The Effects of an Online Patient Portal on Nurses’ and the Health Care Team's Work in an Outpatient Oncology Setting: A Qualitative Study
Bibliographic record
Abstract
BackgroundIn November 2022, Alberta Health Services launched a new province-wide electronic health record, Connect Care (Epic), with a tethered patient portal, MyAHS Connect, across all Cancer Care Alberta sites. Oncology patients now can view their health record (including results), view and manage appointments, enter data directly into their chart, and securely message their health care team.PurposeTo explore how an online patient portal effects nurses and the health care team's work in an outpatient oncology setting.MethodsA descriptive qualitative method was used for this research study. 15 health care providers were recruited (12 registered nurses, 2 medical oncologists, 1 clerical worker). Data was analyzed using thematic analysis with a technology-in-practice sociomaterial theoretical perspective informing our approach.ResultsThree main themes were generated: the invisibility of nurses' responsibility of supporting patient portal use, access to the portal shapes a new type of patient, and MyAHS Connect is as good as the networks of care provision in which it is embedded.ConclusionThis qualitative study details how patient access to the portal changed the ways that health care providers are working but the degree of this change was highly influenced by patient use of the portal, staff's use of the electronic health record, and the greater system context. This research highlights the substantial role of nurses when patient portals are used in health care practice settings.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".