The Introduction and Evaluation of Mobile Devices to Improve Access to Patient Records
Bibliographic record
Abstract
Prompt and efficient access to patient records is vital in providing optimal patient care. The Cancer Agency Information System (CAIS) is the primary patient record repository for the British Columbia Cancer Agency (BCCA) but is only accessible on traditional computer workstations. The BCCA clinics have significant space limitations resulting in multiple healthcare professionals sharing each workstation. Furthermore, workstations are not available in examination rooms. A novel and cost-efficient solution is necessary to improve clinician access to CAIS. This prompted the BCCA and the Provincial Health Services Authority (PHSA) Information Management Information Technology Services (IMITS) team to embark on an innovative provincial collaboration to introduce and evaluate the impact of a mobile device to improve access to CAIS. The project consisted of 2 phases with over 90 participants from multiple clinical disciplines across BCCA sites and other PHSA facilities. Phase I evaluated the adoptability, effectiveness, and costs associated with providing access to CAIS using desktop virtualization via Citrix. Citrix is a server solution that provides remote access to clients via the Web or to dummy terminals in a network. Phase II incorporated the feedback and findings from Phase I to develop a customized mobile application. Phase II also addressed privacy and security requirements and included additional users and workflows. This is explored in this chapter.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".