Health Care Professionals’ Experience of a Digital Tool for Patient Exchange, Anamnesis, and Triage in Primary Care: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Despite a growing body of knowledge about eHealth innovations, there is still limited understanding of the implementation of such tools in everyday primary care. OBJECTIVE: The objective of our study was to describe health care staff's experience with a digital communication system intended for patient-staff encounters via a digital route in primary care. METHODS: In this qualitative study we conducted 21 individual interviews with staff at 5 primary care centers in Sweden that had used a digital communication system for 6 months. The interviews were guided by narrative queries, transcribed verbatim, and subjected to content analysis. RESULTS: While the digital communication system was easy to grasp, it was nevertheless complex to use, affecting both staffing and routines for communicating with patients, and documenting contacts. Templates strengthened equivalent procedures for patients but dictated a certain level of health and digital literacy for accuracy. Although patients expected a chat to be synchronous, asynchronous communication was extended over time. The system for digital communication benefited assessments and enabled more efficient use of resources, such as staff. On the other hand, telephone contact was faster and better for certain purposes, especially when the patient's voice itself provided data. However, many primary care patients, particularly younger ones, expected digital routes for contact. To match preferences for communicating to a place and time that suited patients was significant; staff were willing to accept some nuisance from a suboptimal service-at least for a while-if it procured patient satisfaction. A team effort, including engaged managers, scaffolded the implementation process, whereas being subjected to a trial without likely success erected barriers. CONCLUSIONS: A digital communication system introduced in regular primary care involved complexity beyond merely learning how to manage the tool. Rather, it affected routines and required that both the team and the context were addressed. Further knowledge is needed about what factors facilitate implementation, and how. This study suggested including ethical perspectives on eHealth tools, providing an important but novel aspect of implementation.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".