For hospital leaders tasked with managing unexpected surges in patient demand, the ability to anticipate and adapt to rapidly changing circumstances has become more essential than ever. What if we could predict potential bottlenecks in patient flow in real time – and prevent them before they occur?
While the pandemic has put critical care capacity under the spotlight like never before, hospitals around the world have long faced challenges with bed and staffing shortages to meet demand for acute care. Emergency departments (EDs) in many countries struggle with overcrowding even under regular circumstances. Intensive care units (ICUs) may be operating at or near capacity. All too often, waits and delays are the result – causing frustration, anxiety, and potentially harmful outcomes in patients, while adding to the pressure for staff [1].
It can be tempting to think that the solution lies in adding more beds or more staff. But typically, the problem is not merely one of resources. It’s also about better managing the beds you have. The real challenge is often one of patient flow: anticipating and knowing when to transition a patient from one care setting to the next.
It’s a highly complex and dynamic orchestration challenge, with many moving parts. Which patient waiting in the ED should get the next ICU bed? Which patient in the ICU can I safely move to a step-down unit to free up a bed? And who is ready to be discharged for home monitoring?
Managing patient flow requires an enterprise-wide view across different parts of the hospital or hospital network. However, that’s often precisely what’s missing today. With clinical and operational data dispersed across disparate systems, care teams lack wider situational awareness beyond their unit or department. It’s this lack of readily available and actionable information that can hamper patient prioritization, slow down patient transitions, and lead to unforeseen bottlenecks in patient flow.
The COVID-19 crisis has exposed and exacerbated many of these challenges. But it has also given rise to smart ways of tackling them. Healthcare providers have embraced centralized care collaboration models, sharing data in real time to visualize untapped capacity and facilitate patient transfers. And they’re not just relying on that data to get an overview of what’s happening from moment to moment. They’re also using it to forecast and prepare for future demand. For example, hospitals have successfully used predictive models to estimate the number of beds, equipment and staff needed for COVID-19 patients in the ICU and other hospital wards [2,3].
As we begin to think beyond the pandemic, there’s a unique opportunity to embed these data-driven practices into the everyday management of patient flow – from hospital admission all the way to hospital discharge and, ultimately, monitoring in the home. Using the power of AI and predictive modelling, we can extract relevant patterns and insights in patient flow and patient care needs from vast amounts of real-time and historical hospital data. After initial validation, the resulting algorithms can be updated on a regular basis to take recent trends and circumstances into account, thereby further optimizing predictive value. This enables hospital leaders and patient flow coordinators to orchestrate care more effectively across settings and rapidly adapt to changing circumstances.
Here’s what that may look like for one patient’s journey.
