The cloud-based solutions are being used by incident command centres for predictive planning and capacity creation capabilities. Credit: Adhy Savala on Unsplash.

Artificial intelligence-based patient flow automation systems provider Qventus has deployed its Covid-19 solutions to help hospitals manage surge during the ongoing crisis.

The solutions include Localized COVID-19 Model & Scenario Planner, Critical Resource Mission Control, ICU & Med-Surg Capacity Creation and Length of Stay Optimization.

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These cloud-based solutions are being used by incident command centres for predictive planning and capacity creation capabilities, said the company.

Qventus added that the solutions are available for new clients to implement within weeks.

Qventus co-founder and CEO Mudit Garg said: “As we work with leading health systems, two common themes emerge: the need to plan for and get visibility into the current status of critical hospital resources, and the need to free up these resources – to create ‘virtual’ capacity – to mitigate the impacts of a surge in demand.

“These capabilities are proving to be essential during the Covid-19 outbreak, and will remain important as systems manage through the uncertain environment that will occur with the removal of social distancing, resumption of elective surgeries, flu season, and potential seasonal resurgence of the coronavirus.”

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The company’s Localized COVID-19 Model & Scenario Planner enables prediction of the impact of the coronavirus pandemic on a specific hospital or region, such as new cases and capacity.

Hospitals can use the solution to optimise resources as it can estimate demand for beds, ventilators and PPE depending on the recent local geography data.

Designed to facilitate load balance, the Critical Resource Mission Control solution deliver real time insights into requirement for key resources such as ICU beds, ventilators and negative airflow isolation rooms.

Meanwhile, ICU & Med-Surg Capacity Creation is meant to reduce pressure from ICUs and generate capacity in nursing (Med-Surg) units.

It leverages machine learning to offer early recommendations of patients in ICU who can be moved to nursing units. Also, the solution reviews and prioritises discharge candidates for central review, thereby expediting discharge and unlocking beds.

Length of Stay Optimization uses AI-powered discharge planning and advanced process analytics to develop virtual capacity via decreasing length of stay. It is intended to support demand increases in the future and cut excess costs.