Acceptability of Guided Symptom Entry and Asynchronous Clinical Communication Software Among Primary Care Staff: Qualitative Study
Bibliographic record
Abstract
Background: Patients often communicate with primary care centers remotely (eg, by telephone or email) before seeking in-person care. A comparatively novel addition might be patient-facing symptom entry websites, where subsequent questions are automatically guided by previous responses. However, the acceptability of such systems to health care staff remains unclear, particularly in terms of what features staff perceive as useful. Objective: This study aimed to investigate a patient-facing algorithm-guided symptom-entry software (developed by Certific OÜ, Estonia), which also supports subsequent asynchronous communication, for its acceptability and perceived utility to primary health care providers. Methods: In-depth and open-ended interviews were conducted in 8 primary care centers in Estonia, including 8 nurses and 6 doctors, 3-6 months after the implementation of a novel patient-facing website. Transcripts were coded inductively, using grounded theory and phenomenological approaches to uncover themes most salient to providers. Two family doctors provided feedback on the final analysis. Results: Staff perceived unstructured communication (via email and phone calls) as a burden that increased their cognitive load. Sometimes, this arises out of the perceived mismatch between needing to identify and document critical symptom information and being unable to standardize the supply of such information, due to a heterogeneous and unpredictable communication processes whose duration, quality, and risk of miscommunication are hard to predict and control. All interviewees expressed the desire that more patients initiate their remote query via the algorithm-guided symptom-entry software. The software was reported to satisfy perceived feature needs for patient verification, privacy and data security, editable plain-language symptom summaries of symptoms, and integration with prewritten response templates (particularly for staff who were nonnative speakers). Safety of the new software was perceived as high, on account of integration alongside traditional telephone requests. Staff reported the challenge that great effort was needed to persuade patients to use the website. Among perceived challenges, some providers reported difficulty in onboarding patients, digital literacy gaps, and limited time savings. While previous research has criticized poorly designed multiple-choice systems, our findings suggest that an appropriately designed and personalized multiple-choice system can be preferable to health care staff, as they may lower cognitive demands and enhance well-being. Conclusions: Interviewed primary health care staff felt that this symptom entry software was acceptable and desirable. They valued a perceived reduction in cognitive demands. This holds promise for increasing staff well-being and increasing efficiency, which needs to be quantified in future studies.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".