Mobile Application Design Based on Natural Language Processing to Improve the Quality of Health Services

Achmad Ridwan (1), Zain Nizam (2), Daniyar Satybaldy (3)
(1) Universitas Muhammadiyah Kudus, Indonesia,
(2) Universiti Malaysia Sarawak, Malaysia,
(3) Al-Farabi Kazakh National University, Kazakhstan

Abstract

The increasing demand for efficient and personalized health services has driven the integration of advanced technologies into healthcare systems. Mobile applications leveraging natural language processing (NLP) offer promising solutions to improve patient communication, diagnostic accuracy, and service delivery. Despite advancements, challenges remain in developing user-friendly applications that address diverse healthcare needs. This research focuses on designing a mobile application based on NLP to enhance the quality of health services, emphasizing usability, accuracy, and accessibility. The study employs a user-centered design approach combined with experimental evaluation. The application was developed using Python-based NLP libraries, integrating features such as symptom analysis, medical query responses, and appointment scheduling. A prototype was tested with 150 participants, including patients and healthcare professionals, to evaluate performance metrics such as response accuracy, user satisfaction, and system reliability. The findings indicate that the NLP-based application achieved an 85% accuracy rate in interpreting medical queries and a 90% user satisfaction rate. Participants reported improved communication with healthcare providers and faster access to relevant medical information. However, challenges such as handling complex medical terminology and ensuring data privacy were noted. The study concludes that NLP-powered mobile applications have significant potential to improve health service quality by enabling efficient and accurate communication between patients and providers. Addressing challenges related to data security and expanding linguistic capabilities will be essential for future development. The research underscores the importance of integrating advanced technologies to meet the evolving needs of the healthcare sector.

Full text article

Generated from XML file

References

Abdulhamid, N. G. (2023). Can Large Language Models Support Medical Facilitation Work? A Speculative Analysis. ACM International Conference Proceeding Series, Query date: 2025-03-17 22:15:00, 64–70. https://doi.org/10.1145/3628096.3628752

Ahamad, S. S. (2022). A Secure and Resilient Scheme for Telecare Medical Information Systems With Threat Modeling and Formal Verification. IEEE Access, 10(Query date: 2025-03-17 22:15:00), 120227–120244. https://doi.org/10.1109/ACCESS.2022.3217230

Allida, S. (2020). mHealth education interventions in heart failure. Cochrane Database of Systematic Reviews, 2020(7). https://doi.org/10.1002/14651858.CD011845.pub2

Alodhayani, A. A. (2021). Culture-Specific Observations in a Saudi Arabian Digital Home Health Care Program: Focus Group Discussions with Patients and Their Caregivers. Journal of Medical Internet Research, 23(12). https://doi.org/10.2196/26002

Bedwa, M. (2024). Comparative Performance Analysis of Large Language Models for Deployment in Women’s Healthcare Mobile Applications. Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference, DELCON 2024, Query date: 2025-03-15 08:49:34. https://doi.org/10.1109/DELCON64804.2024.10867016

Chan, A. (2022). Digital interventions to improve adherence to maintenance medication in asthma. Cochrane Database of Systematic Reviews, 2022(6). https://doi.org/10.1002/14651858.CD013030.pub2

Chen, X. (2020). CompRess: Composing overlay service resources for end-to-end network slices using semantic user intents. Transactions on Emerging Telecommunications Technologies, 31(1). https://doi.org/10.1002/ett.3728

Clark, T. (2019). Virtual course for the Americas: Healthcare technology planning and management over the life cycle. IFMBE Proceedings, 68(3), 325–328. https://doi.org/10.1007/978-981-10-9023-3_58

Devi, T. (2021). A Biometric Approach for Electronic Healthcare Database System using SAML - A Touchfree Technology. Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021, Query date: 2025-03-17 22:15:00, 174–178. https://doi.org/10.1109/ICESC51422.2021.9532874

Dicastillo, E. L. D. (2019). Development and Evaluation of a Telematics Platform for Monitoring of Patients in Ambulatory Major Surgery. Telemedicine and E-Health, 25(2), 152–159. https://doi.org/10.1089/tmj.2017.0296

Dones, V. (2025). The Effectiveness of Telemedicine in Hypertension Management of Adults in Rural Communities: A Systematic Review and Meta-Analysis. Physiotherapy Research International, 30(1). https://doi.org/10.1002/pri.70014

Du, Y. (2023). “They Can’t Believe They’re a Tiger”: Insights from pediatric speech-language pathologist mobile app users and app designers. International Journal of Language and Communication Disorders, 58(5), 1717–1737. https://doi.org/10.1111/1460-6984.12898

Fauk, N. K. (2022). Barriers to Accessing HIV Care Services in Host Low and Middle Income Countries: Views and Experiences of Indonesian Male Ex-Migrant Workers Living with HIV. International Journal of Environmental Research and Public Health, 19(21). https://doi.org/10.3390/ijerph192114377

Fernandez, I. D. (2024). Developing Components of an Integrated mHealth Dietary Intervention for Mexican Immigrant Farmworkers: Feasibility Usability Study of a Food Photography Protocol for Dietary Assessment. JMIR Formative Research, 8(Query date: 2025-03-17 22:15:00). https://doi.org/10.2196/54664

Garg, N. (2020). Technology in healthcare: Vision of smart hospitals. Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, Query date: 2025-03-17 22:15:00, 346–362. https://doi.org/10.4018/978-1-7998-3053-5.ch016

Glenton, C. (2024). Healthcare workers’ informal uses of mobile phones and other mobile devices to support their work: A qualitative evidence synthesis. Cochrane Database of Systematic Reviews, 2024(8). https://doi.org/10.1002/14651858.CD015705.pub2

Gupta, M. (2023). The potential of artificial intelligence in the healthcare system. Innovative Engineering with AI Applications, Query date: 2025-03-15 08:49:34, 101–129.

Gupta, V. (2023). Chatbot for Mental health support using NLP. 2023 4th International Conference for Emerging Technology, INCET 2023, Query date: 2025-03-15 08:55:51. https://doi.org/10.1109/INCET57972.2023.10170573

Hameed, A. Z. (2023). A hybrid Fifth Generation based approaches on extracting and analyzing customer requirement through online mode in healthcare industry. Computers and Electrical Engineering, 106(Query date: 2025-03-15 08:55:51). https://doi.org/10.1016/j.compeleceng.2022.108550

Haoues, M. (2023). Machine learning for mHealth apps quality evaluation: An approach based on user feedback analysis. Software Quality Journal, 31(4), 1179–1209. https://doi.org/10.1007/s11219-023-09630-8

Khan, R. (2022). A Prospective Study of Federated Machine Learning in Medical Science. EAI/Springer Innovations in Communication and Computing, Query date: 2025-03-15 08:55:51, 105–116. https://doi.org/10.1007/978-3-030-85559-8_7

Kolcu, M. ?. B. (2023). Health 4.0. Accounting, Finance, Sustainability, Governance and Fraud, Query date: 2025-03-13 09:34:59, 109–119. https://doi.org/10.1007/978-981-99-1818-8_9

Laumer, S. (2020). Chatbot acceptance in healthcare: Explaining user adoption of conversational agents for disease diagnosis. 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019, Query date: 2025-03-15 08:55:51. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087110190&origin=inward

Lee, H. (2022). Predicting User Satisfaction of Mobile Healthcare Services Using Machine Learning: Confronting the COVID-19 Pandemic. Journal of Organizational and End User Computing, 34(6). https://doi.org/10.4018/JOEUC.300766

Lester, R. T. (2025). Natural language processing to evaluate texting conversations between patients and healthcare providers during COVID-19 Home-Based Care in Rwanda at scale. PLOS Digital Health, 4(1). https://doi.org/10.1371/journal.pdig.0000625

Lin, X. (2024). Construction of A Smart Hospital Innovation Platform Using the Internet + Technology. Alternative Therapies in Health and Medicine, 30(12), 495–505.

Mahdavi, A. (2023). Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review. Shiraz E Medical Journal, 24(11). https://doi.org/10.5812/semj-139627

Makovhololo, P. (2020). A Framework to Guide ICT Solution for Language Barrier in South African Healthcare. Journal of Cases on Information Technology, 22(2), 1–17. https://doi.org/10.4018/JCIT.2020040101

Manoharan, G. (2024). AI-Powered Chatbots for Mental Health Support. Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024, Query date: 2025-03-15 08:49:34, 436–440. https://doi.org/10.1109/IC3I61595.2024.10829185

Mendo, I. R. (2021). Machine Learning in Medical Emergencies: A Systematic Review and Analysis. Journal of Medical Systems, 45(10). https://doi.org/10.1007/s10916-021-01762-3

Mishra, S. K. (2024). Role of federated learning in edge computing: A survey. Journal of Autonomous Intelligence, 7(1). https://doi.org/10.32629/jai.v7i1.624

Ouerhani, N. (2020). SPeCECA: a smart pervasive chatbot for emergency case assistance based on cloud computing. Cluster Computing, 23(4), 2471–2482. https://doi.org/10.1007/s10586-019-03020-1

Ouerhani, N. (2022). Towards a smart pervasive conversational agent for COVID-19 psychological assistance based on NLP. 2022 International Conference on Technology Innovations for Healthcare, ICTIH 2022 - Proceedings, Query date: 2025-03-15 08:55:51, 59–63. https://doi.org/10.1109/ICTIH57289.2022.10112030

Ouerhani, N. (2023). Towards a French Virtual Assistant for COVID-19 Case Psychological Assistance Based on NLP. Lecture Notes in Networks and Systems, 715(Query date: 2025-03-15 08:55:51), 199–207. https://doi.org/10.1007/978-3-031-35507-3_20

Ranieri, A. (2022). Complementary role of conversational agents in e-health services. 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings, Query date: 2025-03-15 08:55:51, 528–533. https://doi.org/10.1109/MetroXRAINE54828.2022.9967603

Rehman, I. U. (2021). Features of mobile apps for people with autism in a post covid-19 scenario: Current status and recommendations for apps using ai. Diagnostics, 11(10). https://doi.org/10.3390/diagnostics11101923

Ruma, J. F. (2023). Outdoor patient classification in hospitals based on symptoms in Bengali language. Journal of Information and Telecommunication, 7(3), 336–358. https://doi.org/10.1080/24751839.2023.2196106

Sanjeewa, E. D. G. (2021). Understanding the hand gesture command to visual attention model for mobile robot navigation: Service robots in domestic environment. Cognitive Computing for Human-Robot Interaction: Principles and Practices, Query date: 2025-03-15 08:55:51, 287–310. https://doi.org/10.1016/B978-0-323-85769-7.00003-3

Sarker, I. H. (2021). Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Networks and Applications, 26(1), 285–303. https://doi.org/10.1007/s11036-020-01650-z

Shi, J. (2021). Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study. Frontiers in Medicine, 8(Query date: 2025-03-15 08:49:34). https://doi.org/10.3389/fmed.2021.781781

Xu, Y. (2022). A healthcare-oriented mobile question-and-answering system for smart cities. Transactions on Emerging Telecommunications Technologies, 33(10). https://doi.org/10.1002/ett.4012

Zaki, W. M. A. W. (2019). Smart Medical Chatbot with Integrated Contactless Vital Sign Monitor. Journal of Physics: Conference Series, 1372(1). https://doi.org/10.1088/1742-6596/1372/1/012025

Zhu, Y. (2022). Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics. IEEE Journal of Biomedical and Health Informatics, 26(4), 1422–1431. https://doi.org/10.1109/JBHI.2022.3163013

Authors

Achmad Ridwan
achmadridwan@umkudus.ac.id (Primary Contact)
Zain Nizam
Daniyar Satybaldy
Ridwan, A., Nizam, Z., & Satybaldy, D. (2025). Mobile Application Design Based on Natural Language Processing to Improve the Quality of Health Services. Journal of Computer Science Advancements, 3(1), 33–44. https://doi.org/10.70177/jsca.v3i1.1626

Article Details

Most read articles by the same author(s)