Automating a program for the evidence-based reimbursement of oncology drugs across a complex network: Benefits and challenges.
Bibliographic record
Abstract
39 Background: Ontario hospitals are reimbursed for IV chemotherapy through Cancer Care Ontario’s (CCO) New Drug Funding Program (NDFP). By 2009, 54 indications (annual budget $195MM) were managed through largely paper based processes. A new web based system (eClaims) was developed focusing on clinic workflow and integration to chemotherapy ordering systems. Interfaces were developed for CCO’s OPIS and commercial systems (HL7v3). eClaims provides users with clinical best practice, pre-approval, immediate adjudication and simple means of tracking outstanding claims. The benefits and challenges are described. Methods: Evaluation used several strategies: debriefs after each deployment; post-go live user surveys and lessons learned workshops. Results: eClaims was deployed in 80 hospitals over two years. At most sites (50/80) treatment data flows from CPOE systems to eClaims in near real time. Over 50% of claims are machine adjudicated. Newly approved indications can be posted within hours. The main learnings during the deployment process were the need to understand and adjust for hospital specific factors and the unique business relationships among clusters of hospitals. Survey responses were received at a 19% response rate. The later deployment groups reported greater satisfaction than earlier adopters with more positive responses in all categories. Workshop key theme was the need to match complex clinical workflows with design/build processes. Secondly, evaluation of historical data before migration is necessary. Conclusions: Introducing an application into complex, varied clinical workflows is difficult. The phased approach to deployment and evaluation worked, allowing for increasingly smooth go lives. Future work revolves around balancing user needs through eClaims modifications vs simplifying clinical processes to make the tool more usable.
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 | 0.046 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".