Staying Ahead of the Surge

1. Healthcare systems today are dealing with unprecedented operational strain. From your perspective, what are the biggest structural inefficiencies preventing hospitals from responding effectively to surges in patient demand?

Healthcare delivery is inherently complex, with hundreds of variables across capacity, staffing, beds, patient needs and more. This complexity drives two big challenges: forecasting and resource utilization. The ability to predict what’s coming is critical because even a single bottleneck can strain resources and create a ripple effect across the care continuum. Connected to that challenge is efficient resource use. Lack of visibility into what’s likely to happen across the system makes it challenging to enable thoughtful forward planning. But creating a complete, predictive operational picture isn’t easy. That’s because data is often fragmented across electronic systems and, in some cases, manual tracking tools. This is where AI can make a meaningful difference, helping hospitals connect disparate data points and generate a clear, predictive view.

2. AI is increasingly being positioned as a decision-support tool rather than just an automation layer. How is AI changing the way hospital leaders approach operational strategy, resource allocation, and real-time care coordination?

Finding more ways to enhance operational efficiency and increase access to care is more important than ever. Health systems are increasingly facing resource constraints, rising costs, and growing patient volumes driven by an aging population and the rise of chronic diseases.

At GE HealthCare, we have been using machine learning- and AI-enabled technology to help health systems maximize their resources and increase access to care. The impact is real. We are seeing health systems increasingly use AI-enabled technologies to inform operational strategy, resource allocation, and real-time care coordination. This includes helping organizations understand which care setting is most appropriate for each patient—balancing individual care needs with the effective use of resources.

Duke Health is a good example. We have worked with Duke to implement AI-enabled operational software to enhance data-driven operational decision-making and improve access to care. That work has helped the health system more efficiently assign the right staff to the right place and level of care, resulting in a 66% decrease in bed request-to-bed assigned time. With high accuracy in predicting staffing needs two weeks in advance, they also were able to create capacity for 500 additional patients annually, enhancing access to care without facility expansion.

3. Many health systems still struggle with fragmented data environments. How important is interoperability in enabling AI-driven command centers to deliver actionable insights across departments such as emergency care, inpatient services, and staffing operations?

Health systems face a data paradox. They are data rich but aren’t harnessing the full operational value of that data because the data is fragmented and multi-modal—living in different formats across siloed systems and devices. Interoperability has been a barrier, but we are increasingly seeing the rise of software solutions that are vendor agnostic—precisely to serve this purpose of orchestrating multiple systems to drive better insights and efficiencies. But it’s critical that any new technology consider the workflow, too. Health system leaders and care teams don’t want one more screen or app—they need a solution that sits inside existing workflows for the technology to make sense and be adopted.

4. Predictive analytics is becoming central to hospital operations. How can AI help organizations move from reactive crisis management to proactive surge planning and capacity forecasting?

Surge planning and capacity forecasting are two important use cases we hear from health system leaders, and where we’ve seen AI make a big difference. To give you an example, Children’s Mercy Kansas City is a level one freestanding pediatric hospital. As pediatric demand increased over the past several years, the health system faced rising operational pressure to provide timely access to care while maintaining safety, equity, and quality. Admission delays, discharge variability, and limited system-wide visibility had constrained Children’s Mercy’s ability to consistently align demand with available resources, particularly during periods of peak census and seasonal viral surges. To address this, they launched the Patient Progression Hub with support from GE HealthCare. The Hub is a centralized operating model bringing together real-time data, AI-enabled predictive insights, and cross-functional teams.

Since launching the Hub, Children’s Mercy has achieved significant gains in access, patient flow, and operational efficiency, including more predictable and accurate discharge planning across units.  By improving patient flow and discharge processes, Children’s Mercy created capacity to care for 300+ additional patients in just seven months. They have also been able to predict the winter surge within a week. Doing this has helped them decrease deferrals and help more families.

5. Staffing shortages remain one of the most pressing challenges in healthcare delivery. In what ways can AI help optimize workforce utilization without increasing clinician burnout or compromising quality of care?

AI can help hospitals move from reactive staffing decisions to more proactive workforce planning. When leaders have better visibility into where demand is rising, where capacity is constrained and where bottlenecks may emerge, they can make earlier decisions about staffing, bed placement, transfers and care progression. For example, Duke Health has used AI-enabled operational software to support staffing plans and better align resources to patient demand. This has allowed their reliance on temporary labor to drop by 50%, enabling consistent care from familiar providers.

The important point is that AI should not add work. It should reduce friction, support better coordination and help leaders place staff where they are needed most — helping protect both operational efficiency and quality of care.

6. Hospital bottlenecks often originate in one department but quickly impact the entire care continuum. How can AI-powered operational intelligence help health systems identify and resolve these cascading disruptions before they escalate?

This is very true and we see this all the time. First, you need to be able to forecast the bottlenecks across beds and departments. Then AI can help to determine how to prioritize patients or when to extend hours. These activities are very hard to do in the moment. You need several hours of notice, so you need to be able to see that far in advance.  AI can also be prescriptive in suggesting how the bottleneck can be resolved down to the specific patient that may need to be prioritized for an exam or a patient who may be better served in a community hospital.

7. What role do command centers play in creating enterprise-wide visibility across patient flow, bed management, discharge planning, and resource coordination?

The technology needs to connect across the silos in the medical record, but also across staff scheduling systems which there are multiple and asset information. Centralized teams in a command center help to prioritize enterprise-wide coordination. They leverage the technology to support departments on what is a priority for the day and orchestrate the flow.

8. As AI adoption accelerates, healthcare leaders are increasingly focused on measurable outcomes. Which operational and clinical KPIs should organizations prioritize when evaluating the success of AI-enabled care management solutions?

Increasing access and decreasing length of stay are two critical KPIs that health system leaders we speak to track closely. That focus is becoming even more urgent: according to the American Hospital Association, inpatient volumes increased 5.3% in 2025, outpatient visits rose 9.8%, and total hospital expenses grew 7.5% — more than twice the rate of hospital price growth. We are also working with health systems to use AI-enabled hospital operations to drive intentional departmental growth.

9. There is growing interest in combining generative AI with operational analytics. How do you see generative AI evolving within hospital command centers and clinical operations over the next few years?

Over the next few years, I think advances in AI, including generative AI, will help bring clinical and operational decision-making closer together. Today, many hospital operations teams can see patient movement and capacity data, but often lack the wider clinical context that affects care progression including imaging status, device data, care team notes, staffing constraints or discharge barriers.

The opportunity is to combine operational analytics with richer clinical insight so leaders can better understand not only where bottlenecks are forming, but why they are forming and what actions may help resolve them. Generative AI could help summarize complex information, surface patterns across systems and make operational insights easier for teams to interpret and act on.

At GE HealthCare, we’re also exploring how generative and agentic AI can bring together perspectives from different domains to help evaluate complex healthcare scenarios. The goal is not an autonomous hospital, but a more connected one — where AI supports human decision-making with clearer, more actionable insight.

10. Trust and transparency are critical in healthcare AI deployment. How can organizations ensure that AI recommendations remain explainable, clinically relevant, and aligned with human decision-making processes?

Trust starts with a clear principle: AI in healthcare should support people, not replace them. Recommendations need to be grounded in the clinical or operational context, explainable to the teams using them, and designed with appropriate human oversight. That is especially important because AI is not one technology; different models and use cases carry different risks, which means oversight, validation and governance need to be tailored to the setting where the technology is being used.

For healthcare leaders, responsible AI deployment should focus on fundamentals like safety, reliability, transparency, privacy, security, explainability and fairness. In practice, this may take form as trust-building measures like documenting how systems are developed and validated, giving users meaningful information about how an output was generated, grounding AI responses in relevant data to reduce the risk of hallucinations, and maintaining clear accountability for decisions. The goal is not to make AI the decision-maker, but to make it a trusted decision-support tool that helps clinicians and operational leaders act with greater confidence while keeping humans firmly in control.

11. Health equity is becoming a strategic priority for many providers. Can AI-driven operational systems help reduce disparities in patient access, wait times, or care delivery outcomes across different populations?

AI-driven operational systems can play an important role in improving access by helping health systems use existing capacity more effectively. By giving teams clearer visibility into patient flow, resource constraints, and potential delays, these tools can help hospitals identify where access challenges are emerging and act earlier to address them. For example, using AI-enabled technology, Children’s Mercy achieved results including a 59% increase in inpatient volume, an 80% reduction in delayed admissions, and a 44% reduction in patients left without being seen compared to 2022—helping the organization expand access to care across the region.

12. Cybersecurity and data governance concerns continue to influence digital transformation strategies in healthcare. How should hospitals balance the need for real-time AI insights with the responsibility of protecting sensitive patient and operational data?

Hospitals need AI-enabled systems that are designed with privacy and security at the foundation — including clear data access controls, encryption, auditability, validation processes and alignment with applicable healthcare regulations. As AI-enabled care becomes more integrated into hospital operations, it increases the risk surface. However, that same technology can also create new opportunities to raise the bar on cybersecurity and governance while improving efficiency, coordination and outcomes.

13. From your experience at GE HealthCare, what distinguishes healthcare organizations that successfully scale AI initiatives from those that struggle to move beyond pilot programs?

Organizations that successfully scale AI tend to treat it as an operational transformation, not a technology pilot. They start with a clear problem to solve, define ownership across clinical, operational and IT teams, and measure success against outcomes that matter — such as access, throughput, length of stay, productivity and patient flow.

Capturing these gains requires clear ownership and strong operational discipline. Setting basic performance expectations, monitoring real-world results and treating AI with the same rigor as any other critical infrastructure can help ensure it delivers consistent value.

14. Looking ahead, how do you envision AI reshaping the future hospital ecosystem in terms of operational resilience, patient experience, and system-wide care delivery efficiency over the next decade?

Over the next decade, AI has the potential to make hospital care more personalized, coordinated, and seamless—not by replacing clinical judgment, but by helping care teams better anticipate what each patient needs and when. Today, many hospitals still operate reactively across fragmented systems, limited visibility, and constant capacity pressure. AI-enabled tools can help shift that model by turning data into earlier, clearer signals, allowing teams to identify potential delays, resource constraints, or changes in patient needs before they disrupt care. For patients, that could mean smoother transitions, fewer unnecessary delays, and care delivered in the most appropriate setting; for clinicians and operations teams, it means better coordination across departments and clearer workflows that help prioritize what needs attention next. Ultimately, the future hospital will not just be more digital—it will be more connected, with AI helping health systems move from isolated decisions to coordinated action across the enterprise.

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