Determination of Patient Waiting Time and Associated Factors at General Out-Patient Department (GOPD) of Irrua Specialist Teaching Hospital (ISTH), Irrua, Nigeria - An Electronic Medical Record Setting
Bibliographic record
Abstract
Background: Prolonged waiting typically affects patients’ satisfaction and may deter subsequent utilization of healthcare facility. There is a paucity of information regarding waiting time in hospitals where the Electronic medical record is been utilized particularly from the patients’ viewpoint. The aim of this study was to assess the waiting time and associated factors at the GOPD clinic of Irrua Specialists Teaching Hospital, (ISTH) where the EMR is fully adopted and operational. Subjects and Methods: An institutional-based descriptive cross-sectional study was carried out among 318 adult patients aged 21 and above who attended the GOPD of ISTH, Irrua, Nigeria. Patients were selected by simple random sampling and a structured questionnaire was used to collect data at the last service point. Data collected were cleaned and analyzed using the Statistical Package for Social Sciences version 20. Level of significance was set at p < 0.05. Results: The average waiting time for the clinic was 217 ± 37 minutes. Patients who were first-timers took much longer time (226.1min) than returning patients (206.1min) (p = 0.019).The primary reasons for the delay were: the clinic's bad physical layout, with the radiology and laboratory department located far from the consultation rooms, the instructional services provided to medical students during consultations, and the lack of reliable intranet connectivity with the EMR. Conclusion: Patients visiting the GOPD clinic experienced considerably long waiting hours even with the full implementation of the EMR. The foremost cause of this delay was the unstable EMR intranet at both the service point and payment stations. The EMR intranet connectivity should be improved upon to provide seamless services and reduce waiting time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".