Managing Resource Utilization Cost of Laboratory Tests for Patients on Chemotherapy in Johns Hopkins Aramco Healthcare
Bibliographic record
Abstract
ABSTRACT Introduction Laboratory testing is a fundamental diagnostic and prognostic tool to ensure the quality of healthcare, treatment, and responses. This study aimed to evaluate the cost of laboratory tests performed for patients undergoing chemotherapy treatment in the oncology treatment center at Johns Hopkins Aramco Healthcare in Saudi Arabia. Additionally, we aimed to reduce the cost of unnecessary laboratory tests in a 1-year period. Methods This was a quality improvement study with a quasi-experimental design using DMAIC methodology. The intervention strategy involved educating staff about adhering to the British Columbia Cancer Agency (BCCA) guidelines when ordering laboratory tests for chemotherapy patients, then integrating those guidelines into the electronic health record system. Data were collected for 200 randomly selected cases with 10 different chemotherapy protocols before and after the intervention. A paired t test was used to analyze differences in mean cost for all laboratory tests and unnecessary testing before and after the intervention. Results A significant cost reduction was achieved for unnecessary laboratory tests (77%, p < 0.01) when following the BCCA guidelines. In addition, the mean cost of all laboratory tests (including necessary and unnecessary) was significantly reduced by 45.5% (p = 0.023). Conclusion Lean thinking in clinical practice, realized by integrating a standardized laboratory test guided by BCCA guidelines into the electronic health record, significantly reduced financial costs within 1 year, thereby enhancing efficient resource utilization in the organization. This quality improvement project may serve to increase awareness of further efforts to improve resource utilization for other oncology treatment protocols.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".