Telehealth Nursing: Application of Usability Methods to Maximize Quality Patient Outcomes
Bibliographic record
Abstract
Danica Tuden is a Clinical Analyst/User Experience Specialist. She has also been a registered nurse for over 25 years, practicing in a variety of settings such as acute care, community health senior’s assisted living and telehealth nursing. The move to telenursing was important to her decision to pursue a master’s degree in Health Information Science at the University of Victoria in BC. During this education, she became very interested and passionate in the area of usability engineering methodologies, particularly in usability testing and heuristic evaluation. Danica’s thesis is concerned with developing a framework to support telenurse practice and in doing so, uses clinical simulation and a post cued recall approach interview as the basis for her data collection in order to understand telenurse’s decision making processes. Danica will be speaking about telehealth nursing and how usability methods are important to utilize in the SDLC (systems development life cycle) of the EMR (electronic medical record) and clinical decision support tool that telenurses use during a patient call encounter. Danica currently works at Provincial Health Services Authority (PHSA) as clinical analyst, particularly in the electronic medical record and clinical documentation components of Cerner applications. She has also worked at IBM and HealthlinkBC in the area of usability. References: 1) Arnaert, A. & Macfarlane, F. (2011). Telehealth nursing in Canada: Opportunities for nurses to shape the future. McGill University, University Street: Wilson Hall.
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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 | 0.055 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".