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Enregistrement W2331801689 · doi:10.1097/01720610-201007000-00001

Physician assistants, economics, and workforce modeling

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

Notice bibliographique

RevueJAAPA · 2010
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWorkforceHealth careCounterintuitiveSupply and demandWorkforce planningPer capitaQuality (philosophy)Public relationsBusinessEconomicsMedicinePolitical scienceEconomic growthPopulationEnvironmental health

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,840
Score d'incertitude au seuil0,464

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,052
Tête enseignante GPT0,256
Écart entre enseignants0,204 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2010
Routes d'admission1
Résumé présentoui

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