AN INVESTIGATION OF THE IMPACT OF MEDICAL TECHNOLOGY ON PHYSICIAN SERVICE EXPENDITURES By
Bibliographic record
Abstract
1 Innovations in technology and subsequent changes in clinical practice have often led to increases in healthcare costs. The objective of this paper is to assess the role of health technology intensity and adoption in the evolution of average health expenditures on physician services as well as on the changes in the distribution of expenditures by age and sex. We used patient-level administrative data on physician service expenditures in the Canadian Province of Ontario for the years 1 994 to 2004. The data set provides information about diagnoses, treatments, and payments to physicians with corresponding service dates, according to patient age and sex. We developed an algorithm to classify services into three levels (High, Medium, and Low) of technology and decompose changes in expenditures into these three categories of services. We found that those over the age of 65 received more high technology treatments than the younger population. Moreover, females of all ages were more likely to have medium and high technology treatment than males. Overall, the increases from applying high technology accounted for almost 60 % of the growth of Ontario government health expenditures on physician services during the period investigated.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".