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Intelligent Patient Appointment Schedules
Salma Elhag1, Lama Althagafi1, Shroog Almouabdi1
1Information Systems Department, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Healthcare (Basel, Switzerland)
|May 13, 2026
Summary
This study introduces an Intelligent Patient Appointment System (IPAS) that significantly reduces hospital booking times and manual work. The new system enhances patient satisfaction and operational efficiency through advanced AI and process analysis.
Area of Science:
- Health Informatics
- Artificial Intelligence in Healthcare
- Operations Research
Background:
- Hospital appointment systems face challenges with long patient waits, manual processes, and inefficient resource use.
- These inefficiencies decrease patient satisfaction and overall healthcare delivery effectiveness.
Purpose of the Study:
- To develop an Intelligent Patient Appointment System (IPAS) to address inefficiencies in hospital scheduling.
- To leverage AI and process analysis for automated patient triage and appointment booking.
Main Methods:
- Utilized Business Process Analysis (BPA), Bizagi modeling, SWOT, TQM, and Six Sigma DMAIC methodologies.
- Integrated Machine Learning (ML) and Natural Language Processing (NLP) with a BioBERT-BiLSTM model for symptom analysis and specialist matching.
- Validated the system's performance through Bizagi simulations.
Main Results:
- Simulations demonstrated a 96.3% reduction in appointment booking time (from 155 to 5.73 minutes).
- Human intervention in the booking process was reduced by 70%.
- Observed improvements in patient satisfaction and process capability.
Conclusions:
- The Intelligent Patient Appointment System (IPAS) shows significant potential for improving healthcare scheduling efficiency based on simulation data.
- Further real-world validation is recommended to confirm these efficiency gains.
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