Monitoring Health Parameters for Critical Patients and Raising Alerts to Reduce Fatality Rate

• Introduction: This case study delves into how EA Technologies developed a proof of concept (POC) for an old-age healthcare center in Hong Kong. The POC aimed to enhance medical treatment efficiency by continuously monitoring health parameters, medication intake, and patient habits. By leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms, the solution demonstrated the automatic generation of alerts and preventive actions to mitigate health deterioration risks.
• Background: The client, an old-age care center in Hong Kong, provides high-quality healthcare services to elderly patients. They sought to implement an alert system based on diverse health parameters and patient behaviors. This system would operate around the clock, monitoring patients’ various metrics, medication adherence, and taking preventive measures when necessary.
• Problem Statement: The client needed to eliminate the manual recording of health parameters and medication intake, which was inefficient and time-consuming. Their primary objective was to expedite data collection and validation, thereby reducing operational costs and improving overall healthcare efficiency.
• Objective: The primary objective was to develop an automated alert system that would trigger alerts when any health parameter exceeded predefined thresholds. This system aimed to prevent the deterioration of patient health and reduce the fatality rate among elderly patients.
• Solution: EA Technologies’ solution team analyzed the client’s challenges and proposed the development of a proof of concept (POC) to address them:
• Analysis: The team recognized that manual recording of various health parameters and medication intake was not efficient and consumed substantial time. They proposed a streamlined approach involving the use of sensors to continuously capture health parameters. These data would then be uploaded to a central server, where AI and ML algorithms would assess the data against predefined thresholds.
• Alert Generation: When any health parameter breached the predefined threshold, the system automatically generated alerts. These alerts were transmitted to the respective doctors’ mobile phones, ensuring immediate attention to critical patient conditions.
• Preventive Measures: The system also provided recommendations for preventive actions based on historical data and patient-specific profiles. This proactive approach aimed to reduce the risk of health deterioration.
• Conclusion: EA Technologies successfully implemented a proof of concept (POC) that addressed the client’s need for continuous health monitoring and alert generation. By automating the process and leveraging AI and ML algorithms, the healthcare center can now react promptly to any parameter breach, ultimately reducing the fatality rate among elderly patients. This case study highlights the critical role of technology in enhancing patient care and improving healthcare outcomes.