The role of cognitive bandwidth in more cost-effective CT scan usage: A study in reducing “prevention costs”
Bibliographic record
Abstract
Background: Research has shown preventative care measures aimed at reducing healthcare costs can actually increase them. The objective of this study was to observe the relationship between cognitive capacity and preventative CT scan use relative to best practice. Methods: Conducted in July 2012, the study involved a retrospective analysis of 825 consecutive head CT examinations performed over one month by ED physicians crossing three shifts in a large Detroit medical center. Military Acuity Model data mining and modeling techniques were used to examine the relationship between CT head yield to order timing in terms of cognitive capacity and decision fatigue relative to health risk reconciliation. Results: The study showed the number of CT scans ordered increased as physician shifts progressed, while the test value to clinical management decreased. Cognitive capacity was assumed to be at its highest at the start of physician shifts (when Decision Fatigue was lowest); Results indicated physicians were better able to evaluate CT scan use risks and trade-offs. Translating the study results into actual dollar amounts showed an opportunity cost of 26.7%, or more than $43,000 per month, for CT head/brain scan use alone. Conclusions: Ensuring the management of CT scan usage at the levels of efficiency demonstrated when the study physicians were performing at the level of their In-House Best Practice, is critical to maintaining high levels of patient care at the least possible cost. There is more than $1 million in potential annual cost savings attributable to this phenomenon of cognitive bandwidth affecting radiology decision-making for the average U.S. hospital.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".