Leveraging Predictive Analytics to Support Maintenance of Medical Devices

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By Dhaval Shah and Simran Mittal

The medical device maintenance market is not usually seen as a strategic, growing or robust part of the medical industry. Since medical devices require frequent quality checks to ensure devices are working as desired, device health is a field that should receive more attention from all healthcare entities, including device manufacturers as well as payers and providers. As the industry is in constant demand to adopt emerging technologies to improve overall clinical outcomes and customer satisfaction, having a strategy in place to help providers maintain or upgrade medical devices appropriately will support an ongoing relationship between all parties. 

Traditionally medical device maintenance is driven by on-premise maintenance of devices by field service engineers. However, since the onset of COVID-19, healthcare providers have been seeking ways to rapidly change that approach, which opens the door to make device maintenance a strategic imperative within organizations. Medical device organizations not only want to remotely manage their devices but also want to ensure they can service the devices even before they fail – or provide recommendations regarding available upgrades. 

Addressing Medical Device Maintenance Challenges

To become a true strategic partner, medical device organizations must first assess how they can evolve their maintenance processes to use technology and predictive tools most effectively. To begin, there must be an underlying understanding of when a device is trending towards a possible failure. By incorporating predictive analytics into servicing strategy, medical device organizations can reduce the number of times a field service engineer must visit provider sites to perform a thorough analysis of the device. This approach will only improve the quality and reliability of the device itself but extend its “life” and provide additional safety and improved performance. Further, through the adoption of innovative technology solutions, medical device organizations can reduce field service visits up to 20% while achieving significant savings due to proactive maintenance of devices. 

Introducing predictive strategies into medical device maintenance processes can also have a positive effect on compliance with industry regulations related to patient safety. Predicting device failure can help organizations anticipate and address possible safety risks and anomaly detection before they make a larger or more critical impact. 

5 Critical Steps to Properly Implement a Predictive Analytics Solution

Implementing an effective predictive analytics strategy into your medical device maintenance processes may seem like a daunting task, but by following these five critical steps, proper integration will seem less overwhelming. 

1. Connect devices to analytics platform: Determine if any legacy medical devices are not connected to other clinical and operational systems. Then, ensure IoT gateway is set up to efficiently enable these devices to exchange data with connected systems. Most device organizations may already have connected devices, so for such organizations, start with descriptive device data analytics and then move towards advanced analytics solutions for device failure prediction and service assistants. 
2. Track device KPIs: Tracking device health key performance indicators (KPIs) or measures will help ensure overall device effectiveness aligns with the medical providers’ needs and allow for further improvement and quality management. KPIs to track should include utilization, downtime or potential risk. 
3. Identify device anomalies: Detecting adverse device behavior can assist in the overall device health prediction and proactively monitor potential risks or threats. Prediction of device breakdown can also help medical device organizations to minimize unnecessary field visits.
4. Monitor device health score: Consistently reviewing a device’s health score can not only help identify those devices that need additional proactive maintenance but help prevent further deterioration. 
5. Build service assistants: Assisting technologies like a chatbot or other virtual assistants can be leveraged to help field service technicians and engineers to streamline processes and systems, but these operating bots must be nimble and capable to quickly adapt to changes and varying feedback. 

Using data to effectively drive proactive maintenancedepends on the mix of device and operational data currently being generated by medical device service teams and medical providers. Most data will fall in one of two categories: 

• Structured Data: The year of production, make, and model of the device, warranty period, machine utilization​, inventory, CRM, etc.​
• Unstructured Data: Maintenance history, device or logs, temperature​, flow pressure, coolant levels or other sensors data.

By combining the two types of data into highly customized predictive algorithms, medical device companies can identify devices showing deviation from normal behavior based on their age, service history, use and more. Predictive analytics solutions can provide device health alerts days, weeks, or even months before actual failure. 

The devices that require maintenance can be proactively scheduled for servicing before they go offline and cause the medical provider unanticipated downtime. In addition, medical device organizations can improve field service engineer’s efficiency by providing them AI-enabled service assistant tools. 

An added benefit to proactive maintenance is that medical device organizations can help optimize inventory levels of spare parts stocked at facilities. By integrating data from operational systems such as enterprise resource planning (ERP) or inventory management systems, a device organization can limit inventory tie up while ensuring out of stock parts does not occur. 

The Way Ahead for Medical Device Maintenance

Medical device organizations need to create a unified device data platform to act as a single source of truth across devices that combine device, clinical and operational data. Once the unified data is available, organizations will be able to generate business insights and understand trends (utilization, downtime, labor cost, shipping cost, etc.) to drive proactive maintenance of devices. 

Dhaval Shah is senior vice president and head of the medical technology market at CitiusTech. He has over 18 years of experience in IT outsourcing, with a strong focus on the US healthcare market and holds a Masters degree in Bio-Medical Engineering from Wright State University.

Dr. Simran Mittal is the consulting head of the medical technology market at CitiusTech. She has over 13 years of IT experience and has an MBA from Tata Institute of Social Sciences in Mumbai, India.