What are the policy options for regulating private equity involvement in health care? A review of policies implemented or considered in seven high-income countries
Bibliographic record
Abstract
Over the past two decades, private equity investment in health care has increased substantially. Proponents argue that private equity can optimize and improve health services, while critics warn that the business model of these firms is not aligned with the social values of care delivery and has harmful consequences for health systems and patients. It remains unclear to what extent - and how - subnational, national and supranational governments have attempted to regulate this activity. The purpose of this study therefore was to identify examples of implemented and proposed policy options for regulating private equity activity within health care, with the goal of elucidating the policy options available to regulators. We conducted a narrative review to identify proposed or implemented policy instruments in selected high-income countries, grouping them by type using a conceptual framework based on the works of Milton Friedman and Avedis Donabedian. Our search identified several examples of proposed or implemented policy options for addressing private equity activity in the countries under review. Most of these intervention examples fall into the category of disclosure, while only one focused on regulation of outcomes. Our study suggests that while some countries have started to develop policy interventions to directly address the role of private equity in health care, other countries do not specifically regulate private equity activity.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".