Exploring Barriers and Facilitators of Inter-Professional Communication in Health IT-Supported Patient-Centered Care Systems
DOI:
https://doi.org/10.54536/ajmsi.v5i2.8063Keywords:
Communication Barriers, Communication Facilitators, Deep Learning, Health Information Technology, Interprofessional Communication, Machine Learning, Patient-Centered CareAbstract
Interprofessional communication is essential for improving care coordination, patient safety, healthcare quality, and collaborative decision-making in Health IT-supported patient-centered care systems. Effective communication among healthcare professionals can reduce clinical errors and enhance patient outcomes, while communication barriers may negatively affect healthcare delivery. This study aims to explore barriers and facilitators to inter-professional communication in Health IT-supported patient-centered care systems using machine-learning and deep-learning classification models. For this research, a primary dataset was developed containing 3,000 records with 28 variables, including demographic characteristics, professional roles, Health IT utilization factors, communication practices, organizational support factors, workflow integration measures, and patient-centered care indicators. The study applied supervised machine learning and deep learning methods using a 70% training, 15% validation, and 15% testing split. Three classification models—Neural Network, BiLSTM, and CNN-BiLSTM—were used to classify Barrier Level and Facilitator Level into High, Moderate, and Low categories. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, ROC curve, and precision-recall analysis. The results show that the Neural Network model achieved the highest overall performance with an average accuracy of 97.33%, focusing on Facilitator Level classification, which achieved 94.67% accuracy. CNN-BiLSTM achieved an average accuracy of 94.33%, while BiLSTM achieved 84.89% accuracy. The findings suggest that Health IT usability, interoperability, workflow integration, communication openness, role clarity, team trust, training quality, and technical support are important factors influencing interprofessional communication outcomes. The study concludes that machine learning-based classification models can effectively identify communication barriers and facilitators and support improved collaboration, communication effectiveness, and patient-centered care delivery in Health IT-supported healthcare environments.
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Copyright (c) 2026 Sabrina Momota Saima, Md. Mahmud Al Hasan, Noopur D Costa, Dipika Mazumder, Refaya Mahmuda, Mariom Oyshe
This work is licensed under a Creative Commons Attribution 4.0 International License.
