APAC Medical Technology companies must manage the exponential growth of both data volumes and sources to maximise their growth opportunity. Smart devices, point-of-care devices in the home, artificial intelligence (AI) and machine learning are all driving this trend, making the choice of interoperability and analytics platform a critical one.
By 2022, the size of the APAC MedTech industry is expected to climb by 8.8% a year to US$157 billion, according to the Asia Pacific Medical Technology Association. But despite this growth opportunity, APAC MedTech companies face a number of challenges.
To start with, COVID-19 has changed the rules. Rapid deployment of technology and innovation, for example,are destined to become part of the new normal, as is increased use of data to inform healthcare decision making.
Beyond that, however, the MedTech sector must manage the exponential growth of both data volumes and the number of data sources that products must integrate with to be successful.
Data growth rates are higher in the healthcare sector than almost anywhere else. According to the IDC white paper, “Data Age 2025”, healthcare data is projected to grow faster than in manufacturing, financial services or media, experiencing a compound annual growth rate (CAGR) of 36 percent through to 2025.
Smart healthcare devices and point of care devices located in patients’ homes are just some of the drivers of this trend, with data captured from them allowing real-time monitoring of the patient’s condition.
This exponential data growth presents a major challenge for data scientists at medical device manufacturers, who often spend as little as 20% of their time on actual data analysis.
Interoperability a “stumbling block”
According to John Kelly, Regional Manager, MedTech for InterSystems, the bulk of data scientists’ time is spent tracking down, cleaning and reorganising huge volumes of data stored in multiple silos and in different formats and standards.
“Interoperability – in other words the ability of disparate software to exchange and make use of information – is key,” says Kelly. “Unfortunately, the lack of interoperability between systems has been a major stumbling block for innovation. We are investing a lot of effort into solving this problem to make sure that our customers have clean, compliant and complete data available for advanced analytics.”
When developing MedTech solutions, compliance with different healthcare standards is also paramount, says Kelly, so that solutions can fit easily into existing healthcare environments.“No matter how innovative a new solution may be, if it cannot slot neatly into existing healthcare infrastructure and facilitate the sharing of data between different systems and solutions, adoption is likely to be low.”
Data privacy and security are further considerations, including data management and consent principles that give patients control over their own data. And because MedTech is a global industry, European General Data Protection Regulation(GDPR) and California Consumer Privacy Act(CCPA) concerns, such as who owns patient-generated data, must also be addressed. Standards compliance, with high levels of scalability, needs to be front of mind even if you start with a proof of concept.
Technologies like artificial intelligence (AI) and the Internet of Things are also introducing new cybersecurity challenges. To address these, regulators around the world are introducing stringent guidelines on cybersecurity as a distinct process within each stage of development, from R&D through to systematic identification of risks during practical usage.
Artificial Intelligence that improves with use
Meeting these challenges will create new opportunities for MedTech companies. With increased data volumes, data quality, and consumer trust, AI will become more reliable.Couple this with machine learning and you will get a virtuous cycle where the more practice AI algorithms get, the better they will become.
AI and machine learning are already proving themselves in maximising the effectiveness of high-value medical equipment. For example, Mercy Radiology in New Zealand, working with Ferrum AI, has deployed two AI algorithms which use machine learning to improve over time with use. Operating in a second read capacity for radiologists, the two use-cases are in lung nodules on CT scans and limb fractures on X-ray.
The algorithms have improved the quality of reporting and “there has been positive engagement from clinicians as well,” says Lloyd McCann, CEO of Mercy Radiology and Head of Digital Health for Healthcare Holdings Limited.
Ultimately, however, for MedTech companies to seize the opportunities afforded by healthcare data, they must not only be able to handle increasing volumes and types of data, and deal with privacy and security concerns, but ensure that data is fit for purpose.
The success of data innovations based on AI and machine learning in particular will succeed or fail on the quality of the data that is used, and that makes the choice of data platforms that manage it a critical business decision.