Digital Health Global Trends — Part 1

Ohad Mark Stoller
Sep 4, 2018 · 3 min read

Reviewing mega trends in the digital health field and their barriers for adoption

The medical industry is a mature industry that has numerous companies that lack the resources to innovate internally; hence the reason why it receives external investments. Today, as we witness the digital health industry reach the stage of maturity, each pillar of the “health” umbrella have been recognising the significance of innovation. This proves to show the reason behind the rise of investments into deals. I’ve focus on two mega-trends, including their barriers for adoption — Below is my review for the first mega trend — Big Data.

Big Data & Analytics

Within the digital health industry, big data refers to the use of data science/analytics techniques to capture and analyze complex datasets in order to positively impact patient care outcomes and optimize business processes. The quest to capture, analyze and leverage the amount of data has become a new gold rush for the tech giants and the medical sectors alike.

In 2017, two legacy firms, CVS and Aetna, concluded a merger in a deal valued $69 billion. This was a strategic decision on both fronts as it combined CVS’ extensive pharmacy and retail footprint with Aetna’s pharmacy benefits and insurance services and enabled access to massive data. The merger reflects the way the healthcare industry is gravitating towards an impatient consumer profile. Therefore, CVS is aiming to adapt itself to the transformation within the industry by leveraging its pharmacy data with Aetna’s data; and provide a more holistic and optimized consumer-centric service.

Over the next decade, we will witness more and more opportunities to gather and retrieve data in the digital health industry, where there is a growing integration of web and social media data, such as interaction data from Facebook, Twitter, health plan websites and smartphone apps. Sensors, smart meters and other devices, will help drive more machine to machine communication and further expense the possibilities for smart medical analysis. Passive biometric and digital tracking technologies (Patient-generated data) improve data analysis tools and transcend the value of patient-generated data. So, while data is important, it’s only part of the puzzle. Still, the main challenges are the quality of health data and a right stratification of the analytics.

“Sensors, smart meters and other devices, will help drive more machine to machine communication and further expense the possibilities for smart medical analysis”

According to a study done by PwC, there isn’t an efficient level of trust in order to freely transfer private data for medical purposes, but it certainly is much easier to gather and analyze big data in the medical health industry. Among those 55 and older, only 19 percent trust a tech company to keep their data secure, compared to 38 percent for those younger than 55 (according to a study held by Rock-Health). Physicians and family members were identified as the two groups of people most trusted to keep health data private, whereas government and tech companies were listed as the least trusted. Similarly, when asked which health entities are most aligned with consumers’ interests, physicians were ranked at the top, while the government and technology companies were closely ranked at the bottom.

According to a study held by Accenture and AMA, 83 percent of US physicians reportedly experiencing some form of cybersecurity attacks, and the healthcare industry has had issues reducing the number of security threats. Cybersecurity threats and data breaches have cost the industry billions both in damages and reputation; which has initiated the patients’

frustration and even larger penalties for healthcare companies could follow. Privacy concerns and cyber-security issues could harm the way the healthcare industry is using big data, implementing AI and machine-learning technologies, and is administering business domains such as innovation. Clearly, the lack of agreed standards on data protection, privacy and security, on both ends: the data and the devices; is preventing the pace of digital health adoption to from gaining momentum.

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