Page 138 - Emerging Trends and Innovations in Web-Based Applications and Technologies

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IV. Proposed Research Model: the early warning of an outbreak or the peak of patient
A. Input Phase influx.
User inputs symptoms via web or mobile interface. A Impact: Better preparedness and faster response time in
machine learning-based analysis processes symptom data in
times of crisis.
real time. Wearable devices provide continuous health
monitoring data. 3. Barriers to Implementation
Conclusion: Although there are benefits, challenges such as
B. Processing Phase
technology literacy, data privacy, and equal access continue
AI-driven triage assigns severity levels and provides
to be a significant challenge.
recommendations. Machine learning models analyse
patterns to predict potential health risks. The system Observations: Rural and poor communities lack the
continuously updates patient health information. infrastructure and financial resources to fully utilize these
applications. Enhanced access to emergency treatment
C. Output Phase
Emergency alerts sent to a healthcare provider and VII. Conclusion:
healthcare workers. Personalized health recommendations AI-driven online healthcare platforms hold immense
including diet and exercise. Direct integration with hospital potential in transforming emergency medical response and
emergency services. daily health management. By integrating machine learning
V. Performance Evaluation: IoT and predictive analytics these platforms provide real-
The performance of the proposed system is evaluated on: time health monitoring, automated triage and improved
patient care. Artificial Intelligence will increase health
Metric Description Expected Outcome Accuracy Correct outcomes more quickly and efficiently at an all-in-one cost
classification of emergency cases > 90% Response Time saving of up to 8 billion dollars. Result Analysis: The
taken to provide recommendations enhanced access to proposed platform will reduce emergency response time by
emergency treatment.
automating the triage and prioritization of cases. Improve
Findings: Improvements in online platforms have diagnostic accuracy through AI-driven symptom analysis.
significantly increased accessibility to healthcare services in Optimize access to healthcare by integrating wearable
emergency conditions. There is fast connectivity to medical devices for real-time monitoring. Enhance communication
professionals and ambulance services or finding nearby between patients and healthcare providers through chatbot-
emergency facilities. based interactions.
Key Metrics: Reduced emergency response times for critical References:
care, improved survival rates, and wider reach in rural and [1] Thakre, K, Rothe, P. The year 2022 saw the work of
poorly serviced areas. Kukade, S, and R. Health Care Chatbot Using NLP and
Flask. Retrieved from Academia.edu.
Expected
Metric Description
Outcome [2] Johnson, N. Weiner, M. Jahng, and A. W. (2005).
Correct classification Radiology of Alzheimer Disease. DOI: 10.1016.
Accuracy > 90%
of emergency cases [3] 2022 marks the year of Ali, L. Chakraborty, and Z.
Response Time taken to provide
< 10 sec Predictive Analytics in Parkinson's Disease. DOI:
Time recommendations
10.1007/s00521.
Resource Efficiency in allocating Optimized
Utilization emergency resources [4] Artificial Intelligence in Healthcare by Raj Kommu,
User Effectiveness of AI Springer, 2021.
High
Satisfaction recommendations [5] Deep Learning for Healthcare by Md. Rezaul Karim,
VI. Result Analysis Packt Publishing, 2018.
1. Telemedicine [6] Hossam H. Sultan, Nancy M. Salem, Walid Al-Atabany
Findings: Telemedicine services are significantly utilized for (2019). "Multi-classification of Brain Tumor Images
distant consultations, particularly during disasters or Using Deep Neural Network." IEEE Access, 7:69215-
pandemics. This has helped in reducing the pressure on 69225. DOI: 10.1109/ACCESS.2019.2919123.
hospitals and ensured continued care.
[7] Bilal Alatas, Moradi Shadi, Tapak Leili (2022).
Outcomes: Reduced overcrowding in emergency rooms and "Identification of Novel Noninvasive Diagnostics
optimal use of healthcare resources. Biomarkers in Parkinson’s Diseases." Hindawi. DOI:
2. AI and Big Data Integration 10.1155/2022/8125631.
Findings: The online platforms based on artificial intelligence
(AI) and big data analytics provide predictive insights, like

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