A literature review on the impact of concierge medicine services on individual healthcare
Bibliographic record
Abstract
The concept of concierge medicine, established in 1996, stands out for its focus on enhancing accessibility to healthcare providers and customized medical services. It is centered on key principles such as giving priority to quality and individualized care, managing a smaller group of patients to ensure improved service accessibility, and nurturing enhanced continuity in personalized treatment. This review explores various aspects of how concierge medicine impacts healthcare, encompassing areas such as patient satisfaction and involvement, health outcomes, preventive care, healthcare expenses, and ethical and legal considerations. While the affirmative influence of concierge medicine on individual healthcare has been evidenced in terms of patient contentment, active patient participation, preventive care, and early identification of illnesses, there remains a dearth of research data to firmly establish the correlation between concierge medicine and health-related outcomes. Moreover, comprehensive longitudinal studies focusing specifically on the economic and policy implications of concierge medicine are currently lacking. Therefore, further research, particularly in the domain of health economics, is crucial to comprehensively comprehend the implications of this approach. Similarly, there is a necessity for studies that can conduct a comparative analysis between the concierge medicine model and traditional healthcare models, aiming to draw more robust and definitive conclusions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".