Data overload in digital health

Posted on 26th January 2020

Written by Anjum Rangwala, Investment Banking, MUFG

Every single day there are millions of data points generated by wearables, monitors, lab tests, medical devices, and doctor visits. According to IBM Watson, the amount of medical data is expected to double every 73 days by 2020. The healthcare landscape has shifted to the point in which consumers are in control like never before and newfound knowledge about their own health is empowering.

As I have alluded to in many of my previous posts, the digital health revolution is paving the way for a fundamental shift towards preventative care and data-driven decision making. This shift towards preventative care leads to a decrease in long-term patient costs and an overall improvement in patient outcomes. More data also leads to more personalised treatment plans, reduction in expenses for healthcare providers, lower follow-up costs for patients, and in some cases, avoidance of unnecessary medical procedures. With this influx of data, companies are working to make sense of it all and integrate it into current healthcare systems.

Clearly there are many advantages to having all this health data, but there are some challenges, such as volume & format of data, integration into existing healthcare systems, and privacy risks, which should not be overlooked.

What are some of the challenges with regards to all of this data generation?

1) Volume & Format

The sheer amount of data produced everyday might be a lot, but not all of it is valuable or actionable. In most cases, data needs to be properly cleansed and normalised to reduce noise and extract value. Data sets are not all created equally and they are not all necessarily reliable either. Understanding these data sets and finding patterns and trends requires strong knowledge of both biology and data analytics, and without this double-pronged approach, progress is guaranteed to be limited. Additionally, for many healthcare organisations, companies, and institutions, data is collected in such large volumes that these organisations can no longer store, process, or send data efficiently.

There is also a risk of data overload. Yes, it is nice that there is an option to look at additional health data, but is all of it necessary, and at what point does the volume of data go from being helpful to overwhelming? When it comes to things like consumer DNA testing, there are many people who prefer to know their results and take any action if necessary, but for others it just creates unnecessary anxiety.

2) Integration

Infrastructure is one of the biggest obstacles in this space. Healthcare infrastructure needs a serious makeover and it was needed years ago. According to a recent report by Health Information and Management Systems Society (HIMSS) Analytics, on average, hospitals use 16 distinct EHR platform, leading to major gaps in data interconnection and transmission.

Electronic health records (EHRs) are not particularly interoperable and lead to issues with synthesising old and new information, multiple data formats, increased physician hours, and inefficient workflows among many others. Unlike other industries, healthcare has requirements for reporting and regulatory compliance — the burden of this becomes exacerbated with additional data points. To add to the burden, many healthcare providers have started building new infrastructure but are keeping a lot of their old software, which leads to additional confusion and lack of advancement towards a truly integrated system.

The concern here is that the exponential growth of health data is sharply outpacing any sort of integration efforts — there is a great opportunity here for enhancement of these systems.

3) Privacy

Privacy and data protection, especially when it comes to health data, are critical when it comes to patient care. Most consumers have some concern over how their personal health information might be used by tech companies or even by their employers. The progress in digital health has been much faster than accompanying cybersecurity measures, as evidenced by multiple instances of data breaches and hacks over the last couple of years.

Extracting value from volume

The growing trove of data may likely house a lot of answers and valuable insights into many health challenges and conditions, but producing all this data doesn’t mean anything if it can’t be interpreted and analysed correctly. It’s clear that the volume of data produced can be overwhelming for all stakeholders and existing systems are not up to speed yet.

In order to overcome some of the challenges mentioned earlier, healthcare providers and tech companies need to leverage data analytics and make a strong push to build out their capabilities in this area. Healthcare organisations need to work on solutions towards interoperability so that they can fully take advantage of the additional data that is being created, rather than be burdened by it. And lastly, there needs to be more clarity when it comes to data ownership and privacy in digital health.

Originally posted here.

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