A Metric for Healthcare Technology Management (HCTM)
Bibliographic record
Abstract
Among key drivers of healthcare reform in Canadian society are the challenges faced by the rapid rate of technological change and its impact on organizational performance in terms of efficiency, cost-effectiveness, and innovation in business and operational processes. However, despite the noted significance of the impact of technological change on healthcare organizations, the challenge of healthcare technology management (HCTM) has received only scattered and marginal attention in the technology management (TM) literature. The lack of formalization in HCTM construct, attributes, and measures motivated an empirical study to develop a metric for HCTM. This metric was then used to assess HCTM practices in teaching hospitals across Canada. The project began with an analysis of developments to date in the fields of Management of Technology and Management of Medical Technology. An extensive literature content analysis generated a set of definitions and attributes of the conceptual TM construct, which was eventually extended to HCTM. A measuring instrument was developed through a formal design process involving expert panel review, pilot testing, instrument refinement, and field-testing to extract and measure HCTM performance indicators. Administration of this metric with the help of the Association of Canadian Academic Health Organizations via a Web-based survey of senior healthcare administrators provided insights into the HCTM status of Canadian teaching hospitals and its relationship with organizational performance.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".