By Heather Annolino, Senior Director Healthcare Practice, Ventiv Technology
Thousands of long-term care facilities in the U.S. face significant shortages of personal protective equipment (PPE) such as masks, face shields, goggles, gloves, and gowns as they seek to safeguard their residents and staff against the spread of COVID-19 and other communicable viruses. As facilities face the challenge of keeping their residents healthy and safe, securing these vital resources can reduce resident safety incidents.
As most residents of these facilities are older (and in many cases less healthy than the overall population), there is a greater risk of an outbreak spreading among residents causing severe illness and even a higher rate of death. To protect residents and staff members against infectious viruses, facilities need to collect and analyze real-time data related to resident safety incidents. This will help secure and manage the medical resources required to treat residents as supply chain disruptions continue.
Predictive analytics and data-discovery solutions can help reduce resident safety events caused by supply chain disruptions at long-term care facilities by improving longstanding processes to eliminate gaps to provide an environment free from harm.
To effectively reduce incidents associated with supply chain disruptions, those working in long-term care would benefit from an integrated risk management solution that uses AI and predictive analytics to remove perceived biases in decision-making. Without proper equipment, they are working under duress, leading to more errors or issues related to resident care. But an analysis of these incidents could reveal supply chain/inventory issues contributing to the incident for assuring adequate PPE availability, testing supplies, pharmaceuticals, qualified staff, and other critical supplies.
For instance, a study published in April 2020 in the International Journal of Medical Informatics found that AI holds the potential for identifying frailty among residents of long-term care facilities. Meanwhile, a McGill University professor in Montreal is leading a team that employs real-time AI to track, monitor, and predict COVID-19 symptoms among high-risk seniors at long-term care facilities. And a lab at MIT has rolled out AI-fueled software that tracks long-term care facilities’ daily activities –to streamline operations and increase safety.
However, it’s in the supply chain where long-term care providers may realize some of the most significant financial benefits from AI-enabled predictive analytics and data-discovery tools. Data shows that the global share of healthcare spending dedicated to long-term care services jumped from 31% in 2001 to 46% in 2015. The data comes from countries belonging to the Organization for Economic Cooperation and Development. This growth underscores the value that AI can bring to the supply chain for long-term care facilities.
As care costs continue to rise, more long-term care communities will continue to struggle to address costs while remaining competitive. Amidst the pandemic, the ability to secure healthcare supplies in a timely and cost-effective manner is essential for the budgeting process and residents’ and staff members’ safety.
Planning for the unknown is vital when decision-making must occur in real-time, and it is crucial to have all the information available to decide what comes next in a crisis. Efficient risk management and resident safety strategy require software systems to capture critical data insight allowing care leaders to track and analyze all variables associated with risk incidents. Ultimately, through enhanced processes, leaders equipped with the correct data can confidently shape and execute a tactical plan to mitigate new and historical resident safety risks and safeguard staff to ensure they are effectively securing needed medical supply inventory.
About the Author
Heather Annolino, RN, MBA, CPHRM, is the Senior Director of Healthcare Practice at Ventiv, where she plays an integral part in developing Ventiv’s Patient Safety solutions.
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