Abstract
Outpatient care plays a crucial role in modern health care, often involving diagnosis, treatment, and follow-up care outside of hospital settings. The complexity of outpatient care coupled with the need for efficient and accurate decision-making, makes expert system a valuable tool for healthcare providers. The system aimed at the development of an expert system that leverages in artificial intelligence and medical knowledge to assist healthcare professionals in diagnosis. Evaluating the system’s ability to provide timely and accurate treatment recommendations, analyzing the user-friendliness of the system’s interface for healthcare practitioners and optimizing workflow processes are also captured. A thorough system study and investigation were done and data collected via interview; analysis about the current system using document and data flow diagrams was carried out. The methodology adopted is Top Down Model approach. Data flow, Entity Relationship diagram, Laravel (which is a dynamic PhP frame work) were the tools used for the development of the software. This work’s finding underscores the potential of expert system to enhance outpatient healthcare by improving diagnostic accuracy and facilitating evidence-based treatment decision. Evaluation with target users depicted that the system is efficient and user friendly.
References
Abdulsalam A., Al-kabi J., Mohammed-Ali A. (2020). Expert System for diabetes with Uncertainty. AIML 05 Conference, 19-21 December 2020, CICC, Cairo, Egypt. The principle and practice of diabetes medicine, fourth edition New York.
Almalki A. and Aldosari B. (2019). Role of Medical Expert system in Health care. In the India context, defense medical scientific information & documentation center (DEMSIDOC), Volv 47, No. 4, Kandan swamy.
Amara A. W. et.al., (2019). Development of a web-based outpatient information system support in oncology. Retrieved from https: www.conferencesworkdevelopedwebtoretrieveinformation.com/outpatient.
Bendavid L., Boeck H., and Phillipe R. (2010). Redesigning the replenishment process of medical supplies in hospitals with radio-frequency identification technology. Business Process Management Journal, 16 (6): 991-1013.
Bhakoo V. and Chan C. (2020). Collaborative implementation of e-treatment process within the health-care supply chain: the Monash Pharmacy Project. Supply Chain Management: An International Journal, 16 (3): 184-193.
Khanam J. and Aris P. (2018). Management of Hypertension in the Digital Era: Small wearable monitoring Devices for Remote Blood Pressure Monitoring. Hypertension 2018 Sept. ; 76(3) :640650. Doi:1161/HYPERTENSIONAHA. 120. 14742. Epub 2018 Aug3.PMID: 32755418 ; PMC7418935.
Laudon, K. (2018). Web Management Information Systems. Macmillan 2018.patient web. detail form and experts management conversions, 209.int.doct 081; London-Nov7 INC.
Li S. et. al. (2017). Expert system for chronic heart failure. Heart disease measure in medical lab tech. Australia.
Liu S., Zhu D. and Zhou E. (2020). Wireless medication history, drug-drug sensors for Medical Applications: and future challenges.10.23919/EuCAP.2017.79orm https://www.mayoclinic.org/diseases-condition/drug-drug-sensor/syc-2023554 6528405.
Peters, F. (2019). Design and implementation of AI reasoning mechanism in the context of patient support. Mac,2367,10.23919/Eu CAP. 2017.7928405, faculty of science in expert learning.
Sander, P. (2016). Health and vital statistics, Hutchison publication. Retrieved from http://heath statistic.com/intuition machine/theory-practice-3426f7edb38.
Sezgin D. and Kucuk Y. (2021). Expert Doctor Verdis: Integrated diabetic medical expert system. Turkish Journal of Electrical Engineering & Computer Sciences (GDPR or HIPAA).
Thron T., Nagy G., and Wassan N. (2007). Evaluating alternative supply chain structures for treatment recommendation. The International Journal of Logistics Management, 18(3): 364-384.
Xie Y. et. al., (2019). Autoimmune machine, rheumatoid arthritis, and systemic lupus erythematosus. Retrieved from https://www.mayoclinic.org/diseases-condition/low-blood-pressure/symptomscauses/syc-202355465.
Zhang X. et. al., (2018). Expert System on chronic obstructive pulmonary diseases in outpatient. Data science from http://Expert-System on chronic obstructive-pulmonary diseases in outpatient.