How health systems can leverage data science to address workforce shortages | Derek Streat

By strategically utilizing data-driven tools, health systems can optimize operations, better balance staff and patient demand, and alleviate the burdens on staff.

Numerous foundational flaws of the U.S. healthcare system were exacerbated by the pandemic and brought to light among the public, including issues around health system staffing.

While workforce turnover and shortages have been a continuous challenge for over a decade, this issue has reached a point of crisis over the past year. The U.S. is currently on track to be short by one million healthcare workers by 2030 and, globally, the shortage is estimated to balloon to a staggering 18 million.

The current crisis has been compounded by several contributing factors and trends over the last few decades. At the forefront is today’s aging U.S. population and, with it, the increased need for long-term healthcare – a demographic trend projected to continue in the coming years.

The influx of older adults, many of whom have complex conditions, in combination with a drastically decreasing number of healthcare workers, has made it difficult for providers to manage demand and for patients to get the care they need.

In addition, the shift toward healthcare consumerization has changed patient expectations. Consumers are now seeking healthcare on their own terms and in new forms. While this trend has driven great innovation in telehealth, direct-to-consumer care, and other patient-centric ways of delivering care, it has also heightened patient expectations and demand of these options.

Patients now expect to be able to find care quickly, conveniently, and digitally, but this challenge is compounded by how many health systems are still working toward digital transformation, making it difficult to fulfill these demands for accessibility.

All of these factors have brought us to today’s dire situation in which patient load is rapidly outgrowing the shrinking healthcare workforce.

Nearly a third of healthcare workers across all health systems are currently considering leaving their roles, and health systems have failed to invest in employee retention.

While the list of actions that health system leaders should take to retain their workforce is vast, a strong place to start is implementing new technology.

By strategically utilizing data-driven tools, health systems can optimize operations, better balance staff and patient demand, and alleviate the historic burden being placed on staff.

Leveraging data to ease the burden on patients and staff

Health systems today have no shortage of data between all the information contained in electronic health records (EHR) and encounter data when a patient interacts with a health system. The issue for the health systems is how to leverage data to gain actionable insights and make improvements for patients and providers.

Many health systems have adopted technology that utilize artificial intelligence (AI) and machine learning (ML) to assist with streamlining everyday tasks. But these tools can be further leveraged to improve upon load balancing, staff allocation, and resource optimization.

Health systems can employ predictive modeling that utilizes AI to intelligently match patients and providers. By applying ML algorithms that consider specific patient needs and provider capacity, the health system can create the optimal match for any type of care scenario.

Leveraging data science for predictive modeling enables the health system to get the most out of its people and resources by allowing physicians to practice at the top of their licenses. Not only does it help to alleviate the impacts of workforce challenges, but it also takes the guesswork out of a patient choosing a specific provider, ensuring that the patient receives the best possible care.

Use cases for data-driven technology

The benefits of leveraging data science have been exemplified by many U.S. health systems since the onset of the pandemic.

Providence was quick to adopt this technology in March of 2020, using data-driven insights to triage patients and strategically load balance between virtual and in-person care appointments. The technology enabled them to flex their workforce from in-person to virtual in mere minutes—because the system was aware of both patient demand and resource supply, it was able to orchestrate the two instantaneously.

This approach allowed physicians to focus on treating patients with severe Covid symptoms, while patients with less severe symptoms or other low-acuity conditions were advised to utilize telehealth and remote monitoring programs. By using these tools, the health system was able to scale their resources and adapt to increased demand at a time of high patient need.

Another success story is with leading health system Froedtert, which uses data-driven technology to intelligently triage musculoskeletal patients between physical therapy and surgery.

Many patients with back pain are unsure what type of care is most appropriate—often seeing an orthopedic surgeon when less invasive therapies offered by physical therapists would suffice. By using data-driven technology to direct patients to their ideal care option, Froedtert has been able to strategically allocate staff, save time for patients and providers, and deliver more appropriate and cost-effective care.

Taking a data-driven approach can also effectively expand the provider workforce, which has been seen at Kaiser.

This technology has enabled the health system to load balance across the country, pairing patients with providers virtually no matter their location or time zone. This approach not only eases the burden on staff in specific locations, but it also ensures that patients get the best care possible by expanding provider access.

Making progress in alleviating workforce challenges

Data-driven technology equips leaders with various metrics that can be monitored to measure health systems’ progress in alleviating workforce challenges.

Net promoter scores (NPS) are key in measuring overall satisfaction, in addition to other important indicators like resource utilization, patient throughput and costs. Though these metrics do not necessarily mean that the workforce shortage is being resolved, they can inform a health system about employing their staff and resources in the most effective way possible to help mitigate the effects of such shortages.

Health systems must be able to innovate and adapt swiftly in response to industry trends and challenges, like the current workforce crisis. Developments in data-driven and AI-enabled technology have already helped to alleviate some of the burden on health systems.

However, there is still much progress to be made in regard to fundamentally fixing the workforce problems that plague healthcare professionals and in ensuring that all patients are able to access and receive adequate care.

With the workforce shortage in healthcare growing rapidly, taking a data-driven approach to staffing, operations, and resource allocation will be the key to offsetting the effects for health systems and helping to create a better experience for both patients and our over-burdened providers.

About the author

Derek Streat is founder, CEO and chairman of DexCare, a data-driven intelligence company focused on healthcare access.