Implementing a Regional Oncology Information System: Approach and Lessons Learned
Bibliographic record
Abstract
RATIONALE: Paper-based medical record systems are known to have major problems of inaccuracy, incomplete data, poor accessibility, and challenges to patient confidentiality. They are also an inefficient mechanism of record-sharing for interdisciplinary patient assessment and management, and represent a major problem for keeping current and monitoring quality control to facilitate improvement. To address those concerns, national, regional, and local health care authorities have increased the pressure on oncology practices to upgrade from paper-based systems to electronic health records. OBJECTIVES: Here, we describe and discuss the challenges to implementing a region-wide oncology information system across four independent health care organizations, and we describe the lessons learned from the initial phases that are now being applied in subsequent activities of this complex project. RESULTS: The need for change must be shared across centres to increase buy-in, adoption, and implementation. It is essential to establish physician leadership, commitment, and engagement in the process. Work processes had to be revised to optimize use of the new system. Culture change must be included in the change management strategy. Furthermore, training and resource requirements must be thoroughly planned, implemented, monitored, and modified as required for effective adoption of new work processes and technology. Interfaces must be established with multiple existing electronic systems across the region to ensure appropriate patient flow. Periodic assessment of the existing project structure is necessary, and adjustments are often required to ensure that the project meets its objectives. CONCLUSIONS: The implementation of region-wide oncology information systems across different health practice locations has many challenges. Leadership is essential. A strong, collaborative information-sharing strategy across the region and with the supplier is essential to identify, discuss, and resolve implementation problems. A structure that supports project management and accountability contributes to success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".