Enhancing Healthcare Management: A Case Study of a Medical Chatbot in Egypt
Bibliographic record
Abstract
In hospitals, receptionists are generally responsible for the following functions: greeting and answering inquiries from visitors, providing them with the appropriate information, and ensuring that hospital staff and patients receive timely and professional communication. This paper presents the implementation and deployment of a medical chatbot designed to replace the traditional receptionist role in hospitals where their visitors speak Arabic. The proposed case study here is an Egyptian hospital. The user can ask questions in text and the answers can be text or voice. The presented chatbot utilizes the power of GPT-4, which represents one of the most powerful large language models (LLMs) available to generate text. This model is merged with prompt engineering capabilities for fine-tuning specific tasks or instructions. This merger has gained traction to enhance model performance and adaptability. The model is integrated with an SQLite database to provide immediate information to patients about doctor availability, examination costs, hospital policies, and more. The chatbot demonstrates a significant potential to streamline hospital operations, improve patient satisfaction, and reduce administrative workload. The evaluation shows a 99% accuracy rate, indicating the high reliability of the system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".