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SENIOR LIVING AND THE USE OF ARTIFICIAL INTELLIGENCE FOR INFECTION PREVENTION AND CONTROL
Because senior living communities are susceptible to infectious
outbreaks, they must continue with adherence to the basic prin- ciples of infection control. Many of the traditional IPC precau- tions used during the COVID-19 outbreak, such as masking, social distancing, manual surveillance, symptom monitoring, testing and using PPE, were effective for stopping the virus from spreading. However, in retrospect, traditional surveillance required much time, staff and medical supplies and was often exhausted and not easily monitored. In looking to the future, improved pandemic preparedness will be critical in navigating pandemic threats. Tech- nology and artificial intelligence applications may become integral to a community’s infection prevention and control program (IPC). Since the pandemic, there has been a heightened awareness
of innovation and technologies emerging in healthcare. There are AI applications to assist communities in their overall infection prevention and control plans.
How technology and AI can assist in infection prevention COVID-19 transformed healthcare delivery in many ways. Sensor-based technologies have provided opportunities for resident virtual assessments and changed caregivers’ approach to deliver and monitor resident care. Mobile health technologies and other sensor-based technologies, when placed in the community and used in combination with staff, have added an extra layer of monitoring for residents in their assisted living or memory care environments. The advantages of AI for infection prevention and control can be noted in healthcare-associated infection (HAI) surveillance programs. AI can identify infections early and design customized antimicrobial stewardship strategies for residents. AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling the development of tailored IPC interventions.
AI risk and challenges
AI tools show promise for improving senior health care by predicting health trajectories, recommending treatments and automating administrative tasks. The rapid evolution of AI presents unlimited opportunities. However, operators must be mindful of challenges and risks associated with the use of AI, such as:
• Data access. Developers experience difficulties obtaining the high-quality data needed to create effective AI tools.
• Bias. Limitations and bias in data used to develop AI tools can reduce their safety and effectiveness for different groups of patients, leading to treatment disparities.
• Scaling and integration. AI tools can be challenging to scale up and integrate into new settings because of differences among communities and resident populations.
• Socioeconomic inequality. Algorithms may create an opportunity for abuses, such as sparking concerns for job loss because of automation.
• Limited human integration. AI cannot replace the benefits of human companionship and emotional connection.
• Lack of transparency. AI tools sometimes lack transparency.
• Privacy. As more AI systems are developed, large quantities of data will be in the hands of more people and organizations, adding to privacy risks and concerns. Privacy may not be assured for virtual care or AI, and personal health information may be at risk of breach.
• Uncertainty over liability. The multiplicity of parties involved in developing, deploying and using AI tools is one of several factors that have rendered liability associated with the use of AI tools uncertain.
• Training. AI works by learning from the data it is fed. The data the systems receive must be accurate. It is important to remember who is inputting the data. Are cultural biases being passed on to the system? If the system is designed poorly, it can misdiagnose.
• Regulatory and legal challenges. AI is subject to regulatory and legal considerations; compliance with privacy regulations is complex.
• Data issues and cyber breaches. Data collection and ethics must go hand in hand. Protecting residents from any data leaks is also integral to the safe use of AI in medicine. There should always be full disclosure to residents. They need to be made aware that their information is being used to feed an AI algorithm.
• History and volume of data. AI requires massive data sets in order to “learn,” thus needing volumes of resident health information for training and validation.
16 SENIOR LIVING EXECUTIVE SEPTEMBER/OCTOBER 2023
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