It’s a typical annual occurrence. As summer wanes and winter beckons, patient volumes surge. Influenza always accounts for a large part of the seasonal influx, however this winter may bring a triple threat. Not only is the flu predicted to be particularly aggressive, but a resurgence of coronavirus and an outbreak of childhood respiratory syncytial virus (RSV) will certainly put a significant strain on hospital capacity.
It’s going to be a tough winter. Warnings from industry organizations and media outlets suggest a busy time.
The World Health Organization and the European Center for Disease Prevention and Control warned that the continent is probably entering a new coronavirus wave, which will coincide with a resurgence of the flu.2
In the US: NBC News spoke with six doctors across five states, California, Illinois, Massachusetts, North Carolina, and Rhode Island, all of whom said pediatric hospital bed capacity has been strained due to an influx of RSV patients.3
Most cases of COVID, flu and RSV are likely to be mild, but together they may sicken millions of Americans and swamp hospitals, public health experts warn.4
Healthcare leaders must do everything they can to plan and prepare for the increased demand for services. Freeing up capacity so that staff can ensure all patients get the care they need is imperative.
With the challenges of tighter budgets, staffing shortages and the complexity of running an interconnected hospital system, responding to the surge in demand this winter will be difficult. Issues will arise that include long wait times, overwhelmed staff, bed shortages, and even the possibility of turning away patients. Finding an optimal solution requires testing, measuring and quantifying a variety of options for improved operational workflow. But this must not come at the expense of continuous service.
Optimization can now be accomplished through the use of a digital twin, a digital replica of healthcare operations. The digital twin uses real-life variability to build a virtual model. This is achieved by utilizing historic data sets and converting them in to a real-time, minute-by-minute simulation. Experts gather statistics of the virtual hospital and run analyses to drive actionable insights to optimize hospital operational improvements and quantify benefits of viable solutions before they are implemented.
By accurately depicting real-life operations, various winter resilience initiatives can be tested to understand which will work. Insights will determine to what degree they ought to be implemented to be most effective – delivering care at the right time to the right place. This gives healthcare leaders confidence they are prepared for the up-coming winter surge.
Digital twins provide answers to key questions that will soon be essential to every healthcare enterprise’s digital strategy.1
The key to solving the surge problem is the ability to predict demand into the future (at least two weeks out) and to manage staff based on those demands. Demand forecasting is the analytic approach that works to predict hospital demand so that resources can be optimized in response. Demand forecasting goes beyond traditional analysis of demographics, population growth and disease incidence and delves into the more complex sequence of events that drive supply and demand. Its job is to uncover inefficiencies and hidden friction points. Simulation, or digital twin, is a related tool that’s come to prominence in forecasting the accuracy of demand estimates. Its strength lies in the ability to aggregate large qualities of historical data and provide a real-time view to ‘what-if’ scenarios.
Amidst this year’s ever-increasing winter pressures, it important to ensure the most efficient and effective use of limited health service resources. Use of robust simulation (such as creating a digital twin) is important to test prospective scenarios in a safe virtual environment.
In the United Kingdom, a recent article titled, NHS to roll out winter war rooms as it prepares for busiest period ever5, showcases the new ‘care traffic control centers’ designed to support the expected surge.
“The new 24/7 control centers – dubbed ‘data driven war rooms’ – are expected to be rolled out in every local area and will manage demand and capacity by continuously tracking beds and attendances – a move that will see all activity and performance reviewed for the first time.
Matthew Taylor, chief executive of the NHS Confederation, said: ‘This winter could be the toughest on record for the NHS, which is exactly why services are working together early on to make sure patients get the care they need, where they need it most.’”
How can we know the initiatives will work and are of the right magnitude to scope with a winter surge? Philips digital twin offers a platform in which winter initiatives can be tested in a safe virtual environment to determine if the transformative measures are sufficient to ensure patients gets the care they need, when and where they need it most.
Techniques such as digital twin have been available for decades, but despite ample research which demonstrates potential, their application in health services has been limited. Now, creating a digital twin is easier given improvements in technology. Healthcare leaders are urged to use the digital twin capability to help manage capacity this winter.
Setting a plan in motion based on insights delivered can soften the impact of winter’s arrival.
1 Accenture Digital Health technology Vision 2021, June 7, 2021, The power of massive, intelligent, digital twins
www.accenture.com/us-en/insights/health/mirrored-world
2 www.washingtonpost.com/travel/2022/10/17/covid-wave-holiday-travel/
3 www.nbcnews.com/health/health-news/surge-rsv-virus-fills-hospitals-can-severely-sicken-babies-rcna52082
4 www.nytimes.com/2022/10/23/health/flu-covid-risk.html
5 www.nationalhealthexecutive.com/articles/nhs-roll-out-winter-war-rooms-it-prepares-busiest-period-ever
Sudin Kansakar Modeling and Simulation Lead
Sudin’s experience includes over 12 years of successfully heading complex data-led healthcare transformation programs.
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