Medical devices need to operate around the clock reliably. Medical devices are not only life-improving but mainly life-sustaining. So it is important to reduce unexpected outages and avoid very expensive emergency visits of field service engineers.

Even from an experience with a customer delivering devices for smaller clinics to measure blood values or urine values, I can say that it is becoming more and more important that wearables measuring blood pressure are reliable and do not fail so that there is no demanding timeframe needed for their maintenance. The same applies for more complex devices even at a higher degree.

Predictive Maintenance is based on data generated and transmitted from medical devices whereby manufacturers can monitor the status of a medical device and recognise if it is operating normally or abnormally and proactively predict a possible failure and plan to intervene before the device breaks down. Sensors may record the environmental conditions such as the temperature of the medical device, the humidity, any kind of vibrations, etc.

 

Experience from a recent project

I have been involved in one project for more than a year focused on building up a big data, AI and service analytics platform. The data scientists are looking at all the data and exploring new AI-powered predictive models to predict future failures on device components associated with big cost impacts.

 

Optimising maintenance schedules

If you want to predict future outages, you need data. That is one of the reasons why many companies invest in the IoT, which creates data from various sensors. Imagine a smartphone. How many sensors does one have? It is amazing; such a small device has approximately 6–8 different sensors. With these sensors, data companies are building new applications.

If you consider a medical device, there are so many factors that can impact the functioning of a product. Sensors measure humidity, temperature in the operating area and so on. These all influence the time between maintenance events. In some countries, some elements fail quicker than others, and this need to be predicted and mitigated. The trick with predictive maintenance is to predict as precisely as possible when a system break will hit your customer; it should not predict the future event too far in advance nor in the reactive phase of handling.

 

Improvement of Customer Satisfaction

Automated data collection and processing makes it possible for a service provider to extract insights from the devices’ statuses, operations and behaviour. These insights unlock a tremendous potential: Manufacturers of medical devices can grow by refreshing their current product-related offerings by adding new digital services. According to a BCG study, companies that can successfully reposition their service capabilities stand to generate substantial standalone revenues with profit margins in the 35% to 40% range—more than 50% for best-in-class performers—while also improving customer satisfaction and providing a source of competitive differentiation.

 

Main benefits of Predictive Maintenance

 

First-Time Fix Rate improvements

Higher customer satisfaction rates

Less travel costs and less capital locked in stocks

 

What does the customer need to bring along at the start?

  • Quality and quantity of data
  • Data engineering aspects need to be given

 

How ERNI can support in this area

  • Business & Technology Consulting
  • Innovation Services
  • Project Management
  • Architecture Advisory
  • IoT Engineering, Big Data Engineering, ML Engineering
  • UX, Front-End Developers Agile Coaching

Would you like to optimise your service-providing strategy related to medical devices or health-measuring appliances?

better ask ERNI

News from ERNI

In our newsroom, you find all our articles, blogs and series entries in one place.

  • 22.11.2023.
    Newsroom

    Recognising trends: An insight into regression analysis

    Data plays a very important role in every area of a company. When it comes to data, a distinction is made primarily between operational data and dispositive data. Operational data play an important role, especially in day-to-day business. However, they are not nearly as relevant as dispositive data. This is because these data are collected over a longer period of time and provide an initial insight into the history or the past.

  • 08.11.2023.
    Newsroom

    Why do we need digital transformation for medical devices?

    For hospitals, it is not up for discussion as to whether they want to digitalise. The increasing age of the population in western countries and the progressive shortage of medical professionals mean that without digitalisation, the healthcare system will not be able to provide the quality that patients want in the future.

  • 25.10.2023.
    Newsroom

    Mastering the challenges of mobile app testing: Strategies for efficient quality assurance

    Discover the unique challenges faced in testing mobile applications and learn how to overcome them effectively. From selecting suitable devices and operating systems to leveraging cloud-based test platforms, test automation and emulators, this article provides seven essential strategies for optimising your mobile app testing process.

  • 11.10.2023.
    Newsroom

    Incorporating classical requirements engineering methods in agile software development for a laboratory automation system

    Traditional agile methodologies can sometimes struggle to accommodate the complexity and regulatory requirements of laboratory automation systems, leading to misalignment with stakeholder needs, scope creep, and potential delays. The lack of comprehensive requirements documentation can result in ambiguous expectations and hinder effective communication among cross-functional teams.

  • 27.09.2023.
    Newsroom

    Unveiling the power of data: Part III – Navigating challenges and harnessing insights in data-driven projects

    Transforming an idea into a successful machine learning (ML)-based product involves navigating various challenges. In this final part of our series, we delve into two crucial aspects: ensuring 24/7 operation of the product and prioritising user experience (UX).

  • 13.09.2023.
    Newsroom

    Exploring Language Models: An overview of LLMs and their practical implementation

    Generative AI models have recently amazed with unprecedented outputs, such as hyper-realistic images, diverse music, coherent texts, and synthetic videos, sparking excitement. Despite this progress, addressing ethical and societal concerns is crucial for responsible and beneficial utilization, guarding against issues like misinformation and manipulation in this AI-powered creative era.

  • 01.09.2023.
    Newsroom

    Peter Zuber becomes the new Managing Director of ERNI Switzerland

    ERNI is setting an agenda for growth and innovation with the appointment of Peter Zuber as Managing Director of the Swiss business unit. With his previous experience and expertise, he will further expand the positioning of ERNI Switzerland, as a leading consulting firm for software development and digital innovation.

  • data230.08.2023.
    Newsroom

    Unveiling the power of data: Part II – Navigating challenges and harnessing insights in data-driven projects

    The second article from the series on data-driven projects, explores common challenges that arise during their execution. To illustrate these concepts, we will focus on one of ERNI’s latest project called GeoML. This second article focuses on the second part of the GeoML project: Idea2Proof.