Towards a systematic approach to resource optimization management in the healthcare domain
Bibliographic record
Abstract
Increasing patient demand, constrained physical resources, and a rising cost of operations are imperative concerns in healthcare management which require improvements to the way medical services are provided to the public.The urgency of the problem in Ontario, Canada has forced the Provincial Government to put a plan in place to increase access and reduce wait times for major health services including cancer surgery, cardiac procedures, cataract surgery, hip and knee replacements, general surgery, paediatric surgery, and MRI and CT exams (Ontario, 2008).The main directives of the plan include four goals: Operations of the Image Guided Therapy (IGT) Department of the Hospital for Sick Children ("SickKids"), Toronto, Canada have been taken as a sample object in the study.The IGT department provides valuable diagnostic and therapeutic data using procedures that involve different forms of anesthesia or sedation administered to the patients (Khaiter et al., 2015).The study demonstrated that none of the investigated optimization algorithms was able to minimize the IGT schedules with regard to all selected time-based performance criteria.Each algorithm generated schedules which are more efficient from the perspective of a single performance indicator, but not optimal for the others.It is reasonable to assume that specific features of the IGT department (i.e., multi-server environment and variable-length blocks) make the optimization of heir scheduling a complex non-trivial problem requiring a hybrid approach that combines several optimization techniques.
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 | 0.042 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".