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Record W2055999055 · doi:10.1001/jama.298.15.1797

Is P4P Really FFS?

2007· article· en· W2055999055 on OpenAlexaffabout
Walter P. Wodchis, Joseph S. Ross, Allan S. Detsky

Bibliographic record

VenueJAMA · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

IT IS COMMON FOR SOCIETAL TRENDS AND PREFERENCES TO be recycled. Men’s neckties have varied from thin to wide and back to thin again. Government policies have favored individual responsibility, then social responsibility, and again individual responsibility. In health care, policy makers have swung between concern about access and concern about cost, attempting to increase or decrease medical care supply in response. Until 1980, there was widespread belief in a fairly severe physician shortage in Canada and the United States. By 1990, it was thought that too many physicians were performing too many services. Now, in the 2000s, physician shortages are again the topic of discussion. In the 1960s and 1970s, health policy makers were concerned about access and institutionalized a system that expanded insurance for patients (thus separating them from the true costs of care) and organized a reimbursement system based on fee for service (FFS) and costs incurred. As access expanded, so did health care expenditures. By the 1980s and early 1990s, health care costs in Canada and the United States were thought to be too high. The view that supply created its own demand was common and best epitomized by Roemer’s law: hospital bed utilization reflects supply. In his article on “protecting the commons,” Hiatt criticized the FFS model as having significant negative externalities for society, providing too much health care at the expense of other social services. A solution was to pay physicians and hospitals a flat amount to provide care through prospective case payments, capitation, or global budgets rather than per service. The 1990s were filled with reforms that used 2 mechanisms to correct the problem of rising health care costs and “protecting the commons”: (1) managed care, the insertion of third-party management tools that included incentives for reducing care and (2) competition between health care systems and “plans” for patients on the basis of cost and quality. Together these ideas were known as “managed competition,” an approach originally proposed by Enthoven. The objectives of managed competition, capitation, and global budgets (supply-side constraints) were to reduce unnecessary care without reducing necessary or appropriate care. In managed care, health care administrators would be able to control which patients received which types of care to increase the proportion of care that was deemed appropriate. Theoretically, managed competition made sense, but it quickly fell out of favor in many circles, particularly among the public, precisely because it was successful at achieving its goal; ie, it effectively reduced the provision of care and kept health care costs from continuing to escalate. Restricting only unnecessary care proved to be quite difficult. The RAND Health Insurance Experiment provided an analogous example of the relationship between (consumer) cost sharing (a demand-side intervention) and appropriateness. Increased patient co-payments reduced all health care service use, not just ineffective or unnecessary utilization. It seems that both supply-side and demand-side attempts to control costs provide financial barriers that limit both necessary and unnecessary treatment. Now, as the pendulum appears poised to swing away from cost concerns and back toward improving access, the Institute of Medicine’s Quality Chasm and Rewarding Performance reports have broadened priorities, also focusing attention on improving quality. To improve both quality and access, a new idea has been promoted: pay for performance (P4P).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.003

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 head0.063
GPT teacher head0.293
Teacher spread0.231 · 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.

Study designNot applicable
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".

Quick stats

Citations27
Published2007
Admission routes2
Has abstractyes

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