Hospitals continue to struggle with patient flow due to staffing shortages and inefficient discharge processes. This report explores how AI-powered capacity management tools address these challenges by forecasting demand, optimizing beds, and improving coordination to boost operational performance and ROI.
Obstacles to scaling AI-powered capacity management across health systems
AI implementation depends on a health system’s strategy and factors like operations, tech readiness, finances, culture, and change capacity.
1. The AI Co-Developer: An innovation hub or venture arm that invests in startups to help shape custom AI solutions instead of purchasing ready-made products.
Key AI opportunities in patient flow:
Customizable AI models with system-aligned workflows and goals
Potential for financial return through equity stakes in the AI company
Early adoption for competitive edge
Influence over product development to fit internal needs
Core barriers to implementation:
Requires substantial internal expertise and resources for implementation
Solutions may be less proven in early stages, needing strong change management
Investment returns are long-term and not guaranteed
This mindset in action: Engage with AI vendors open to co-development or early adoption and integrate their solutions into strategic initiatives to ensure flexibility, alignment with system needs, and long-term healthcare goals.
2. The Enterprise Integrator: System-wide AI that works to make a significant upfront investment for long-term transformation.
Key AI opportunities in patient flow:
System-wide visibility into patient flow to reduce inefficiencies
Fast, seamless integration with existing IT and HER systems
Continuously improving AI models for better performance
Cost savings through automation and resource optimization
Core barriers to implementation:
Higher upfront costs and longer ROI than point solutions
Needs strong IT governance and leadership support for success
Cultural resistance (e.g., staff adoption is critical for success)
This mindset in action: Leverage existing EHR and IT infrastructure by choosing scalable AI solutions with quick time to value, and implement them through a phased, long-term strategy that drives performance and cost efficiency.
3. The Focused Implementer: Target specific pain points with lower-risk investments and faster ROI over system-wide transformation.
Key AI opportunities in patient flow:
Faster implementation – launch in months, not years
Lower financial and operational risk than enterprise AI
Delivers clear results (e.g., fewer excess days, higher provider efficiency)
Acts as a proof of concept for broader AI adoption
Core barriers to implementation:
Limited initial scalability-may need upgrades for system-wide use
Poor workflow integration can limit AI impact
May require customization to meet system needs
Making the most of this mindset: Start with targeted areas like discharge or bed use and choose AI solutions with quick implementation to deliver measurable results within months.
Four Universal Strategies Can Drive Successful Adoption
Fix processes before investing in AI to avoid automating bad practices
Workflow variability means not all sites are ready for AI, which works best when built on structured operations—so it’s critical to lay the right foundation first.
Example: OhioHealth operates 16 hospitals and 200+ ambulatory sites, but only implemented Qventus’ AI discharge management at its two largest hospitals. These were the only sites with standardized rounding practices. Four other hospitals were excluded due to inconsistent workflows. The system focused first on codifying discharge best practices systemwide.
Hardwire cultural adoption into workflows; staff won’t use AI that feels optional
Successful AI integration depends on securing frontline buy-in to bridge the gap between leadership enthusiasm and day-to-day clinical realities. AI optimism is higher among senior leaders (77%) than frontline managers (50%), but this gap can hinder adoption when end users aren’t equally engaged.
Example: At Nebraska Health, nurses previously struggled to identify patients ready for discharge. After partnering with Palantir, real-time discharge data eliminated manual searches and improved workflow visibility. Lounge usage surged by over 2,000%, prompting construction of a new facility. This shift was driven by frontline engagement, not just executive interest.
Use AI to prevent future bottlenecks, not just report on past problems
AI for patient flow and capacity should deliver real-time, predictive insights—beyond retrospective reports—to help hospitals proactively manage demand, especially in OR scheduling where surges are common.
Example: Geisinger partnered with Opmed.ai to optimize OR scheduling, boosting accuracy by 96% and improving case duration predictions by 30%. Opmed reduced inefficiencies and enabled real-time shift planning aligned with staff and patient needs.
Geisinger’s experience shows that AI transforms scheduling into a strategic, real-time tool by balancing surgical volumes, integrating workflows, and enabling predictive, system-wide improvements.
If in doubt, target AI investments in primary care and discharge planning first
To optimize efficiency and financial performance, health systems should deploy AI in high-volume areas like primary care and discharge planning, where impact and ROI are most measurable.
Example: Presbyterian used AI-driven referrals to streamline patient flow by embedding guidance into primary care visits, ensuring timely, appropriate specialist access and reducing delays. This improved coordination could generate an estimated $1M–$2M in added specialist revenue.
Similarly, OhioHealth’s use of Qventus for AI-powered discharge planning cut excess hospital days by 20% and saved $1.7 million in six months. This underscores how AI delivers the greatest value when applied to high-volume areas with clear operational and financial impact.
In conclusion: The key to implementing AI in patient flow and capacity management is to start where the need is greatest and integrate AI seamlessly into existing workflows. When done right, AI improves operational efficiency, financial performance, patient outcomes and provider satisfaction—positioning health systems for long-term success in an increasingly data-driven healthcare landscape.