Sharing exogenous data will transform the Healthcare Industry

The explosion of data
If you think of what you can accomplish in 60 seconds, it is probably not much. But if you think of the amount of data that is created in these same 60 seconds, it is a whole different story. Google received over four million search queries per minute in 2015 and Facebook users share over 2,5 million pieces of content and like over four million posts (Gunelius 2014). Besides this, there are a whole bunch of other (online) things happening in this same one minute, which is graphically shown in figure 1.


Figure 1: Illustration of data creation per minute (James, J. 2015)

The healthcare industry develops huge amounts of data as well. Electronic health records, pharmaceutical and life sciences databases, spontaneous reporting systems, consumer-contributed data in social media and wearable sensors connected to mobile devices, (continuously) track health conditions of customers and also create data (Yang & Veltri 2015). This has led to the fact that 90 percent of digital medical data is developed in the past two years (Dilsizian & Siegel 2014) and that 80 percent of the available data is unstructured (Datamark 2013).

Exogenous data
Data used and produced by physicians (clinical data) forms only ten percent of the total amount of data that one person produces over a lifetime. 30 percent of personalized data is formed by genetics data and 60 percent, 1100 terabytes of data, is based on exogenous data (McGovern et al. 2014). This is also shown in figure 2. Exogenous data is data related to behavioral, lifestyle, nutrition, exercises, etcetera, which is usually measured by smartphone applications and patient-controlled medical devices (e.g. smart watches and fitness trackers) (McGovern et al. 2014). This exogenous data, in combination with EHR’s, clinical trials, insurance claims, and other health related data, can be used to give people the opportunity to live longer and healthier, and therefore could improve peoples lives (Robert Wood Johnson Foundation 2015).

Figure 2: amount of exogenous data increases (McGovern et al. 2014)

The Watson Health Cloud
In order to bring all above mentioned data together. IBM has developed a platform, which is called: The Watson Health Cloud. IBM is collaborating with several companies, including Apple, Johnson&Johnson, and Medtronic to create new health-based offerings that leverage information collected from personal health, medical, and fitness devices, which will result in better insights, real-time feedback, and recommendations to improve everything from personal health to acute and chronic care (IBM 2015).

Within this platform there is for example the care manager option. This option integrates Phytel’s patient engagement tools and Apple HealthKit and ResearchKit. By integrating this information, cognitive analysis is applied to disparate types of clinical and individual data and insights for nurses, physicians (assistants), and other care managers so that they are able to closely monitor and council individual patients with complex and costly conditions (IBM 2015).

How does this work?
A patient with chronic heart failure (CHF) receives a personalized plan that includes monitoring physical activity and daily weight tracking. Currently, reporting of this data by patients and evaluating and acting upon this data by physicians is largely a manual process. With the care manager in the platform patients can opt-in to have data collected from wireless-enabled scales, wearable devices, other types of sensors, and from assessments delivered to a patient’s devices. Watson does a cognitive analysis of the patient’s integrated data streams and the care managers receive the insights from this analysis. The final goal is to enhance engagement with patients and to spot and address potential health problems early. The data related to an individual patient’s case is then returned in the Watson Health Cloud, which analyzes which interventions correlate with positive results and applies that knowledge to future care management options. What the platform can do for you and how it integrates data from different sources is also shown in the 4-minute YouTube video:

Is IBM Watson able to improve personal care?

Watson is the first commercially available cognitive computing system and is able to analyze large volumes of data, understands questions posed in natural language, and suggests evidence-based options. The vision of IBM Watson Health is that clinical, genomics, and especially exogenous data give us the opportunity to transform the ways we manage our health (IBM 2015).

What if you could choose to turn your everyday routine, every step or check-up, into health data that you share to help your doctor provide better and more personalised care?

Would you be willing to share? And more important, do you think that sharing personal health data, will enable Watson to provide better insights, real-time feedback, and improve personal health and acute and chronic care?

Sources:

Datamark, (2013). Unstructured Data in Electronic Health Record (EHR) Systems: Challenges and Solutions. [Online] Available at: http://www.datamark.net/uploads/files/unstructured_ehr_data_white_paper.pdf [Accessed 9 Jan. 2016]
Dilsizian, S. E., & Siegel, E. L. (2014). Artificial intelligence in medicine and cardiac imaging: Harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current cardiology reports, 16(1), 1-8.
Gunelius, S. (2014). The data explosion in 2014 minute by minute—infographic. [Online] Available at: http://aci.info/2014/07/12/the-data-explosion-in-2014-minute-by-minute-infographic/ [Accessed 16 Jan. 2016]
IBM (2015). Watson in healthcare. [Online] Available at: http://www-05.ibm.com/innovation/uk/watson/watson_in_healthcare.shtml [Accessed 17 Jan. 2016]
James, J. (2015). Data never sleeps 3.0. [Online] Available at: https://www.domo.com/blog/2015/08/data-never-sleeps-3-0/ [Accessed 16 Jan. 2016]
McGovern, L., Miller, G., Hughes-Cromwick, P., Mays, G., Lantz, P., Lott, R. (2014). The relative contribution of multiple determinants to health outcomes. Health Affairs (Robert Wood Johnson Foundation), 33, 2.
Robert Wood Johnson Foundation (2015). Data for health. Learning what works. [Online]. Available at: http://www.rwjf.org/content/dam/farm/reports/reports/2015/rwjf418628 [Accessed 28Jan. 2016]
Yang, C. C., & Veltri, P. (2015). Intelligent healthcare informatics in big data era. Artificial intelligence in medicine, 65(2), 75-77.