TRIMEDX Vice President of Product and Portfolio Management TJ Kubricky wrote an article published in Unite.AI about how AI is redefining device availability in health systems. He highlights how predictive insights, automation, and smarter data use can improve uptime, optimize maintenance strategies, and better support frontline clinical care.
What if hospitals could achieve near-perfect device availability without ballooning costs—and without clinicians even noticing the shift? Artificial intelligence (AI) is already making this a reality by forecasting maintenance needs, improving device utilization, and automating scheduling in ways that reduce friction across healthcare workflows.
As more devices become network-connected, the possibilities to apply AI and machine learning (ML) advancements to healthcare technology management (HTM) are expanding rapidly. These technologies will empower clinical engineering teams to ensure medical devices are available, working properly, and easily located the moment they are needed. Optimizing device availability can prevent revenue loss for health systems while also improving the patient experience by reducing delays or cancelations.
The challenge of device availability
Despite its critical role in ensuring quality patient care and maximizing health system revenue, medical device availability remains a persistent challenge. Fragmented systems, workforce constraints, and a lack of inventory visibility often leave clinicians and clinical engineering teams spending valuable time searching for devices. Unforeseen device failures and equipment downtime can lead to canceled procedures, delayed diagnoses, and lost revenue. Health systems can reduce or eliminate many of these problems by embedding automation and AI-powered technology into clinical engineering workflows.
Reducing unexpected breakdowns and avoidable device damage
Increasing complexity and connectivity in medical devices has opened the door to innovative solutions that can prevent avoidable device damage and unexpected breakdowns. Through remote device diagnostics, problems can be anticipated before they lead to failures–reducing downtime and improving asset utilization.
Health systems should consider working with an expert partner that uses AI analysis and event detection to spot early warning signs of equipment issues before they’re visible to human technicians. These predictive work systems continuously monitor devices 24/7. When a warning sign is detected, the systems can proactively provide troubleshooting steps and automatically schedule maintenance around patient care. Through advanced predictive analytics, TRIMEDX technology helps divert at least 1,000 downtime events annually. Not only does this improve overall device availability, but automated maintenance also ensures devices are being cared for properly, extending the lifespan and maximizing the value of a health system’s clinical assets.
In addition, AI-driven analysis of repair history can identify preventable errors that occur during clinical use. For instance, improper cleaning and handling of ultrasound probes can cause lens cracks. AI can detect these patterns and alert health systems if the same error is happening multiple times. Organizations can then implement specific training to prevent the mistakes from happening again. This ensures devices like ultrasound probes remain operational and available, while reducing the cost of replacing damaged equipment.
Elevating visibility and real-time medical device tracking
A comprehensive and accurate view of medical device inventory is the foundation of effective medical device management and reliable device availability. Additionally, health systems spend about 25% of their capital budgets on medical equipment, making visibility and utilization critical to financial performance.
TRIMEDX has found health system inventory inaccuracies may be as high as 40%. When health systems lack visibility into their clinical asset inventory, it leads to inefficient use of existing assets, increased operational and capital costs, and missed opportunities to improve device uptime and patient throughput. Artificial intelligence can enhance real-time location system (RTLS) data for devices across fragmented systems.
Advanced medical device tracking technologies do more than pinpoint location—they provide insights into true utilization, helping health systems identify underused assets, reduce waste, and unlock significant financial savings. Advanced AI technologies can ensure more complete, reliable device records and continuously evaluate assets across multiple care sites. Intelligent algorithms can seamlessly ingest RTLS data, device performance metrics, network activity, and patient scheduling to determine true utilization.
These insights empower health systems to position each device where it delivers the greatest value. Fragmented systems and inaccurate inventories often result in equipment sitting idle in one location while its urgently needed elsewhere. By ensuring precise device allocation across the health system, organizations can maximize capital investments, reduce unnecessary purchases, and unlock significant operational efficiencies.
AI models can proactively predict equipment needs and ensure the right devices are available at the right moment. This can eliminate or reduce avoidable delays and lost revenue due to rescheduled or canceled patient procedures.
Seamless availability enhances patient satisfaction and allows clinicians to stay focused on patient care, confident the equipment they need will be ready and functioning. A McKinsey study found 20% of nursing time could be optimized through technology enablement. By taking advantage of these innovative solutions, organizations can enable targeted service, optimize technician workflows, and allocate resources more effectively, ensuring devices are ready when needed without overextending budgets or staff.
Supporting the human workforce behind device availability
Advanced AI-powered tools allow BMETs to focus on strategic tasks by automating routine work like paperwork and repetitive manual tasks. When clinical engineering teams have access to automated documentation, automated test results, smart work order prioritization, and centralized work order information, they can focus on the highest value work. AI can also synthesize complex equipment manuals into concise, actionable worklists, helping technicians quickly understand tasks and build knowledge on the job.
These technologies allow the clinical engineering workforce to transition from fixers to strategic partners focused on risk-based maintenance and continuous performance monitoring. In addition, they can develop new competencies in data analytics, cybersecurity, and AI tools. Enabling BMETs to focus on fulfilling, proactive work will help health systems leverage their expertise more effectively to keep devices up-and-running.
Artificial intelligence is already transforming the way clinical engineering teams manage medical devices. Organizations making use of AI-powered solutions will see availability become more predictable, maintenance more proactive, and operations more efficient. By integrating intelligent automation into clinical engineering workflows, hospitals can make sure critical equipment is operational and accessible when care demands it. Health systems that harness the power of AI are creating a more resilient, cost-effective healthcare environment that supports both operational and financial goals and better patient outcomes.