060

1,262

567

1,076

384

896

201

713

183

365

182

70,940

76,090

80,130

84,100

85,570

87,690

89,430

91,650

93,480

95,450

97,030

98,740

100,500

101,490

102,590

103,650

104,520

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

5,510

SOURCE. Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health Education, and We/fare, Washington,

D.C.: Health Resources Administration, DHEW publication No. (HRA)19-633, p,140.

Table8.–Basic, High,and Low Projections of the FMG Active Supply

Basic High LOW

Year FMG Canadian FMG Canadian FMG Canadian

1975. . . . . . . . . . . . 76,090 5,510 76,090 5,510 76,090 5,510

1980. . . . . . . . . . . . 89,430 5,510 92,340 5,510 86,270 5,510

1985. . . . . . . . . . . . 98,740 5,510 104,340 5,510 92,910 5,510

1990. . . . . . . . . . . . 104,520 5,510 112,580 5,510 96,320 5,510

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of

Health, Education, and We/fare, Washington, D.C; Health Resources Administration, DHEW pubiication No. (HRA)

19-633, PP. 140-142.

estimates in table 9 have lower projections of

the graduate supply and higher projections of

the FMG supply than the estimates in table lo.

This is despite the optimistic projections of

cavitation funding and even further curtailment

of the FMG supply that underlie the table 9 projections.

Interestingly enough, projections from the

Bureau of Health Manpower (BHM) made in

1974 (USDHEW, 1974) were similar to those

made in its report to the President and Congress

(USDHEW, 1979b) but the contribution from

the graduate supply was lower and that from

FMGs higher. In other words, the Bureau’s

previous estimates, made before the 1976 law

curtailing FMG immigration, are more internally consistent with the estimates taking into consideration the effect of the 1976 law. So even

though the aggregate projections of supply are

similar for these different sets of assumptions,

the contribution of the components of the aggregate estimates has differed significantly.
Ch. 2–Supply Ž 2 1

Figure 1.— Diagram of Projection of Supply of Active Physicians Through 1990

Projected Base year U.S. medical Canadian Foreign Deaths Emigration

active = active + graduates + medical + medical - and -

supply

o f

supply (USMGS) graduates graduates retirement temporary

1974 (CMGS) (FMGs) FMGs

USMG = FY E

a

– Attritio n FMG =

Permanent + Temporar y

visa visa

Deaths Emigration

Total separations = and + of temporary

retirements FMGs

Derived from

estimates of

percent inactive

(retirees) and

percent mortality

of MDs by age

and sex cohort

aFy E = first.year enrollment

SOURCE Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, Education, and Welfare,

Washington, D C Health Resources Administration, DHEW publication No (HRA) 79.633, p, 112.
22 . Forecasts of Physician Supply and Requirements

Table 9.—Supply of Active Physicians (MD and DO) by Country of Medical

Education Using Basic Methodology: 1974 and Projected 1975-90

Category 1974 1975a 1980 1985 1990

Number of active physicians

All active physicians . . . . 362,500 377,400 447,800c 523,600 596,800

U.S.-trained . . . . . . . . . . . . . . . . 286,000 295,800 352,800 419,300 486,900

MD. . . . . . . . . . . . . . . . . . . . . . 272,400 281,700 335,100 396,100 457,000

DO. . . . . . . . . . . . . . . . . . . . . . 13,600 14,100 17,700 23,200 29,900

Canadian-trained MDs . . . . . . . 5,600 5,500 5,600 5,600 5,600

Foreign-trained MDs. . . . . . . . . 70,900 76,100 89,400 98,700 104,500

Rate per 100,000 population

All active physicians. . . . . 171.1 176.8 201.5 224.8 245.1

U.S.-trained . . . . . . . . . . . . . . . . 135.0 138.5 158.8 180.0 200.0

MD. . . . . . . . . . . . . . . . . . . . . . 128.6 131.9 150.8 170.1 187.7

DO. . . . . . . . . . . . . . . . . . . . . . 6.4 6.6 8.0 10.0 12.3

Canadian-trained MDs . . . . . . . 2.6 2.6 2.5 2.4 2.3

Foreign-trained MDs. . . . . . . . . 33.5 35.6 40.2 42.4 42,9

Percent distribution

All active physicians. . . . . 100.0 100.0 100.0 100.0 100.0

U.S.-trained . . . . . . . . . . . . . . . . 78.9 78.4 78.8 80.1 81.6

MD. . . . . . . . . . . . . . . . . . . . . . 75.1 74.6 74.8 75.6 76.6

DO. . . . . . . . . . . . . . . . . . . . . . 3.8 3.7 4.0 4.4 5.0

Canadian-trained MDs . . . . . . . 1.5 1.5 1.3 1.2 0.9

Foreign-trained MDs, . . . . . . . . 19.6 20.2 20.0 18.9 17.5

available estimates for 1975 and 1976 for active U. S,-trained MDs are 282,800 and 290,900 respectively; active FMGs are estimated at 76,200 and 79,700 resPOctivOIY. Ac.

tive Canadian-trained MDs are estimated at 5,500 for both years.

bAssume5 that the percent active of the AMA “not classified” MDs is the same as the percent “professionally active” Of the classified MDs includlng those with address unknown.

coriglnal table added this column incorrectly to total 477,800.

Population figures used (in millions): 1960: 185.4; 1970: 206.1; 1974: 21 1.9; 1975: 213.5; 1980: 222.2; 1985 232.9; 1990: 243,5.

SOURCE: /nferirn Report of tfre Graduate Med/ca/ .Educat/on rValiona/ Adwsory Cornrn/ttee to the Secretary, Deparfrnent of Health, Educat/err, and We/tare, Washington,

D. C.: Health Resources Administration, DHEW publication No, (HRA) 19-633, p,144.

Table 10.—Supply of Active Physicians (MD and DO) by Country of Medical

Education Using Basic Methodology: Actual 1974, 1975; Projected 1980.90

Category 1974 1975 1980 1985 1990

Number of active physicians

All active physicians. . . . . 362,500 378,600 444,000 519,-joo 594,000

U.S.-trained . . . . . . . . . . . . . . . . 286,000 296,700 353,600 42,$,400 495,700

MD. . . . . . . . . . . . . . . . . . . . . . 272,400 282,600 335,900 40”t ,100 465,900

DO. . . . . . . . . . . . . . . . . . . . . . 13,600 14,011 17,700 23,300 29,800

Canadian-trained MDs . . . . . . . 5,600 5,700 6,000 6,100 6,200

Foreign-trained MDs. . . . . . . . . 70,900 76,200 89,400 86,500 92,100

Rate per 100,000 population

All active physicians. . . . . 171.1 177.3 199.3 2;)1 .7 242.4

U.S.-trained . . . . . . . . . . . . . . . . 135.0 138.9 158.7 181.3 202.3

MD. . . . . . . . . . . . . . . . . . . . . . 128.6 132.3 150.8 171.4 190.1

DO. . . . . . . . . . . . . . . . . . . . . . 6.4 6.6 7.9 10.0 12.2

Canadian-trained MDs . . . . . . . 2.6 2.7 2.7 2.6 2,5

Foreign-trained MDs. . . . . . . . . 33.5 35.7 37.9 37.8 37.6

SOURCE: A Report to the Pres/derrt and Congress on the Srafus of Hea/th ProfessIons Personnel m the Ur?/ted States, Washington, D. C.: Bureau of Health Manpower,

Health Resources Adminlstratlon, DHEW publication No. (HRA) 79-93, p A-25.
Ch. 2–Supply “ 23

SPECIALTY SUPPLY

Recall that aggregate supply was prepared in

two matrices. The first matrix projects graduates and foreign additions utilizing estimates of

first-year enrollments, student attrition, other

medical-school-related trends, and the model of

FMG immigration. The second matrix distributes the graduates among medical specialties

through projections of first-year residency

trends, and computes deaths and retirements of

active practitioners among the specialties.

Comparable disaggregation of the data on

DOS has not been developed, although estimates of total DO supply have been made. The

DO distribution between primary care specialties is difficult to predict because of the lack of

basic data on graduate training positions and

because the graduate osteopathic training system is changing. In addition, MD residency programs accept DOS, which could lead to increasing specialization by younger DOS.

Presently, about 58 percent of DOS are in

primary care. If DO graduates enter first-year

residency programs in the same proportion as

projected for MDs, by 1990 only 52 percent

would be in primary care. If DOS continue current trends in graduate osteopathic training, 64

percent would be in primary care in 1990

(USDHEW, 1979b). Although these are significant percentage differences, the absolute differences are not large. Out of a total DO supply

of 30,000 in 1990, the 52-percent figure corresponds to about IS, SOO primary care DOS,

and the 64-percent figure corresponds to 19,000.

This is in contrast to 1990 estimates of total MD

and DO supply of 600,000 and a primary care

MD supply of 240,000.

The projections for MD speciaIty distribution

of the aggregate supply depend principally on

first-year residency trends and on the attrition

rates of the various specialties and subspecialties.

The data sources for current specialty supply

are summarized in

tions are obtained

Board certification

memberships. The

table 11. Specialty designafrom the AMA master file,

data, and specialty society

AMA file contains self-designation of specialty, tending to overestimate

specialty supply and underestimate general

practice supply. And, as only the primary activity/specialty is identified, nothing is known

about patient care time spent in the identified

specialty or in activities usually associated with

other specialties. About half of the physicians

identified in the AMA files are not identified in

the Board certification data. Also, Board certification data and especially society membership

data result in duplicate counting, as physicians

can belong to more than one specialty board or

society. There are 22 medical and surgical

boards and over 130 specialty societies.

BHM uses the AMA master file as its basic

source, with the 1974 active supply as the starting point.

First-year residency trends, which are used to

project additions of MD graduates (foreign and

domestic) to the specialties, contain three

assumptions: 1) that the first-year residency

distributions for 1968, 1970-74, and 1976, can

be used to predict future first-year residency

trends; 2) that first-year residency counts for

particular specialties are duplicative in the sense

that a proportion of these residents do not go on

to complete that specialty training, but move on

to subspecialization or to another specialty

altogether; and 3) that some residency positions

are shared by different institutions, which also

leads to duplicative counting. Considering the

kinds of interpretation problems that accompany trying to project specialty distribution

among active practitioners from first-year residency positions, the better method would be to

analyze final-year residency counts, but the

AMA does not keep year-by-year accounts of

medical graduates, and first-year residency data

represent the best available data.

Residency data sources and comments on

their strengths and limitations are summarized

in table 12. The principal data source is the

Directory of Accredited Residencies.

The particular years chosen to establish

trends, 1968, 1970-74, and 1976, are the most

recent years on which to base such calculations,
24 Ž Forecasts of Physician Supply and Requirements

Table 11 .–Data Sources on Physician Specialty Supply

Data sources Strengths Limitations

1. The American Medical Association Most complete source of

Master File, —Contains data on all data on allopathic physiknown MDs in the United States, ob- cians. Published and uptained by surveys performed every 3 dated annually providing

to 4 years, and updated annually by trend data.

selected mailings to specific physicians for whom a change in status

has been indicated.a

Physicians are listed by self-designation as to their specialty, activity, and

location according to how they

spend the majority of their time. Sixty-eight specialities are included

within which eight activity categories are included.

2. The American Osteopathic Associa- Most complete sources of

tion MAster File.—Contains informa- data on Osteopathic Dhvsition on both member and nonmember osteopathic physicians as to location and updated annually. Augmented by surveys performed in

1956, 1967, 1971, and 1976 which

yielded additional data on specialty,

age, and activity status.e

3. Licensure data.—Provides data on

numbers of physicians Licensed by

State. Disaggregate by whether or

not physician attended a U.S. or foreign medical school.

4. Board certification data.—Gives information the numbers of MDs certified by the 22 medical and surgical

boards and the numbers of DOS cer-

. .

cians.

Updated annually, and thus,

only sources of trend data

on osteopathic physicians.

In some cases the data are

comparable to AMA data.

Contains data on physicians

who have received licenses;

therefore, one can be sure

all uncredentialled physicians are excluded.g

Published and updated

annually, so trend data are

available.

Most objective criteria of

physicians postgraduate

training in specific specialty

areas.

tified by the 14 Osteopathic specialtv

boards. h Published and updated

5. Specialty society membershjps,—

Includes numbers and distributions

of MDs in over 130 specialty

societies. t

annually so trend data are

available.

Gives some indication of

physician’s interests in

specific areas of medicine

not revealed in other AMA

specialty classifications.

Published and updated annually.

Self-designation of specialty gives no indication of specific training in the area and also tends to overestimate

specialty manpower, and underestimate general practice manpower. Published data provide no information

on the time devoted to other specialty areas and activities making it difficult to determine full-time equivalent

manpower. b

Accuracy of data on FMGs is debatable as is the accuracy of specialty distributions because increasing numbers of physicians are being relegated to the “nonclassified” category.cd

Can be difficult and/or expensive to obtain unpublished

tabulations.

Published data usually 2 years out of date.

The problems associated with self-designation relating

to AMA data also apply to AOA data.

Specialty data only available for survey years, and when

published contains information up to 3 years out of date.

Accuracy of specialty data questionable because large

numbers of physicians are relegated to the nonclassified category.f

Not always comparable to AMA data.

Underestimates true physician supply since it excludes

all physicians who are not licensed, such as some of

those in teaching and administration and research, and

some FMGs who are providing important service despite

their unlicensed status.

No information on specialty and practice activity of

licensed physicians.

Duplication often occurs between various State licensure boards.

Excludes almost half of MD supply as reported by AMA

and 4/5 of the DO supply as reported by AOA.

Duplicate counting occurs due to certification by more

than one specialty board.

Does not necessarily represent physician’s present

specialty activities.

Gives no indication of physician’s training or background in a specific specialty area represented by the

society.

Duplicate memberships often occur.

Does not necessarily represent the present activities of

the physician.

aAmerlcan Medical Association Physician Master Fde, American Medical Asso. ‘1976 D/rectory, American Osteopathic Assoclatlon, Chicago, 1976

clatlon, Chicago, III., 1977.

bFor example, a physician may report hfs or her professional activltleS m a tYP1.

cat workweek consisting of 30 hours of patient care and 20 hours of teaching

and research, and In addltlon specialty activity IS reported as 25 hours of inter.

nal medicine and 25 hours of dermatology. This precludas determination of

number of FTE phyaiclans m dwect patient care

cAccordlng to cohoti study of physicians Immlgralmg to the United states between 1961 and 1971 an estimated 33 percent of 27,710 Immigrants In the cohort ware not on the AMA mastar Me J C Klelnman, Physician Manpower

Oata. “The Cese of the Mkssmg Foreign Medical Graduates,” Medtcal Care,

12 W& 1974. Others beheve that the AMA does account for all FMGs I Butler

and M. Schaffner, “Foreign Medical Graduates and Equal Access to Medical

Care,” Med/ca/ Care, 9(2) 136-43, March. April 1974

dFrom 25s in 197o to 30,129 (n 1976 for MDs, Louis J Goodman, phYSIClan Lk.

fr~buflon and Medlca/ Llcerrsura m the US, 1976 Chicago, American Medical

Assoclatlon, 1977

fFrom s01 In 1971 to 653 in 1976. M E Altenderfar, OstaOpath/C Physic/arrs In

the U S A Reporf of a 1971 Survey, BHRD, DHEW publication No (HRA) 75-60,

1975 and Amertcan Osteopathic As.soaatlon, 1974 Master File, Liaison Corn.

mlttee on Osteopathic Information, Osteopatfwc Manpowar Information I@

ect, final report, May 20, 1977

gAt present it IS estimated that there are about 36,500 physicians In the country

who do not hold a regular State license. LouIs J Goodman, Ostrdxmon of

Phys)c!ans, 1976, p 577

hFor MDS the American Medical Association, Pro/da of A4edtcal Pr8Ct/Ce 1977,

Chtcago, 1977, p 101 For DOS sea, L/awon Commtttea on Osteopathic fm

formation, Osteopathic Manpower /rrformat/o? Pro/ect, Final Report, May 20,

1977

‘In 1974, over 130 such societies existed, In which thera were 342,090 members

representing 104 percent of all active physicians during that year. American

Medical Assoclatlon, Profde of Med/ca/ Pracf/ce 197>76 Chicago, 1976.

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, Education,

and We/fare, Washington, D. C,: Health Resources Administration, DHEW publication No. (HRA) 79-633, pp. 101-103.
Ch. 2—Supply • 25

Table 12.—lnternship and Residency Data Sources

Data sources Strengths Limitations

1. Directory of Accredited Residencies and Most complete source of

previous editions of the Directory of Ap- data available on MDs in

proved Internships and Residencies. — training.

Contains data on distributions off first-year

and total residents by specialty (30 listed), Published and updated

country of education, and affiliation status annually.

of hospital.

Also lists numbers of positions offered and

filled by specialty and affiliation and numbers of positions offered for the forthcoming year.a

2. American Osteopathic Association Almanac.—Contains data on residents in osteopathic hospitals by specialty and institution. b

3. Council of Teaching Hospitals. — Provides

data on interns and residents by institution. c

4. National Intern and Resident Matching

Program.— Provides information on

specialty distributions of first-year and

other residents in AMA-approved hospitals

who participate in the program.d

Most complete source of

data on DOS in training.

Published and updated annually.

Provides distributions of

residents by institution.

Provides distributions of interns and residents by institution.

Published and updated annually.

Timely, 1976 data available in

1976.

Provides indications of student specialty and institutional preferences.

Timely, 1976 data available in

1976.

Usually it is 2 years out of date.

Does not provide distributions of residents by

institutions.

Physicians listed as first-year residents in some

specialties may in fact be the second or third year

of training.

includes only first-year and total counts—intervening years not given. No data on fellowships.

Does not provide disaggregated data on residents

by years in t raining.

No information provided on DOS training in nonAOA-approved programs such as AMA-approved

hospitals.

Does not provide distributions of resident specialty or years in training.

Does not provide trend information on unmatched

graduates and foreign medical graduates variously estimated at 10 to 30 percent of the total firstyear resident supply.e f g

aArnerican Medical Association, L1/recfory of Approved Internships and ‘J. S. Graettinger, “Graduate Medical Education Viewed From the National lnResfderrts 1975-1976, Chicago, 1976. tern and Resident Matching Program,” J. Med. Educ,, 51, September 1976,

b American Osteopathic Association, “Almanac, Supplement to Volume 76,” fB, Biles, communication to staff of the Senate Committee on Labor and Public

1975 Journal of the American Osteopathic Association, April 1977. Welfare, June 6, 1976.

ccouncil of Teaching Hospitals, Directory, 1976, Association Of American gNIRMp does provide data for 1977 and 1978 on unmatched U.S. graduates. The

Medical Colleges, January 1976.

d Amer i can Medical Association, D/rectory of Approved InternShips and

program plans to collect such information periodically on all U.S. graduates,

both those who use as well as those not using NIRMP.

Residencies.

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, Education, and Welfare, Washington,

D. C.: Health Resources Administration, DHEW publication No, (HRA) 79-633, pp. 105-108,

but they also are unfortunate choices in the

sense that major changes were occurring in addition to the general drive to increase the aggregate supply of physicians and particularly

those in primary care. In 1971, the AMA decided to terminate the free-standing internship

after July 1, 1975, and instead to integrate the

first year of graduate medical education into

specific residency programs. During this time,

the number of first-year residency positions increased dramatically. Most of this increase occurred in the primary care specialties, especially

internal medicine (see figures 2 and 3). It would

be reasonable to presume that much of this

growth was not related to interest in primary

care as a career. Instead, the first year of

primary care residency training most likely substituted for the internship of previous years.

This overcounting of specialists through the

use of first-year residency data is not a phenomenon solely related to the discontinuation
26 . Forecasts of Physician Supply and Requirements

Figure 2.—Trend Data on Number of First-Year

Residents: Total, Primary, Nonprimary Care

Specialties; 1960, 1968, 1974, and 1976

Figure 3.—Trend in Number of First-Year Allopathic

Residents in Four Selected Specialties

aNOrlprlnlary specialties are total less primary care specialties.

bprimav care specialties include general and family practice, internal medicine, and pediatrics.

SOURCE: interim Report of the Graduate Med/cal Education National Advisory

Committee to the Secretary, Department of /fea/th, Educat/on, and

We//are, Washington, D. C.: Health Resources Administration, DHEW

publication No. (HRA) 79-633, p. 51.

of the free-standing internship. It has been

known for some time, although hard to quantify, that some graduate trainees take a second

first-year residency in a more specialized area of

the same specialty or move on to more advanced training in another specialty altogether.

For example, there is an observed 22-percent increase between the first and second year in the

surgical specialties (USDHEW, 1979a).

The way in which the overcounting is minimized is to adjust the first-year residency data in

the Directory of Accredited Residencies by subtracting the appropriate subspecialties from the

general residencies 1 year later. These adjustments are performed for internal medicine, pediatrics, general surgery, psychiatry, and pathology (USDHEW, 1979a).

For internal medicine, 9 percent of first-year

residents are subtracted first, this percent is

assumed to take another first-year residency in a

5,00(1

4,000

3,000

2,000

1,000

0

1950 1955 1960 1965 1970 1975 1980

Year

SOURCE: Interim Report of the Graduate Medma/ Education Nat/ona/ Advisory

Committee to the Secretary, Department of Hea/th, Educat/on, and

We/fare, Washington, D. C.: Health Resources Administration, DHEW

publication No. (HRA) 79-633, LI. 50,

medical subspecialty or in another specialty. Of

the remainder, 25 percent is assumed to go on to

subspecialty training. Thus, a total of about 32

percent (25 percent of the remainder of firstyear residents after subtracting 9 percent, plus

the original 9 percent of the total) of all internal

medicine first-year residents are subtracted and

“lost” to medical subspecialties or other specialties.

More recent data estimates that only 38 percent of first-year internal medicine residents end

up in general internal medicine, as compared to

the 68 percent summarized above (USDHEW,

1979a). However, these percentages are not

directly comparable because of the way in

which internal medicine subspecialties are

counted or not counted as primary care. The 68

percent remaining in primary care internal

medicine excludes gastroenterology, pulmonary

disease, cardiovascular disease, and allergy, but
Ch. 2–Supply “ 27

includes allergy and immunology, diabetes,

endocrinology, geriatrics, hematology, immunology, infectious diseases, neoplastic diseases, nephrology, nutrition, oncology, and

rheumatology (table 13). Whether a subspecialty of medicine is included in the internalmedicine primary care count is of crucial importance, as the first-year residency distribution

of primary care specialties is heavily weighted

toward internal medicine. Internal medicine

comprises more than so percent of all first-year

primary care residency positions (table 13).

The adjustment to pediatric first-year residency positions is 9 percent, representing losses

to pediatric allergy and pediatric cardiology.

The surgical figures are adjusted downwards by

62 percent, representing all surgical specialties

except obstetrics-gynecology and ophthalmology. Pathology is adjusted downwards by 2.7

percent, representing forensic pathology. And

psychiatry is adjusted downwards by 20 percent, representing child psychiatry. The “miscellaneous” category is assumed to remain proportionate to the overall numbers of MDs throughout the projection period. The adjusted and unadjusted first-year residency distribution for

1974 is summarized in table 13.

The same adjustments made for the 1974 year

are made for all historical years, 1968, 1970-74,

1976, that are used to establish the trend. For

1968, the use of these specific adjustment

percentages may not be very relevant, since it

was several years before announcement by

AMA of its intention to discontinue the internship.

A more technically oriented summary is provided in the following excerpt (with table

numbers changed to match the sequence of this

report) from the Graduate Medical Education

National Advisory Committee (GMENAC) interim report (USDHEW, 1979a). Note that the

trend in distribution of residents among the

various specialty training programs assumes a

similar trend for 1974 to 1980-81 as the base

years of 1968-74 (which were also modified to

include 1976 data). After 1980-81, the residency

distribution is held constant. BHM’s justification for the constant distribution after 1980-81 is

primarily that the base years which have been

chosen to establish the trend covered 6 years, so

the trend extrapolation is limited to 6 years.

The total projection method is summarized in

figure 4. Tables 16 and 17 summarize specialty

projections for MDs to 1980, 1985, and 1990.

Projections of filled first-year residencies were

made by extrapolating the results of simple

linear regression applied to the trend in filled

first-year residency percent distributions for the

years 1968, 1970-74, and 1976. The procedure

was applied for each specialty individually except for the category “miscellaneous,” which

was assumed to remain constant at 6.7 percent

(see tables 14 and 15). Also, rates were developed separately for U. S., Canadian, and other

medical school graduates.

In these regression analyses, the slope of the

regression line was computed from historical

trends, and the constant term (base year) was

taken from the first-year residency distribution

of 1974, adjusted for the duplication caused by

physicians first taking a residency in a general

area and then in a specialty (table 13). Where

this adjusted value differed significantly from

the original vaIue, as in general surgery, the

yearly rate of change (slope) was decreased by

the ratio of the unadjusted to the adjusted value.

The degree to which simple linear regression represents historical trends in individual specialties

is reflected in the F and R2 values displayed in

tables 14 and 15. Most specialty trends are adequately “explained” using this statistical method. However, recent cultural, political, and

fiscal interventions have affected certain specialties so that they behave erratically, and therefore have statistically nonsignificant F and R2

values for a linear fit. In two cases, U. S./CMG

general practice and radiology, the linear trend

produced actual negative percent values. These

values were set and held at zero for these two

specialties. This is a reasonable assumption since

general practice is being replaced by family practice, and general radiology is being replaced by

the diagnostic and therapeutic training programs.

The effects of recent legislation (Public Law

94-484) have not been evaluated and incorporated into the projections. However, the percent

of U.S. /CMG filled first-year residencies in primary care for 1980 are projected to meet the legislative mandates.
Table 13.–First.Year Residency Distribution With Subspecialty Adjustment: Sept. 1,1974

AMAa Adjustments Adjusted AMA

US/CMGs FMGs US/CMGs FMGs US/CMGs FMGs

Specialty Number Percent Number Percent Number Percent Number Percent Number Percent

(1) (2) (3) (4) (5) (6) (7) (8) (9) (lo)

Total active physicians. . . . . . . . . . . 13,519 100.0 5,216 100.0 12,626 100.0 4,755 100.0

Primary care. . . . . . . . . . . . . . . . . . . . . . . . 5,978 44.2 1,746 33.5 4,735 37.5 1,394 29.3

General practice . . . . . . . . . . . . . . . . . . 23 0.2 139 2.7 23 0.2 139 2.9

Family practice . . . . . . . . . . . . . . . . . . . 1,131 8.4 68 1.3 1,131 9.0 68 1.4

Internal medicineb. . . . . . . . . . . . . . . . . 3,591 26.6 962 18.4 – 1,144 –306C 2,447 19.4 656 13.8

Pediatrics. . . . . . . . . . . . . . . . . . . . . . . . 1,233 8.4 577 11.1 – 99 – sad 1,134 9.0 531 11.2

Other medical specialties. . . . . . . . . . . . . 335 2.5 46 0.8 1,155 9.1 266 5.6

Dermatology . . . . . . . . . . . . . . . . . . . . . 248 1.8 16 0.3 248 2.0 16 0.3

Pediatric allergy. . . . . . . . . . . . . . . . . . . 46 0.3 13 0.2 46 0.4 13 0.3

Pediatric cardiology . . . . . . . . . . . . . . . 41 0.3 17 0.3 0.3 17 0.4

Internal medicine subspecialtiese . . . . — — + 82 0 + 220f 8 ; ; 6.5 220 4.6

Surgical specialties. . . . . . . . . . . . . . . . . . 4,395 32.5 –

1,454 2;.9 3,280 26.0 936 19.7

General surgery. . . . . . . . . . . . . . . . . . . 1,803 13.4 836 16.0 – 1,118 – 5189 685 5.4 318 6.7

Neurological surgery. . . . . . . . . . . . . . . 114 0.8 15 0.3 114 0.9 15 0.3

Obstetrics and gynecology . . . . . . . . . 742 5.5 288 5.5 742 5.9 288 6.1

Ophthalmology . . . . . . . . . . . . . . . . . . . 468 3.5 36 8.7 468 3.7 36 0.8

Orthopedic surgery. . . . . . . . . . . . . . . . 547 4.0 62 1.2 547 4.3 62 1.3

Otoiaryngology . . . . . . . . . . . . . . . . . . . 227 1.7 43 0.8 227 1.8 43 0.9

Plastic surgery. . . . . . . . . . . . . . . . . . . . 148 1.1 36 0.7 148 1.2 36 0.8

Colon andrectalsurgery. . . . . . . . . . . . 0.1 10 0.2 20 0.2 10 0.2

Thoracicsurgery . . . . . . . . . . . . . . . . . . % 0.7 50 1.0 97 0.8 50 1.1

Urology . . . . . . . . . . . . . . . . . . . . . . . . . . 232 1.7 78 232 1.8 78 1.6

Otherspecialties. . . . . . . . . . . . . . . . . . . . 2,808 20.8 1,970 3;: : 3,456 27.4 2,159 45.4

Anesthesiology . . . . . . . . . . . . . . . . . . . 367 2.7 348 6.7 367 2.9 348 7.3

Neurology . . . . . . . . . . . . . . . . . . . . . . . . 252 1.9 109 2.1 252 2.0 109 2.3

Pathology . . . . . . . . . . . . . . . . . . . . . . . . 397 2.9 410 7.9 – 11 – l l h 386 3.1 399 8.4

Forensic pathology. . . . . . . . . . . . . . . . 17 0.1 7 0.1 17 0.1 7 0.1

Psychiatry . . . . . . . . . . . . . . . . . . . . . . . 952 7.0 612 11.7 – 180 –llG i 771 6.1 496 10.4

Child psychiatry. . . . . . . . . . . . . . . . . . . 189 1.9 189 1.5 98 2.1

Physical medicineand rehabilitaticm . 29 : : ; !% 1.8 29 0.2 93 2.0

Radiology . . . . . . . . . . . . . . . . . . . . . . . . 88 0.7 137 2.6 88 0.7 137 2.9

Diagnostic radiology. . . . . . . . . . . . . . . 452 3.3 101 1.9 452 3.6 101 2.1

Therapeutic radiology. . . . . . . . . . . . . . 65 0.5 55 1.1 0.5 55 1.2

Miscelianeousj. . . . . . . . . . . . . . . . . . . . — — — — + 84 0 +316k 8 : ; 6.7 316 6.7

aDirectO~OfACCredited Residencies, AMA,Chicago, 1977.

blncludesa~ergy and immunology, diabetes, endocrinology, geriatrics, hematology, immunology, infecvided by 1973 FYRs(2,6g8) in general surge~ is 62percent, the proportion subtracted out of the 1974

FYRsin general surgery.

tiousdiseases, neoplasticdiseases, nephrology, nutrition, oncology, and rhematology. hlg74FyRs(24) in forensic pathology divided by 1973 FYRs(896) in pathology is2.7 percent, theproporc Forexplanation ofgand 2$percent adjustments, seetext. tionexcluded from the1974FYRsin pathology.

d1974FyR9in pediatfica~ergy and pediatric cardiology(l17) divided by 1973 FYRsin pediatrics (l,899)is i1974FYR9

(287) inchildps ychiatvdivided by the 1973 FYRs(l,472) in psychiatvis20 percent, the pro8.9 percent, theproportion excluded from the 1974 FYRsin pediatrics. ,portion subtractedout of the 1974 FYRsin psychiatry.

elnclude.g gastroenterology, pulmonary disease, cardiovascular disease, andallergy. Ilncludes aerospace medicine, public health, general preventive medicine, occupational medicine,

fl,~figure from footnotec. “other,’’a ndunspecified.

g1974 FYRs (1,679) in surgical Subspecialties (excluding obstetrics/gynecology, and ophthalmology) di- ‘For explanation, see text.

SOIJRCE: Interim Report of the Graduate Medical ~duc~~io~ ~S’tkVM/ AdvisoW Committee to the Secretary, Deparfrnerrt of Health, Educatiorr, and We/fare, Washington, D. C.: Health Resources Administration,

DHEW publication No. (HRA) 79-833, pp. 124-125.

I
Ch. 2–Supply “ 29

Figure 4. -Projection of Active Physicians

by Specialty

Adjustment of

~G regression results

, :. . to 1980-81. Theredistribution

is held constant

residencies

resldenc Ies ~.

, ‘ , . . .

$

..;’ $ :, * - , , 1 . ’

Judgments and ““ Adjustment so Application of

analysis of “ that percent projected

special circum- specialty adjusted firststances - distribution - year residency

sums to 100°/0 distribution to

.> graduates over

all specialties

,

, .

.? > . . ‘.,

Addition to Net active

active phy - separations due

sician supply :. to retirement ,

by specialty m

, ,

- . **. q.$j .: ,* -., ,, & ! J ‘: ~. ,. . . 7. . z ‘ , . , . _ : . . : ., } . . . +. ! , . . . ,; ,i. , ;f ,? ‘

‘“

SOURCE: Interim Report of the Graduate A.fed/ca/ Education Naf/ona/ Advisory

Cornrn/tree to fhe Secretary, Depar?nrenf of Health, Educaf/on, “and

We/fare, Washington, D. C.. Health Resources Admlnistratlon, DHEW

publication No (HRA) 79-633, p 122,

As mentioned, judgment was used in specific

instances where straightforward extrapolation

appeared to produce intuitively unreasonable results. Such was the case with specialties showing

a curvilinear trend. This trend was terminated in

1977 for these specialties, not only because the

projected numbers appeared unreasonable, but

also because the historical data in no instance

showed a strong curvilinear trend in one direction that lasted more than 3 years. The USMG

radiology trend was allowed to fall to zero by

1980 because of the reported phasing out of

general radiology as a prerequisite for entry into

one of the radiology subspecialties.

It is readily acknowledged that such use of regression in this analysis implied an assumption

that the conditions underlying and responsible

for past trends will also be in force in the future.

Even though the situation is rapidly changing in

the GME environment, it is nonetheless believed

that such projections, when interpreted in a cautious manner, can be of value as “baseline” estimates, indicating what the specialty configuration might be if residency developments continue as they have in the past.

Using this approach, extrapolations of each

distinctive residency trend were developed. Because each residency category was projected

separately, however, a few minor changes had

to be made to adjust the overall distribution to

the control total for all residencies. In other

words, when the projected percentage distribution of residencies did not add to 100 percent

because of unusually strong trends in one specialty, the specialties which remained constant

(10 out of 29 in the case of USMGS) in the historical trend period were adjusted slightly to

make up the difference.

For several reasons, this methodology was

employed for the period of 1980-81. Thereafter,

the 1980-81 residency distribution was held constant. One reason for this is that extrapolation is

not statistically justified for longer periods in the

future than are represented by the historical data

on which it is based, in this case 6 years. Another reason is that most trends of the type observed have a tendency to level off after their initial spurt. (Additional research and trend analysis is continuing on this aspect of the projections. )

These assumptions are the working assumptions of the Division of Manpower Analysis,

BHM, for specific purposes. They have not been

endorsed by GMENAC, which will develop its

own assumptions concerning requirements

rates, foreign medical graduates projections,

specialization rates, cavitation grants to medical

schools and other issues. It is GMENAC’S intent

to investigate and, as needed, modify these assumptions (USDHEW, 1979a).
30 . Forecasts of Physician Supply and Requirements

Table 14.—Percent Distribution of U.S/CMG First-Year Residency Projections

Using Simple Linear Regressions (1976 actual, 1977-81 projections)

Specialty 1976a 1977 1978 1979 1980 1981 Fb R2b

Totalc . . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0

Primary care . . . . . . . . . . . . . . . . . . . . . 43.2 43.8 46.0 48.1 50.2 — 52. 2

General practice. . . . . . . . . . . . . . . . 0.1 0.0 0.0 0.0 0.0 —

0.0 18.2 .78

Family practice. . . . . . . . . . . . . . . . . 12.0 13.5 15.1 16.6 18.1 19.5 385.9 .99

Internal medicine. . . . . . . . . . . . . . . 22.6 21.2 21.7 22.2 22.7 23.2 10.1 .67

Pediatrics . . . . . . . . . . . . . . . . . . . . . 8.5 9.1 9.2 9.3 9.4 9.5 9.6 .66

Other medical specialties. . . . . . . . . . 9.8 9.6 9.5 9.7 9.8 9.9 – –

Dermatology. . . . . . . . . . . . . . . . . . . 1.6 1.7 1.6 1.6 1.5 1.5 2.7 .36

Pediatric allergy. . . . . . . . . . . . . . . . 0.4 0.4 0.4 0.4 0.4 0.1 .02

Pediatric cardiology. . . . . . . . . . . . . 0.2 0.3 0.2 0.4 0.2 0.2 2.6 .34

Internal medicine subspecialtiesd . 7.6 7.2 7.3 7.5 7.7 7.8 16.1 .76

Surgical specialties. . . . . . . . . . . . . . . 22.5 21.8 20.7 19.4 18.0 16.9 – –

General surgery . . . . . . . . . . . . . . . . 4.8 4.2 3.8 3.4 3.0 2.6 60.3 .92

Neurological surgery. . . . . . . . . . . . 0.6 0.6 0.5 0.5 0.4 0.3 24.9 .83

Obstetrics and gynecology. . . . . . . 5.9 5.8 5.8 5.7 5.6 0.4 .07

Ophthalmology. . . . . . . . . . . . . . . . . 3.1 2.8 2.6 2.3 5.6 1.8 500.0 .99

Orthopedic surgery . . . . . . . . . . . . . 3.4 3.6 3.4 3.2 3.0 2.8 .61

Otolaryngology. . . . . . . . . . . . . . . . . 1.4 1.4 1.3 1.1 1.0 0.9 3:: : .88

Plastic surgery . . . . . . . . . . . . . . . . . 1.0 1.1 1.1 1.1 1.1 8.8 .40

Colon and rectal surgery . . . . . . . . . 0.1 1.1 0.2 0.2 0.2 22.7 .82

Thoracic surgery . . . . . . . . . . . . . . . 0.7 0.6 0.6 0.2 0.5 0.5 5.4 .52

Urology . . . . . . . . . . . . . . . . . . . . . . . 1.4 1.4 1.3 1.2 1.1 5.3 .51

Other specialties . . . . . . . . . . . . . . . . . 24.6 24.6 23.8 22.9 22.1 21.2 – –

Anesthesiology . . . . . . . . . . . . . . . . 2.2

— —

2.0 1.7 1.4 1.1 0.9 113.9 .96

Neurology . . . . . . . . . . . . . . . . . . . . . 1.8 1.8 1.6 1.5 1.3 6.1 .55

Pathology . . . . . . . . . . . . . . . . . . . . . 3.2 2.9 2.8 2.7 1.4 2.5 11.7 .70

Forensic pathology . . . . . . . . . . . . . 0.1 0.1 0.1 0.2 0.2 0.2 0.5 .11

Psychiatry . . . . . . . . . . . . . . . . . . . . . 4.8 4.5 3.9 3.3 2.7 2.2 26.0 .84

Child psychiatry . . . . . . . . . . . . . . . . 1.3 1.5 1.5 1.5 1.5 1.4 0.01 .002

Physical medicine and

rehabilitation . . . . . . . . . . . . . . . . 0.4 0.2 0.2 0.1 0.0 5.1 .50

Radiology . . . . . . . . . . . . . . . . . . . . . 0.2 0.0 0.0 0.1 0.0 0.0 57.1 .92

Diagnostic radiology . . . . . . . . . . . . 3.5 4.8 5.0 5.3 5.5 .28

Therapeutic radiology . . . . . . . . . . . 0.4 4.6 0.5 0.5 0.5 0.5 0.07 .02

Miscellaneous . . . . . . . . . . . . . . . . . 6.7 6.7 6.7 6.7 6.7 6.7 – –

aActual figures.

%he degree to which simple linear regression represents actual historical

trends in the individual specialties is reflected in the F and fly values. CFigUreS may not total due to independent rounding.

dlncludes gastroenterology, pulmonary disease, cardiovascular disease, and

ailergy.

elncludes aerospace n’wdklrre, public heaith, general preventive medicine, occupational medicine, ‘*other,” and unspecified.

The “F Test,” as applied to the regression on historical residency data, measures the statistical significance of the linear trend as an estimate of the past

changes in the number of first-year residents by specialty 1968-76. Vaiues of F

greater than 6.6 are statistically significant at the 95-percent confidence level.

R’, the square of the Pearson product-moment correlation coefficient, is frequently referred to as “The Correlation Index.” On a scale from zero to one, it

measures the degree to which the linear trend estimates the actual changes in

the number of first-year residents, by specialty, 1968-76.

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, Education, and Welfare, Washington,

D. C.: Health Resources Administration, DHEW publication No. (HRA) 79-633, pp. 127-128.

LOCATIONAL DISTRIBUTION

Where physicians reside and practice medi- The limitations of the data sources in simply

cine is generally obtained from the same data describing where physicians live and practice

sources as for aggregate and specialty supply. are similar to the data limitations in describing

Distribution is usually described at the State, aggregate and specialty supply. In addition, decounty, or Health Service Area (HSA), and, for scribing locational distribution by States, by

comparative purposes, quantified as a physi- counties, by metropolitan versus nonmetropolician-to-population ratio. tan areas, and even by HSAS may be most con-
Ch. 2–Supply ● 3 1

Table 15.—Percent Distribution of FMG First-Year Residency Projections Using Simple Linear Regressions

(1976 actual, 1977.81 projections)

Specialty 1976a 1977 1978 1979 1980 1981 Fb RZb

Total:. . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0 100.0

Primary care . . . . . . . . . . . . . . . . . . . . . 33.2 31.6 32.1 32.6 33.1 33.6

General practice. . . . . . . . . . . . . . . . 4.4 3.5 3.6 3.7 3.8 3.9 0.7 - .12

Family practice. . . . . . . . . . . . . . . . . 3.1 3.1 3.5 3.9 4.3 4.7 27.2 .87

Internal medicine. . . . . . . . . . . . . . . 13.2 13.3 13.1 12.9 12.7 12.5 2.0 .28

Pediatrics . . . . . . . . . . . . . . . . . . . . . 12.5 11.7 11.9 12.1 12.3 12.5 1.3 .21

Other medical specialties . . . . . . . . . . 5.4 5.2 5.2 5.1 4.9 4.7 — —

Dermatology. . . . . . . . . . . . . . . . . . . 0.3 0.2 0.2 0.2 0.2 0.1 6.7 .57

Pediatric allergy. . . . . . . . . . . . . . . . 0.3 0.3 0.3 0.3 0.3 0.3 0.1 .02

Pediatric cardiology. . . . . . . . . . . . . 0.4 0.3 0.3 0.3 0.3 0.2 10.4 .68

Internal medicine subspecialtiesd . 4.4 4.4 4.4 4.3 4.2 4.1 2.2 .31

Surgical specialties. . . . . . . . . . . . . . . 20.5 19.0 18.2 17.6 17.0 16.2 — —

General surgery. . . . . . . . . . . . . . . . 7.0 6.6 6.4 6.1 5.9 5.7 16.4 .77

Neurological surgery. . . . . . . . . . . . 0.8 0.5 0.5 0.5 0.5 0.5 0.1 .02

Obstectrics and gynecology . . . . . . 5.4 5.0 4.6 4.1 3.7 3.3 138.7 .97

Ophthalmology. . . . . . . . . . . . . . . . . 0.5 0.6 0.5 0.5 0.5 0.4 3.6 .42

Orthopedic surgery . . . . . . . . . . . . . 2.0 1.7 1.7 1.8 1.9 1.9 2.3 .31

Otolaryngology. . . . . . . . . . . . . . . . . 0.9 0.9 0.9 0.9 0.9 0.9 0.7 .13

Plastic surgery . . . . . . . . . . . . . . . . . 0.8 0.9 0.9 1.0 1.0 1.0 41.3 .89

Colon and rectal surgery . . . . . . . . . 0.3 0.3 0.3 0.4 0.4 0.4 9.5 .65

Thoracic surgery . . . . . . . . . . . . . . . 0.9 0.8 0.7 0.6 0.5 0.4 14.9 .75

Urology . . . . . . . . . . . . . . . . . . . . . . . 1.8 1.7 1.7 1.7 1.7 1.7 .00005 .00001

Other specialties . . . . . . . . . . . . . . . . . 40.9 44.3 44.5 44.8 45.1 45.5 — —

Anesthesiology . . . . . . . . . . . . . . . . 6.2 6.6 6.2 5.9 5.5 5.2 6.5 .56

Neurology . . . . . . . . . . . . . . . . . . . . . 1.9 2.3 2.3 2.4 2.4 2.5 2.7 .35

Pathology . . . . . . . . . . . . . . . . . . . . . 7.1 7.5 7.2 6.8 6.5 6.2 6.7 .57

Forensic pathology . . . . . . . . . . . . . 0.2 0.2 0.2 0.2 0.2 0.2 0.4 .10

Psychiatry . . . . . . . . . . . . . . . . . . . . . 8.5 9.2 9.4 9.6 9.9 10.1 1.5 .23

Child psychiatry. . . . . . . . . . . . . . . . 2.0 2.3 2.5 2.7 2.9 3.1 55.5 .92

Physical medicine and

rehabilitation . . . . . . . . . . . . . . . . 2.5 2.5 2.7 2.9 3.0 3.2 38.5 .89

Radiology . . . . . . . . . . . . . . . . . . . . . 1.3 1.6 1.2 0.8 0.5 0.1 29.5 .86

Diagnostic radiology . . . . . . . . . . . . 3.0 3.6 4.1 4.6 5.1 5.6 239.7 .98

Therapeutic radiology . . . . . . . . . . . 1.5 1.8 2.0 2.2 2.4 2.6 154.7 .97

Miscellaneous. . . . . . . . . . . . . . . . . 6.7 6.7 6.7 6.7 6.7 6.7 – –

aActual figures. The “F Test:’

%he degree to which simple Iinear regression represents actual historical

as applied to the regression on historical residency data,

measures the statistical significanceof the linear trend asan estimateof past

trends intheindiwdual specialties is reflected in the Fand Revalues. changes in the number of first-year residentsby specialty 1966.76, Valuesof F

cFlguresmay not add tototal dueto independent rounding. greaterthan 6,6 are statistically significant at the95-percent confidence level,

dlncludes gastroenterology, pulmonary disease, cardiovascular disease, and Ry, the squareof the Pearson product-moment correlation coefficient, isfreallergy. quently referred teas’’TheCorrelation index’’Onascale from zero to one, (t

elncludes aerospace medicine, public health, general preventive medicine, oc- measures the degreeto which the Iinear trend estimates the actual changes in

cupatlonal medicine, “other,” and unspecified, the number of first-year residents, by specialty, 1968-76.

SOURCE: Irrtermr Report of the Graduate Medical Education Natfona/ Adwsory Corrrrrr/ttee to the Secrefary, Departrnerrt of ffea/tfr, Educa(/err, and We/fare, Washington,

D.C : Health Resources Administration, DHEW publication No. (HRA) 79-633, p 129-130

venient from a data availability point of view,

but it does not necessarily follow that physicians are available to the populations they are

matched against. Nor are populations identified

on these bases comparable, and one area (e. g.,

county) may have people with significantly different health problems than people in other

areas. So in addition to the basic problem of

being able to count the numbers of practicing

physicians and their clinical specialties in an

identified area, there is the additional problem

of whether these physicians actually provide

medical services to the designated population

(including whether they may be providing services to people in adjacent areas). This qualification becomes important when such comparative data are used to implement programs

that single out “health manpower shortage

areas” for support. In these aid programs, a specific physician-to-population ratio is chosen as

the cutoff point and used in conjunction with

other indices of medical need to determine
32 . Forecasts of Physician Supply and Requirements

Table 16.—Active Physicians (MD), by Major Specialty Group:

Actual 1974; Projected (under the basic assumption) 1980-90

Projected

Specialty group Base year 1974 1980 1985 1990

Number of active physicians

Total . . . . . . . . . . . . . . . . . . 348,960 430,150 500,340 566,940

Primary care. . . . . . . . . . . . . . . . 133,240 166,790 203,370 239,830

Other medical specialties. . . . . 18,930 26,580 33,800 41,080

Surgical specialties. . . . . . . . . . 97,720 113,200 122,160 129,610

Other specialties. . . . . . . . . . . . 99,070 123,580 141,050 156,410

Percent distribution

Total . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0

Primary care. . . . . . . . . . . . . . . . 38.2 38.8 40.6 42.3

Other medical specialties. . . . . 5.4 6.2 6.8 7.2

Surgical specialties. . . . . . . . . . 28.0 26.3 24.4 22.9

Other specialties. . . . . . . . . . . . 28.4 28.7 28.2 27,6

Rate per 100,000 population

Total . . . . . . . . . . . . . . . . . . 165.5 193.1 213.8 231.3

Primary care. . . . . . . . . . . . . . . . 63.2 74.9 86.9 97.9

Other medical specialties. . . . . 9.0 11.9 14.4 16.8

Surgical specialties. . . . . . . . . . 46.3 50.8 52.2 52.9

Other specialties. . . . . . . . . . . . 47.0 55.5 60.3 63.8

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of

Hea/th. Education. and We/tare. Washington. D. C,: Health Resources Administration. DHEW tmblicatlon No, (HRA) . . . ,

79-633; p. 155.

“ .

whether an area is eligible or not for aid. This

concept of “shortage” is a question more basic

to comparing supply with requirements and is

discussed in the chapter on requirements.

The distribution of physicians by the most

common methods of description and quantification is summarized in tables 18 and 19 and figure S. Table 18 provides physician-to-population ratios for selected specialties in the aggregate and for the States with the highest and lowest ratios. Table 19 contrasts non-Federal MDs

in metropolitan with nonmetropolitan counties.

And figure 5 contrasts the supply of surgeons

and primary care physicians as grouped by

HSAS. DHHS also compiles these statistics

through a “GINI index, ” a statistical tool that

expresses unevenness as a single number. To

compute the GINI index, the percentage of the

total population is graphically accumulated on

one axis and the percentage of practitioners

similarly accumulated on the other axis, starting

with the area with the lowest physician-topopulation ratio and going to the area with the

highest ratio. If the distribution were perfect,

the result would be a 45-degree “line of equality. ” The GINI index is the ratio of the area between the actual curve and the line of equality to

the total area under the line of equality

(USDHEW, 1979b).

The GINI index value varies between zero, indicating no maldistribution, and 1.0, indicating

the greatest possible maldistribution. In general,

smaller index values (indicating less unevenness)

can be expected when making comparisons

among larger geographical units. This can be

seen in the following GINI index for active nonFederal MDs in 1973:

By State (50 States) . . . . . . . . . . . . . . . . . . . . ..0.161

By Census-Defined State Economic Area

(173 areas) ... , . . . . . . . . . . . . . . . . . . . . .0.292

By county (3,071 counties). . ...............0.361

(Source: USDHEW, 1979b)

Osteopathic physicians (estimated at 17,700

in 1980 out of a total supply of active physicians

of 447,800) are unevenly distributed among the

States because they were not allowed to practice

in some States until recently and because of the

limited number of schools. In 1977, Michigan

had 2,760 osteopaths, Alaska, only 7. More

than 20 States had less than 100 osteopathic

physicians and students.
Ch. 2–Supply ● 3 3

Table 17.–Supply of Active Physicians (MD), by Specialty: Actual 1974; Projected 1980-90

Number of Dhvsicians Percent distribution

Specialty 1974 1980 1985 1990 1974 1980 1985 1990

Total active physicians. . . . . . . . 348,960 430,150 500,340 566,940 100.0 100.0 100.0 100.0

Primary care. . . . . . . . . . . . . . . . . . . . . 133,240 166,790 203,370 239,830 38,2 38.8 40.6 42.3

General practice . . . . . . . . . . . . . . . 46,530 39,290 32,870 26,350 13.3 9.1 6.6 4.6

Family practice . . . . . . . . . . . . . . . . 9,480 22,380 39,190 56,480 2.7 5.2 7.8 10.0

Internal medicine . . . . . . . . . . . . . . 54,780 73,280 91,020 108,530 15.7 17.0 18.2 19.1

Pediatrics. . . . . . . . . . . . . . . . . . . . . 22,460 31,830 40,290 48,470 6.4 7.4 8.1 8.5

Other medical specialties. . . . . . . . . . 18,930 26,580 33,800 41,080 5.4 6.2 6.8 7.2

Dermatology . . . . . . . . . . . . . . . . . . 4,470 5,830 6,720 7,610 1.4 1.4 1.3 1.3

Pediatric allergy . . . . . . . . . . . . . . . 480 870 1,210 1,500 0.1 0.2 0.2 0.3

Pediatric cardiology . . . . . . . . . . . . 590 850 1,030 1,200 0.2 0.2 0.2 0.2

Internal medicine subspecialtiesa. 13,120 19,030 24,850 30,730 3.8 4.4 5.0 5.4

Surgical specialties . . . . . . . . . . . . . . 97,720 113,200 122,120 129,610 28.0 26.3 24.4 22.9

General surgery. . . . . . . . . . . . . . . . 32,100 34,700 35,210 35,140 9.2 8.1 7.0 6.2

Neurological surgery . . . . . . . . . . . 2,990 3,470 3,360 3,710 0.9 0.8 0.7 0.7

Obstetrics and gynecology . . . . . . 22,080 26,620 30,040 33,230 6.3 6.2 6.0 5.9

Opthamology. . . . . . . . . . . . . . . . . . 11,220 12,630 13,210 13,730 3.2 2.9 2.6 2.4

Orthopedic surgery. . . . . . . . . . . . . 11,550 14,280 16,170 17,890 3.3 3.3 3.2 3.2

Otolaryngology . . . . . . . . . . . . . . . . 5,870 6,640 6,980 7,310 1.7 1.5 1.4 1.3

Plastic surgery. . . . . . . . . . . . . . . . . 2,330 3,370 4,280 5,150 0.7 0.8 0.9 0.9

Colon and rectal surgery . . . . . . . . 680 800 890 980 0.2 0.2 0.2 0.2

Thoracic surgery . . . . . . . . . . . . . . . 2,100 2,750 3,080 3,350 0.6 0.6 0.6 0.6

Urology. . . . . . . . . . . . . . . . . . . . . . . 6,790 7,960 6,620 9,150 1.9 1.9 1.7 1.6

Other specialties. . . . . . . . . . . . . . . . . 99,070 123,580 141,050 156,410 28.4 28.7 28.2 27.6

Anesthesiology. . . . . . . . . . . . . . . . 13,330 15,600 16,210 16,830 3.8 3.6 3.2 2.9

Neurology . . . . . . . . . . . . . . . . . . . . 4,200 6,070 7,360 8,520 1.2 1.4 1.5 1.5

Pathology. . . . . . . . . . . . . . . . . . . . . 12,310 15,860 18,120 20,020 3.5 3.7 3.6 3.5

Forensic pathology. . . . . . . . . . . . . 220 360 540 700 0.1 0.1 0.1 0.1

Psychiatry . . . . . . . . . . . . . . . . . . . . 24,740 28,560 29,900 30,690 7.1 6.6 6.0 5.4

Child psychiatry . . . . . . . . . . . . . . . 2,730 4,460 5,970 7,730 0.8 1.0 1.2 1.3

Physical medicine and

rehabilitation . . . . . . . . . . . . . . . . 1,780 2,450 2,780 2,990 0.5 0.6 0.6 0.5

Radiology. . . . . . . . . . . . . . . . . . . . . 11,900 11,710 10,950 9,970 3.4 2.7 2.2 1.8

Diagnostic radiology. . . . . . . . . . . . 3,650 8,180 13,440 18,660 1.9 2.7 3.3

Therapeutic radiology. . . . . . . . . . . 1,200 2,000 2,760 3,420 : : : 0.5 0.6 0.6

Miscellaneous . . . . . . . . . . . . . . . . 23,010 28,320 33,030 37,670 6.6 6.6 6.6 6.6

al”~ludes allergy, cardiovascular disease, gastroenterology, and Pulmonary disease,

blncludes aerospace medlclne, general preventive medicine, occupational medicine, public health, unspecified, and “other Specialties.”

NOTE: Figures may not add to subtotals and totals due to independent rounding.

SOURCE: Intermr Report of the Graduate Medical Educat/on National Advisory Cornffr/ttee to the Secretary, Department of Health, Educat/err, and Welfare, Washington,

D.C : Health Resources Administration, DHEW publication No. (HRA) 79-633, p. 153.

These estimates of MD and DO locational

distribution do not address the question of future distributional patterns. Such predictive efforts are used for programs which intend to

place physicians in identified areas of shortage

and for which such shortage designations also

make the identified areas eligible for governmental (Federal and State) funds.

Prior to the Health Professions Educational

Assistance Act of 1976, different criteria had

been developed for designation as a Health

Manpower Shortage Area (HMSA) for BHM

programs and as a Medically Underserved ..Area

(MUA) for the Bureau of Community Health

Services’ (BCHS) programs. Following the passage of the 1976 Act, these definitions have been

consolidated under the HMSA designation.

Thus, once designated a HMSA, such areas:

1) would be eligible for National Health Service

Corps (NHSC) staffing of Corps practice site, 2)

would be areas in which students who borrowed

money under health professions student loan

programs could practice in lieu of repaying the

loans in money, 3) would be eligible for grants

in various health manpower training programs,
34 . Forecasts of Physician Supply and Requirements

Table 18.–Patient Care MDs (non-Federal) by Selected Specialties and for High and Low States (1976)

MDs per 100,000

Specialty population, all States

All specialties . . . . . . . . . . . . . . . . .

Primary carea. . . . . . . . . . . . . . . . . .

General and family practice. . . . . .

internal medicine . . . . . . . . . . . . . .

Pediatrics . . . . . . . . . . . . . . . . . . . .

Obstetrics and gynecology . . . . . .

General surgery . . . . . . . . . . . . . . .

Psychiatry . . . . . . . . . . . . . . . . . . . .

Ophthalmology. . . . . . . . . . . . . . . . .

Orthopedic surgery. . . . . . . . . . . . .

137.0

58.4

23.9

22.2

8.9

9.3

13.5

9.0

4.9

4.9

Anesthesiology. . . . . . . . . . . . . . . . 5.3

Ratio for highest State Ratio for lowest State

New York. . ...........198 South Dakota. . . . . . . . . .......78

New York. . . . . . . . . . . . . 84 Alaska . . . . . . . . . . . . . . . . . . . . .35

California . . . . . . . . . . . . 30 Alabama. . . . . . . . . . . . . .......19

Massachusetts . . . . . . . . 43 South Dakota . . . . . . . . . . . . . . . . 7

Maryland . . . . . . . . . . . . . 17 South and North Dakota. . . . . . . 3

Maryland . . . . . . . . . . . . . 16 South Dakota . . . . . . . . . . . . . . . . 3

New York. . . . . . . . . . . . . 20 South Dakota and Alabama. . . . 2

New York. . . . . . . . . . . . . 20 South Dakota and Alabama. . . . 2

New York. . . . . . . . . . . . . 7 South Dakota . . . . . . . . . . . . . . . . 2

Massachusetts, Connect- South Dakota, Alabama, and

icut, and California. . . 7 Mississippi . . . . . . . . . . . . . . . 3

Massachusetts. . . . . . . . 9 South Dakota. . . . . . . . . . . . . . . . 1

aDeflned as general arlcJ family practitioners, Internists, and pediatricians

SOURCE. /nter/m Report of the Graduate Med/ca/ Education National Adv/sory Committee to the Secretary, Department of Hea/tn, Educat/on, and We/fare, Washington,

D.C : Health Resources Adminlstratlon, DHEW publication No. (HRA) 79-633, pp. 65-87,

Table 19.—Non.Federal Physicians (MD) Providing Patient Care

in Metropolitan and Nonmetropolitan Areas 1963.76

Number of physicians MDs per 100,000 population Percent of MDs

All Metro- Non- All Metro- Non- All Metro- NonYear counties politan metropolitan counties politan metropolitan counties politan metropolitan

1963 . . . . . . 225,427 178,403 47,024 120.3 144.2 73.8 100.0 79.1 20.9

1964 . . . . . . 232,067 184,298 47,769 122.0 146.8 73.9 100.0 79.4 20.6

1965 . . . . . . 239,482 189,211 48,271 123.2 148.7 73.6 100.0 79.7 20.3

1966 . . . . . . 241,473 192,871 48,602 123.7 148.9 74.1 100.0 79.9 20.1

1967 . . . . . . 247,256 200,880

1968b . . . . . 236,458

46,376 125.4 150.0 73.3 100.0 81.2 18.8

192,242 44,216 118.7 141.6 69.7 100.0 81.3 18.7

1969 . . . . . . 245,368 200,247 45,121 121.8 145.7 70.5 100.0 81.6 18.4

1970 . . . . . . 252,778 206,676 46,102 124.2 148.7 71.5 100.0 81.8 18.2

1971 . . . . . . 261,335 217,187 44,148 127.5 152.9 70.1 100.0 83.1 16.9

1972 . . . . . . 266,587 225,424 41,163 128.5 152.9 68.6 100.0 84.6 15.4

1973 . . . . . . 270,412 231,529 38,883 129.1 150.9 69.4 100.0 85.6 14.4

1974 . . . . . . 276,070 235,994 40,076 130.9 153.3 70.4 100.0 85.5 14.5

1975 ..., . . 287,837 249,218 38,619 134.8 156,9 74,1 100.0 86.6 13,4

1976 . . . . . . 294,730 255,102 39,628 137.4 158.2 74.4 100.0 86.6 13.4

aFor 1963.66, metropoi itan Counties include those in SMSAS on basis of 1962 population and nonmetropol itan counties include those adjacent to metrOPOlitan counties

and isolated rural and semirural counties.

For 1967-76, metropolitan counties include those in SMSAS on basis of 1967 population and nonmetropolitan counties include potential metropolitan counties and all

others outside SMSAS.

b Beginnlng in 1968, the AMA changed its methods of Classlfylng physicians to reflect the number of hours spent in various actiwties and .Specialties. This resulted in a

loss in physicians in patient care with corresponding Increases in physicians in “other activities” and inactive.

Based on annual reports on the distribution physician in the United States by the American Medical Association, 1963-67.

SOURCE: /nterim Report of the Graduate Medical Education Naoonal Advisory Comm/ttee to the Secretary, Department of Hea/th, Education, and We/fare, Washington,

DC.: Health Resources Administration, DHEW publication No. (HRA) 79-633, pp. 91-92.

4) would be eligible or given preference for

grant funds for several BCHS programs such as

the urban and rural health initiatives, and

5) would be the only areas in which rural health

clinics could be certified for reimbursement of

nurse practitioner and physicians’ assistant services under Medicare and Medicaid.

Through the 1976 Act shortage designation

for eligibility for NHSC physician services is

now available not only to geographic areas (the

old emphasis on alleviating rural shortages), but

also to population groups and institutional settings of care. The former include Native Americans, migrants, and the aged. The latter include

hospitals, state mental health facilities, rehabilitation facilities, long-term care facilities, community health centers, community mental

health centers, migrant health centers, and Federal and State prisons.
Ch. 2–Supply Ž 35

Figure5.— Frequency Distribution of Physician Availability Indexes—primary Care .

Physicians and Surgeons for the 204 HSAs

69

5

<.39

48

Surgeons

Primary care

physicians

47

39

1 35

3

4

9

.4 .59 .6 .79 .8 0.99 I 1.0 1.29 1.3 1.59 1.6 1.99 >2.0

Below equivalent share for Above equivalent share for

target population target population

The availability index is a weighted average of the ratio between the portion of the Nation’s physicians

in each of the HSA’S counties and the portion of the Nation’s population living in each of those counties. If the HSA has attracted a portion of the Nation’s physicians equivalent to its portion of the U.S. ’S

population, its physician availability index would be 1.0.

SOURCE. Inter/m Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health, Education, and Welfare, Washington D.C. Health Resources Administration, DHEW publication No, (HRA) 79-633, p. 95.

From this wide array of potential shortage for mental health facilities and osteopaths also

areas, the NHSC program had to develop sub- are included, although not specifically in their

sets that would actually receive Corps attention. projections, which concern allopathic physician

NHSC includes obstetrician-gynecologists as supply.

primary care physicians, in addition to family For nonmetropolitan areas there is a primary

practitioners, general practitioners, pediatri- care physician-to-population designation ratio

cians, and internists. Psychiatrists are included of 1:3,500, which means that only those areas
36 ● Forecasts of Physician Supply and Requirements

with that ratio or higher (fewer physicians)

would be eligible for Corps staffing. The target

ratio is 1:2,000. Once designated and selected

for Corps staffing, Corps physicians would be

provided until a ratio of 1:2,000 was achieved

(USDHEW, 1978a).

As part of the process of planning for how

much effort is needed in specific practice settings, estimates must be made of the numbers

and types of physicians who will settle in these

areas voluntarily. DHHS, through the joint efforts of its BHM of the Health Resources Administration and the Office of Planning, Evalua-

. tion, and Legislation of the Health Services Administration, has developed a computerized

model to project the number and distribution of

active, non-Federal, office-based patient care

physicians in the so States and all counties from

1972 through 1990.

As the Health Professions Educational Assistance Act of 1976 enlarged the definition of

HMSAS, separate estimates are made for: 1)

rural counties, 2) urban areas, 3) Federal and

State prisons, 4) State mental hospitals and

community mental health centers, and 5) the Indian Health Service. The computerized model is

used to project future supply in rural counties,

but it cannot be used to project supply in the

other categories. Hence, other methods must be

used for these other categories. For prisons,

mental health facilities, and the Indian Health

Service, the sponsoring agencies have provided

estimates of both supply and requirements. Essentially, these agencies forecast a steady state

supply (USDHEW, 1978a).

The NHSC task force’s estimates of the supply of primary care physicians in rural and urban areas are summarized below.

Rural areas.—The projections are based on:

1) existing supply (DOS and MDs, domestic and

foreign graduates); 2) anticipated deaths, retirements, and relocation of existing physicians; 3)

anticipated graduations, specialty choices, and

practice locations; and 4) anticipated population growth. Beginning with 1972 as the base

year, total physician and primary care and nonprimary care physician-to-population ratios are

projected for each county (urban as well as

rural) for each year. The method is thus similar

to aggregate and specialty supply, with the

added factor of accounting for where physicians

locate.

Counties are assumed to maintain population

as fixed proportions (derived from 1972 data) of

their respective State populations.

Starting with the 1972 active, non-Federal,

office-based, patient-care physicians in each

county, year-by-year reductions from deaths

and retirement are calculated.

The number of new physicians expected to

enter practice is estimated by the method described earlier for aggregate supply. These numbers are summarized in table 20.

Total new physicians entering practice each

year are reduced for: 1) Federal employment, 2)

nonpatient care activities, and 3) practices in

areas other than the so States.

The physician distribution among the States

for 1973-87 (since updated to the 1990’s) is based

on the historical pattern of distribution for graduates from 1965 to 1969, modified for percent

changes in State populations projected for 1972

to 1987.

The percent of physicians entering practice

from 1973 to 1987 as primary care specialists is

Table 20.—New Physicians Entering

Practice, 1973-87

Year USMG FMG DO

1973 . . . . . . . . . . . . . . 8,367 3,665 472

1974 . . . . . . . . . . . . . . 8,974 5,081 485

1975 . . . . . . . . . . . . . . 9,551 5,202 649

1976 . . . . . . . . . . . . . . 10,391 2,709 587

1977 . . . . . . . . . . . . . . 11,613 3,799 702

1978 . . . . . . . . . . . . . . 12,714 5,265 809

1979 . . . . . . . . . . . . . . 13,561 3,517 908

1980 . . . . . . . . . . . . . . 13,607 3,895 964

1981 . . . . . . . . . . . . . . 14,598 1,903 996

1982 . . . . . . . . . . . . . . 15,048 2,187 1,029

1983 . . . . . . . . . . . . . . 15,346 1,849 1,208

1984 . . . . . . . . . . . . . . 15,789 2,372 1,308

1985 . . . . . . . . . . . . . . 16,354 2,034 1,377

1986 . . . . . . . . . . . . . . 16,956 2,246 1,458

1987 . . . . . . . . . . . . . . 17,241 1,908 1,496

SOURCE: Memorandum from the Chairmm, NHSC Needs Task Force A, to the

Director, Bureau of Community Health Services, Health Services Administration; the Deputy Director, Bureau of Health Manpower,

Health Resources Administration; and the Chairman, NHSC Needs

Task Force, Washington, D. C,, May 26,1978.
Ch. 2–Supply Ž 37

projected to increase to the proportions listed in

table 21. This should be distinguished from the

percent of the total physician supply that is projected to be in primary care (tables 16 and 17).

Physicians are projected to enter practice in

county classes in the percentages summarized in

table 22.

Newly entering physicians are allocated to individual counties by specialty according to the

1974 observed pattern of 30-to 44-year-old physicians of the same specialty. This new supply is

added to the existing supply, modified yearly

for attrition from deaths and retirements.

Table 21 .—Percent of New Physicians

Expected To Enter Primary Care

Percent in

Year primary care

1973 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2

1974 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.4

1975 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.1

1976 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.5

1977 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.5

1978 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.2

1979 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.8

1980 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46.0

1981 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.1

1982 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50.2

1983 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2

1984-87. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2

SOURCE: Memorandum from the chairman, NHSC Needs Task Force A,to the

Director, Bureau of Community Health services, Health Services Administration; the Deputy Director, Bureau of Health Manpower,

Health Resources Administration; and the Chairman, NHSC Needs

Task Force, Washington, D.C., May26,1978.

Urban area.—Predicting the future supply of

physicians for urban areas in order to assess the

need for additional physicians is not computed

on a county basis. If measured by county, the

number of primary care physicians is usually

adequate, so the needs in urban areas are measured by the needs of certain population groups

which have financial and sociocultural barriers

to access instead of the geographic barriers of

rural areas, for which physician-to-population

ratios serve as substantial proxy measures.

Thus, an estimate of the number of physicians

required to meet the needs of metropolitan lowincome areas as defined by the Bureau of the

Census is used.

Such identified low-income area populations

declined from 17,936,000 in 1974 to 16,554,000

in 1976, or a decline of 3.8 percent per year. But

the task force concluded that the decrease is not

expected to continue indefinitely and that there

is a current trend for physicians in central cities

to move to the suburbs. It therefore assumed

that the decrease in low-income population will

be offset or more than offset by the emigration

of physicians from the inner city. In other

words, present supply as expressed in physicianto-population ratios also predicts what the future supply will be. This average is 13.3 fulltime-equivalent primary care physicians per

100,000populationo

Parenthetically, it was determined that 42.3

primary care physicians per 100,000 population

Table 22.—Projected County Classes of Newly Practicing Physicians

MD

County classa Family practice Primary careb Non primary care DO

1 . . . . . . . . . . . . . . . 2.7 0.2 0.1 3.0

2 . . . . . . . . . . . . . . . 7.5 2.0 0.5 6.7

3 . . . . . . . . . . . . . . . 10.6 5.1 1.6 6.4

4 . . . . . . . . . . . . . . . 9.4 6.9 3.7 5.2

5 . . . . . . . . . . . . . . . 2.3 2.2 1.2 2.2

6 . . . . . . . . . . . . . . . 26.8 19.5 24.6 17.6

13.2 12.8 13.1 13.9

7:: : : : : : : : : : : : : : : 21.9 35.5 37.0 40.1

9 . . . . . . . . . . . . . . . 5.6 15.0 18.2 4.9

100.0 100.0 100.0 100.0

aAMA demographic county classification (1-4 = rural; 5-9 = urban).

bExcluding family pra~tlce, This definition of ‘Jprima~ care” includes obstetrics-gynecology, in addition tO Qenerd praCtlCe,

family practice, internal medicine, and pediatrics.

SOURCE: Memorandum from the Chairman, NHSC Needs Task Force A, to the Director, Bureau of Community Health Services, Health Services Administration; the Deputy Director, Bureau of Health Manpower, Health Resources Administration; and the Chairman, NHSC Needs Task Force, Washington, DC., May 26,1978.
38 ● Forecasts of Physician Supply and Requirements

(a staffing ratio of 1:2,000) would be needed in

these low-income areas and that the 13.3 number meant that 31.4 percent of need was already

met. The analysis then goes on to say that separate analyses of the underserved population’s

“usual source of care” resulted in the figures in

table 23 and confirmed the 31.4-percent figure.

The analysis then goes on to equate the sum

of care from “hospital, “ “neighborhood health

center, ” and “none” with unmet need of 62.6

percent of the population and goes on to estimate additional physicians needed on this basis.

Yet only 8.6 percent of the 62.6 percent received

no care. Hospital care does not represent “unmet need” but involves the question of what is

appropriate care. And since this analysis was

made to estimate the number of NHSC physicians that might be placed in these areas, identiSUMMARY

Comparing the methods for estimating supply

with those used for estimating requirements, we

would expect more certainty in the supply projections. Yet the foregoing description of supply

projections shows that there are many weaknesses in the data bases, some questionable assumptions underlying the projections, different

interpretations given to some commonly used

terminologies such as “primary care” and “fulltime-equivalent, ” etc.

The description of how supply projections are

made can quickly become quite detailed. So let

us summarize: 1) the components and primary

assumptions of the supply estimates, and 2)

some definitional problems that are linked to

substantive issues and which are compounded

by weaknesses in the data.

Supply is the sum of practicing physicians

and additions of foreign and domestic medical

and osteopathic school graduates (there are no

foreign additions to the osteopathic supply). Attrition from deaths and retirements for the practicing physician component is estimated by agespecific rates. For specialty supply, the same

death and retirement rates are applied to each

specialty.

Table 23.—Usual Source of Care

for Urban Underserved Areas

Percent

Private physicians. . . . . . . . . . . . . . . . . . . . . . 31.4

Hospital (emergency room and

outpatient treatment). . . . . . . . . . . . . . . . . 31.3

Neighborhood health center . . . . . . . . . . . . . 22.7

None. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.0

Total. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0

SOURCE: Memorandum from the Chairmanj NHSC Needs Task Force A, to the

Director, Bureau of community Health sewices, Healtfl se~ices A&

ministration; the Deputy Director, Bureau of Health Manpower,

Health Resources Administration; and the Chairman, NHSC Needs

Task Force, Washington, D. C., May 26,1978.

fying “neighborhood health center” care as “no

care” must mean that the presumption is that

such centers will be staffed only by NHSC

physicians in the future.

Additions to supply are the sum of foreign

and domestic graduates. Estimates of first-year

enrollments and attrition prior to graduation

are made to arrive at the number of domestic

graduates. The high, low, and basic first-year

enrollment estimates all assume full cavitation

funding by 1981, and 4, 7, or 10 new medical

schools after 1977-78. For FMG additions, the

Canadian addition is currently estimated to

equal losses from death, retirement, and emigration. Estimates of the addition to supply from

other FMGs rely heavily on the presumed impact of the 1976 Act, which contained major

restrictions on FMG immigration. It should be

noted that the resulting total projections of supply made before and after the 1976 Act have not

varied greatly (estimated at approximately

600,000 in 1990). The component projections,

however, have varied wiclely. In essence, present projections of domestic graduates are larger

than previous estimates, and present projections

of FMG additions are less than prior estimates.

In the current projections, the assumption of full

cavitation funding is not very realistic and tends

to increase the supply projections. On the other

hand, the additions from the FMG supply may

be too optimistic in terms of legislative impact
Ch. 2–Supply Ž 39

on decreasing this source of supply, particularly

with the large pool of U.S. citizens studying

medicine abroad, for whom immigration restrictions are not applicable, although they have

to pass a competency exam in order to practice

medicine in the United States.

Additions to specialty supply use projections

of first-year residency trends to allocate foreign

and domestic graduates among the specialties.

The predictive power of first-year residency

choices is a problem because they may not reflect ultimate specialty practices. This is particularly true for internal medicine, where at least

one-third of the first-year residency positions is

used as a general medicine traineeship for physicians ultimately subspecializing or choosing

another specialty altogether. The trend in

specialty choice is determined by the trend

reflected in the years 1968, 1970-74, and 1976,

years in which major changes were occurring in

the structure of postgraduate medical training

programs. Statistical techniques also limit the

applicability of these trend years up to 1981, at

which point the distribution is held constant for

future years.

Methods for predicting the locational distribution of physicians are generally similar to

those for aggregate and specialty supply. For

rural areas, the active, non-Federal, office-based

physician supply in each county is reduced for

deaths and retirements. The supply of new physicians is allocated to individual counties by specialty according to the 1974 observed patterns

of 30- to 44-year-old physicians of the same

specialty (counties are allocated to nine classifications from rural to urban). For urban (inner

city) areas, physician supply is assumed to decrease (no numbers given), reflecting continued

emigration to the suburbs. For prisons, mental

health ,services, and the Indian Health Service,

future supply is generally assumed to hold constant at its present rate.

In addition to absolute numbers, a relative

standard is used, the physician-to-population

ratio, which is also commonly expressed as the

number of physicians per 100,000 population;

e.g., 1:1,000 or 100/100,000. This ratio is used

to provide a more complete quantification of

supply; i.e., we need to know not only the

numbers of physicians in practice, but also the

populations which they serve.

For aggregate and specialty supply, the Census Bureau’s Series II (or Series III, which projects slower growth) estimates of population are

used. As these reflect the 1970 Census, more accurate information will be available from the

upcoming 1980 Census.

For locational distribution, the population

estimates try to be more specific, as supply

estimates are part of programmatic efforts to identify HMSAS. For rural areas 1972 State population estimates are used, and counties are assumed to maintain fixed proportions of their

respective State populations. For urban areas,

the population is that identified by the Bureau of

the Census as metropolitan low-income areas.

Although these low-income populations have

declined (17,936,000 in 1974 to 16,554,000 in

1976, or a decline of 3.8 percent per year), the

physician-to-population ratio is assumed to

hold constant in the future because of the

previously mentioned expectation of continued

emigration of physicians out of the inner cities.

It should be obvious that supply, as referenced to population, depends not only on the

physician supply projections, but also on the

population projections. An example is the distinction between projections of physician supply

which include or exclude Federal physicians. In

projecting supply for rural areas, table 19 estimated that there were 287,800 active non-FederaI MDs in 1975. Table 9 estimates that there

were 363,400 active MDs (including Federal) in

197s (377,500 minus 14,100 DOS). The 287,800

figure was used to allocate physicians among all

counties. However, in subtracting the Federal

physician supply, no effort was made to decrease the population by a proportional amount

(these Federal physicians were active and presumably providing patient care). In addition,

1967 population estimates were used in table 19,

whereas table 9 used population estimates that

included projections of population growth.

On the physician side of the ratio, table 9

assumed that a portion of “not classified” MDs

were active; whereas table 19 excluded this category from its count (approximately 30,000 phy-
40 ● Forecasts of Physician Supply and Requirements

sicians, or about 10 percent). In addition, table

9 included DOS, table 19 did not.

So even though at first glance the differences

in the number of active physicians represented

in tables 9 and 19 (377,500 v. 287,800) might

seem accounted for from the exclusion of Federal MDs, “not classified” physicians, and DOS in

table 19, the method of quantifying the population also contributed to the different physicianto-population ratios (176.8/100,000 v. 134.8/

100,000).

We have already seen that there are definitional problems associated with quantifying

physician supply. These definitional problems

will take on even more significance once we

begin to quantify requirements and try to match

that with supply. Two basic problems are involved: 1) the amount of patient care that is attributed to the average physician, and 2) the

type of patient care provided.

The first problem is usually couched in terms

such as “productivity” or “full-time-equivalent, ” which attempt to provide a common reference by which the number of physicians can

be equated to a certain volume of patient care

services. For example, physicians’ assistants in

prisons were assumed to be equal to 0.5 physicians (USDHEW, 1978a). In this case, a physician was equal to a full-time-equivalent (FTE),

whatever the particular hours or number of patients seen by prison physicians. Implicit in this

definition are assumptions on physician productivity. Other uses of FTE are more explicit. Indiana uses a definition of a FTE primary care

physician as a general or family practitioner in

the age group 35 to 39, which has the highest

output in terms of visits per year for that

specialty (Hindle et al., 1978). A more common

method is to use average productivity figures by

specialty, either as measured by the average patient care hours worked per week (hospital and

ambulatory care), the number of patients seen

per week (usually expressed as the number of

ambulatory visits), or both. And still another

method is to estimate what percent of time is

spent on nonpatient care activities and subtract

that percentage from the total (aggregate or by

specialty) supply. These productivity or FTE

estimates are crucial when comparing supply

with requirements, because they are the basic

methods underlying the comparison. Given the

same basic numbers of physician supply as provided through “head counts” by the methods

summarized earlier, whether supply equals, falls

short, or exceeds demand obviously depends on

the productivity assumptions applied to the

physician.

The definition of “primary care” involves

more than the simple identification of which

specialties “qualify” for that designation. Yet we

have already seen that what specialties count as

primary care is quite confusing. Even if we limit

the specialties to general practice, family practice, general internal medicine, and general

pediatrics, there can be great variations in the

quantification of primary care physicians, because over 50 percent are in the internal medicine category. Yet, at various times, some subspecialties of internal medicine are included and

some are not. For example, table 13 excludes

dermatology, gastroenterology, pulmonary disease, cardiovascular disease, and allergy from

the primary care internal medicine subspecialties, but includes allergy and immunology, diabetes, endocrinology, geriatrics, hematology,

immunology, infectious diseases, neoplastic diseases, nephrology, nutrition, oncology, and

rheumatology.

The Institute of Medicine (1978) reviewed 38

definitions of primary health care, and concluded that primary care cannot sufficiently be

defined by the location of care, by the provider’s

disciplinary training, or by the provision of a

particular set of services. It then goes on to

elaborate on what it considers primary health

care’s five essential attributes: 1) accessibility,

2) comprehensiveness, 3) coordination, 4) continuity, and 5) accountability.

In a study examining the general care content

of different specialty practices, the data was disaggregate into five components: 1) first encounter, 2) episodic care, 3) principal care, 4)

consultative care, and 5) specialized care (Aiken

et al., 1979). Principal care was defined as:

There is evidence of continuity; the physician

reports having seen the patient before and considers him or her to be a regular patient. Com-
Ch. 2–supply • 41

prehensiveness is suggested, since the physician

indicates that he or she provides most of the patient’s care.

Principal care thus falls short of the Institute

of Medicine’s definition of primary health care.

Obviously, quantifying the supply and requirements for specific physician specialties will differ between these definitions, and they will substantially affect the quantification of specialty

distribution. -

This difference also

specific assumptions on

If many specialty types

Table 24.—Estimated Principal”Provider

Patient Loads of General Practitioners, Family

Practitioners, and General Internists

Average number

Specialty

Of Persons covered

per physician

General practitioner. . . . . . . . . . . . . . . . 870- 965

Family practitioner. . . . . . . . . . . . . . . . . 1,004-1,127

General internal medicine . . . . . . . . . . . 468- 523

.

SOURCE. L. H Aiken et al , “The Contribution of Specialists to the Delivery of

Primary Care,’” 1979, table 4.

points out the use of

FTEs and productivity.

are providing principal

care, the use of FTEs will serve as proxy measures for some part of the total demand for the

specific specialties. But the different specialties

may also have different productivity rates. For

example, internists generally see sicker and

older patients than seen by general and family

practitioners, and their average patient loads

may be considerably less than the latters’ (table

- . i

specialties qualify, 3) the proportions within

each specialty which provide principal health

care, and 4) the use of different productivity

values for each specialty. And different requirements projections also easily result when

these factors, in addition to the specialty care

responsibilities of each specialty, are used to

translate these FTE/productivity values into

head counts for each specialty. And similar es4Z4) . timates must be made for the supply head

Different results can be easily obtained on: counts in order to ultimately compare the sup1) what exactly is primary health care, 2) which ply with the requirements projections.
3 .

Requirements
3.

Requirements

INTRODUCTION

Faced with what seemed to be a straightforward question on what our country’s physician

supply is and will be, we have found that supply

projections are not definitive and need to be

revised periodically. And in somewhat circular

fashion, supply projections can be substantially

dependent on deliberate Federal actions at the

same time that Federal actions are somewhat dependent on supply projections.

Even if there were agreement on the assumptions underlying the supply projections, what

the projections mean in terms of meeting the requirements for physicians would still be unclear, as the numbers represent self-designated

specialties, general estimates of physicians in actual patient care activities, and general estimates

of the locational distribution of the supply of

physicians. In order to compare supply with requirements, common standards of quantification (e.g., full-time-equivalents (FTEs), what

counts as primary care and what types of physicians provide it, etc. ) must be applied to both

estimates.

The limitations imposed by definitional problems compound the problems that arise from the

specific assumptions underlying the supply projections. Reaching agreement on the assumptions underlying the projections (e.g., the future

prospects for cavitation and its effect on the

number of domestic graduates) is a separate

question from agreeing on questions such as

what counts as primary care and how the volume of primary care is to be translated into

specific numbers of physicians in the various

specialties. In other words, the separate projections for supply and requirements and comparisons between the two have both best-guess and

value-laden assumptions undergirding the methodologies.

The first task in estimating requirements

for physicians is to decide what method should

be adopted. A 1977 review of the literature

(USDHEW, 1977a) concluded that the approaches could be categorized into one of four

groupings and pointed out that the first relates

to treatment of all medical needs, as determined

by disease prevalence and morbidity data, and

that the other three categories deal with the demand for care, as derived from the opinions of

health professionals, calculations of service requirements and manpower productivity, and

observed staffing patterns in prepaid group

practice settings (health maintenance organizations, or HMOS). The four categories were defined as:

●

●

●

Medical need based ratio. —A ratio which

describes the number of health professionals required to care for a given population

if all disease conditions that require treatment (existing utilization plus unmet needs)

are actually treated. Data on morbidity or

disease incidence and prevalence must be

used.

Professional judgment based ratio. — A

ratio which reflects the opinion of health

professionals or health manpower experts

regarding the number of health professionals needed to meet the expressed demands

for care of a given population. This cluster

encompasses “ideal,” “adequate,” and

“minimally acceptable” ratios. The standard is derived from an aggregate assessment of the manpower situation, without

detailed consideration of utilization or productivity factors.

Demand/productivity based ratio. —A ratio which describes the number of health

professionals needed to care for a given

population, as derived from specified assumptions about services demanded

and/or manpower productivity. Calculations may account for changes in technology, health insurance coverage, composition of the population, utilization of allied

health personnel, and similar factors.

45
46 ● Forecasts of Physician Supply and Requirements

● HMO based ratio. —A ratio which directly

reflects or is derived from observed staffing

patterns of prepaid group practice.

This review concluded that three of the four

approaches required special “leaps of faith:”

Need-based standards are appropriate for

planning only if consumers are both able and

willing to express all medical problems or demands for services. Acceptance of professional

judgment based standards requires blind faith in

the knowledge and foresight of those consulted—especially ironic in that “medical expertise” (knowledge of diagnosis and treatment requirements) is only one of many types of information needed to estimate manpower requirements. Adoption of HMO based standards (adjusted HMO staffing patterns) assumes that

most care will be provided through this delivery

mode which integrates both financial and delivery system variables.

The fourth approach, the calculation of requirements estimates based on explicit utilization and productivity assumptions, requires no

leaps of faith, but neither does it provide unambiguous guidance. It is viewed, nevertheless, as

the most valid approach available for estimating

health manpower requirements. Its value to the

health planner is that it focuses attention on the

parameters which determine manpower requirements. In doing so, it lays the groundwork for

integrating manpower with health system planning. In this broader context, policies can be formulated to influence these parameters and modify health manpower requirements in a socially

desirable manner.

Another criticism frequently leveled at projections of the future requirements for physicians is that the modeling effort may be a static,

snapshot picture of the health care system and

does not sufficiently account for change. This

criticism is most frequently raised when a single

physician-to-population ratio, as derived from a

particular modeling effort, is used to project

future requirements. The use of a fixed ratio

represents the conclusion that physician requirements should (or would) change in direct proportion to population growth. A fixed ratio

does not take into account any trends toward increasing per capita use of physician services.

Thus, the use of a fixed physician-to-population

ratio versus the use of changing ratios will lead

to quite different estimates of the future requirements for physician services. If changing

ratios are used: 1) the rate of change adopted

will have a significant effect on the calculation

of future requirements, and 2) at some point, a

leveling off of the rate of change has to occur.

Table 25 compares supply with requirements

using the studies in the literature review just

cited (USDHEW, 1977a) that led to the highest

estimates of requirements. Each of these studies

estimated requirements for a specific year: for

the HMO method, estimates were made for

1972; for the need-based method, estimates

were for 1974; for professional judgment, 1975;

and for demand/productivity, the year chosen

was 1980. As these projections excluded osteopathic physicians, the supply column in table 25

is limited to allopathic physicians (MDs). Taken

together, table 25 shows that there was an approximate balance between supply and requirements in the 1970’s.

Table 26 is modified from a 1970 analysis

(Hansen, 1970) of physician requirements projections for 1975, the year which is the basic

starting point for current projections. Again, we

see a fairly wide range of requirement estimates,

with the midrange approximately in balance

with the actual supply.

Table 25.–Comparisons of Aggregate Physician (MD)

Supply With Requirements Using Different Modeis

Rate/100,000 Target year Total supply

HMO. . . . . . . . . . . . . . . . . 153.6 1972 321,000 333,000

Need-based. . . . . . . . . . . 167.8 1974 355,600 351,000

Professional judgment. . 187.3 1975 400,000 366,000

Demand/productivity . . . 182.8 1980 407,000 430,000

(projected)

SOURCE: See text.
Ch. 3—Requirements ● 47

Table 26.—1960’s Projections of Physician Requirements in 1975

(actual supply in 1975 equals 378,000)

Deficit ( – ) or

Projection study Requirements supply surplus ( + )

Bane Committee Report (1959)FJ . . . . . 330,000 (minimum) 312,800 – 17,200

318,400 – 11,600

Bureau of Labor Statistics (1966)b . . . 305,000 — —

Fein (l967)c. . . . . . . . . . . . . . . . . . . . . . 340,000 to 350,000 361,700 + 21,700 to + 11,700

372,000 to 385,000 – 10,300 to – 23,300

National Advisory Commission on

Health Manpower (1967)d . . . . . . . . 346,000 (minimum) 360,000 + 14,000

Bureau of Labor Statistics (1967)’ . . . 390,000 360,000 – 30,000

Public Health Service (1967)f

. . . . . . . . 400,000 360,000 – 40,000

425,000 – 65,000

asUrQeOn General,~ cOn~Ult~nt G~~U~ on M’di~al Education, Frank Ban’, chairman, Physicians for a Growing America

(Washington: Government Printing Office, 1959).

bus, Bureau of Labor Statistics, “America’s Industrial and Occupational Manpower Requirements, 1964-1975.” In: The OUfIook for Technological Change and Employment, Appendix Volume I to Technology and the American Economy, report of the

National Commission on Technology, Automation, and Economic Progress (February 1966).

cRashi Fein The Doctor Shortage: An E~onom/c Ana/ysis (Washington: The Brookirlgs institution, 1967).

dNatlonal A&vlso~ Commission on Health Manpower, Report, vol. Il. (Washington: Government prlntin9 Office, 1~7)

t? Bureau of Labor Statistics, f-fea/th ~anpower 1966-1975, A Study of Requirements and SUPP/Y (Washington: Government

f[~~~~n~e~~~$~e’w~c~; Health Manpower, Perspectives 1967, (Washington: GpO. 1~7)

SOURCE: W, L. Hansen, “An Appraisal of Physician Manpower Projections,” Inquiry 7: 102-113, 1970.

Two other observations are of interest. Fein’s

estimates are provided in two sets of numbers.

The first set, 340,000 to 350,000 is based on

population growth alone. This would be analogous to the use of a fixed physician-to-population ratio to predict, in 1967, the demand in

1975. The second set of numbers, 372,000 to

385,000, is based on an increase due to all factors. It would be analogous to including further

increased requirements due to increasing per

capita utilization of physician services. Finally,

it should be noted that the estimates of “requirements” of the Bureau of Labor Statistics (BLS)

were revised markedly between 1966 and 1967,

increasing from 305,000 to 390,000. In essence,

it was a judgment that, given rising demand for

physician services, 85,000 more physicians

could be employed.

ECONOMIC MODELS

The Bureau of Labor Statistics Model

In pursuit of quantifying future “appropriate

demand” or “requirements,” little attention has

been paid on past and current balances or imbalances between the supply of and requirements for physician services. Policy has been

1ess concerned with whether the projections

were correct than with the effect of such projections on current decisionmaking. Thus, this assessment concentrates on the two modeling efforts that will have the most impact on Federal

health manpower policy, the sustained modeling and projection activities of the Bureau of

Health Manpower’s (BHM) Division of Manpower Analysis, and the yet-to-be completed

deliberations of the Graduate Medical

Education National Advisory Committee

(GMENAC).

The U.S. Department of Labor’s BLS provides health occupations are medicine; dentistry;

projections of demand and training needs for nursing; medical technologists, technicians, and

the major occupations every 2 years (BLS, assistants; therapy and rehabilitation; and other

1979a). Thirteen occupational groupings are health occupations. These projections relate

analyzed, including the health occupations. The manpower to projected economic demand (ex-
48 ● Forecasts of Physician Supply and Requirements

penditures) as provided by the Bureau’s model

of the future economy, which projects the future

gross national product (GNP) and its components—consumer expenditures, business investment, governmental expenditures, and net exports; industrial output and productivity; the

labor force; average weekly hours of work; and

employment for detailed industry groups and

occupations.

Current projections are based on the following assumptions:

●

●

●

●

the institutional framework of the U.S.

economy will not change radically;

current social, technological, and scientific

trends will continue, including values

placed on work, education, income, and

leisure;

the economy will gradually recover from

the higher unemployment levels of the mid1970’s and reach full employment (defined

as an unemployment rate of 4.7 percent in

1985 and 4.5 percent in 1990); and

trends in the occupational structure of industries will not be altered radically by

changes in relative wages, technological

changes, or other factors.

Beginning with population projections by age

and sex from the Bureau of the Census, projections of the total labor force are derived by

using expected labor force participation rates

for each group. The labor force projection is

then translated into the level of GNP that would

be produced by a fully employed labor force.

The GNP projection is then divided among its

four major components—consumer expenditures, business investment, governmental expenditures, and net exports. Each component is

broken down by producing industry. Medical

care falls under the consumer expenditure component. Estimates of future output per hour are

derived from productivity and technological

trends in each industry, and industry employment projections are derived from the output estimates. These projections are then compared

with employment projections through the use of

regression analysis. Comparison of the two

methods is used to identify inconsistencies of

one method with past trends or with the Bureau’s economic model, and the projection is adjusted as needed. Projections of industry employment are translated into occupational employment projections for each industry (201 industry sectors and 421 occupation sectors). The

growth rate of an occupation is determined by:

1) changes in the proportion of workers in the

occupation to the total work force in each industry, and 2) the growth rate of industries in

which an occupation is ccmcentrated.

In addition to occupational employment projections, attrition from the existing work force is

calculated to estimate average annual replacement needs for each occupation over the projection period.

Supply estimates assume that past trends of

entry into the particular occupation will continue. These estimates are developed independently of the demand estimates; i.e., wages do

not play a major role in equating supply and demand.

Table 27 summarizes these projections. 1985

projected employment (demand) of 520,000

physicians, up from 375,000 in 1976, equals the

projected supply (see tables 9 and 10).

Table 27.—Bureau of Labor Statistics Projections

of Physician Supply and Requirements, 1985

Employment, 1976 . . . . . . . . . . . . . . . . . . . 375,000

Projected employment, 1985 .., . . . . . . . 520,000

Percent growth, 1976-85 . . . . . . . . . . . . . . 37.8

Average annual openings, 1976-85 . . . . . . 21,800

Growth . . . . . . . . . . . . . . . . . . . . . . . . . . (16,000)

Replacement . . . . . . . . . . . . . . . . . . . . . (5,800)

Available training data:

Projected 1976-85

1975-76 (annual average)

MD degree . . . . . . . 14,163 15,997

DO degree . . . . . . . 806 1,128

SOURCE: Occupational Projections and Training Date, Bureau of Labor Statistics, Department of Labor, Washington, DC., Government Printing

Office, Bulletin 2020, 1979, pp. 64-65.

BLS, in its forthcoming revisions of its estimates to include 1990 projections, will adopt the

midpoint of the range of projections from the

BHM model for physician requirements. BLS

considers the BHM model as more sophisticated

for two reasons. First, while the BLS model incorporates population as a variable, no consideration is given to the varying utilization

rates of the demographic components. Women
Ch. 3—Requirements ● 49

use more physician and hospital services than

men, and older people require more care than

the young. The BHM model does capture these

aspects of demand. Second, the BHM model

estimates the effects of possible price elasticity

for physician services and provides a narrow

range of the resultant expected demand for

physician services.

BLS also points out that both its and BHM’s

models cannot measure and project the capacity

of physicians to stimulate demand. A downward bias in the projections would result if

physician-stimulated demand reflects real need

for medical care. To the extent that physicianstimulated demand reflects unnecessary care,

the demand estimates would introduce an upwards bias to the projections.

BLS also points out two other factors which

would bias the BHM projections in an upward

direction. The inclusion of data points from

1968 and 1969 for projecting utilization rates

reflect the sharp growth in utilization attributable to Medicare and Medicaid startup. (As

we will explain later, we agree that 1968 and

1969 data points should be excluded in determining utilization trends, but not for the reason

given by BLS). Second, BLS points out that as

the coinsurance (that part of fees paid by the patient) has been and is projected to continue

dropping, the effective price of physician services has dropped as well. BHM’s assumption of

a constant price elasticity means that a percentage drop in coinsurance from 20 to 10 percent

would result in the same percent increase in the

demand for physician services as would a drop

in coinsurance from 50 to 40 percent. A more

likely effect would be that consumer demand

would gradually be saturated as the percentage

coinsurance decreases (BLS, 1979b).

The Bureau of Health Manpower Model

BHM uses a demographic projection method

which makes certain assumptions about medical

care utilization and physician productivity to

arrive at its estimates for physician requirements. The general method is not specific to

physicians and is applied to 29 general types of

health manpower, ranging from selected MD

specialties to broad allied health groups. Although specialty-oriented estimates for physicians can be calculated from the total population’s utilization of specific types of care by

cross-multiplying the projected size of each population subgroup by its associated utilization

rate, the method is presently not considered reliable enough to use in projecting specialty-byspecialty requirements.

The basic assumptions are:

●

●

●

●

●

●

No major unforeseen events wiIl occur in

the projection period, including the enactment of a comprehensive program of national health insurance (NHI).

Supply and demand were in balance in

1975. As we have seen from tables 25 and

26, past estimates are in genera] agreement

with this assumption.

Physician productivity does not change

substantially between 1975 and 1990.

Price elasticity, the sensitivity of demand to

net price, remains constant between 1975

and 1990. (Different choices of price elasticity of demand coefficients, representing

alternative rates of consumer response to

price changes for a given personal health

service, are used to present the range of

estimates. )

Nondollar costs of obtaining care do not

change substantially during the projection

period.

No major health care or manpower substitution occurs between service categories.

The model is developed in three stages which,

in practice, may be separated or combined as

desired. The most rudimentary stage is termed

the “framework.” It estimates future demand

from the anticipated impact of population

growth and demographic shifts (age, sex, income distributions) on the economic demand for

medical services, with physician productivity

assumed to remain the same. In this first or

framework stage:

. The U.S. population is projected to 1980,

1985, and 1990 by age, sex, and income

subgroups in 40 components (5 age by 4 income by 2 sex groupings).
so ● Forecasts of Physician Supply and Requirements

●

●

For each of the 40 subgroups, a utilization

rate for each of 20 types of health service

settings (e.g., general medical office visit,

inpatient hospital admission, nursing home

stay, vision care, laboratory services, etc. )

was estimated from recent National Health

Interview Survey data and other sources

for the 1975 base year and the future years.

The percentage increase in utilization for

each of the 20 types of care is obtained by

summing over the population and dividing

the future year utilization by the 1975 base

year utilization for that type of care.

The second stage, called the “baseline” configuration, attempts to factor the effects of historical trends in per capita utilization of medical

services into the general model. In this second

stage:

●

●

●

Utilization, by each of the 20 care types, is

adjusted to account for future increases

that represent continuation of recent

trends. Economic and noneconomic trends

are analyzed separately. Economic trends

interpret the effects of price and insurance

copayment charges on changes in utilization. As noted earlier, the price elasticity is

assumed to be constant over the period

1975-90. The utilization projections in each

of the 20 care types are then individually

adjusted for the future years, based on

these projections of price and coinsurance

payment. After removing price effects from

the past utilization trends, the remainder

(representing education, consumer preference, patient-care physician supply, etc. ) is

the noneconomic trend. This noneconomic

effect is assumed to continue in a linear

fashion into the future and per capita utilization adjustments for each care type are

made.

The adjusted increases in future care utilization are then applied to the 1975 estimates of the distribution of the 29 manpower categories in each of the 20 care

types to give a preliminary estimate of the

future manpower required to provide the

projected care utilization.

Adjustments are then introduced to account for trends in the manpower required

to provide a given amount of care utilization. For physicians, it has been previously

noted that the assumption is that productivity remains constant from 1975-90.

● Demand is then summed over the 20 types

of care to arrive at the baseline forecast of

future demand.

These estimates assume no major departure

from historical experience. The third stage, the

“contingency” modeling capability, is a separate

component that has been used to explore the

possible impact on the economic demand for

physician services of the following contingencies:

●

●

●

The

The impact of NHI can be estimated by adjustments to the utilization increases

through new economic trend adjustments

which provide for different NHI copayment

possibilities.

Expanded-function task delegation can be

factored in by new adjustments to the manpower staffing trends based on alternative

assumptions of midlevel practitioner employment and their productivity rates.

Expanded use of HMOS is treated by alternative estimates of the population enrolled

in HMOS in future years and separate utilization rates, trends, and staffing assumptions for this part of the population.

Framework

To predict the impact of population growth

and demographic shifts, the model starts with

the basic assumption that supply and demand

were in balance in 1975 and calculates per capita

utilization rates for each of 40 population

segments (age by sex by income categories) with

respect to 20 forms of health care. Table 28

displays the population matrix utilized; table 29

indicates the various health care categories.

Note that the BHM health care categories include care setting (office-based, short- and longterm hospital care, nursing homes) as well as

types of care (pediatric, surgical, psychiatric).

Thus, the capability of the BHM model to make

distinctions among physician specialties is not

very fine-grained. The basic distinctions made

are between general medical, pediatric, obstetrics-gynecology, psychiatric, vision, surgical,
Ch. 3—Requirements . 51

Table 28.—Population Matrix Used in the BHM Model

Family income

Under $5,000 $5,000-$9,999 $10,000 -

$14,999 $15,000 and over

Males

Under 14

14-24

25-44

45-64

65+

Females

Under 14

14-24

25-44

45-64

65+

SOURCE. JWK International Incorporated, Evaluation of Project SOAR (Supply, Output, and Requirements), draft report,

DHEW contract No. HRA 232.78-0140, 1979.

Table 29.—Health Care Categories

Used in the BHM Model

Setting Form of care

Medical office

Short-term hospital

Long-term hospital

Other care settings

Noncare settings

General care

Pediatric care

Obstetric-gynecological care

Psychiatric care

Vision care

Other medical office care

Outpatient care

Surgical care

Medical care

Psychiatric care

Other long-term hospital care

Nursing home care

Dental care

Veterinarian services

Optometric care

Podiatric care

Other patient care, not elsewhere

specified

Pharmacy services

Laboratory services

Noncare activities, not elsewhere

specified

SOURCE JWK International Incorporated, Evaluation of Project SOAR (Supply,

Output. and Requirements), draft report, DHEW contract NO HRA

232;

78-0140, 1979

podiatric care, and “all other. ” Finally, it should

be noted that the overall model applies to 29

categories of health manpower (including veterinary medicine). Accordingly, all 20 health

care categories do not come into play in estimating physician demand.

Utilization rates are “calculated for each applicable category of health care for the 40

population subgroups (based on recent National

Health Interview Survey data, etc.). Changes in

population size and in demographic distribution

(age by sex by incomes) are calculated for future

years. 1975 utilization rates for each category

are then projected onto the future population

estimates and the results are summed to obtain

expected utilization of services.

Based on population and demographic shifts,

physician demand is expected to increase by 9.5

percent between 1975 and 1990 (Cultice, 1979)

yielding an estimated demand of 414,336 in

1990. This corresponds to a projected increase

in the population of 10.5 percent in the same

period. Of the 414,336 physicians, 229,276 are

expected to be in demand in office-based settings

(versus 205,196 in 1975), and 126,008 in shortterm hospital care (versus 117,573 in 1975). The

remaining 59,052 physicians are expected to be

in demand in other care settings.

The anticipated population changes between

now and 1990 that are most pronounced and appear most likely to impact on utilization patterns are: 1) an aging population, and 2) strong

signs of upward income mobility, measured in

constant (1970) dollars.

Although the U.S. population is aging, that

does not mean the 1990 population will be an

elderly one. Growth in the over-65 category

will, in fact, be small (from 9.5 percent of the

total population to 12.3 percent). Rather, the

bulk of the population will be in the prime of

adulthood, 25 to 44. The percentage of Americans in this age category is projected to grow
52 . Forecasts of Physician Supply and Requirements

from 23.6 to 32.9 percent. Correspondingly, the

percentage of persons under 25 is projected to

drop sharply (from 46.3 to 35.4 percent).

The projected shift in income distribution is

more dramatic than the age shift. The percentage of individuals whose family income, measured in 1970 dollars, lies below $10,000 is projected to shrink from 50.6 to 20.2 percent, while

the percentage of those with family incomes of

$15,000 or greater is projected to grow from 23

to 58.1 percent—a complete reversal of the percentages in the below $10,000 and above

$15,000 groups respectively.

These population changes are summarized in

table 30.

Changes in the age and income composition

of the population can be expected to bring about

changes in patterns of utilization of medical

services. Specifically, we can anticipate that

those forms of care more heavily used by the

elderly and by those with higher incomes will

grow more rapidly than those forms used by the

young and by the poor, and that physician services most closely associated with the former will

also experience a higher rate of growth. Thus,

the greatest growth rates are projected for nursing home and podiatric care (both utilized more

heavily by the elderly) and for psychiatric, vision, and dental care (services used more by

those with higher incomes). Conversely, the

lowest growth projections are for pediatric care

and hospital outpatient care (more frequently

used by the poor). The population shift into the

25 to 44 age bracket is also expected to increase

demand for ob-gyn care somewhat, because

these are the childbearing years. It is worth observing that though the U.S. population is aging, we will not be faced with providing for the

medical needs of a heavily geriatric citizenry as

of 1990. The major growth is going to be in the

age group 25 to 44, which is traditionally a comparatively healthier age group relative to both

children and the elderly.

It is also noteworthy that most of the health

care areas projected to grow as a result of demographic shifts are areas in which services are not

provided by physicians (dental care, podiatry)

or only partially so (vision care, nursing home

services).

The projected growth rates for the 20 types of

health services is summarized in table 31. Note

that the growth factor attributable solely to

overall population growth is 110.5 percent; i.e.,

the population will have grown 10.5 percent between 1975 and 1990. Thus, surgical care is projected to grow at barely over the overall population growth rate (0.4 percent higher), and both

general medical office-based care (108.9 percent) and medical care in short-term hospitals

(103.3 percent) are projected to grow at less

Table 30.—Projected Shifts in Age and Income Distribution, 1970-90

Percent distribution, by yeara

1970 1975 1980 1985 1990

Age

Under 14. . . . . . . . . . . . . . . . . . . 26.8% 23.20/o 20.50/. 19.5% 19.4%

14-24 . . . . . . . . . . . . . . . . . . . . . 19.5 20.8 20.5 18.5 16.0

25-44 . . . . . . . . . . . . . . . . . . . . . 23.6 25.1 28.1 31.1 32.9

45-64 . . . . . . . . . . . . . . . . . . . . . 20.6 20.4 19.8 19.2 19.4

65 + . . . . . . . . . . . . . . . . . . . . . . 9.5 10.5 11.1 11.7 12.3

Total . . . . . . . . . . . . . . . . . . 100.O% 100.070 100.O% 100.0% 100.0°4

Incomeb

Under $5,000 . . . . . . . . . . . . . . . 19.90/0 16.6 ”/0 11.6% 9.9% 8.6%

$5,000 -$9,999 . . . . . . . . . . . . . . 30.7 23.4 20.2 16.9 11.6

$10,000 -$14,999 . . . . . . . . . . . . 26.4 24.1 24.3

$15,000- and over . . . . . . . . . . .

21.2 18.7

23.0 35.8 43.7 51.9 58.1

Total . . . . . . . . . . . . . . . . . . 100.0% 100.O% 100.070 100.O% 100.O%

acOlumnS may not add due to rounding.

b

ln 1970 dollars.

SOURCE: JWK International Incorporated, Ev81u~f/orr of Project SOAR(Supp/y, Output, and Requirements), draft report, DHEW contract No. HRA 232-78-0140, 1979,
Ch.3- RequirernetItS ● 5 3

Table 31 .—Projected Utilization

Growth Factors, 1975-90

Projected

growth 1975-90

Medical off ice

General care . . . . . . . . . . . . . . . . . . . . . . . . . . 108.9

Pediatric care . . . . . . . . . . . . . . . . . . . . . . . . . 101.1

Ob-gyn care. . . . . . . . . . . . . . . . . . . . . . . . . . . 120.5

Psychiatric care . . . . . . . . . . . . . . . . . . . . . . . 124.7

Vision care..... . . . . . . . . . . . . . . . . . . . . . . . 123.8

Other care . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.0

Short-term hospital

Outpatient care. . . . . . . . . . . . . . . . . . . . . . . . 95.6

Surgical care.... . . . . . . . . . . . . . . . . . . . . . . 110.9

Medical care . . . . . . . . . . . . . . . . . . . . . . . . . . 103.3

Long-term hospital

Psychiatric care . . . . . . . . . . . . . . . . . . . . . . . 118.9

Other care . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110.5a

Other care settings

Nursing home care. . . . . . . . . . . . . . . . . . . . . 127.3

Dental care. . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.6

Veterinarian services . . . . . . . . . . . . . . . . . . . 110.5a

Optometric care . . . . . . . . . . . . . . . . . . . . . . . 116.4

Podiatric care . . . . . . . . . . . . . . . . . . . . . . . . . 127.2

Other care . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110.6a

Noncare settings

Pharmacy services . . . . . . . . . . . . . . . . . . . . . 111.7

Laboratory services . . . . . . . . . . . . . . . . . . . . 110.5a

Noncare activities. . . . . . . . . . . . . . . . . . . . . . 110.5a

aAge-, sex-, and Income-specific utll!zatlon rates areei(her inappropriate orunavallable for these categories The growth shown (110.5) IS that attributable

solely to theoverall growth of thepopulat!on.

SOURCE. JWKlnternatlonal incorporated,.EvaluatiOfl of Pro/ect SOAR(Supp/y,

OIJtpuL and Requ/rernenfs), draft report, DHEW contract No. HRA

232.78-0140,1979.

than the overall population growth rate of 110.5

percent.

These projections deal with utilization of

medical services as an expression of economic

demand, not medical need. We do know, however, that higher income has traditionally been

associated with improvements in health status;

i.e., less need for medical care. Tables 32 t0 35

give some indication of the differential health

status and utilization patterns of high- and lowincome groups. Clearly, low-income groups are

sicker and use more medical services, particularly hospital care, than higher income groups.

An evaluation completed under contract to

BHM of its general demand model (JWK International, 1979) raises the question whether

“new arrivals” in the upper income brackets

would exhibit the same patterns of utilization or

the same underlying patterns of medical need as

long-time members of upper income categories.

Conceivably, the “new arrivals” in the upper income categories might lag in exhibiting the

lower utilization rates of the higher income

brackets.

We might also question whether the relationship between utilization of medical services and

income is indeed constant over time, which is

what is implied by projecting 1975 income utilization rate differentials to the 1990 population.

If we had done such an exercise 15 years ago

(that is, attempted to project 1975 utilization

from a 1960 data base) we would have underestimated the actual utilization rates of lowincome groups. Over this time period the utilization rates of the poor have risen more rapidly,

and, in the area of physician visits, the poor

now use more services than the nonpoor,

whereas, previously they used less (see table

34).

These questions are raised not so much to critique the assumptions that went into the BHM

framework model—which are quite plausible

assumptions —but merely to point out that even

the most plausible of assumptions may finesse a

great deal of uncertainty.

The Baseline Configuration

The “baseline” configuration factors in the effects of historical trends in per capita utilization

of medical services. Two major component

trends are distinguished in the analysis. The

price-related component is that portion of the

change in utilization that is expected to result

when changes in the price of a particular form of

care or medical service affect consumer decisions to seek that care or service. The non-pricerelated component is a residual that measures

the effects of all other possible influences combined, including such factors as changes in the

accessibility of care, changes in population,

changes in consumer taste and preference,

changes in medical technology, environmental

changes, and changes in disease prevalence and

incidence. The non-price-related component is

the greater of the two.

The language of the BHM model is the language of economics, which tends to treat medical care like any other “consumer product, ”
54 • Forecasts of Physician Supply and Requirements

Table 32.—Prevalence of Selected Chronic Conditions Reported

in Health Interviews, by Family Income (United States)

Chronic Heart Hyper- Impairment,b Hearing Vision

Arthritis Asthma bronchitis Diabetes conditions tensiona back or spine impairment impairment

Family income (1969) (1970) (1970) (1973) (1972) (1972) (1971) (1971) (1971)

Number per 1,000 persons 17-44 years

Under $5,000 . . . 46.9 34.1 28.4 11.4 32.5 48.9 59.4 55.4 43.2

$5,000-$9,999 . . 40.5 23.6 22.3 8.7 23.3 40.8 50.5 44.0 31.7

$10,000-$14,999 38.7 24.4 21.8 8.4 22.5 35.9 47.4 39.3 28.7

$15,000 and over 35.9 26.8 23.7 8.0 24.3 29.8 42.4 35.8 30.9

Number per 1,000 persons 45-64 years

Under $5,000 . . . 297.8 53.5 44.2 74.1 139.3 172.7 102.8

$5,000-$9,999..

158.9 114.1

200.3 33.5 38.7 43.8 92.5 125.4 67.2 118.1 57.4

$10,000-$14,999 163.7 23.7 29.0 37.8 74.3 121.3

$15,000 and over 159.8

62.3 107.3 45.9

22.7 30.3 30.5 66.6 105.3 52.2 85.9 48.9

awithout heart involvement,

bExcept paralysis.

SOURCE: National Center for Health Statistics, “Selected Reports from the Health Interview Survey,” Vita/ and Hea/th Statistics, Series 10.

Table 33.—Number of Disability Days per Person

per Year by Family Income (United States, 1973)

Restricted

Family income activity days Bed disability days Work-loss days

Days per person ages 17-44 years

Under $5,000. . . . . . . . . . . . . 21.1 8.3 6.5

$5,000 “ $9,999. . . . . . . . . . . . 14.6 5.7 5.9

$10,000 -$14,999. . . . . . . . . .

$15,000 and over. . . . . . . . . .

11.9 4.8 4.8

11.4 4.4 4.6

Days per person ages 45-64 years

Under $5,000. . . . . . . . . . . . . 45.7 15.5 7.5

$5,000 “ $9,999 . . . . . . . . . . . 25.1 8.7 7.3

$10,000 -$14,999 . . . . . . . . . .

$15,000 and over. . . . . . . . . .

16.9 5.9 5.5

14.0 4.5 5.3

SOURCE: National Center for Health Statistics, “Current Estimates from the Health Interview Survey. 1973.” Vital and Health

Statistics, Series 10, No. 95; and unpublished data.

. .

with the assumption that utilization is generated

by consumer demand. It has frequently been

argued, however, that much utilization of medical care is physician generated. The model does

take such factors into account. What it does not

do is differentiate between consumer- versus

provider-generated changes in per capita utilization of medical care.

The price-related utilization trend is calculated through out-of-pocket costs to the consumer. Over the years, the price of health care

relative to the Consumer Price Index (CPI) will

have risen for some forms of care and declined

for others. In all (or almost all) instances,

however, the actual out-of-pocket expenses to

the consumer will have declined due to the increasing use of private and public health insurance. For those forms of care for which the net

price to the consumer has declined, per capita

utilization would be expected to increase.

We will not describe in any detail the technical aspects of utilization trend analysis. Conceptually, however, the process involves the following steps:

●

●

The observed utilization trend over the

baseline time period (1968-76) is factored

into its price- and non-price-related components.

A linear least squares regression equation is

fitted to the non-price-related utilization

trend and the resulting straight line is extrapolated forward in time to 1990.
Ch. 3—Requireme?zts ● 55

Table 34.—Number of Physician Visits per Year by Poor and Not Poor Status

and for Whites and Others (United States, 1964 and 1973)

Total Whites All others

Aae and year Poor Not Poor Poor Not poor Poor Not poor

Number of physician visits per person per year

17-44 years

1964 . . . . . . . . . 4.1 4.7 4.5 4.8 3.3 4.2

1973 . . . . . . . . . 5.7 5.0 5.8 5.0 5.6 4.8

45-64 years

1964 . . . . . . . . . 5.1 5.1 5.2 5.1 4.9 4.6

1973 . . . . . . . . . 6.3 5.4 6.1 5.4 7.1 5.3

Percent with no physican visits in past 2 years

17.44 years

1964 . . . . . . . . . 24.2 18.1 23.2 17.7 26.6 22.9

1973 . . . . . . . . . 13.4 12.8 13.1 12.7 14.5 13.5

45-64 years

1964 . . . . . . . . . 29.2 21.7 28.0 21.3 33.1 29.0

1973 . . . . . . . . . 20.6 16.9 21.4 16.9 17.0 16.9

Definition of poor IS based on family Income. Under $3,000 in 1964.

Under $6,000 in 1973

In each case, this included about one.fifth of the population.

SOURCE National Center for Health Statistics, unpublished data from the Health Interview Survey

Table 35.—Number of Discharges From and Average Length of Stay in Short-Stay

Hospitals, by Income and Color (United States, 1964 and 1973)

Total Whites All others

Age and year Poor Not poor Poor Not poor Poor Not poor

Number of discharges per 1,000 population

17.44 years

1964 . . . . . . . . . 181 161 188 164 163 132

1973 . . . . . . . . . 198 148 190 148 223 149

45.64 years

1964 . . . . . . . . . 146 148 159 151 102 111

1973 . . . . . . . . . 225 152 238 153 174 133

Average length of stay in days

17-44 years

1964 . . . . . . . . . 6.9 6.3 6.8 6.2 7.1 8.0

1973 . . . . . . . . . 6.4 6.0 6.0 5.9 7.2 7.0

45-64 years

1964 . . . . . . . . . 14.4 9.7 12.8 9.5 22.6 13.5

1973 . . . . . . . . . 12.8 9.3 12.3 9.0 15.3 13.0

Definition of poor is based on family Income Under $3,000 in 1964

Under $6,000 in 1973

In each case, this Included about one-fifth of the population

SOURCE National Center for Health Statistics, unpublished data from the Health Interview Survey

● In a separate process, the historical values ● The projected net price to the consumer is

of coinsurance and price, defined as the then applied to a demand curve with an asratio of CPI for health care to CPI for all sumed elasticity. This demand curve relates

items combined, are calculated. These val- the price (to the consumer) of a given form

ues are then multiplied to yield the pro- of care to the demand for that care. Based

jected net price (out-of-pocket costs) to the on that relationship, the extrapolated nonconsumer for the year or years in question. price-related utilization is adjusted upward

–r 1 -
56 . Forecasts of Physician Supply and Requirements

or downward to reflect the estimated impact of price changes during the years

ahead.

Current trend analysis employs “high” and

“low” assumptions of price elasticity. Thus, two

alternative price-adjusted utilization growth

trends are projected. Table 36 shows alternative

utilization growth rates based on high- and lowelasticity assumptions for four types of medical

care.

The utilization trend analysis is an extremely

important element in the BHM general demand

model. 1990 demand is projected to be 596,217

as compared to a 1975 demand of 378,376. of

the 217,841 additional physicians, 181,881, or

83 percent of the difference, is attributable to the

assumptions made about rising per capita utilization. Table 37 summarizes the projected rise

in physician demand between 1975 and 1990,

separated into population/demographic and per

capita utilization trend components.

The projection of the per capita utilization

trend is perhaps the most problematic aspect of

Table 36.—Estimated Growth in Per Capita

Utilization, Four Forms of Health Care

Projected per capita

utilization in 1990 relative to

baseline utilization in 1975

High Low

elasticity elasticity

Medical office services . . . . . . 1.45 1.37

Short-term hospital services. . 1.38 1.29

Dental office services . . . . . . . 1.28 1.29

Community pharmacy services 1.58 1.50

SOURCE: JWK International Incorporated, Eva/uatiorr of Project SOAR (SIJpp/y,

Output, arr~ Efequiremerrfs), draft report, DHEW contract No. HRA

232-78-0140,1979.

Table 37.—increase in Demand From

Population and Per Capita Utilization

Changes, 1975 to 1990, BHM Model

1975 1990

Demand . . . . . . . . . . . . . . . . . . 378,376 596,217

(assumed equal (projected)

to supply)

Increase. . . . . . . . . . . . . . . . . . —— 217,841

(projected)

Population effect . . . . . . . . (35,960)

Per capita utilization effect (181,881)

SOURCE: See text.

the BHM general modeling effort because of the

difficult philosophical and methodological issues involved in identifying and projecting

trends.

Methodologically, one problem is that the

non-price-related utilization trend includes

changes in per capita utilization that occur as a

result of demographic changes, including age

and income shifts. Thus, the BHM model double

counts the effects of age and income, and the

estimates are inflated accordingly.

While this problem is relatively easy to correct (and the model is in the process of being adjusted so that age- and income-related trends

will be counted only once), the sensitivity of the

estimates to alternative starting dates for trend

projection is more difficult to remedy.

The first task in trend analysis is to identify a

time period for which data will be collected and

analyzed. Until recently, the time period covered began in 1966 and extended through the

latest year for which suitable data were available (currently 1976). The starting date was

moved forward to 1968 because of concern that

the increases in utilization of medical services

that occurred during the startup period of Medicare-Medicaid tend to misleadingly inflate the

trend data when 1966 is used as a starting point.

If the desire is to remove the unique historical

impact of Medicare-Medicaid startup from the

projection of a longer term societal trend in

utilization, it is questionable whether moving

the starting date of the trend analysis from 1966

to 1968 is sufficient. With respect to Medicaid,

many States did not yet have their Medicaid

programs fully in operation in 1968, and important amendments to the original legislation were

also passed during this period, most notably,

expanded nursing home coverage.

The choice of starting date is extremely important because, for most kinds of medical care

(short-term hospital care excepted), the slope

and even the direction of the trend line changes

rather dramatically if the starting date is moved

from the late 1960’s to the early 1970’s.

Figures 6 to 9 summarize unadjusted per

capita utilization and high and low elasticity,

non-price-related utilization for the years 1966
Ch. 3—Requirements • 57

Figure 6.— Per Capita Utilization of Physician Office Services, 1966-76

Per capita utilization

(visits per year)

5.00

4.75

4.50

4,25

4.00

3.75

3.50

3.25

3.00

.

.

.

.

.

.

/

/

● ❞ ● ✍ ✎

J * ● -% ● - ●

— -- - # -

/ Legend “ -“ M

*

-“ \

/

Historic utilization

‘\ ● /

— (unadjusted )

Non=prJce-relaWd utilization “~ , ~ “

‘ ~

- (Iow-elasticity )

— * — Non-price-related utilization

(Mgh-eia$ticity)

I 1 1 I I I I I 1 I

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE JWK International. Inc , Evaluation 0f Project SOAR (Supply, Output, and Requirements), draft report, DHEW contract No

HRA 232.780140, 1979

to 1976. Only one set of data points (utilization

of short-term hospital services) displays a consistent, natural linear trend. In the case of the

other services (physician office visits, dental

care, community pharmacy), the utilization

data being extrapolated do not consistently display a pattern for which linear extrapolation

can be justified.

The mathematics of trend projection (linear

regression analysis) involves fitting a straight

line to the data points. Clearly, such a conceptual simplification of reality is less distorting of

the world’s true complexity when the data

points themselves tend naturally to conform to

a straight line pattern when graphically portrayed. Philosophically, the issue is one of consistency. Trend projection in general and the

methodology of linear regression in particular

assume that reality—in this instance per capita

utilization of medical care—exhibits a reasonably consistent pattern; whether it be one of increasing, decreasing, or remaining steady.

Figures 10 to 13 take the high-elasticity data

points of figures 6 to 9 and fit straight lines to

the data points for the years 1966-76, 1968-76,

and 1971-76. Except for short-term hospital

services, distinctly different trends can be projected, depending on the starting date. For physician office services, a start date of either 1966

or 1968 is seen to yield a distinctly upward

trend, whereas a start date of 1971 shows a

downward slope. In the case of dental office

visits, starting the trend analysis in 1968 produces a flat projection; starting in 1971 produces

a decline. For community pharmacy services,

the trend lines started in 1966 and 1968 show a

sharp upward rise even though per capita utilization has been declining since 1973.

The magnitude of the disparities in per capita

utilization growth produced by different start

dates is quantified in table 38.
Figure 7.— Per Capita Utilization of Short”Term Hospital Services, 1966-76

Per capita utilization

(admissions per year)

0.150

0.125

0.100

0.075

/ -

.- ● ✍ ✎

● ✏

✍ ‘ - * 0 “

/

Legend

—-*~*- ” — Historic utilization [unadjusted)

------ Non-price-related utilization (Iow-elasticity)

-*—c- Non-price-related utilization (high-elasticity)

I 1

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE JWK International Inc Eva/uaflon of Pro/ect SOAff (SuPPfY, Output, and F?equlrements), draft report. DHEW contract No

HRA.232.78-0140, 1979

Clearly, the considerable variability of these

results (except in the case of short-term hospital

services) points to the need for caution in extrapolating utilization trends to project future

physician demand, A slight change of 1 or 2

years in the time period covered in the trend

analysis can produce enormous differences in

the projections. It is evident, however, that the

choice of a pre-1970 start date followed by

linear trend extrapolation is bound to result in

drastically different estimates from estimates

which use a post-1970 start date.

BHM’s review of its general demand model

(JWK International, 1979) suggested that instead of linear extrapolation, a logarithmic fit

would produce a somewhat better trend extrapolation (for all but short-term hospital services),

less sensitive to the Medicare-Medicaid startup

years. Table 39 shows the difference in the nonprice-related per capita utilization trend between 1975-90 produced by employing logarithmic versus linear extrapolation.

The review concluded, however, that, while it

is possible to reduce the disparities produced by

selecting different start dates for trend analysis

by using an alternative form of extrapolation, it

was difficult to rationalize the use of a particular

functional form of trend fitting, and that there

was simply no reason a priori to expect the data

to follow a logarithmic as opposed to a linear

(or any other) pattern. In addition to mathematical retooling, the evaluation suggested

more use of human judgment rather than mechanistic methods, perhaps through projections of

what would be “most likely, ” based on classic

Delphi techniques or simply averaging the response of a suitably selected group of knowledgeable individuals.
Figure 8.— Per Capita Utilization of Dental Office Services, 1968-76

Per capita utilization

(visits per year)

2.00

1.75

1.50

1.25

1.00

---- -

/

\

“~

— Historic utilization (unadjusted)

- Non-price-related utilization

(loweksticity)

— * — Non-price-related utilization (high-elasticity)

I I I I 1 I I 1 I I

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE JWK International, Inc Eva/uat/on of Project SOAR (SUPPIY. OufPUf and Recrulremerrts), draft report, DHEW contract NO

HRA.232.78.0140, 1979

BHM states that the reasons it continues to

use the 1968 start date are: 1) the historical observation of rising per capita use of physician

services, 2) the more base years used to establish

the trend, the sounder the methodology of trend

extrapolation, and 3) its conclusion that the

1968 and 1969 data points do not reflect Medicare and Medicaid startup activities (Cole,

1980).

The problem, however, is not whether these

statements are correct or not. Instead, the problem stems from the specific rate of increase in

per capita utilization of physician services that

were used to calculate physician demand in addition to that derived from demographic

changes. We have simply pointed out that

BHM’s use of 1968 as the starting point results in

a trend line radically different from actual utilization data from 1971 to 1976 (see figure 10). It

is necessary to point this out, because the projections of physician demand issued by BHM

combine the estimates of demand due to demographic changes with that due to increasing per

capita use of physician services without an indication of the very different results that occur if

the starting date for trend projections is changed

by just 2 or 3 years. Even BHM’s internal evaluation has suggested that more human judgment, rather than mechanistic methods, be

used.

Contingency Modeling

The contingency modeling capability has

been used to explore the possible impact on economic demand for physician services from: 1)

alternative forms of NHI, 2) various rates of

growth of the HMO movement, and 3) increased use of midlevel practitioners or “task

delegation. ”

The effects of NHI on utilization are assumed

to be mediated through a lowering of the out-of-
60 ● Forecasts of Physician Supply and Requirements

Figure 9.—Per Capita Utilization of Community Pharmacy Services, 1966-76

Per capita utilization

(prescriptions per year)

7.5

7.0

6 . 5

6 . 0

5.5

5---0

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURE JWK International, Inc , Evaluation of Project SOAR (Supply, Output, and Requirements), draft report, DHEW contract No

HRA-.232-.78-.0140, 1979.

pocket costs to consumers for medical services

of various types. It is assumed that coinsurance

under NHI would be lower than it would be

without NHI. Clearly, the greater the gap between the average coinsurance rates projected

for future years in the absence of NHI and seems

likely under NHI, the more utilization would be

expected to rise under NHI.

Three NHI plans have been modeled, assuming rates of coinsurance of 5, 10, and 15 percent.

A recent rough estimate of the impact of NHI in

1990 assuming a 10-percent coinsurance rate

with and a 25-percent coinsurance rate without

NHI, employing a log linear fit, results in a 13-

percent upward demand shift.

Trend estimates suggest, however, that overall, as coinsurance falls, the gap between out-ofpocket costs to the consumer with or without

NHI is narrowing. This means that while those

portions of the population who currently have

little or no medical insurance would surely experience a great drop in out-of-pocket expenses

and would increase their utilization of medical

services accordingly, in the aggregate, most

Americans would not experience such a major

drop in out-of-pocket costs. Thus, if the coinsurance rate continues to decline without NHI,

the expectation would be for the eventual impact of NHI on utilization and physician demand to be less, the longer the delay in enacting

an NHI plan, particularly if the plan enacted

had a comparatively high coinsurance rate (e.g.,

15 percent rather than 5 percent).

With respect to the impact of HMO growth

on utilization, currently available data on HMO

growth suggest that only 6 percent of the population can be expected to belong to HMOs in

1990. This is not enough to appreciably lower

utilization or physician demand.
Ch. 3—Requiremetlts ● 6 1

Figure IO.— Non-Price-Related per Capita Utilization Trends, Physician Office Services, 1966-76

Non-price-related

per capita utilization

4.00

3.75

(

3.50

3.25

T

*

0

.

F

.- - . :- <; “

.?’ q?-~(wtwwed M16&76 ;

}- .>0

7 . -

,., . #. ‘ -. 0

*. ~ . :! ~. . : I J I 1 I I 1 I

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE JWK /nternat/ona/, Inc., Eva/uat/on of Project SOAR (Supply, Output, and Requirements), draft report, DHEW contract No HRA.232.

78-0140, 1979.

In the case of task delegation to midlevel

practitioners, the productivity enhancement factor is estimated at 30 percent. Here again, however, the effect of expanded task delegation is

projected to be negligible in 1990—on the order

of 2 to 3 percent. This minor effect is due to the

projected limited supply of nurse practitioners

and physicians’ assistants, based on current

training levels.

Productivity

Finally, productivity of physicians in 1990 is

assumed to remain the same as productivity of

physicians in the base year 1975. Productivity in

the base year is addressed indirectly in the form

of a staffing matrix which shows the number of

units of manpower engaged in each separate

form of health care activity during that year.

Because productivity is assumed to be constant,

the ratio of services to manpower will be the

same in 1990 as in 1975, and it is on this basis

that manpower estimates are generated. The

total number of estimated active physicians as

of 1975 was obtained from the American Medical Association (AMA), with the various specialties regrouped to provide a more compact

typology. Physicians were then allocated to particular specialties and care settings based on care

profiles. As a result, the numbers used are not

head counts of physicians claiming particular

specialties, but are estimated FTE physicians

providing a particular type of care. For example, the percentage of time the average general

practitioner (GP) devotes to providing ob-gyn

care is assigned to the ob-gyn category, while

the estimated percentage of GP practice devoted

to pediatric care is assigned to pediatric care.

Thus, physician demand is calculated in terms

of the nature of the services provided, as well as
62 . Forecasts of Physician Supply and Requirements

Figure 11.- Non-Price-Related per Capita Utilization Trends, Short-Term Hospital Services, 1966-76

Non-price-related

per capita utilization

0.110

0.105

0.100

(

0.095

0.090

0.085 1 I I I I 1 1 I {

)

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE. JWK International, Inc .Evaluation of Project SOAR (Supply, Output, and Requirements), draft report, DHEW contract No

HRA-.232-78-0140, 1979

in terms of physician specialty categories. Table figures are derived from the “framework”

40 shows the allocation of physicians by type model; i.e., the projected growth in utilization

and setting of care in the 1975 base year. Table in table 41 is based only on anticipated popula41 is an illustrative computation of the 1990 tion growth and demographic shifts.

manpower demand for pediatricians. These

THE GRADUATE MEDICAL EDUCATION NATIONAL

ADVISORY COMMITTEE MODEL

As we have seen, the BHM estimates are

products of a market-oriented approach that

tries to predict the future economic demand for

medical services if current trends in utilization

continue without major disruption. In contrast,

GMENAC seeks to define physician requirements in terms of the type and amount of care

that medical professionals believe should be

utilized in 1990, in light of available data and

medical judgment as to the prevalence of biologic conditions and the ability of the medical

profession to provide useful therapeutic and

preventive care. The main aim of the GMENAC

modeling effort is to generate estimates of physicians trained in particular specialties so that

graduate medical education programs can be revamped accordingly. Table 42 summarizes the

specialties and subspecialties for which estimates are being planned or considered by

GMENAC. Of these categories 14 to 26 are ex-
Ch. 3—Requirements ● 6 3

Figure 12.—Non-Price Related per Capita Utilization Trends, Dental Office Services, 1968-76

Non-price-related

per capita utilization

1.75

1.50

1.25

1.00

13

Legend

 High-elasticity series-observed 196&76

--- High series-linear trend 1968-76

— 0 — High series-linear trend 1971-76

1

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE JWK International. Inc. Evaluation of Project SOAR (Sum/Y. Output. and Requirements), draft report, DHEW contract No

HRA-232-78.01 40, 1979

pected to be completed (McNutt, 1979). While

an aggregate estimate of physicians required in

1990 is not the principal objective of GMENAC,

such a number can readily be generated simply

by adding the estimates for each specialty, once

those numbers are available and if all specialties

are covered.

As of this time, no estimates for any specialty

group are available from GMENAC. The estimates should be released in 1980, provided

GMENAC undergoes no further delays and

meets its scheduled date for publication of the

final report.

The formal definition of “need for care” employed by GMENAC is as follows:

An individual is said to need medical care if a

pathologic finding exists or if the individual will

benefit from such care. Need for care thus refers

to: 1) persons with a given morbidity for whom

intervention by a physician is appropriate for diagnosis and treatment, and 2) persons without

morbidity for whom preventive services are appropriate.

Thus, in the GMENAC model, populationbased estimates of morbidity (biological need)

are adjusted to determine the proportion of persons with a given morbidity who are in need of

physician intervention. In addition, the quantity

and type of preventive services appropriate for

certain population subgroups are normatively

estimated. Further, the model takes into account

other uses such as insurance physical examinations and visits by the “worried well. ” The result

is what GMENAC terms an “adjusted-needs”

model which is used in conjunction with U.S.

Census population projections to estimate the

need for physician care in 1990. Figure 14 illustrates the procedure of arriving at an adjusted

needs estimate for one particular type of biological condition, varicose veins.
64 ● FOrecast of Physician Supply and Requirements

Figure 13.—Non-Price.Related per Capita Utilization Trends, Community Pharmacy Services, 1966-76

Non-price-related

per capita utilization

7.0

6.5

6.0

5.5

(

5.0

)

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

Year

SOURCE JWK International, Inc , Evaluation of Project SOAR (Supply, Output, and Requirements), draft report, DHEW contract No.

HRA-.232-.78.0140, 1979

Table 38.—Dependence of Trend Projections on

Alternative Starting Dates in the Baseline Data

Projected growth in non-price-related per

capita utilization, 1975-90 (1975 = 100)

Start date

Elasticity 1966 1968 1971

Physician office services . . . . . High 116.3 123.0 89.9

Low 123.1 127.3 95.4

Short-term hospital services . . High 123.8 131.4 123.4

Low 123.9 129.9 129.9

Dental office services. . . . . . . . High NA 97.5 56.3

Low NA 123.3 106.9

Community pharmacy services High 135.6 129.4 100.1

Low 140.1 134.1 105.6

SOURCE: JWK International Incorporated, Evaluation of Project SOAR (Supplyr Output, and Requirements), draft report,

DHEW contract No. HRA 232-78-0140, 1979,
Ch. 3—Requirements ● 6 5

Table 39.—Comparison of Linear Versus Logarithmic Extrapolation of Utilization Data

R

2 (percentage of variance Projected growth in non-price-related per

Form of services explained by regression) capita utilization, 1975-90 (1975 = 100)

and price elasticity Linear extrapolation Logarithmic extrapolation Linear extrapolation Logarithmic extrapolation

Physician office

High . . . . . . . . . . . . . . . . . 43.20/o 43.50/0 116.3 103.1

Low . . . . . . . . . . . . . . . . . 65.7 66.2 123.1 104.4

Short-term hospital

High . . . . . . . . . . . . . . . . . 75.7 73.5 123.8 104.4

Low . . . . . . . . . . . . . . . . . 90.1 88.5 123.9 104.3

Dental office

High . . . . . . . . . . . . . . . . . 00.7 00.4 97.5 101.7

Low . . . . . . . . . . . . . . . . . 59.1 60.3 123.3 106.5

Community pharmacy

High . . . . . . . . . . . . . . . . . 76.4 77.6 135.6 110.6

Low . . . . . . . . . . . . . . . . . 81.1 82.3 140.1 111.3

SOURCE: JWK International Incorporated, Evaluation of Project SOAR (Supply, Output, and Requirements), draft report, DHEW contract No. HRA 232.78-0140, 1979

Use of such adjusted needs estimates has important implications. If, for example, we compare estimates of physician requirements based

on biological need and appropriateness of medical intervention with estimates based on projecting current patterns of utilization of physician services into the future, we can anticipate

some differences in the types of physician services on which the estimates of overall physician

requirements would be based. The GMENAC

model, for example, implies that patients will

not receive physician services merely because

they want such services and can pay for them;

i.e., factors that translate into “effective economic demand” in a market-oriented model.

Using the GMENAC model, a proportion of the

current and future economic demand for care

might be discounted, because the persons seeking physician services might lack sufficient

biological need for services or might be seeking

services inappropriate to their biological condition, or might be seeking care for biological conditions for which no useful physician interventions are presently available. From what is

known about current patterns of utilization,

such a downward adjustment of “economic demand” to meet standards of true biological need

and appropriate, useful physician intervention

would have the greatest impact on primary care

(defined as “first contact” physicians). The reason is that a high volume of complaints seen by

primary care practitioners are nonserious, selflimiting conditions for which no effective medical treatment currently exists (e. g., “colds,”

nonbacterial sore throats, and similar conditions). However, the adjusted needs approach

does take into account some proportion of the

demand for care that is generated by the socalled “worried well” and persons with vague

symptoms, probably psychological in origin,

for which the patient seeks a physical cause and

medical cure.

Conversely, there are also medical conditions

for which beneficial interventions are available

which do not, however, generate demand. The

would-be patient may be unaware of the condition or of the availability of effective treatment

or preventive cure, or, for whatever reason, has

chosen not to seek it. On this dimension, a needbased model would tend to overestimate physician requirements.

The GMENAC model accordingly provides

for some downward adjustment of medical need

estimates to conform to patterns of future utilization that can realistically be anticipated,

even though such adjustments imply an acceptance that the true medical need for physician

services will never be wholly met. Finally, much

has been written in recent years concerning over

and unnecessary utilization of medical services

that is physician rather than consumer generated. To the degree that over and inappropriate

utilization are factors in current patterns of

utilization and ongoing trends in utilization,

economic demand models include such unnecessary services in projecting physician requirements. In contrast, an adjusted need-based mod-
66 Ž Forecasts of Physician Supply and Requirements

Table 40.—Allocation of Physicians by Type and Setting of Care for the 1975 Base Year, BHM Model

Medical office

General Pediatric

Total care care Ob-gyn care Psch. care Vision care Other care

Physicians (MD). . . . . . . . . 340,280 46,493 21,453 16,255 15,080 8,820 76,406

Generala. . . . . . . . . . . . . 116,430 36,476 9,932 2,895 1,081 . — 26,995

Pediatric. . . . . . . . . . . . . 21,746 568 12,061 —— —— 271

Obstetrics-gynecology.

——

21,731 2,634 –– 12,964 79 ——

Opthalmolgy . . . . . . . . .

——

11,129 —— —— —— —— 8,820 ––

Psychiatry . . . . . . . . . . . 26,502 –– —— —— 13,837 ––

Surgeryb. . . . . . . . . . . . .

——

76,017 3,516 –– 396 63 –– 24,003

Secondary speciaiistc. . 48,322 3,299 –– —— —— 25,137

Noncare specialist . . . .

——

18,403 —— —— —— ——

Physicians (DO). . . . . . . . .

—— ——

14,532 11,072 47 47 35 24 464

Short-term hospital Long-term hospital

Out pt. care Surgical care Medical care Psychiatric care Other care

Physicians (MD). . . . . . . . . 8,481 63,701 35,680 9,334 3,314

Generala. . . . . . . . . . . . . 5,660 1,351 21,292 3,103 1,476

Pediatric. . . . . . . . . . . . . 638 49 6,179 131

Obstetrics-gynecology. — —

.——

5,156 —— .——

Ophthalmology . . . . . . . . –—

——

1,991 —— .——

Psychiatry . . . . . . . . . . .

——

1,746 —— 1,392 6,231

Surgeryb. . . . . . . . . . . . . ——

——

45,289 ——

Secondary specialist. .

-—— ——

437 4,374 6,124 -—— 1,707

Noncare specialist . . . —— 5,491 693

Physicians (DO). . . . . . . . .

-—— ——

312 254 1,210 -—— 166

Other care settings Noncare settings

Nursing Dental Vet. opt. Pod. Other Pharm. Non care

home care care care care care Lab service service activities

Physicians (MD). . . . . . . . . 594 –– –– –– –– 2,611 4,309 —— 27,749

Generala. . . . . . . . . . . . . 594 –– –– –– –– –– –-– –– 6,115

Pediatric. . . . . . . . . . . . . —— —— —— —— —— —— –-— —— 1,849

Obstetrics-gynecology. —— —— —— —— —– —— —— —— 898

Ophthalmology . . . . . . . —— –— —— —— —— —— –-— —— 318

Psychiatry . . . . . . . . . . . —— —— —— —— —— —— –-— —— 3,276

Surgeryb. . . . . . . . . . . . . —— —— —— —— —— —— –-— —— 2,750

Secondary specialist. . —— —— —— —— —— —— –-— —— 7,244

Noncare specialist ., . —— —— —— —— —— —— 4,309 —— 5,299

Physicians (DO). . . . . . . . . –– –– –– –– –– –– –-– –– 901

alnCludeS ~enera~ and family practice, internal medicine, and “sPeClaltY un” dlatrlc allergy, pediatric cardiology, pulmonary d!seases, radiology, diagnostic

speclfled” (presumed to be predominantly In primary care) radiology, therapeutic radiology, neurology, physical medlclne and rehablllta.

blncludes general surgery, neurological surgery, orthopedic sur9erY, otolarYn. tion, and “other specialties. ”

gology, plastlc surgery, colon and rectal surgery, thoraclc surgery, urology, dlncludes occupatlona[ medlc!ne, general preventwe medlclne, publ!c health,

and anesthesiology. aerospace medlclne, pathology, and forensic pathology

clncludes allergy, cardiovascular diseases, dermatology, 9aStrOenter0109Y, PeSOURCE: JWK International Incorporated, Eva/uaffon of Pro/ect SOAR (SUpp/y, Output, and Requirements), draft report, DHEW contract No. HRA 232.78-0140, 1979.

cling effort such as GMENAC’S tries to factor

out some unnecessary services from its estimates.

There is controversy over the definition of

“unnecessary” services. The GMENAC model

would presumably reflect expert opinion in respect to whether particular conditions require a

physician visit, and whether these conditions

could benefit or not from further treatment. Figthat are involved in translating adjusted medical

need estimates into projections of physician requirements by specialty. Epidemiological data

on the frequency of specific biological conditions in the population are used as the starting

point. Data on conditions that are known to be

treated by physicians in a given specialty or specialty groups are selected based on analyses of

current practice content by self-designated specialists and estimates of the training content in

ure 15 summarizes the component processes each specialty.
Table 41.— Illustrative Computation of Manpower Requirements

1. According to table 40, the Nation’s pediatricians were involved in 1975 in the following

forms of health care activity, in the numbers shown:

Number of pediatricians

engaged in this activity (1975)

Medical office

General care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Pediatric care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Other care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Short-term hospital .

Outpatient care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Surgical care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Medical care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Long.term hospital

Other care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Other settings

Noncare activities, not elsewhere specified . . . . . . . . . . . . . . . .

568

12,061

271

638

49

6,179

131

1,849

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21,746

2. Runs conducted by the Division of Manpower Analysis indicate that between 1975 and 1990,

utilization of each of the foregoing forms of care will have undergone the following growth

(or reduction):

Projected utilization growth

factor (1975-90) —

Medical office

—

General care. . . . . . . . ~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108.9

Pediatric care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.1

Other care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.0

Short-term hospital

Outpatient care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95.6

Surgical care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110.9

Medical care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.3

Long-term hospital

Othercare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110.5

Other settings

Noncare activities, not elsewhere specified . . . . . . . . . . . . . . . . 110.5

3. Applying these projected growth factors to the correspcmding 1975 supply of pediatricians,

the following table of projected 1990 manpower requirements is produced:

Number of Projected 1990

pediatricians Projected manpower

engaged in this utilization requirements (column

activity(1975) growth, 1975-90 — ltimes column2) —

Medical office

General care. . . . . . . . . . . . . . . . . . . . . 568 108.9 619

Pediatric care. . . . . . . . . . . . . . . . . . . . 12,061 101.1 12,194

Other care. . . . . . . . . . . . . . . . . . . . . . . 271 111.0 301

Short-term hospital

Outpatient care . . . . . . . . . . . . . . . . . 638 95.6 610

Surgical care. . . . . . . . . . . . . . . . . . . . . 49 110.9 54

Medical care. . . . . . . . . . . . . . . . . . . . . 6,179 103.3 6,383

Long-.term hospital

Other care. . . . . . . . . . . . . . . . . . . . . . . 131 110.5 145

Other settings

Noncare activities, not elsewhere

specified. . . . . . . . . . . . . . . . . . . . 1,849 110.5 2,043

Total. . . . . . . . . . . . . . . . . . . . . . . . 22,349

SOURCE JWK International Incorporated. Evaluation of Pro/ecf SOAR (SuPP/Y Outpuf and RequlremefrfsL draft report.

DHEWcontract No liRA232.78-Ol@. 1979
68 ● Forecasts of Physician Supply and Requirements

Table 42.—Specialty Areas and Subspecialties

for Which Requirements Estimates Are Being

Planned or Considered by GMENAC

Obstetrics-gynecology

Dermatology

Adult medical care

Family practice

General internal medicine

Allergy and immunology

Hematology

Cardiovascular disease

Infectious disease

Endocrinology and metabolism

Nephrology

Pulmonary disease

Gastroenterology

Rheumatology

Medical oncology

Pediatric medical care

Family practice

General pediatrics

Allergy and immunology

Pediatric hematology-oncology

Pediatric nephrology

Pediatric endocrinology

Pediatric cardiology

Neonatal-perinatal medicine

Otolaryngology

General surgery

Colon and rectal surgery

Orthopedic surgery

Thoracic surgery

Ophthalmology

Urology

Neurosurgery

Plastic surgery

Pathology

Radiology

Psychiatry and neurology

Anesthesiology

Preventive medicine

Nuclear medicine

SOURCE Interim Report o/ the Graduate Medical Education National Advisory

Committee to the Secretary, Department o/ Health, Education, and

We/fare, Washington, D.C. Health Resources Administration, DHEW

publication No (HRA) 79-633, p 206

These data on current incidence and prevalence of conditions, it is important to note, are

subject to various limitations in terms of validity and reliability. The following quote, taken

from the workbook prepared for the general

surgery advisory panel, is illustrative:

There is a general problem in the national data

sets with coverage of conditions treated by general surgeons. Population based clinical examination data provide the best source of data for

estimates of incidence and prevalence of disease

and injury. Such data for general surgery conditions are limited, however. The data set that

Figure 14.—lllustrative Procedure for Arriving at

Adjusted Needs Estimates

Total U.S. population

A. Persons with one or more episodes

of varicose veins

SOURCE GMENAC Workbook for General Surgery Panel, 1979.

contains the most extensive coverage of conditions is the Health Interview Survey (HIS).

However, conditions in HIS are self-reported

and thus represent an individual’s knowledge

and perception of a condition, and not necessarily an accurate measure of disease or injury. In

addition, in HIS, reporting of morbidity is contingent upon a person’s taking one or more of

various actions, such as restriction of usual activities, bed disability, work loss, or seeking

medical advice. A further problem exists in obtaining reliable estimates for low-prevalence

conditions (less than 1 to 2 percent of the population), The sample of persons with rare conditions is usually small, and thus these estimates

tend to be unreliable. Since many of the general

surgery conditions occur with low frequency in

the total population, they are difficult to estiimate accurately. In general, the available morbidity estimates presented for the general surgery conditions are thought to be underestimates

of actual morbidities in the U.S. population.

Given the limitations of the national morbidity

data, the estimates of the proportions of persons

with general surgery conditions is presented to

the panel for review and revision.

Accordingly, the GMENAC advisory panels

of experts for each specialty area use their professional judgment to take account of possible
Ch. 3—Requirements ● 6 9

Figure 15.—GMENAC Model for Estimating Physician Requirements

I

I

I

I

Data sources

P4

SOURCE: Interim Report of the Graduate Medical Education National Advisory Committee to the Secratary, Department of Health, Education, and

Wa/fare, Washington, D.C.: Health Resources Administration, DHEW publication No. (HRA) 79%33, P. 195.

over, under, or misreporting of conditions. For

example, estimates of the frequency of venereal

disease would be adjusted upwards, since the

frequency of these diseases is known to be under-reported.

In the next phase the advisory panels of experts are asked to estimate the probable frequency of these same conditions in 1990, based

on the data on current frequency adjusted by

their judgments concerning changing disease

patterns, host responses and technology, efficacy of preventive strategies, and any other factors they might believe to have an important impact. The specialty panel of experts also estimates what proportion of episodes of a given

condition should receive a physician’s care in

1990 and what proportion of these should be

seen by the panel’s medical specialty (e. g., general surgery, general pediatrics, psychiatry) versus some other specialty. Here the panel members again employ their own intuitive judgments

concerning the frequency of self-limited conditions, the availability of effective therapeutic or

preventive care, and any other such factors that

might be expected to influence the degree of benefit persons with particular conditions might be

expected to derive from receiving care from a

physician and from a particular type of specialist. In making these and other similar kinds of

judgments, specialty panels are instructed to

think in terms of the average patient. A modi-
fied Delphi process is used to achieve consensus.

The final product emerging from the deliberations of the expert panel at this phase is a list of

diseases, diagnoses, preventive activities, operations, and counseling requirements expressed in

terms of population rates and disease or diagnostic categories.

GMENAC staff then apply these estimates of

medical need to census projections of the size,

age, and sex distribution of the U.S. population

in 1990. The GMENAC model apparently does

not consider future changes in income distribution and the impact these changes might be expected to have on population health needs. Adjustments for the unusual needs of some groups

of people as well as those previously excluded

from the health care system are introduced at

this phase of the model.

In the next phase, the panels of experts determine norms of care for each disease or diagnostic category. Here again, the panels will have

available to them data on actual utilization rates

from a variety of sources such as HMOS and the

National Ambulatory Medical Care Survey’s

published research studies. Each panel may recommend increases or decreases in the prevailing

rates of utilization, based on its perceptions of

what constitutes good medical care and what

technology is likely to be available in the future.

The norms of care maybe expressed as visits per

episode of illness or annual encounter rate per

chronic condition or some other unit of service.

During this phase of the study the panels also

consider which conditions can be treated in the

office, which require hospitalization, and what

is the appropriate length of hospital stay.

Again, each panel examines existing data on utilization (e.g., Hospital Discharge Survey) and

adjusts it up or down for its estimates of appropriate care for the average case. Finally, the

panels estimate the proportion of inpatients and

office visits that can be delegated to physicians’

assistants or nurse practitioners. Although actual figures are not available, GMENAC staff

report that the panels have been willing to delegate significant amounts to paraprofessionals

compared to what is currently delegated.

GMENAC staff predict that the specialty panels

will recommend increased task delegation and

will specify where increased task delegation to

paraprofessionals is most appropriate.

Perhaps the single most problematic aspect of

the GMENAC modeling effort occurs in the next

phase. This is the reconciliation of conflicting

estimates by the various specialties as to what

proportion of a given disease or diagnostic category “belongs” to each specialty. The extent of

the problem is likely to be mitigated somewhat

because each specialty panel contains a few representatives from other specialties. In particular, generalists (general practitioners, family

practitioners, and general pediatricians) are represented on the specialty panels (surgery, dermatology, etc. ) and vice versa. This is important because it is essential that a specialty’s

estimate of the conditions it should handle

match those of the generalists who make the referrals. At this point, it is difficult to determine

how many problems there will be in mediating

disputes between specialties. An indication of

the complexity of the task facing these panels is

that only 14 to 26 of the 37 specialties listed in

table 42 are expected to be completed by the end

of 1980.

Among the difficult questions that must be

mediated during this phase is the issue of how

much primary care should be provided by secondary and tertiary care specialists. The issue is

a knotty one that cannot be easily settled. One

reason is that wider geographic distribution of

subspecialists, outside major cities, virtually requires a part-time practice of the subspecialty

for some percentage of these physicians, because the conditions treated by subspecialists

are comparatively rare,

The final task in the modeling process is to

translate estimates of the volume of physician

services into FTE physicians and then into actual head counts. FTEs are arrived at by dividing service estimates allotted to each specialty

by the expected productivity for each physician

in that specialty. Productivity may be expressed

in terms of encounters, operations, or some

other unit, depending on which is most appropriate.

As in the BHM model, it is generally assumed

that physician productivity will be the same in
1990 as it is now, although this depends on each

specialty panel. GMENAC staff review the

available data on the typical physician practice

profile and arrive at estimates of productivity

for the various kinds of services the specialty

provides. Table 43 displays the average practice

profile of general surgeons and the preliminary

productivity estimates to be used to calculate

FTE general surgeons. It should be noted that, in

addition to the estimate of 43 office visits per

week, alternative numbers of 77.2, 58, and 51

were also cited from the data. Finally, some estimates are also made of the productivity enhancement for physicians of employing physicians’ assistants and nurse practitioners and the

productivity gains from organizational arrangements such as group practice and the interaction

between these two factors.

Overall, the productivity estimates used in

the GMENAC model are somewhat problematic

for two reasons: 1) the data sources on which

productivity estimates are based often exhibit

considerable disagreement (in part because the

definitions of service units vary; for example,

some measures of time allotted to surgical

operations may count operating room time

only, while other measures may include all the

care associated with a procedure including preoperative and postoperative office and hospital

visits) and 2) little information is available

about trends in productivity over time, particularly by specialty.

Table 43.—The Average Practice

Profile of General Surgeons

Hours

Hours worked

Average number of weeks worked per year 47.0

Average number of hours worked per week 52.0

Time allocation within week

Hours in hospital . 31.3

Hours In operating roo m 11,5

Hours In Inpatient vlslts. . . . . . . . . . . . . 19.8

Hours In office. ... . . . . 13.4

Other professional time. . . . . . . . . . . . . 7.3

Total professional time . . . . . . . . . . . . 52.0

Weekly productivity

Office visits per wee k 43

Inpatient vlslts per wee k 45

Operations per week. . . . . 3.4

Operations per week (CRV units). . . . 34,4 CRV

SO~JRCE GMENAC General Surgery Workbook

The final step in the GMENAC model is the

conversion of FTE physicians into actual head

counts. In essence, this involves making some

additional allocation to cover those physicians

who do not practice full time but are instead involved in full- or part-time research, teaching,

or administration or have taken some time out

from practice for continuing education or other

activities.

As a complement to the modeling effort,

GMENAC commissioned a study of selected elements of consumer dissatisfaction with health

care. The study, based on a scientifically designed opinion survey, was carried out by researchers at the Center for Health Administration Studies of the University of Chicago

(USDHEW, 1979c). It is not known how

GMENAC plans to incorporate the report’s

findings into its final estimates, if indeed it plans

to do so at all. The report does, however, contain some interesting findings with potential implications for manpower policy.

Broadly speaking, the report suggests that

there is a tradeoff relationship between physician productivity and consumer satisfaction,

and that a decrease in current productivity levels might result in greater patient satisfaction.

The assumption here is that if physicians saw

fewer patients, they would be able to spend

more time with patients. Presumably, with

more time, they would be better able to express

concern, courtesy, and consideration; improvements in the quality of the doctor fpatient relationship that the data indicate some patients believe are needed.

Tables 44 and 4!5 and figures 16 and 17 summarize the findings of the consumer study in

respect to levels of consumer satisfaction/

dissatisfaction with various aspects of care.

Note that the single major source of consumer

dissatisfaction, high out-of-pocket costs (figure

16), is not particularly amenable to solution via

manpower policy. Table 45 indicates that ethnic

minorities are more dissatisfied with all aspects

of care than the majority white population.

Finally, the study found that consumer perceptions of the availability of care correlated

highly with actual data on physician availabili-

,— - — 1-
ty. In other words, people were more likely to cian-to-population ratios. Consumer percepreport that their area lacked sufficient doctors in tions of physician shortages were particularly

areas where there actually were lower physi- sensitive in the case of medical specialists.

Table 44.— Proportion of Persons Whose Experience With Physician Visits Is Beyond the Critical Threshold

Aspect of visit Crtt ical threshold

Travel time . . . ., . . . . .. ..30 minutes or more

Appointment time . . . . . . . . . . . Over 2 weeks

Waiting time . . ., . . . . . . . . . . . . .. .30 minutes or more

Time with doctor . ... Less than 10 minutes

Information from doctor . . . . . . . A Ilttle or nothing

Out-of-pocket costs. ... ... ... ... .. $10 or more

Percent of persons beyond

critical threshold

1170

10

27

28

27

38

Number of pat Ients beyond

critical threshold (in millions)

1 5

14

37

39

37

52

SOURCE The Consumer V/ewpo/nt Whaf IS Hea/lh Care and What do We Wanf7 & A Response, prepared - for the Graduate Medical Education National Advisory Corn

mlttee Washington D C Health Resources Admlnlstratlon DHEW publication No (H RA) 79.632, p 13

Table 45.—Percent of Ethnic Groups Dissatisfied With Aspects of the Medical Care System

Aspects of the medical care system –

Waiting time for Office waiting time Out-of-pocket cost Humaneness and

Ethnic group———-

an appointment to see the doctor of the medical visit quality of the visit

Majority white . . . . . . . . . . . . 1 50/0 270/o 360/o -

10%

Urban black . . . . . . . . . . . . . . . . 27 38 43 15

Rural southern black. . . . . . . . . 26 39 45 12

Spanish heritage, Southwest 33 32 39 16

SOURCE The Consu-mer V/ewpo/rrt ‘“What IS Hea/fh Care and—W;at do We WarrP & A Response, prepared fOr the Graduate Medical Education National Advisory Corn.

mlttee Washington, D C Health Resources Admlnlstrat(on, DHEW publication No (HRA) 79.632, p 19

COMPARISON OF THE BHM AND GMENAC MODELS

The two major modeling efforts currently underway, those of BHM and GMENAC, exemplify two quite different philosophical approaches

to the task of estimating future physician requirements. The GMENAC approach is to estimate how many physicians would be needed to

provide appropriate services to meet the population’s medical need for care. Estimates of

medical need and appropriate services are in

turn based on a combination of the frequency of

particular illnesses and conditions and of medical judgment as to which of these conditions

can benefit from medical services, by amount

and type. In contrast the BHM approach is an

economic modeling effort that treats medical

care as it would any other market commodity.

The aim is to predict the economic demand for

medical services and the number of physicians

that would provide those services based on currently observable patterns and trends in medical

care consumption.

Second, the GMENAC approach is a deliberate goal-setting effort based on the Committee’s

estimates of the numbers and types of physicians’ services that should be provided or at

least be available to the American citizen in

1990. Thus, the estimates GMENAC arrives at

will be “target numbers, ” goals GMENAC will

be recommending that governmental and private sector activities be directed toward realizing.

In contrast, the BHM projects current behavior trends into the future. It is not a goaloriented modeling effort, and its estimates are

therefore not intended to be target numbers for
Figure 16.—Consumer Satisfaction With Physician Services

Out-of-pocket

cost of care

Time waiting to see

the doctor

Information given by doctor

about what was wrong

Time between calling for

and receiving appointment

Time spent with the doctor

Amount of concern doctor

seemed to have

Quality of care patient felt

was provided

Cost of traveling to the

doctor’s office

Time to travel to the

doctor’s office

Courtesy, consideration

shown by receptionist

Courtesy, consideration

shown by doctor

Courtesy, consideration

shown by nurses

The overall visit to the doctor

Among those who saw a physician in a 12-month

period, percentage dissatisfied/satisfied with

various aspects of the visit.

Dissatisfied 3 7 % Satisfied 63%

28“A 72 %

18’-’/0 82 %

160/0 84 0 / 0

16% 84 “/o

130/o 87°10

13% 8 7 %

13 “/0 870/0

12 % 880/0

9% 91%

8% 92 %

7 0/0 93° 0

12% 8 8 %

To determine whether

people are satisfied or

dissatisfied with their

medical care, the research

group asked questions of

all persons who had visited a

doctor during the past

year. The questions probed

impressions of specific

aspects of the visit.

“Responses were very

skewed toward the positive

end of the scale, ” the

group reports, For the total

population, cost iS the

greatest cause of

dissatisfaction Overall,

the chart reveals the U.S.

population iS genera//y

satisfied.

NOTE Because of the large sample size, the true’ percentages of satisfied and dissatisfied consumers in the population are unlikely to vary by more than 1 percent

SOURCE The Consumer Viewpoint ‘ What iS Health Care and What do We Want” & A Response, prepared for the Graduate Medical Education National Advisory Corn

mittee Washington, D C Health Resources Administration, DHEW publication No (HRA) 79-632

policymaking purposes. The BHM modeling ef- able or should be a policy goal to satisfy any

fort is probably best understood as an ongoing particular level of economic demand for physiprocess of monitoring factors and trends that cian services in 1.990. The model simply generare or presently seem most likely to affect the ates an estimate of how many physicians it

future economic demand for medical services. would likely take to provide the medical servThus, BHM physician estimates represent the ices that are likeIy to be utilized by the Amernumbers of physicians that would satisfy speci- ican population in 1990, if particular conditions

fied levels of future economic demand for physi- and trends existing now are assumed to continue

cian services. on into the future.

To say that the BHM modeling effort is not The assumption that conditions and trends

goal-directed is to say that the model itself does that characterize the present and recent past will

not recommend or even assume that it is desir- continue on into the future is probably the single
Figure 17.—Consumer Satisfaction With Physician Services, by Nature of the Experience

Aspect of the visit

Time to travel to the

doctor’s office

Time between calling for

an appointment and the

appointment

Time waiting to see the

doctor

Among those who saw a physician in a 12-month period,

percentage dissatisfied with various aspects of the visit classified

by the nature of the experience.

Percentage dissatisfied

— While people generally

express satisfaction with

Less than 15 minutes 4“/ 0 most aspects of their medical

experiences, there comes a

15 to 30 minutes 1 30/0 time for most when they cross

a threshold of tolerance.

30 minutes to 1 hour 37 ”/0 At that point, satisfaction

turns to dissatisfaction.

More than 1 hour 350/o

Each variable—travel time,

cost, amount of time spent

with the doctor, appointment

waiting time—tras its

Up to 2 days 9 % special threshold. This chart

matches a range of

3 days to 2 weeks 2 1 % experiences with a set of

variables and shows levels

More than 2 weeks 43% of dissatisfaction. Overall, it

captures some sense of t he

— dynamics of the doctorUp to 30 minutes 13“/0

pat/en/ relationship.

30 minutes to 1 hour 59 ”/0

More than 1 hour 850/0

NOTE Because of the small number of people sampled in these categories, the true percents may Iikely vary by as much as 10 percent for this subgroup in the population Other figures in the table are unlikely to vary more than 3 points

SOURCE The Consumer Viewpoint “What is Health Care and What do We Want?” & A Response, prepared for the Graduate Medical Education National Advisory Committee, Washington, D C Health Resources Administration. DHEW publication No (HRA) 79632

most important assumption made by the BHM

model. This assumption might be characterized

as a sort of “law of societal inertia” which states

that the future is going to look alot like the present and that any major differences between the

present and the future are going to be the outcome of changes already underway that are observable as ongoing trends. The major problem

is that unforeseen events, developments, interventions, decisions, etc., are quite common and

are therefore quite likely to cause the future to

deviate from both present conditions and from

the outcome of currently ongoing trends.

Taken together, the uncertainty factors that

affect modeling efforts make it advisable to

view the results generated as “benchmark” estimates rather than hard predictions. Another

way of viewing these estimates is to think of

them as “if-then” numbers, as in “if Americans

continued to utilize medical services in the current per capita amounts, how many physicians

would be in demand in 1990?” It would also be

accurate to characterize the BHM projections as

providing a baseline or yardstick against which

the comparative size and impact of particular

sorts of changes—especially deliberate policy

interventions—can be measured.

The BHM model also) includes separate “contingency” estimates. These are intended to

gauge the probable impact on future demand for

physician services of major changes that appear

either likely or quite possible. Current contingency modeling efforts focus mainly on predicting the impact of alternative NHI plans. This

points out that the political process and its policy outcomes are among the major uncertainty

factors affecting predictive modeling. The fact

that the accuracy of current predictive modeling

efforts is highly dependent on the unknowable

outcomes of political decisions yet to be made
simply underscores the point made earlier that

these estimates should be viewed as “benchmark” or baseline estimates.

As a goal-directed modeling effort,

GMENAC’S most important core assumptions

are that reasonable estimates of appropriate

utilization of medical services and the numbers

and types of physicians needed to provide those

services can be derived from a combination of

empirical data and professional judgment concerning “medical need. ” The standard of medical need being applied to the determination of

appropriate utilization of services requires a

somewhat stronger presumption of linkage between medical service and improvements in

health outcome than a standard based on a volume of services provided, not because they are

expected to produce beneficial effects in most instances, but because in some instances they

might yield improvements and in the rest of

cases are believed to do no harm. The standard

being used would tend to be more conservative

in estimating the medical need for such marginally beneficial services, especially where the

medical problem or illness is a nonserious condition.

An illustration of how this theoretical difference translates into practice can be seen in the

deliberations of GMENAC’S dermatology panel

on the treatment of acne. In its first round, the

panel’s initial estimate of medical need assumed

that every case of acne should be seen by a dermatologist, and that a typical case would require six visits annually (which assumes medication and the need to monitor its effects). In

subsequent rounds of discussion, however, the

panel determined that this number was excessive and revised their initial estimate downward

to reflect a more conservative definition of need.

In lowering their original estimate of need for

dermatologists to treat acne, the panel took into

account such factors as the nonserious, selflimiting character of a high proportion of acne

cases, the fact that medical treatment is most

likely to produce improvement in severe cases

and, finally, the fact that nonserious cases of

acne can be treated as safely and efficaciously

by appropriately trained generalists as by specialists in dermatology.

As a goal-directed model more concerned

with defining what should be rather than forecasting what is likely to be, the GMENAC effort

need not be as concerned with the problems

posed by uncertainty factors as the BHM trend

projection and contingency models must be. It is

also relevant that the goals being formulated are

for the comparative near-term future. Thus,

GMENAC’S definitions of medical need are

based on the assumptions: 1) that Americans

will continue to have the same medical problems at the same demographic (e.g., age, sex,

social class) rates in 1990 as they do now, and 2)

that there will be no major breakthroughs in

medical knowledge or technology that will seriously alter current medical practice. While these

assumptions are probably reasonable for a period covering little over a decade, they might well

become more doubtful if the time span were expanded.

More problematic for a near-term, goal-directed model than the uncertainties of the future

are the reality constraints of the past and present. What we mean by this is that societies are

not like marching bands: policy makers cannot

blow the whistle and expect society (or one of its

major subunits such as the health system) to execute a 90-degree turn in formation. Yet in a

sense that is what efforts to establish and

achieve collective goals frequently assume can

be accomplished. Clearly, the more greatly a

goal-directed estimate differs from anticipated

supply and the shorter the time period available, the more we must implicitly assume that

90-degree societal pivots are possible; at least if

the goal is taken seriously as one we ought to try

to achieve.

A much more serious reality constraint is that

there may be insufficient “play” or “slack” in the

system to permit actual attainment of a physician “requirements” estimate that deviates drastically from the currently projected 1990 supply.

At issue is what policy researchers term the relative “malleability” of key variables. The possibility of attaining a goal within a given period of

time is dependent on the malleability of supply

factors. Supply factors, however, are not highly

malleable. The reason for this is, that, as 1990 is

only 10 years away and physician training re-
quires a long Ieadtime, most of the 1990 supply

is already locked in. And even though 40 percent of the physicians in practice in 1990 will

have completed training since 1979 (Jacoby,

1980), major changes in graduate medical education cannot be expected to take place and

have a significant impact on the specialty distribution of the 1990 physician supply. Perhaps future goal-oriented modeling efforts should pay

explicit attention to the relative “malleability”

of key variables. In so doing, they might provide alternative estimates, signaling, on the one

hand, goals that are capable of attainment within the allotted time span and, on the other hand,

goals that are considered desirable but would require a longer time frame and are thus best considered as signaling the appropriate direction for

deliberate change but not taken as immediate

targets.

So far we have discussed only one set of reality constraints (i.e., limitations on the malleability of the health manpower supply) that impinges on the feasibility of a goal-oriented

model of physician requirements. There are

other factors as well. One is the difference between the types of conditions that ought to be

seen by particular types of physicians and actual

patterns of physician use. For example, a nontrivial portion of the current caseload of generalist physicians is composed of nonserious, selflimiting conditions that medical treatment can

do little to cure or ameliorate (e.g., “colds”).

These cases would tend to be discounted based

strictly on medical need. There is little reason to

expect however, that, in reality, patients would

rapidly be reoriented to stop bringing such complaints to physicians or that physicians would

refuse to see patients with such complaints.

Thus, GMENAC’S normative model accounts

for some need to provide services to the “worried well, ” and the BHM estimates include present use of medical services by the “worried well”

in its projections.

The need to pay attention to both goals and

reality leads naturally to a consideration of the

complementarily of goal-driven and trend projection modeling. Because each of these two major modeling efforts is oriented toward different

purposes and focuses on rather different variables, they are, in truth, more complementary

than competing. As such, each model’s results

can aid our interpretation of the other’s.

The GMENAC model focuses on translating a

normative definition of medical need into appropriate rates of utilization of medical services,

while the BHM model looks on medical care as a

“consumer good” and treats empirical trends in

utilization of medical services as a proxy for

economic demand. If the BHM demand estimates should prove significantly greater than

the GMENAC estimates, this would suggest that

there are powerful factors at work that are

pushing utilization of medical services beyond

the level medically necessary and appropriate

for “good” care. This would then raise the

policy question of what percentage—if any—of

the projected future economic demand for medical services over and above the professional

judgment-based estimates of medical need

should be considered legitimate. Conversely, if

the BHM demand estimates should prove significantly less than the GMENAC estimates, this

would suggest that there remain and will remain

in the near future significant barriers to obtaining medically necessary care. Finally, if the

BHM and GMENAC demand estimates prove to

be in rough parity, this would suggest that the

economic demand for services is more or less in

line with professional estimates of the medical

need for physician services.

Obviously, since the GMENAC model has

yet to generate any numbers, it is impossible to

say at the present time which of these three

alternatives will prove to be the case. We can

say, however, that the most likely occurrence

would appear to be rough parity or a BHM demand estimate that is significantly greater than

the GMENAC aggregate estimate. The major

reason for anticipating that the BHM estimate

will most likely prove greater than or at least

equal to the GMENAC estimate is that one of

the major variables in the BHM model is a projected trend toward rising per capita utilization

rates for medical services, independent of demographic changes and projected changes in price.

In contrast, the GMENAC model assumes no

major changes in medical need apart from

changes in medical need induced by demograph-
ic shifts (e. g., an aging population), between

now and 1990; hence no medical rationale for

steadily rising per capita utilization of services.

Thus, in order for the GMENAC estimate to

logically come out larger than the BHM estimate, one would need to assume that there is

curretztly such a large unmet medical need for

services, that, despite the trend of rising per

capita utilization rates, assumed in the BHM

model, considerable unmet medical need will remain in 1990.

What is a reasonable estimate of requirements

from the BHM economic model which might approximate aggregate adjusted need from the

GMENAC modeling effort? Recall that BHM

now projects demand at approximately 600,000

physicians in 1990, or what the supply will be.

We saw (table 37) that this represented an increase of 217,841 over the 1975 figure of

378,376; 35,960 was due to an increasing and

changing population, and 181,881 due to projected increases in per capita utilization trends.

That is, without increasing per capita utilization, demand in 1990 would be roughly

415,000.

We also saw (figures 10 through 13) that the

large increase attributed to rising per capita

utilization would nearly disappear if pre-1970’s

data were deleted from the trend base. But we

would not want to discount this increase entirely for several reasons: 1) the possibility of NHI,

2) possible decreases in the average physician’s

PRODUCTIVITY

Both BHM’s and GMENAC’S modeling efforts emphasize the amount of medical services

that either will (based on predictions of trends)

or should (based on normative determinations

of medical need) be used in the future. However,

estimates of the number of physicians required

are derived by dividing projected use by physician productivity. With the exception of task

delegation to physicians’ assistants and nurse

practitioners, which would enhance physician

productivity and thereby reduce aggregate physician requirements, neither modeling effort explores possible changes in productivity and their

effects on requirements estimates. Rather, both

workweek, and 3) increasing the time physicians spend with patients.

The possible effects of an NHI program have

previously been summarized. Physicians currently average longer workweeks than most of

the rest of the working population. Bringing the

physician workweek more into line with the

present patterns of work productivity of the

labor force in general would lower productivity.

Alternatively, the cushion of excess physicians

might enable physicians to see fewer patients

and spend more time with each one. According

to the National Center for Health Statistics,

about half of all office visits to physicians in

both 1973 and 1977 lasted 10 minutes or less.

With smaller patient loads, doctors might be

able to use the additional time to provide patients with more information, education, and

counseling.

For these reasons, it is difficult to estimate

physician requirements. If one takes projected

population changes alone, requirements in this

model would be for 415,000 physicians. Some

contingency is necessary to account for such factors as NHI, decreased working hours for physicians, and more time spent with patients per

visit. How much of a contingency is necessary is

a matter of judgment, and the reader can come

to his or her own conclusion on what it should

be over and above the increase in requirements

due to population growth.

models basically assume that physician productivity will remain constant through 1990. The

BHM model does this by assuming that the ratio

of practicing physicians to total output of physician services will be the same in 1990 as it was in

197.s. In the case of GMENAC, the main effort

has been toward choosing the most reliable and

accurate measures of current productivity as reflected in various empirical studies, Its modeling

effort makes explicit assumptions about the

average workweek, patient visit rates, etc., by

each medical specialty.

But physician requirements estimates are

highly sensitive to changes in productivity
(Reinhardt, 1975). As an illustration of this sensitivity, if we were to postulate that the appropriate ratio of physicians-to-population in 1970

was 185 per 100,000 (the actual ratio in the bestendowed areas) and percent growth in productivity kept pace with percent growth in per capita use of physician services, then the physicianto-population ratio would remain constant indefinitely. However, as Reinhardt points out:

If in 1970 a set of policies could have been implemented such that average annual growth in

physician productivity during the following two

decades were one percentage point higher than

the annual growth in the per capita utilization of

physician services, then the required ratio at the

end of the forecast horizon would have been

only 151 physicians per 100,000. Relative to a

forecast based on maintenance of the base-year

ratio of 185 per 100,000 and for a population of

roughly 250 million in 1990, this turn of events

would have led to a reduction of about 85,000 in

the number of MDs that would otherwise have

been “required.” The corresponding number for

1980, based on a projected population of 225

million, is 40,500. These figures must surely

strike one as significant, especially if held up

against the annual number of medical graduates

(between 15,000 and 16,000) likely to be produced during the next several decades (Reinhardt, 1975).

Reinhardt’s analysis was primarily concerned

with the impact of substantial gains in productivity that might occur as a result of organizational changes in physicians’ practices and of

task delegation to nurse practitioners and physicians’ assistants. His research also provides

some data suggestive of a possible relationship

between growth in physician supply and decreases in physician productivity. Table 46 (reproduced from Reinhardt, 1975) provides some

data on relationships between physician supply,

physician productivity, and financial factors

such as average visit fee and average annual

physician income. If physician supply increases

(item #l) but demand for services (per capita

use) remains constant (item W), then productivity (measured in patient visits per MD) will

drop (item #3). The data suggest that physicians

then may charge more per visit (item #7),

though not necessarily enough for physicians’

incomes to reach the same level as in areas with

fewer physicians and/or greater demand for

services (item #8).

A different approach to the question of physician productivity and its relationship to requirements estimates is to examine trends in physician productivity. According to Medical Economics magazine’s Continuing Survey, comparing workweeks in 1965 versus 1976, office-based

physicians spent 2 hours less with office patients, 2 hours less on housecalls, and 1 hour less

on hospital rounds and consultations in 1976.

Median time spent on all professional activities

(including activities other than patient care) in a

typical workweek fell from 64 hours in 1965 to

60 hours in 1976 (Owens, 1977).

More recently, Medical Economics reported

that the number of office visits has continued to

decline. Over the period 1974-78, office-based

physicians were seeing 8 fewer patients per week

in 1978 as compared to 1974, a median weekly

number of 126 rather than 134 (Owens, 1979).

Generally, two hypotheses are given to explain recent decreases in productivity and to

predict further decreases. One hypothesis is that

physicians, as most other Americans, would

prefer to work less and enjoy more leisure time.

The other hypothesis is if growth in physician

supply outpaces growth in the demand for physician services, then declines in physician productivity may occur as a means of bringing supply and demand into balance.

In the latest survey (Owens, 1979) 57 percent

of the office-based physicians stated that they

believed they were practicing at full capacity.

Of this 57 percent, a minority (amounting to 12

percent of the entire sample of physicians surveyed) said they would prefer not to practice at

peak productivity. Of those surveyed 43 percent

said that they were not practicing at peak productivity. Of these, a majority said they did not

want to practice at full capacity. Of the entire

sample 18 percent, however, stated that they

were not practicing at full capacity but would

prefer to do so. Among the specialties, 31 percent of urologists, 24 percent of general surgeons, and 24 percent of otolaryngologists said

that they were not practicing at peak productivity and would prefer to do so.
Table 46.—Regional Differences in Certain Health-Care Statistics, United States, 1969-70

—

Item No.

East-North

Year New England

1970

1969

1969

1969

1969

1969

1969

1967

1969

1969

1970

1969

1970

1968

1970

7.7

5.4

4.4

19.2

31.8

$4,469.00

Central

115

(0.71 )

2,495

2.151

6.611

(1 .38)

4,799

(1 .42)

2.65

3.07

1.8

17.4 0/0

$6.29

8.05

6.94

7.76

9.32

$47,000.00

7.6

5.5

4,0

19,4

35.4

$4,306.00

East-South

Central

95

(0.59)

2,568

2,303

8,408

(1 ,75)

6.052

(1 .79)

3.27

3.65

2.1

19.4 0/0

$5.21

7.20

5.40

6.85

7.60

$41,963.00

8.0

5.8

4.1

20.9

40.5

$3,146.00

According to Medical Economics, the finding

that almost one-fifth of the physicians surveyed

felt that they were not practicing at full capacity

but would prefer to do so “suggests that maldistribution of medical manpower plus the growing number of new doctors—nearly 35,000 have

joined the ranks of office-based MDs over the

past 5 years—may already have left physicians

short of patients in some areas” (Owens, 1979).

In sum, the available evidence suggests that

both physician preferences and the increasing

number of physicians are contributing to declining productivity. Yet, some physicians still feel

overworked, which suggests that maldistribution remains.

Decreased physician productivity, it is important to note, is not necessarily undesirable. If a

physician is practicing in an underserved area,

then high productivity is likely to reflect overwork. Under these conditions of chronic overwork, decreased productivity would probably

represent increased quality. As the physician

supply increases and unmet demand slackens,

then decreases in productivity would, at some

point, begin to represent not better quality care,

but inefficiency. Table 47 summarizes one effort

to quantify this relationship for primary care

physicians’ services (Walker and Armondino,

1977). Additional research that would increase

our understanding of this relationship would be

important because of the cost implications.

The possibility of further decreases in physician productivity has important, though largely

unexplored, implications for the problem of the
Table 47.—Shortage, Adequate, and Surplus Levels of Primary Care’ Physicians

Average primary

care visits

per hour

6

4

2

Criteria

designation

Shortage -

Adequate

Surplus

SOURCE J E C Walker and N L Armondlno. The Prtrnarv Care Physm/an Issues (n D/sfr/bul/on, Lawrence, Kan Connectlc U! Health Services Research Series, No 7,

1977

locational maldistribution of physicians. Arguments about the likely effects on physician

shortage areas of increasing the aggregate supply of physicians have tended to focus on two

alternative hypotheses. One hypothesis is that,

ultimately, the law of supply and demand will

force physicians to move into what are currently shortage areas as long as growth in supply

outpaces growth in demand for services in areas

that already have high physician-to-population

ratios. The alternative hypothesis is that physicians have the capability to generate demand for

their services, and, if exercised to any significant

degree, this capability would decrease the pressure on physicians to move out into less attracLOCATIONAL

The application

GMENAC models

REQUIREMENTS

of both the BHM and

has relevance primarily at

the national level. Shortages will always remain

in specific service areas no matter how “correct”

the balance between national supply and requirements are and even if supply exceeded requirements substantially. Yet locational estimates must be made at the national level: 1) to

plan for the National Health Service Corps

(NHSC) to meet some part of this requirement

and 2) to provide guidelines and eligibility criteria for Health Manpower Shortage Area

(HMSA) designations. Consequently, estimates

of the requirements for physicians are used to

determine need and serve as the starting point

for shortage area designations, augmented by

other criteria that represent barriers between the

physician and the population he/she is expected

to serve.

tive areas as aggregate physician supply increased. Whatever the case, enormous increases

in the aggregate physician supply cannot be

assumed to guarantee that an eventual solution

to the problem of locational shortages will

“naturally” occur. An attractive metropolitan

area where the physician-to-population ratio is

high enough to satisfy the highest reasonable

levels of medical need or consumer demand for

care can nevertheless continue to absorb many

additional physicians if individual productivity

decreases. Otherwise stated, it is quite likely

that we could have a large “oversupply” of

physicians in the aggregate in future years and

still have shortages in particular locations.

We

tional

have seen that future supply for locadistribution is estimated in similar

fashion as national supply. Subtraction of the

estimated supply from estimated requirements

equals total unmet need.

Need in 1990 has been estimated at 16,400 primary care physicians and psychiatrists

(USDHEW, 1979d). Primary care is defined as

non-Federal MDs and DOS providing direct patient care who practice principally in general or

family practice, general internal medicine, general pediatrics, and obstetrics-gynecology. At

current budget levels, NHSC scholarship recipients now in the pipeline will result in 3,950

NHSC physicians in the field by 1990. Through

NHSC scholarships and combining 1,150 physicians expected to volunteer with 900 midlevel

practitioners (assumed to each equal 0.5 physi-
cians), 34 percent of need will be met. Assuming

a 10-percent conversion rate from NHSC to private practice in underserved areas, 1,000 physicians, or 6 percent of need, will be met. Finally,

assuming current levels of 2,000 physicians in

federally funded health centers, another 12 percent of need will be met in 1990. Together, these

sources are expected to provide 52 percent of the

projected need of 16,400 in 1990. These projections are summarized in table 48.

“Need” as defined for purposes of projecting

future HMSAS should not be confused with the

need for physicians based on estimates of a

given population’s economic demand or medical

need for services, as described in the analysis of

the BHM and GMENAC modeling efforts. In

the case of shortage area projections, two physician-to-population ratios are used as criteria to

determine the level of need for primary care

physicians in an area:

●

●

Designation ratio. —The actual minimum

ratio of active, non-Federal, patient care

physicians engaged in primary care to the

civilian population of an area below which

an area is considered to have a shortage of

health manpower sufficient to justify its

being counted as a shortage area in the

model.

Staffing ratio. —The theoretical maximum

ratio of active non-Federal, patient care

physicians engaged in primary care to the

civilian population of an area used as a

standard above which an area is considered

to have adequate health manpower so that

additional Federal intervention with NHSC

staffing is no longer necessary (USDHEW,

1978a).

“Need” is the number of physicians required

to reach the staffing ratio for all designated

areas. The designation ratio is based on equity

considerations and reflects that quarter of the

United States having the least number of primary care physicians. It has been set at 1:3,500.

The staffing ratio establishes a limitation upon

the extent of Federal involvement and specifies

the relationship between the service demands of

the population and the primary care physicians

available to provide those services. It has been

set at 1:2,000.

The designation ratio of 1:3,500 means that

areas with smalIer ratios would not be included,

including areas with ratios between 1:3,500 and

1:2,000.

However, because criteria for making HMSA

designations were expanded under the 1976 Act

to include, in addition to manpower-to-population ratios, other indicators of need such as infant mortality rates, access to health services,

health status, income level, and the number of

foreign medical graduates practicing in the area,

the method for projecting future shortage areas

and their physician needs has been adjusted in

the following way:

Comparison of projected areas with actually

designated areas showed that the projection

model missed part-county areas designated upon

factors other than strict physician to population

ratios. The physician to population ratios, strictly determined, fell within a range from 1:2,000

to 1:3,500. Therefore, the unmet need for counties with ratios between 1:2,000 to 1:3,500 is

used as a proxy for part-county rural areas.

(USDHIW, 1978a).

Table 48.—Need for Primary Care’ Physicians and Psychiatrists in 1990

Current NHSC Conversion from NHSC ‘Current level of

Total schoiarshl p to private practice in physicians [n federally Unmet

need recipients Volunteers u nderserved areas funded centers need

16,40C 3,950 –

1,600b- 1,000 2,000 7,850

1 000/0 4 8.550 (520/o) - 480/0

aGeneral &d family Practice gen~-ral Internal m-edlclne, general pedlatrlcs, and obs(itrlcs-gynec”ology

blrlc[Udes 1300 mldlevel pract[t loners, each equal to O 5 physlclan

SOURCE Outyear Stze of the National Health Service Corps (N HSC)– DECISION MEMORANDUM. from the Assistant S@C.

retary for Health and the Acting Assistant Secretary for Plan nlng and Evaluation to the Secretary for Plan nlng and

Evaluation to the Secretary, Washington. D C , Spring 1979
The result is that, taking the year 1980, unmet

need from reducing the ratio from 1 :3,500 (or

more) to 1:2,000 would be 5,659 primary care

physicians, with an additional 3,o37 from the

proxy measure for part-county rural areas.

These designation and staffing ratios are applied to metropolitan and nonmetropolitan

areas. The staffing criteria for correctional institutions were partly based on the needs identified

by the Federal Bureau of Prison’s Medical Director’s office. DHHS’S Alcohol, Drug Abuse, and

Mental Health Administration provided the 600

workload unit estimates and the 1:20,000-

30,000 ratio. The Indian Health Service estimates were based on an expected increase for

primary care and psychiatric physician needs of

3 percent yearly. These criteria are summarized

in table 49.

These designation and staffing ratios were

used to arrive at the estimated need in 1990 for

16,400 primary care and psychiatric physicians.

These projections have been used to plan for

future staffing of NHSC. The great majority of

future NHSC physicians will come from medical

school scholarship recipients obligated to serve

year-for-year in the Corps. The emphasis is

therefore on recruiting first-year medical students, as the total obligation will be 4 years.

Table 49.—Criteria for Unmet

Need Calculation by Area

Designation Staffing

Area ratio ratio

Nonmetropolitan . . . . . . . . . 1 :3,500 1 :2,00 0

Metropolitan. . . . . . . . . . . . . 1 :3,500 1 :2,000

Correctional institutions

Primary care . . . . . . . . . . . 1:1 ,000 1 :500

Psychiatry. . . . . . . . . . . . . 1 :2,000 1:1 ,000

State mental hospitals

(psychiatrists). . . . . . . . . . 600 workload 600 workload

unitsa/FTE units/FTE

Community mental health

centers (psychiatrists). . . 1 :30,000 1 :30,000

Indian Health Service . . . . . all officially 3-percent

recognized yearly

tribes increase

aTotal workload units ‘= average daily !npatlent census + 2X (number o-f lnPa

tlent admissions per year) + O 5x (number of admissions to day care and out

patient services per year)

SOURCES Memorandum from the Chairman, NHSC Needs Task Force A, to the

Director, Bureau of Community Health Services, Health Services

Admlnlstratlon, the Deputy Director, Bureau of Health Manpower

Health Resources Admlnlstratlon, and the Chairman, NHSC Needs

Task Force, Washington, D C , May 26, 1978, and 42 CFR sec 5

Currently, an option has been adopted whereby

the Corps will consist of 8,300 physicians and

2,800 physician extenders in 1990, with the

understanding that the target could be readjusted to 15,000 physicians and 2,800 physicians’ assistants and nurse practitioners after a

3-year study. The latter would meet almost all

projected need, but not until 1995. The 8,300-

physician target would require about 13 percent

of each medical school class through 1983. The

15,000 physician target would require recruiting

25 percent of each class by 1986 (USDHEW,

1979d).

The primary care physician-to-population

designation ratio of 1 to 3,500 is employed as a

major criterion in the process of determining

whether a particular area qualifies for official

designation as an HMSA and thus eligible for

NHSC placements and other aid. It is not, however, the only criterion employed, and areas

with lower physician-to-population ratios may

qualify for designation under certain conditions.

This is best illustrated by the criteria for

geographic areas. A sample of the specific methodologies for meeting these criteria should illustrate the point. Detailed criteria can be found in

the Code of Federal Regulations, title 42, section

5, appendix A.

Three criteria must be met for designation:

1.

2.

3.

The area is a rational area for the delivery

of primary medical care services.

One of the following conditions prevails

within the area:

(the area has a population-to-primary

care physician ratio of at least 3,500:1,

(The area has a population-to-primary

care physician ratio of less than 3,500:1

but greater than 3,000:1 and has either

unusually high needs for primary medical care services or insufficient capacity

of existing primary care providers.

Primary medical care manpower in contiguous areas is overutilized, excessively

distant, or inaccessible to the population

of the area under consideration (42 CFR

sec. 5, app. A).
Ch. 3–Requirements ● 8 3

“Rational area for the delivery of primary

care” includes:

i. A county, or a group of contiguous counties whose population centers are within

30 minutes travel time of each other.

ii. A portion of a county, or an area made up

of portions of more than one county,

whose population, because of topography, market or transportation patterns,

distinctive population characteristics, or

other factors, has limited access to contiguous area resources, as measured generally by a travel time greater than 30 minutes

to such resources (42 CFR sec. 5, app. A).

“Insufficient capacity of existing primary care

providers” will be met if at least two of the

following criteria are documented:

a. More than 8,000 office or outpatient visits

per year per FTE primary care physician

serving the area.

b. Unusually long waits for appointments for

routine medical services (i. e., more than 7

days for established patients and 14 days

for new patients).

c. Excessive average waiting time at primary

care providers (longer than 1 hour where

patients have appointments or 2 hours

where patients are treated on a first-come,

first-served basis).

d. Evidence of excessive use of emergency

e.

f.

room facilities for routine primary care.

A substantial portion (two-thirds or more)

of the area’s physicians do not accept new

patients.

Abnormally low utilization of health services, as indicated by an average of 2.0 or

less visits per year on the part of the area’s

population (42 CFR sec. 5, app. A).

Several points should be noted in comparing

the actual criteria used for HMSA designation

with the methods and assumptions used to model and project the number of shortage areas anticipated in future years. First, the model for

predicting future shortages uses the county as

the geographic base to calculate physician-topopulation ratios. The HMSA designation process uses a “rational service area” as the geographic base. A “rational service area, ” as we

have seen from the regulations, could be a county or it could be an area larger or smaller than a

county. In sum, the definition of a “rational

service area” contains considerable flexibility to

permit responsiveness to local conditions in

making the actual HMSA designations.

Additional flexibility to respond to local conditions is introduced by permitting areas to

qualify as HMSAS if they have a physician-topopulation ratio lower than 1:3, 500 but greater

than 1:3,000 as long as they can show either unusually high needs for primary medical care

services or insufficient capacity of existing primary care providers.

One final factor that differentiates the methods used in projection of future shortage areas

from those used in the actual official designation

process is that, in order for an area to actually

receive official designation as an HMSA, a request for designation must come from the local

level. Thus, a request for HMSA designation

serves as a preliminary indicator that there is interest at the local level in obtaining NHSC

physicians, However, remember that HMSA

designation is necessary not only for assignment

of NHSC physicians, but also that such designated areas: would be areas in which students

who borrowed money under health professions

student loan programs could practice in lieu of

repaying the loans in money; would be eligible

for grants in various health manpower training

programs; would be eligible or given preference

for grant funds for several Bureau of Community Health Services programs such as the urban

and rural health initiatives; and would be the

only areas in which rural health clinics could be

certified for reimbursement of nurse practitioner

and physicians’ assistant services under Medicare and Medicaid. So HMSA designation does

not necessarily mean that NHSC physicians will

be recruited to provide services in these areas.

The model had predicted a need for 14,033

primary care physicians in 1979, 8,839 in nonmetropolitan areas, and 5,194 in metropolitan

areas (USDHEW, 1978a). The actual number of

HMSAS designated in 1979 was 1,711, with a

totaI primary care physician need of 11,336. Of

the HMSAS, 1,226 were in nonmetropolitan
84 ● Forecasts of Physician Supply and Requirements

areas, with a need for 5,368 physicians, and 485

were in metropolitan areas, with a need for

5,968 physicians (Reid, 1980). Thus, there was

an overestimate of physician need in nonmetropolitan areas and an underestimate of need in

metropolitan areass. However, a request for

designation must come from the local level, so

the difference between the predicted need for

14,033 primary care physicians and the actual

need for 11,336 is not surprising.

How well do the HMSA designation and

staffing criteria relate to the use of primary care

physician services? Some physician capacity utilization surveys have recently been made available. Salient findings from the surveys are:

The analysis also examined the influences of

exogenous factors on practice characteristics of

HEW-designated physician shortage area counties—defined primarily in terms of physicianpopulation ratios. In practical terms, none of the

fully or partially designated shortage counties

studied gave evidence of excess demand in the

traditional economic sense, Physicians’ office

hours in these shortage areas were about the

same as those of physicians in nonshortage

areas. Nor were patient waiting times, for appointment and in the office, significantly different from those in nonshortage areas. However, a decreasing waiting time to appointment

as the supply of general practice physicians increases is some indication of excess demand in

shortage areas. It was found, in fact, that shortage area physicians had slightly fewer patient

visits than physicians at large. (This result is

substantiated by a recent study of the Health

Service Administration that reported similar

observations of productivity from NHSC physicians placed in shortage areas. ) A control for

population density (used as a proxy for travel

distance to see a physician) made little difference

in these results (reference and footnotes omitted)

(USDHEW, 1978b).

The NHSC experience, however, could be reversed over time. Table 50 disaggregates physician encounters/physician by: 1) self-support

ratio categories, 2) initial staffing years, and 3)

sites with and without midlevel practitioners.

Bearing in mind that the NHSC experience was

only 5 years old at the time of the study, the

table shows that: 1) patronage of NHSC sites

builds over time, patient demand being positively and significantly related to the number of

years the sites have been in operation, 2) the

more mature sites tended to have higher productivity per provider, and 3) sites that were approaching the capability to be financially selfsupporting showed higher productivity levels

per provider.

Table 50.—Physician Encounters per Physician and Physician and Physician Encounters

per Physician Hour by Selected Cohorts, National Health Service Corps (FY 1976)

Self- supporta ratio categories

—— —..—. —— .—

Initial staffing year

Sample

sites 1 2 3 1972 1973 1974 1975 1976

Physician encounters/physician 4,664 7,092 4,568 3,524 6,144 5,164 5,140 3,912 2,804

(all sites). . . . . . . . . . . . . . . . . . (130) (30) (52) (48) (25) (25) (27) (64) (2)

Physician encounters/physician 4,428 6,420 4,048 3,440 5,544 5,780 5,392 3,792 2,804

(sites with no PEsb). . . . . . . . . (94) (23) (40) (31) (12) (6) (18) (56) (2)

Physician encounters/physician 5,272 9,304 6,296 2,888 6,700 4,420 4,644 4,772 —

(sites with PEs) . . . . . . . . . . . . (36) (7) (12) (17) (13) (5) (9) (8) –

Physician encounters/

physician hour. . . . . . . . . . . . . 1.8 2.8 2.1

(all sites). . . . . . . . . . . . . . . . . . (:3;) ; ; ) & (48) ;“: ) (11) ( & ) (%:) (2)

Physician encounters/

physician hour. . . . . . . . . . . . . 2.6 5.2 2.9 1.4 3.3 2.6 2.8 1.1 —

(sites with PEs) . . . . . . . . . . . . (36) (7) (12) (17) (13) (5) (9) (8) –

Physicians encounters/

physician hour. . . . . . . . . . . . . 2.2 2.1 2.0 2.7 2.9 2.4 1.0 2.0

(sites with no PEs). . . . . . . . . . (94) (%) (40) (31) (12) (6) (18) (56) (2)

ase~f.support ratios measure thi relation between the total revenues from all so;-;ces and total costs experienced at sites al a (Jlven time ‘Category 1 Sites are the most

self.supporting, dlmlnlshlng to category 3

bphyslclan extenders, or mldlevel PraCtltlOnerS

SOURCE f-l L Heaton, et al Comparaf/ve Cosf and Flnanc/af Analysts of Ambulatory Care Provders, GEOMET Inc report No HF360, DHEW contract No HSA.74.68,

1976
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