The Effect of Mobile Payment on Payment Waiting Time for Outpatients With Medical Insurance: Historically Controlled Study
Bibliographic record
Abstract
Background Waiting for a long time to make payments in outpatient wards and long queues of insured patients at the checkout window are common in many hospitals across China. To alleviate the problem of long queues for payment, many hospitals in China have established various mobile apps that those without health insurance can use. However, medically insured outpatients are still required to pay manually at the checkout window. Therefore, it is urgent to use information technology to innovate and optimize the outpatient service process, implement mobile payment for medically insured outpatients, and shorten the waiting time for outpatients, especially in the context of the COVID-19 epidemic. Furthermore, smartphone-based mobile payment for outpatients with health insurance could be superior to on-site cashier billing. Objective This study aimed to investigate the impact of smartphone-based mobile payment in relation to different aspects, such as waiting time, satisfaction with patients’ waiting time, payment experience, the proportion of those dissatisfied with payment, total outpatient satisfaction, and outpatient volume, and compare mobile payment with on-site payment. Methods This was a historically controlled study. This study analyzed the outpatients’ waiting time to make a medical insurance payment, their satisfaction with the waiting time and payment experience, the proportion of those dissatisfied with payment, and the outpatient volume of patients at Guangzhou Women and Children’s Medical Center 1 year before and after the implementation of mobile payment for medical insurance in January 2021. An independent sample 2-tailed t test was used to compare waiting time, satisfaction with waiting time, and overall satisfaction. Paired sample 2-tailed t test was used to compare monthly outpatient visits. The chi-square test was used to compare the percentages of patients dissatisfied with payment. Results After the implementation of mobile payment for medical insurance outpatients, the patients’ payment waiting time was significantly shortened (mean 45.28, SD 10.35 min vs mean 1.02, SD 0.25 min; t9014=53.396; P<.001), and satisfaction with waiting time and payment experience were significantly improved (mean 82.08, SD 3.17 vs mean 90.36, SD 3.45; t9014=–118.65; P<.001). Dissatisfaction with payment significantly decreased (10.27%, SD 2.18% vs 1.19% vs SD 0.30%; P<.001). The total satisfaction of outpatients significantly improved (mean 86.91, SD 3.23 vs mean 89.98, SD 3.31; t9014=–44.57; P<.001), and the outpatient volume increased (248,105.58, SD 89,280.76 vs 303,194.75, SD 53,773.12; t11=2.414; P=.03). Furthermore, payment efficiency improved, and the number of the on-site cashiers substantially decreased. Conclusions Mobile payment for health insurance significantly shortened patients’ payment waiting time; improved patient satisfaction on waiting time and payment experience and overall satisfaction; reduced the proportion of patients who were dissatisfied with payment and the cashier at the hospital; and increased monthly outpatient volume. This approach was effective and thus worthy of promoting.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".