What Is Different About the Market for Health Care?
Bibliographic record
Abstract
ECONOMICS IS A SOCIAL SCIENCE THAT DESCRIBES HOW society produces and consumes goods and services. It is concerned with both efficiency (getting the most out of a fixed amount of resources) and distribution (who gains and who loses). The use of free markets as a mechanism of promoting efficiency has been touted by the majority of economists since the time of Adam Smith. His theory was that in free markets each individual, pursuing his or her own self-interests, will be led, as if by some “invisible hand,” to produce an allocation that maximizes society’s utility. However, certain conditions are required for free markets to result in efficient allocations. First among these is perfect competition, ie, a market in which no individual buyer or seller can affect the price by his or her actions. Without this condition, structural imperfections or market distortions arise and result in inefficient allocations. For example, the oil market is distorted because the Organization of the Petroleum Exporting Countries (OPEC) has exerted influence on the price of oil by restricting its supply since the 1970s. The market for health care contains many distortions, including asymmetry of information between clinicians and patients; clinicians’ dual roles as patient agent and independent business owner (profiting by ordering or providing certain medical services); the effect of insurance in reducing the apparent cost of health care services to patients (since the full cost of health care is not charged to patients, also known as the “price-wedge” distortion); tax subsidies that have a similar effect on consumers’ decisions to purchase insurance; and monopoly power bestowed on certain professions and, in some countries, health insurance plans, thereby limiting competition. Over the last 40 years, health economists have devoted considerable attention to the impact of these distortions on the market for health care. Feldstein was one of the first to write about the distorting effect of health insurance tax subsidies on market efficiency. Detsky described asymmetry of information and impact of clinician as agent. Arrow noted that when markets fail, public policies are often derived to ameliorate the consequences. In this Commentary we review 3 classic efforts to describe, to quantify, and to propose solutions to market distortions in health care. These efforts framed policy initiatives over the last 40 years and remain relevant to health policy debates today.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".