Sociotechnical Evaluation of a Clinical Transformation Project in a Specialized Cancer Care Centre
Bibliographic record
Abstract
The radiation therapy (RT) department at the British Columbia Cancer Agency - Vancouver Island Centre (VIC) is responsible for delivering radiation treatments to cancer patients from Vancouver Island, which has a population base of approximately 750,000. The purpose of this analysis is to examine a process transformation project undertaken by a VIC clinical champion using a sociotechnical approach and identify factors that influenced the project outcome. Beginning in January 2009, a radiation oncologist at VIC initiated a project to transform the clinical process of generating prescriptions for radiation therapy. The project objective was to replace the paper-based process for radiation therapy (RT) prescriptions with an electronic process to achieve benefits such as increased legibility, accuracy, and accessibility of prescriptions. The electronic prescription (e-Rx) process was designed and developed by health informatics students from the University of Victoria, and the new process was trialed and implemented for approximately half of the new patients seen by the VIC RT department. This pilot implementation was brought to a halt two weeks later, due to concerns raised by the RT department. Using a sociotechnical approach, the authors identify several factors that negatively impacted the project's successful implementation: lack of leadership endorsement and organizational strategy, insufficient formal and informal organizational power of the clinical champion, underestimation of complexity, and inadequate management of the implementation process. Although these factors have been well documented in the literature for large-scale system implementation projects, understanding the way by which they influence smaller-scale process transformation projects in highly specialized clinical settings may help future project managers and coordinators to set such projects up for success.
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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 | 0.059 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".