Design and Evaluation of a Smartphone Medical Guidance App for Outpatients of Large-Scale Medical Institutions: Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: The greatest stressor for outpatients is the waiting time before an examination. If the patient is able to use their smartphone to check in with reception, the patient can wait for their examination at any location, and the burden of waiting can be reduced. OBJECTIVE: This study aimed to report the system design and postintroductory outcomes of the Tori RinRin (TR2) system that was developed to reduce outpatient burden imposed by wait times before examination. METHODS: The TR2 system was introduced at Tottori University Hospital, a large medical facility that accepts a daily average of 1500 outpatients. The system, which links the hospital's electronic medical record database with patients' mobile devices, has the following functions: (1) GPS-based examination check-in processing and (2) sending appointment notification messages via a cloud notification service. In order to evaluate the usefulness of the TR2 system, we surveyed the utilization rate of the TR2 system among outpatients, implemented a user questionnaire, and polled the average time required for patients to respond to call notifications about their turn. RESULTS: The 3-month average of TR2 users 9 months after the TR 2 system introduction was 17.9% (14,536/81,066). In an investigation of 363 subjects, the mean examination call message response time using the TR2 system was 31 seconds (median 14 seconds). Among 166 subjects who responded to a user survey, 86.7% (144/166) said that the system helped reduce the burden of waiting time. CONCLUSIONS: The app allowed 17.9% of outpatients at a large medical facility to check in remotely and wait for examinations anywhere. Hence, it is effective in preventing the spread of infection, especially during pandemics such as that of coronavirus disease. The app reported in this study is beneficial for large medical facilities striving to reduce outpatient burden imposed by wait times.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".