A multinational survey of patient utilization of and value conveyed through virtual symptom triage and healthcare referral
Bibliographic record
Abstract
Objective: To describe the use patterns, impact and derived patient-user value of a mobile web-based virtual triage/symptom checker. Methods: Online survey of 2,113 web-based patient-users of a virtual triage/symptom checker was completed over an 8-week period. Questions focused on triage and care objectives, pre- and post-triage care intent, frequency of use, value derived and satisfaction with virtual triage. Responses were analyzed and stratified to characterize patient-user pre-triage and post-triage intent relative to triage engine output. Results: Seventy-eight percent of virtual triage users were female, and 37% were 18-24 years old or younger, 28% were 25-44, 16% were 45-54, and 19% were 55 years or older; 41.2% completed the survey from the U.S., 12.5% from the U.K., 9.1% from Canada, 5.6% from India, 3.8% from South Africa. Motivations were to determine need to consult a physician (44.2%), to secure medical advice without visiting a physician (21.0%), and to confirm a diagnosis received (14.2%). Forty-three percent were first time users of virtual triage, 36.6% utilized a triage engine at least once every few months or more often. Pre-triage, 40.5% did not know what level of healthcare they were planning to utilize, 33.9% stated they intended to seek a physician consultation, 23.7% engage self-care and 1.8% seek emergency care. Virtual triage recommended 56.8% of patient-users consult a physician, 33.8% seek emergency care and 9.4% engage self-care. In three-fourths, virtual triage helped users decide level of care to pursue. Among 74.1%, triage recommended care different than pre-triage intentions. Post-triage, those who remained uncertain of their care path decreased by 25.4%. Patient-user experience and satisfaction with virtual triage was high, with 80.1% stating that they were highly likely or likely to use it again, and interest in and willingness to use telemedicine doubled. Conclusion: Virtual triage successfully redirected patient-users who initially planned to seek an inappropriate level of care acuity, reduced patient uncertainty of care path, and doubled the percentage of patients amenable to telemedicine and virtual health engagement. Patient-users were highly satisfied with virtual triage and the virtual triage patient experience, and a large majority will use virtual triage recurrently in the future.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".