Machine learning and predictive analytics can help hospitals better prepare for a rapidly aging population
A tsunami is coming, and it’s not made of water. The “silver tsunami” is a term that refers to the rapidly aging U.S. population. As Baby Boomers get older (and live longer), hospitals and Integrated Delivery Networks (IDNs) must ensure they have the staff, space, and supplies to properly care for them.
To get a sense of just how big this wave will be, consider the following:
- Within 10 years, all Baby Boomers in the U.S. will be older than 65 years of age.
- Within 12 years, older adults will outnumber children for the first time ever.
- Within 18 years, elders will comprise 20% of the population.
It’s only natural for people to require more care as they age. Older adults are more than twice as likely to require hospitalization than those in middle age. Additionally, Baby Boomers have higher rates of obesity, hypertension, high cholesterol, and diabetes than previous generations. When multiple conditions are present, hospital stays get more complex and costly.
To prepare for the silver tsunami, hospitals and IDNs must create data-driven master plans. Master plans are living documents by which hospitals determine future needs. They can help hospitals ensure they have the right resources available to handle an influx of older patients.
Creating a data-driven master plan
To prepare for a growing number of older patients, hospitals and IDNs must be able to accurately project everything from ICU bed availability to staffing numbers. Unfortunately, current processes are little more than educated guesswork.
Monte Carlo simulations and other mathematical algorithm models do not consider all possible interdependencies. Meanwhile, manual census planning is rife with potential risks, including human error, and is enormously time-consuming. Plus, it’s often focused on short-term census planning—not long-term demographic shifts.
Instead of relying on outdated manual processes and technologies to plan for the silver tsunami, hospitals and IDNs should use machine learning, predictive analytics, and process simulation—features offered in FutureFlow RX and MedModel. Building a master plan based on historical patient data, predictive analytics, and demographic data is a better and more effective strategy than spreadsheets or even business intelligence software.
Modeling what/if scenarios
Predictive analytics is a form of AI that continually takes raw, historical data and applies it to machine learning algorithms to provide actionable intelligence that can be used for long-term planning related to demographic changes. The more information the system ingests, the smarter it becomes—and the more accurate the recommendations it provides. By automatically combing different factors—including patient data, anticipated population numbers, the average age of local residents, and more—a hospital can conduct a What-if Scenario Analysis (WISA) to model what staff, space, and supplies the population boom will require.
A WISA is created using several data points, including anonymized information from past patients’ electronic health records. It’s based on historical data and can show the impact different variables and process changes can have on hospital operations and census capacity not just in the short-term, but years in advance.
The benefits of digital twins
Digital twins can take a hospital’s master plan to the next level. A digital twin is a virtual replica of a physical object or system—for example, a hospital facility, or the components of that facility and its processes (exam rooms, hospital rooms, and patients). Digital twins can offer an accurate representation of how the silver tsunami is likely to affect capacity needs in the future. Visualizations of workflow in a digital twin can be extremely effective in seeing bottlenecks and the impact of changes before they’re implemented.
WISA and discrete-event simulation (DES) can be modeled in the digital twin. Discrete-event simulation (DES), for example, can predict older patients’ lengths of stays and movements through a hospital based on medical data from previous similar patients. These scenarios allow hospitals to see potential impacts and determine contingency plans based on different population trends. Possibilities can be re-run and re-analyzed as often as needed. Some digital twins allow for a dynamic and visual replay of how patients, staff, and equipment move throughout a clinic or department.
Predictive analytics, digital twins, WISA, and DES are all driven by machine learning capabilities that get smarter over time, resulting in more accurate insights. With this accurate intelligence, hospitals can better understand when additional resources will be needed. These tools can also keep hospitals from over-preparing for the silver tsunami. Paying for staff and supplies that aren’t needed is wasteful and costly. The key to success is a right-sized master plan that can be adjusted as new information comes in. It’s a tool that’s always at your fingertips; no more digging for the right spreadsheet.
The bottom line
The silver tsunami is coming—ready or not. A proactive approach based on actual data can help hospitals know what resources they need to treat older patients effectively. Predictive analytics, DES, WISA, and digital twins are smarter and more efficient means of planning for the population boom than old-school manual methods. These tools can help hospitals fine-tune their master plans, ensuring they have the right amount of space, staff, and supplies to accommodate a wave of older patients.
Learn more about how to create a data-driven master plan to prepare for the silver tsunami. Download our whitepaper: Preparing for the Baby Boomer Boom. To learn more about BigBear.ai’s capacity planning solutions visit FutureFlow RX.