Use of Hospital Appointment Registration Systems in China: A Survey Study
Bibliographic record
Abstract
BACKGROUND: Hospitals have expressed no knowledge of patients' opinions regarding diversified appointment registration systems, despite efforts to develop novel appointment registration systems that assist patients and increase hospital efficiency. Therefore, the aim of this study was to investigate the use of diversified appointment registration systems and explore the factors influencing patients' registration system choices. METHODS: A survey study using a questionnaire was conducted in West China Hospital in February 2012. Outpatients were randomly selected from different hospital departments and the questionnaire was distributed and collected on-site. RESULTS: Data from 1,009 patients were available for analysis. Of these, 63.4% used appointment systems to register while others chose a traditional queuing method to register. 114 telephone (30.4%) and on-site (22.9%) appointments were made, whereas other systems were less used by patients. Between the non-appointment and appointment groups there were significant differences in gender, educational degree, and residence location (P < 0.05), but no significant difference in age (P > 0.05). While the clinical appointment system had the greatest number of appointment days (25.75), the bank's self-service terminal appointment had the least number of appointment days (5.05). Leaflets sent from the hospital (50.70%) and the recommendations of friends or families (40.77%) were the two main ways of knowing about the appointment registration systems. With the exception of those who felt no need to make an appointment (30.12%), not having the capability to use the appointment systems (24.10%) and the lack of a registered health card (34.53%) were the two main reasons for not using appointment registration systems. CONCLUSIONS: Convenience was a major motivation for patients' use of appointment registration systems. Personal knowledge and capability were the two important factors that influenced patients' appointment system choices. Hospitals must improve the design and promotion of appointment registration systems to better facilitate their use.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".