Mobile radiography CQI: an inter-national study.
Bibliographic record
Abstract
Mobile or bedside radiography has been and is a staple diagnostic and follow-up tool used readily by the many medical disciplines, such as cardiology, surgery, orthopedics, pediatrics, neonatology, etc. Ironically, in the past a student or the least qualified technologist was sent to perform the bedside exam. Moreover, it was almost expected that poor but acceptable film quality would result or that repeat films were almost always to be taken. Inefficiency with respect to quality of exam, the time the exam takes, or film repeats can be costly. The price of inefficiency is the cost involved in doing things incorrectly or not in the most efficient manner, i.e., incurring inefficiencies instead of operating in an ideal manner. The purpose of this study was to compare the total cost of inefficiently organized, scheduled and performed mobile radiography at three large teaching hospitals in various locations and of diverse patient loads, as a means of determining how best to increase utilization and performance. The study was performed at the 489-bed New England Deaconess Hospital (NEDH), the 644-bed Sentara Norfolk General Hospital (SNGH), and the 1500-bed Rheinische Westfalische Technische Hochschule (RWTH) in Aachen, Germany. Similar standardized study methods were utilized at all three institutions where extended observation of mobile utilization, areas of inefficiency, time wasted per episode and number of episodes per time period were determined. Data were loggedin at three standardized time periods, summated, and then multiplied by technologist hourly pay rate. This sum was extrapolated over 52 weeks to give the total annual cost of inefficiently organized mobile radiography. For NEDH the cost of total inefficiency was $75,453, for SNGH $49,586, while for RWTH it was $9,519. Eighteen areas of inefficiency were identified and grouped, such as lack of spatial cohesiveness and lack of communication leading to film duplication, etc. While inefficiencies in the delivery of hospital based health care are well known, this study attempts to quantify and determine a dollar value for each process found as inefficient. Key inefficiencies were found to be common at large hospitals no matter whether in the United States or Europe. These impairments are responsible for a disproportionate share of overall inefficiency, and their elimination (achievable by simple solutions) would result in drastic cost reductions (ranging from 40-75% at the institutions studied). Thus this study is important in view of spiralling costs, as it is a key component of total quality management (TQM) in radiology and a continuous quality improvement (CQI) tool for mobile radiology specifically.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".