To deliver effective patient care, hospital staff must have the right equipment in the right place at the right time. But many hospitals struggle with asset management, which, in turn, can affect budget, productivity and quality of care.
To maximize efficiency and manage inventory levels, many hospitals use periodic automated replenishment (PAR) processes. PAR helps ensure that staff have an appropriate amount of equipment or machines on hand when they are needed — rather than having too few (which can compromise patient care) or too many (which can drive up costs).
However, implementing PAR can be complex and requires hospitals to understand where their equipment is at all times and optimize the processes to use those machines. Below, we explore the intricacies of optimizing PAR levels and how artificial intelligence (AI) can help.
The challenge of optimizing equipment availability
Why is maintaining 100% equipment availability so challenging? It comes down to two factors: the environment and technology.
Environment: By nature, patient care is chaotic, complicated, and ever-changing. Even with established processes to keep equipment moving predictably, some patients and scenarios don’t cooperate — making it difficult to keep those procedures on track.
Technology: Traditional asset tracking technology limits staff insight into machine availability — and when machine availability is a concern, departments may hoard equipment to ensure they have enough to provide care. However, when this is done consistently across multiple departments, it can create equipment shortages, which can cause hospitals to incur unnecessary costs through additional machine purchases and rental fees.
Keys to effective equipment distribution
Given these challenges, how do you optimize PAR levels? Effective equipment distribution requires a multi-pronged approach:
Step 1: Equipment location
To allocate and use equipment efficiently, you have to be able to locate it. While this sounds like a simple concept, very few hospitals are able to account for 100% of their equipment inventory at any given time.
Step 2: Process implementation
Once you can track and locate equipment accurately, you need an established process for keeping that equipment efficiently and effectively moving through the system. For example, if you use clean and soiled storage rooms, there must be a documented process for when and how the equipment is processed from patient use to clean status, and eventually gets redistributed.
Step 3: Data-driven decision-making
Once you have data about your equipment usage, you can put it to use. This data can help organizations determine if they have the right number of machines, identify the teams or staff members who fail to correctly put machines back into rotation, and pinpoint departments with more machines than necessary. With the right data, you can make informed decisions about your equipment and processes.
Step 4: Continuous improvement and expansion
Over time, with the right tools and data, hospitals can revolutionize their operations, extending their reach to other departments and locations.
At the heart of this transformation lies technology. Accurate asset tracking technology is the key to locating equipment and streamlining processes. While traditional methods often fall short, AI offers a powerful new approach to optimizing PAR levels.
Enhancing PAR with AI
Accurate and consistent equipment location is fundamental to effectively manage PAR levels. Traditional asset tracking technology often requires extensive infrastructure, making it costly to implement and maintain. To achieve the high levels of accuracy demanded by PAR with this type of technology, hospitals would need to deploy infrastructure in virtually every room and hallway.
A more innovative approach involves AI-powered asset tracking, which delivers room-level accuracy with minimal infrastructure. Utilizing AI as the location intelligence reduces costs and simplifies deployment, making it an ideal solution for PAR management. And, because AI is rapidly advancing, its accuracy generally improves over time.
Not all AI tools are created equally
When trying to distinguish between RTLS platforms that boast AI capabilities, it’s important to distinguish how AI is being used. For example, an RTLS platform built with AI as a reporting tool relies on the underlying data to be accurate and actionable. Unless that underlying data has consistent room-level accuracy, the AI-based analytics will be equally flawed as the underlying data.
RTLS platforms that utilize AI as the location engine provide room-level accuracy at the base data level, ensuring accurate reporting while also reducing infrastructure and maintenance costs.
Cognosos combines AI and machine learning with lightweight infrastructure to deliver consistent, accurate asset-tracking data, so hospitals can improve productivity while continuing to deliver quality patient care. To learn more, download our eBook, “How AI Changed the RTLS Game.”
