This article was written by Dr. Taha Kass-Hout, Global Chief Science and Technology Officer, GE HealthCare, and was originally published on Forbes.
For patients, being admitted to a hospital can often be the beginning of a challenging journey. Test results can take hours—sometimes days—to arrive. Nurses, though compassionate, can often be constantly on the move, and securing even a few minutes with a physician can feel like a luxury. On these occasions, scheduling becomes a game of chance, with key appointments delayed and specialists booked out for months.
This isn’t a failure of any one person or institution. Rather, it’s the consequence of systemic challenges that no single actor can solve alone.
The challenges healthcare faces today
Today, healthcare systems face mounting financial strain, labor shortages and operational inefficiencies that not only increase costs but also limit the attention each patient receives. According to the World Health Organization, chronic diseases now account for 74% of global deaths, a sharp rise from 63% in 2008, leading to an increasing demand for medical services. All of this is unfolding at a time when the number of care providers is shrinking. According to the American Association of Colleges of Nursing, the United States will face a shortfall of 500,000 nurses this year, intensifying the challenges hospitals must navigate.
Hospital bed management is a prime example of how rising patient volumes and operational inefficiencies intersect. Hospitals often face bed shortages because patients who are ready to leave stay longer than necessary, taking up space needed for new patients in the ICU and emergency department. When beds don’t free up in time, it also affects the ICU since recovering patients can’t be moved out, creating a backup. This slows things down throughout the hospital, cascading impacts across multiple departments, including the maternity wing, surgical centers, emergency rooms and other units that all depend on each other to move patients smoothly.
These inefficiencies can inflate costs by prolonging stays in some of the most expensive “real estate” in healthcare. The operational cost for each ICU bed in 2010 alone ran up to $4,300 per day. Specialized stays are even more, making each inefficiency a direct financial hit.
In my role at GE Healthcare, I regularly see how AI is providing powerful solutions to some of healthcare’s most persistent challenges, cutting excess length of stay and improving bed capacity to achieve multi‑million‑dollar annual operating cost savings.
Here are three specific ways AI is helping to enhance hospital operations:
1. Predicting staffing needs
Because AI can detect patterns in data—much like a streaming service recommends your next show—hospitals can now better anticipate patient flow and resource needs. Predictive models analyze historical and real-time data to forecast bed occupancy, discharges and staffing demands, enabling more proactive decisions.
AI is able to generate short- and long-term forecasts, providing care teams with comprehensive visibility across the entire hospital system. GE HealthCare customer Duke Health, for example, used these solutions to cut reliance on temporary labor by 50% and improve productivity by 6%, aligning staff levels more closely with patient needs.
2. Providing recommendations for bed management
Bed management is one of hospitals’ toughest challenges, with patients arriving from emergency departments, procedural areas and transfer lines. Matching each patient to the right bed is complicated by limited availability and more than 20 clinical and operational factors.
For instance, when a unit bed opens and multiple patients are waiting, AI helps staff choose the best match by factoring in urgency, staffing and capacity constraints. The system also alerts teams when an ICU bed becomes available, ensuring the right patient receives timely care. The result: digital solutions and AI can help facilitate faster placements, ER congestion is reduced and costly resources are used more efficiently—without overburdening staff.
This also supports discharge planning by identifying patients who may need post-acute placement and surfacing factors like home health needs or high-cost prescriptions. By flagging barriers early, it can enable smoother, safer discharges, maintaining care quality after patients leave the hospital.
For example, in 2022, AdventHealth was able to reduce transport time to move patients from one location to another within the hospital by approximately 15 minutes and improve patient placement time by over 20 minutes through the use of AI. Today, AdventHealth has seen even more substantial improvement achieved with the help of AI.
3. Helping care teams prioritize decision making
AI-powered offerings can help enhance real-time decision-making processes by providing timely visibility into operational bottlenecks and helping hospitals rapidly resolve critical issues. Just as important, AI can automate clinical data management—extracting, cleaning and organizing chart information in the background—to help generate reports faster and more accurately. By relieving nurses and physicians of manual data entry tasks, these tools can help provide a clearer operational view.
Understanding the hurdles Of AI
Despite its promise, AI adoption in healthcare is not without hurdles. Data privacy standards, upfront integration costs and the extensive staff training required to operationalize new tools can pose challenges that slow deployment.
Robust data governance frameworks, phased roll‑outs and ongoing education programs are all key strategies to help mitigate these challenges. The use of no‑code workflows and natural‑language interfaces is especially helpful in letting clinicians interact with AI, using plain English rather than complex dashboards.
Ultimately, AI is well on track to offer transformative solutions to some of healthcare’s most persistent challenges from staffing shortages to operational inefficiencies. By using AI to optimize resource allocation, streamline clinical data abstraction and enhance feedback loops, hospitals can support their goal of improving patient outcomes, reducing costs and building a more resilient learning healthcare system.
As the demand for medical services continues to rise, healthcare leaders should explore how AI might integrate into their operations as they work to ensure a more sustainable and patient-centered future.
