Clinical Engineering Benchmarking
Bibliographic record
Abstract
In Brief Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities. Operational and financial clinical engineering (CE) data from 253 acute care hospitals were analyzed for indicators that are statistically valid and useful for measuring, monitoring, and improving performance. The sample is mostly composed of nonprofit public and religious hospitals and is evenly distributed among major and minor teaching hospitals and nonteaching institutions. Almost all CE departments manage all biomedical equipment and provide technology management support, but only some manage imaging, laboratory, nonmedical devices, and beds. Clinical engineering departments typically use 2.5 full-time-equivalent employees per 100 staffed beds or 1 full-time-equivalent employee per 4000 adjusted discharges. Administrative support is available only at large departments. Most of the CE budget is typically spent on service contracts, whereas approximately 20% is dedicated to internal labor. One scheduled maintenance and 1 repair are typically performed per capital device per year. Although the ratio of total CE expense and total equipment acquisition costs was confirmed to be a good indicator at around 4%, several other denominators also emerged as valid and, perhaps, even more widely available for comparisons, for example, staffed beds, adjusted discharges, and number of capital devices. Overall, CE budget is around 0.5% of the hospital's total operating budget. Because of uneven data quality and impossibility of validation, each indicator should not be used individually for precise benchmarking. On the other hand, when used together, multiple indicators provide not only valuable ballpark comparisons but also insights into deviations that warrant further investigation for potential uniqueness and/or improvement opportunities.
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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 | 0.022 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".