The Development and Implementation of an Electronic Departmental Note in a Colposcopy Clinic
Bibliographic record
Abstract
Hospital-wide electronic medical records can be limited in addressing clinical department needs. A study was undertaken to examine the development and implementation of an electronic informaton system in a colposcopy unit in a large teaching hospital in Canada. A case study design was used, and 24 semistructured interviews were conducted with nurses and physicians working in the colposcopy clinic and individuals from the information technology team. Interviews occurred in two phases-directly after implementation and again 9 months later. Computerized audit data were gathered to examine usage patterns. The results provide insight into the processes and challenges of defining and capturing information for both clinical and research purposes and creating a standardized referral note. The findings demonstrated some initial uncertainty around roles and responsibilities concerning the electronic system and its integration into clinical routines. After a period of 12 months, and further refinement, it was found that the system was accessible and user-friendly, although some concerns raised during the developmental stage persisted. Audit data revealed that 9 months after its introduction, nurses' adoption of the system rate reached 89%, and physicians, 96%. This study has demonstrated that practitioners in a colposcopy clinic successfully collaborated with information technology specialists and each other to develop and implement a clinical departmental information system. While certain challenges were encountered, nurses and physicians have bought into the system, recognize its potential for research and patient care, and are therefore committed to figuring out how to adapt to the changes in communication both within the clinic and with referring physicians.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".