Asset tracking plays an extremely vital role in the efficient management of healthcare facilities, ensuring that critical equipment, supplies and resources are readily available when needed. However, the current state of asset tracking in the healthcare industry has been riddled with challenges. Many hospitals that have deployed Real-Time Location Systems (RTLS) have faced issues with technology reliability, implementation complexity and high costs. Conversely, many hospitals that have not yet implemented RTLS do indeed recognize its value but are frequently deterred by the traditionally prohibitive price-tag associated with it.
To truly grasp the current state of RTLS technology in healthcare, it’s important to first understand its origins. In this article, we’ll take a trip down memory lane to review the historical journey of RTLS in healthcare and shed light on the characteristics of existing solutions. Afterwards we’ll delve into how artificial intelligence (AI) technology is changing the face of asset tracking by leveraging Bluetooth technology for enhanced accuracy and cost-effectiveness.
The Evolution of RTLS Technology in Healthcare
Historically speaking, the healthcare industry has been at the forefront of adopting RTLS technology, recognizing its potential to streamline operations and improve patient care over the course of recent decades. In the early 2000s, as the technology began to commercialize, hospitals became early adopters of RTLS solutions. However, initial attempts at utilizing Wi-Fi for asset tracking have proven to be less than optimal due to its limited precision and reliability.
Subsequently, various other technologies, including ultrasound and infrared, began to emerge as viable alternative options for achieving room-level accuracy in healthcare asset tracking. Ultrasound-based systems utilized ultrasonic tags attached to assets paired with receivers embedded on ceiling tiles to determine assets’ presence within a room. Not to be outdone, infrared systems also came onto the scene, employing infrared LEDs to establish in-room asset location. While these solutions provide some semblance of room-level accuracy, they require the installation of sensors in every room, a complex and costly deployment process in which patients must be moved from their beds to supplemental locations.
These legacy RTLS solutions also face significant challenges when assets cross open room boundaries, as the signals can propagate into adjacent rooms or hallways, leading to inaccuracies and false readings—certainly not ideal when lives are at stake as they often are in medical facilities. Efforts to address these issues further complicate the infrastructural requirements, with the inclusion of sensors inside and outside doorways. Consequently, these solutions have suffered from deteriorating performance over time coupled with steep installation and maintenance costs.
Limitations of Traditional RTLS Solutions
Despite the adoption of these RTLS technologies in healthcare, their limitations have become all-too apparent. Wi-Fi-based systems, for example, offer only approximate location information, with a margin of error up to 50 feet–a level of inaccuracy that severely hampers asset retrieval efforts in intricate hospital settings and multi-floor environments. Perhaps more important, that lack of precision makes it impossible to leverage the data to optimize and automate asset management processes, like managing periodic automatic replenishment (PAR) levels in a clean room. The extensive infrastructure requirements and high costs associated with ultrasound and infrared systems have further hindered their widespread adoption.
Moreover, the integration of multiple technologies within a single tag, as seen in some later solutions, has only added an additional layer of complexity and fails to deliver the most cost-effective, reliable and straightforward asset tracking solution. This lack of a unified and efficient approach to RTLS has resulted in frustration for hospitals, which either abandon the technology altogether or struggle with limited returns on investment due to the suboptimal performance of their existing systems.
The Promise of AI-Powered RTLS with Bluetooth Technology
In the midst of the challenges faced by traditional RTLS solutions, AI-powered asset tracking systems leveraging Bluetooth technology have arisen as the ultimate game-changer, revolutionizing asset tracking in healthcare by combining Bluetooth technology with AI algorithms to enhance accuracy, reliability and cost-effectiveness.
Bluetooth technology, today quite ubiquitous and incredibly affordable, provides a rock-solid foundation for asset tracking. Nevertheless, traditional Bluetooth-based systems relying solely on signal strength suffer from substantial errors when assets are separated by walls or other obstacles, just like Wi-Fi used to. This limitation has now been neutralized through the application of AI algorithms, which analyze multiple data points to determine asset location accurately.
AI-powered RTLS utilizes a light–but powerful–network of Bluetooth Low Energy (BLE) beacons to achieve real-time accuracy in asset tracking. The system employs machine learning algorithms to interpret received signal strength, asset movement and other relevant data, enabling precise asset location determination. By leveraging AI, these solutions eliminate the need for extensive infrastructure and complicated deployment procedures, providing a cost-effective and scalable asset tracking solution.
Benefits and Applications
The integration of AI on-top-of-Bluetooth-based RTLS in healthcare offers myriad benefits and opens up a wide range of applications. First, the improved accuracy and real-time tracking capabilities drastically reduce the time medical personnel spend searching for assets, leading to enhanced operational efficiency and staff productivity. Critical equipment, such as infusion pumps or defibrillators, can be located promptly, ensuring timely access for patient care.
Second, AI-powered asset tracking enables hospitals and other healthcare facilities to optimize asset utilization and reduce capital expenses. By gaining insights into asset utilization patterns, facilities can make better-informed decisions about asset procurement, redistribution and maintenance. Optimization such as this also reduces the need for excessive inventorying and helps minimize asset loss, including theft. The AI algorithms employed in these next-generation solutions have the ability to automate the asset management processes, like PAR level management which are significant drivers for return and optimization.
Without question, RTLS has the potential to transform asset tracking in the healthcare industry. While traditional solutions faced limitations in terms of accuracy, infrastructure requirements and cost-effectiveness, the arrival of AI-powered RTLS with Bluetooth technology offers a promising glimpse into the future of enhanced patient care and operational excellence.
Adrian Jennings is CPO of Cognosos.