What question did this study set out to answer?

To develop an intelligent patient appointment scheduling system to reduce wait times and improve resource allocation.

May 6, 2026Open Access

Intelligent Patient Appointment Schedules

Key Points

To develop an intelligent patient appointment scheduling system to reduce wait times and improve resource allocation.
Developed an appointment system using Business Process Analysis and Bizagi modeling.
Applied SWOT analysis, TQM, and Six Sigma DMAIC in the design process.
Integrated machine learning and natural language processing with BioBERT-BiLSTM for triage.
Conducted simulations to validate the scheduling improvements.
Booking time reduced by 96.3%, from 155 to 5.73 minutes.
Human intervention decreased by 70%.
Enhanced patient satisfaction and overall process capability.

Abstract

Background: Hospital appointment systems suffer from extended patient waits, manual interventions, and suboptimal resource allocation, reducing satisfaction and efficiency. Methods: This study develops IPAS using Business Process Analysis (BPA), Bizagi modeling for As-Is/To-Be workflows, SWOT analysis, TQM, and Six Sigma DMAIC. It integrates ML/NLP with BioBERT-BiLSTM triage (AUC 0.92, F1 0.87) for symptom analysis, specialist matching, and automated booking, validated via Bizagi simulations. Results: Simulations show booking time was reduced 96.3% (155 to 5.73 min) and human intervention was cut 70%, with enhanced patient satisfaction and process capability. Conclusions: IPAS demonstrates simulation-based gains in scheduling efficiency, pending real-world validation.

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Cite This Study

Elhag et al. (Wed,) studied this question.

synapsesocial.com/papers/69fa989404f884e66b5325cb https://doi.org/https://doi.org/10.3390/healthcare14091195

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