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How IoT Analytics Can Transform the Medical Device Value Chain


By investing in IoT infrastructure and big data analytics capabilities, medical device makers can increase their agility and better inform business decision-making.

Traditionally, manufacturers have introduced new efficiencies to their value chain by employing lean manufacturing and Six-Sigma quality techniques. While this certainly can drive growth, manufacturers can accelerate performance even further by using the Internet of Things (IoT) and big data analytics to inform key business decisions. 

Using data from connected devices and other sources of consumer interactions and transactions (e.g., social media, patient blogs and mobile), device manufacturers can better understand customer likes and dislikes, behaviors and preferences. Such insights can drive more effective product development, as well as sales and marketing initiatives.

To understand the enormity of the data sets and their potential game-changing possibilities, consider the following: 

  • Roughly 72 million wearable devices were shipped in 2015, a figure that will nearly double to 156 million by 2019. Device manufacturers can harness the surge of data around patient-reported outcomes from these devices and associated mobile apps to improve patient well-being and health.

  • The number of product recalls of medical devices has increased nearly 100% from 2003 to 2013. By investing in real-time predictive analytics around global quality and complaints, manufacturers can proactively determine quality issues to reduce the number of product recalls.  

  • According to the U.S. Food & Drug Administration, the number of serious complications from medical device use has outpaced industry growth by 8% each year since 2001. Using predictive analytics capabilities, medical device manufacturers could reduce these complications by informing future product design and proactively improving post-sales service support.

  • A leading medical device manufacturer logs an average of 200,000 product-related customer complaints every year. Imagine the impact on the customer experience and resultant bottom line if the company could predict and proactively address even 10% of these complaints.

  • A total of 85% of physicians and 76% of patients believe “wearables” could help them better manage patient health and potentially improve long-term care. A significant opportunity exists for manufacturers to collaborate with wearable device makers to improve overall care.

  • Success in developed markets varies greatly from emerging economies, which require a very specific understanding of stakeholder needs. By using data mining techniques, such as natural language processing (NLP), medical device manufacturers can cost-effectively obtain this information and thereby influence the product development lifecycle for each market.

When these data sets are assessed in isolation, their meaningful impact on the value chain is limited. By identifying effective ways to integrate these data sets and apply analytics capabilities to them (e.g., predictive analytics and NLP), organizations can more effectively optimize the value chain. By analyzing IoT and an array of big data assets, for example, an insulin pump maker can enhance various components of its value chain (see Figure 1).


Figure 1

Once initial value chain gains are realized, companies can invest in optimization strategies, such as Six Sigma quality and lean manufacturing techniques, to derive additional value (see Figure 2). To uncover the hidden business insights and generate additional value from IoT and big data analytics, companies need to invest in capabilities across people, process and technology. 

For example, from a process standpoint, IT would need to engage with the business much earlier in the lifecycle to build a comprehensive data and analytics strategy. In terms of technology, companies would need to invest in IoT capabilities, such as changing device design (e.g., installing sensors to monitor real-time device usage), and building or buying predictive analytics systems and natural language processing (NLP) tools to analyze the information gathered through these devices. Finally, from a people standpoint, investing in knowledge management and learning systems would go a long way toward improving the organizational adoption of these new capabilities.

Figure 2

To get started, we propose the following five steps:

  • Identify strategic business areas in which decisions are hindered by a lack of or limited use of data.

  • Obtain buy-in from key business stakeholders by developing and operationalizing use cases with demonstrated business value. 

  • Review, validate and prioritize use cases that are aligned with the short- and long-term company strategy.

  • Define and execute a proof of concept.

  • Leverage early wins to build and execute an end-to-end IoT and big data analytics strategy with due consideration to capabilities around people, process and technology.

For more insights, please visit our IoTmedical deviceand analytics sections of our website.

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How IoT Analytics Can Transform the Medical Device Value Chain