The Old Model Doesn’t Work: Revamping Service Delivery for the Medical Device Industry
The size and scope of the medical device industry is rapidly expanding, and as it does, so too does the service function that supports these machines. As the medical device space shifts to more specialized equipment that’s increasingly critical to patient care, it’s time for service providers to shift offerings, too.
The traditional service model where standard service level agreements (SLAs) and routine maintenance was the norm doesn’t work. An increase in sophisticated machines that require equally sophisticated service techs, highly complex service agreements, and ever-changing compliance regulations have left the service sector scrambling to meet demand.
And COVID-19 has added another factor to consider in the short-term. Routine hospital operations have been severely disrupted. Some devices are being pushed to max capacity and uptime is critical for saving lives, while other devices for elective surgery have been shut down, and start-up may pose additional services challenges.
Here’s how service leaders in the medical device industry can address immediate challenges and devise long-term strategies for improved service delivery.
Bridge the skills gap across service teams
The level of expertise required to service today's medical devices and maximize customer uptime is quickly becoming a major hurdle. Recent research by WBR Insights found “60% of medical field service leaders said that servicing or maintaining medical devices has become more complex for their organizations since the start of 2018 when ‘health technology’ became one of the fastest-growing industries for private investment.”
While deeper knowledge is needed, field organizations are struggling to retain skilled employees. Low unemployment has made open positions harder to fill, junior technicians take longer than expected to skill up, and experienced Boomers are hitting prime retirement age — leaving big gaps in service knowledge.
One of the best ways to bridge the skills gap is to leverage artificial intelligence to empower your workforce to make the right decisions in the field. This includes using user-friendly tools to capture and distribute institutional knowledge from expert workers in an intuitive interface designed for a less experienced, digitally native workforce. By leveraging artificial intelligence, closing the skill gap is not only realistic but necessary to meet growing customer service expectations.
Get to the root cause of the problem quicker
Equipment uptime is critical to patient care, and for service teams that work with medical devices, there’s zero room for error. One of the most important factors in improving uptime is identifying the root cause of device issues to ensure that the right corrective action is taken to resolve problems the first time.
While companies have sophisticated manuals and decision trees to help in root cause analysis, more often than not the best solutions to problems can be found in your historical service data — ranging from neatly organized information inside of your CMS or parts database, to lengthy free text comments in your work orders. Taking this real world experience and converting it into an actionable framework is the key to empowering your service workforce to tackle this challenge.
Decision-making frameworks built from real-world experience empowers customer-facing agents to troubleshoot with clients to ask questions like an expert and identify the most likely resolution based on customer input about the problem.
Alternatively, if the issue requires further investigation on-site, these tools ensure techs have the right parts upon arrival — so that the job is completed on the first visit.
Automate complex compliance processes
Employees at medical device companies dedicate hours every day identifying and reporting issues where patients using their devices get hurt to the FDA and other regulatory bodies. As devices are used more and more in patient care, this practice becomes more resource-intensive.
AI tools can automatically flag device incidents, eliminating manual effort and simplifying the FDA reporting process. These tools learn from experience, and can help you identify patterns and reduce the risk of incidents. Plus, AI-powered decision-making frameworks can also play a vital part in guiding techs through workflows that ensure they remain compliant while also completing jobs efficiently.
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