By Jonathan Shoemaker

Hospitals today are fighting a daily battle – not just for resources, staff, or space, but the staffed bed for the acute inpatient stay.

The ability to move patients seamlessly through the acute patient journey is increasingly constrained by post-pandemic realities: staffing shortages, rising costs, and increasingly complex patient needs. These challenges are not going away anytime soon. In fact, a recent study published in JAMA Network Open found that by 2032, the U.S. could face a full-scale hospital bed shortage.
With national hospital occupancy rates now at 75%, dangerously close to the threshold where hospitals begin running out of available beds, health system leaders must act now. The answer isn’t simply more beds – it’s smarter patient flow. Artificial intelligence (AI), predictive analytics, and automation offer new ways to anticipate patient needs, proactively manage capacity, and create a more efficient and coordinated care environment.

The real cost of poor patient flow
Many hospitals still operate under a reactive model when it comes to patient flow, relying on outdated manual processes to manage admissions, transfers, and discharges. This results in avoidable bottlenecks that delay care, decrease efficiency, and impact financial performance.

Consider this: when patients stay in an acute care setting longer than necessary due to delays in discharge or transfer, they not only could have compromised outcomes, but they also occupy beds that could be used for new patients. Hospitals then face situations where patients in the emergency department (ED), transfers, or those awaiting scheduled procedures have nowhere to go. This creates overcrowding in the ED, increases the burden on care teams, and ultimately leads to poorer patient outcomes.

The traditional approach to managing patient throughput is no longer sufficient. Hospitals must move away from manual, siloed decision-making and embrace real-time intelligence that enables proactive, strategic management of patient movement across the hospital and health system. AI and predictive analytics provide the necessary visibility and decision support to improve patient throughput in several ways:

• Real-time capacity awareness: AI-driven platforms continuously analyze real-time and historical data to predict demand surges, identify bottlenecks, and recommend actions to optimize bed utilization.
• Automated admission and discharge prioritization: AI can assess which patients are most in need of admission and which patients are ready for discharge, ensuring that the right patient is in the right bed at the right time.

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