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Physician assistants, economics, and workforce modeling

2010· article· en· W2331801689 on OpenAlexaboutno aff
Roderick S. Hooker

Bibliographic record

VenueJAAPA · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceHealth careCounterintuitiveSupply and demandWorkforce planningPer capitaQuality (philosophy)Public relationsBusinessEconomicsMedicinePolitical scienceEconomic growthPopulationEnvironmental health

Abstract

fetched live from OpenAlex

FigureIf researchers were asked to design a health care system for the United States, what would it look like? Would it be more market driven or have a central authority that governs how many doctors, nurses, and physician assistants per capita should be distributed throughout the country? Economists are the proposed architects because they focus on organizational efficiencies and are not likely to overlook important elements of safety and quality. Statisticians and actuaries would be important players in this futurist research because they use complex models to make predictions. Mathematical models have been used by a host of industries for decades. Microsimulation managers examine all of the elements of supply and demand, then introduce “what if” scenarios to look downstream. Current health care modeling can be nationwide or refined to states, regions, and townships. Such systematic planning exists in most industrialized countries. Some emerging US medical workforce modeling attempts to address this shortcoming. More than 25 medical and surgical specialties have developed workforce predictions that are based on current and emerging trends. Many specialties predict doctor shortages and mention PAs and nurse practitioners (NPs) as important components to meet this demand. Although all forecasts are subject to some degree of uncertainty, exceptions do exist. For example, knowing the birth rate trend provides a fair amount of confidence in the predicted demand for obstetricians and pediatricians for a couple of decades. The US health care system is not centrally composed but relies on the market and generally avoids impinging on states’ rights. No Bureau of Health Professions determines how many medical schools should be in Utah or Missouri. Instead, a national composite of 45% public and 55% privately funded universities produce medical graduates without federal planning. This is in contrast to Canada, Australia, and Great Britain, which have central health-resource planning in various degrees and a predominance of public universities. In highly socialized systems, such as in France and Sweden, the central government oversees the welfare of the people in toto and ensures a defined ratio of doctors to population. Less socialized systems tend to let the invisible hand of markets reign. An engaging debate among workforce analysts is speculating what the US health care system would look like if it were more centrally controlled. For example, what if a national body had discretionary power to decide optimal health care staffing for the good of all? This exercise uses the best information available but with no influence from doctors, PAs, nurses, hospitals, or other interested parties. Politicians and industry leaders would not have a say either. Health services researchers, using comparative effectiveness research, would create the system. The analogy is similar to NASA engineers, instead of pilots, deciding which vehicle can get to the moon most effectively. Although many analysts favor PAs, an optimal ratio of PAs to population has not been determined. The ratio topic is relevant because almost every medical and surgical specialty is facing shortages, and the maldistribution of physicians is worsening. Some medical workforce analysts believe the current expansions of medical schools will eventually meet this demand.1 Others point to 75-year-old evidence that shows that the economic well-being of American society drives the demand for medical services, and supply continues to lag behind. Cooper maintains meeting this demand will require more doctors, PAs, and NPs.2 “Growing supply to meet demand” versus “demand outstripping supply” is a critical debate. Many ongoing economic studies focus on gathering data on doctors to answer these questions; however, more information is needed to include PAs in the equation. Tabletop modeling of the health care needs of the nation requires critical bits of information-and not all of that information is available. A call for PA modeling could not be timelier. Health care reform legislates a federal Medical Workforce Commission to look at provider supply and demand. The PA profession is included because of their part in the national discussion about optimal health care delivery. But, to be more than bit players, the profession must identify accurate annual replacement rates, career spans, retirement patterns, market influences, and shifts in social behavior for PAs. How this sophisticated labor tally is accomplished requires moving beyond the traditional annual census. In addition to a periodic snapshot of the PA profession, a longitudinal cohort is required. Following an anonymous but representative sample of PAs throughout their careers, including refined information about choice and behavior, is needed. The richness of such data can significantly complement the yearly survey and give researchers the granular understanding of PA choice, career mobility, and role delineation needed to shape health care delivery for the future. Such data, when mined properly, is knowledge translation for the benefit of all.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.256
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2010
Admission routes1
Has abstractyes

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