Exploring the business of urology: Is it time for a “Business of Healthcare” curriculum in urology residency programs?
Bibliographic record
Abstract
rology is a relatively small surgical specialty filled with innovative and forward-thinking physicians.Decade after decade, through early adoption of new technologies, urologists have been at the cutting edge of advances in medical and surgical care.Urologists are generally very well-prepared for the rapidly evolving improvements medical and surgical technology.But are graduating urologists prepared for the business side of their practices and careers?After completion of postgraduate training and upon entry into the workforce, are most urologists prepared for the multiple roles and demands that will be placed upon them in today's dynamic healthcare environment?Does residency provide adequate education and evalution of basic business topics and principles that are critical to achieving high performance in one's job as a urologist?Do we adequately train urologists to manage all aspects of the operations of their own practice, including the negotiation and legal execution of employment agreements and contracts?Are urologists adequately prepared to navigate the Canadian and U.S. healthcare landscapes and trends in the delivery of care?Unfortunately, the answer to these questions for most young urologists is, "No."Healthcare costs around the world have become unsustainable.Healthcare spending accounted for an average of 9.0% of gross domestic product (GDP) across the Organization for Economic Co-operation and Development (OECD) countries in 2016, and an astonishing 17.2% of GDP in the U.S. 1 In Canada, healthcare accounts for the largest percentage of provincial and territorial budgets. 2 Today, most Canadians recognize that our provincial and territorial healthcare systems are not financially sustainable.In the business world and in healthcare, value is defined as quality divided by cost.3 Therefore, in order to deliver the greatest possible healthcare value to North Americans, our overriding goals need to be focused on increasing quality of care while reducing the cost of delivering that very same high-quality care.In order to achieve these goals, North American healthcare systems are in need of future physican healthcare leaders who are properly educated in
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".