Physician awareness of diagnostic and nondrug therapeutic costs: A systematic review
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to investigate doctors' knowledge of the relative and absolute costs of diagnostic tests, medical consumables (e.g., syringes or intravenous tubing), and healthcare visits as well as to determine factors influencing awareness. METHODS: For this systematic review, we searched the Cochrane Library, EconoLit, EMBASE, and MEDLINE; reviewed reference lists; and had contact with authors. Studies were included if either doctors or trainees were surveyed, there were >10 survey respondents, costs of diagnostic or therapeutic items were estimated, results were expressed quantitatively, and a clear description was provided of how authors defined Accurate Estimates and determined True Cost. Two authors reviewed each article for eligibility and extracted data independently. Cost accuracy outcomes were summarized, but data were not combined due to extensive heterogeneity. RESULTS: Fourteen articles were included in the final analysis. Cost accuracy was low; 33 percent of estimates were within 20 percent or 25 percent of true cost and 50 percent were within 50 percent or in the 50-200 percent range of the true cost. Country, year of study, level of training, and specialty did not impact accuracy. The cost of items appears to have no impact on the accuracy (Fisher's exact test, p = .41) or pattern of estimation (binomial test, p = .92). CONCLUSIONS: Doctors have a limited understanding of diagnostic and nondrug therapeutic costs, and we could not identify anything that impacts understanding of these costs. More focus is required in the education of physicians about costs and the access to cost information.
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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 | 0.015 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".