Healthcare Chatbot for Symptom Analysis

 

Bridging the Healthcare Gap: Artificial Intelligence and Accessibility Project.

Introduction

Accessibility to healthcare is very essential in the current dynamic healthcare system (Alowais et al., 2023). This research aim is to create a healthcare chatbot that is expected to enhance access to healthcare, especially for the rural and underserved population (Attrey and Levit, 2019). This project aims to create an AI platform for users to understand their medical condition and take appropriate actions by using NLP and ML. Qualitative and quantitative methods will be used in the present research to gauge the impact of the healthcare chatbot in providing access to and earlier diagnosis of healthcare.

Project Description

 This project could be considered a step further in developing the healthcare technology industry that strives to make healthcare more accessible and more effective. The main objective is to develop a healthcare application with NLP and machine learning for symptom analysis. Patients will be required to enter their symptoms and obtain further information and advice on health-related matters. This is even more useful in linking people to healthcare services in poorly populated or disadvantaged geographical locations. Further, healthcare professionals can use this chatbot in the primary diagnosis and triage to help them in the management of the workflows in healthcare settings (Calvo et al., 2017).

 Preliminary Literature Review

The literature review also emphasizes important trends in healthcare technology and chatbots as well as the evolution of NLP and machine learning. Online medical services, remote healthcare, and telehealth have been presented in the healthcare sector due to the COVID-19 pandemic (Taylor et al., 2021). The use of chatbots is crucial in the transformation of the healthcare industry and it will help in the provision of instant medical information and customized responses that will improve communication and user’ satisfaction. New advances in NLP and ML have further improved the performance of chatbots in symptom interpretation diagnosis and treatment recommendations (Bhirud et al., 2019).

Moreover, healthcare chatbots promote patient empowerment and participation by providing quality information about health (Periera and Diaz, 2019). This helps people make decisions about their health. Consequently, the association between patient role and health care specialists becomes like a partnership that ensures better communication and improved health.

Research Inquiries

• Can a healthcare chatbot for symptom analysis advance access to medicinal attention?
• What are the building blocks and functionality of an active healthcare chatbot?
• How else can NLP and ML help to enhance symptom analysis?

Research Design and Methodology

Both quantitative and qualitative methods will be used in this study to answer these research questions.

 Quantitative Phase

Data Collection: The information will be sourced from medical databases; online surveys; and statistics on the use of chatbots. This includes medical data used for training the chatbot model and demographic and preference information and feedback from users.

Algorithm Development: Training and testing of NLP and ML models will be focused on. These include data pre-processing, feature engineering, algorithm selection, training, authentication, and testing. Accurateness, accuracy, recollection, and F1-score are evaluation metrics for the algorithms.

Prototype Development: A quantitative analysis will be carried out to assist in the design of a symptom analysis chatbot prototype, a risk assessment chatbot prototype, and a health recommendations chatbot prototype.

Testing and Validation: Quantitative testing and validation of the prototype will also take place and will include response time, completion rate, and user satisfaction.

 Qualitative Phase

User Feedback: Qualitative data related to the opinions, preferences, and experiences of the users of the chatbot will be collected through interviews, focus groups, and open-ended surveys.

Healthcare Professional Input: Stakeholder surveys will be directed to determine the efficiency and suitability of the chatbot for clinical practice.

Iterative Development: User and healthcare professionals’ comments will be used to further develop the chatbot and modify algorithms and interface to determine if the chatbot is appropriate for the final release.

Resources and Constraints

The project will require access to good academic databases and repositories as well as literature about healthcare chatbots and symptom analysis, NLP, and machine learning. It will be useful to have bibliographic management software as well as tools for literature review and thematic analysis. Challenges may include limited access to certain scholarly publications and bases and the sheer volume of sources. Some of the solutions include interlibrary loans, the support of another researcher in another institution, and proper management of time to deal with these issues (Oh et al., 2017).

Social, ethical, professional, and legal concerns.

The development of a healthcare chatbot is a social, ethical, professional, and legal concern (Xu et al., 2021). It is important to make sure that the chatbot does not exacerbate the already existing inequities in healthcare delivery and quality (Xu et al., 2021). Ethical risks are related to privacy, security, and informed consent of users. Professional concerns include the implementation of the chatbot into practice and its effectiveness. Ethical concerns include HIPAA and other healthcare regulations. 

Conclusion

This research aims to develop a healthcare chatbot that employs NLP and ML technology to improve healthcare access and early disease diagnosis. A mixed-methods approach will assist in exploring diverse aspects of the chatbot’s role in the provision of healthcare services in rural and underserved communities. The project will address the existing research questions and utilize state-of-the-art technologies as well as the social, ethical, professional, and legal elements necessary for the effective implementation and adoption of the healthcare chatbot into healthcare.

References

Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., Alqahtani, T., Alshaya, A.I., Almohareb, S.N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H.A. and Al Yami, M.S., 2023. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), p.689.

Attrey, R. and Levit, A. (2019). The promise of natural language processing in healthcare. University of Western Ontario Medical Journal, 87(2), pp.21–23. doi https://doi.org/10.5206/uwomj.v87i2.1152.

Bhirud, N., Tataale, S., Randive, S., & Nahar, S. (2019). A literature review on chatbots in the healthcare domain. Int J Sci Technol Res, 8(7), 225-231.

CALVO, R.A., MILNE, D.N., HUSSAIN, M.S. and CHRISTENSEN, H. (2017). Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5), pp.649–685. doi:https://doi.org/10.1017/s1351324916000383.

Oh, K.-J., Lee, D., Ko, B. and Choi, H.-J. (2017). A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation. 2017 18th IEEE International Conference on Mobile Data Management (MDM). doi:https://doi.org/10.1109/mdm.2017.64.

Pereira, J., & Díaz, Ó. (2019). Using health chatbots for behavior change: a mapping study. Journal of Medical Systems, 43, 1-13.

Taylor, A., Caffery, L. J., Gesesew, H. A., King, A., Bassal, A. R., Ford, K., ... & Ward, P. R. (2021). How Australian health care services adapted to telehealth during the COVID-19 pandemic: a survey of telehealth professionals. Frontiers in public health, 9, 648009.

Xu, L., Sanders, L., Li, K., & Chow, J. C. (2021). Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR cancer, 7(4), e27850.