The Future of Big Data Analytics
The healthcare industry has been slower than other sectors to embrace computerized information systems like EHRs, in part, because of concerns over the privacy of personal health information. While it was a long time in coming, this evolution is now driving the application of big data to solve big problems in healthcare.
In his 2016 State of the Union address, President Obama announced the U.S.’s commitment to lead the world to find a cure for cancer. Alluding to the country’s success in the race to put a man on the moon in the 1960’s, the ‘cancer moonshot’ is an ambitious initiative being led by Vice-President Joe Biden. A major prong of his plan is to increase collaboration by enabling researchers to share data freely. By breaking down silos, healthcare organizations can work together to expedite the discovery of a cure.
Today, most healthcare organizations use reporting tools descriptively to categorize data, hoping to understand what has gone on historically within their own domain. While descriptive analytics has been useful to identify opportunities to reduce costs and improve efficiencies in organizations, the explosion in healthcare data is spurring the use of predictive analytics and prescriptive analytics.
Rather than look for the causes of past success like descriptive analytics does, predictive analytics tries to answer what will happen given a particular situation. It offers powerful techniques to forecast scenarios and predict health outcomes. It is the use of this type of analysis that will enable the rapid research advancements needed to meet Vice President Biden’s goal of making a decade’s worth of advances in five years. Prescriptive analytics goes even further. It uses data to anticipate events and provide decision-makers with suggestions and alternatives so that they may take advantage of future opportunities or mitigate future risks.
While the CDC’s FluView application let us rapidly see where the incidence of flu is occurring, it is still largely descriptive analysis. To be truly predictive, it would need to indicate the likelihood of a flu outbreak in a given area at a future point. It is the type of analysis that Google was exploring with their Flu Trends website, using data from internet searches to model real-world phenomena.
In order for predictive and prescriptive analytics to enrich health research, it must be possible to take advantage of the 80% of data in the system that is unstructured. By having access to the full spectrum of available data, researchers will be able to realize the promise of personalized medicine that will use our genomic data to support pre-diagnosis as well as improve overall population health by pinpointing the most promising new therapies in the fight against debilitating diseases.
As the market matures for BDA in healthcare, organizations will be under pressure to improve how they manage and share the escalating volume of data under their control. Leveraging this data has real value; the McKinsey Global Institute has estimated that big data could be worth $300 billion to the US healthcare industry from improved quality and efficiencies. Organizations need to start now to establish strong privacy practices that will allow them to take maximum advantage of their wealth of data.
Establishing strong de-identification practices, with special attention on the de-identification of unstructured data, will enable healthcare organization to turn their information into a strategic asset, collaborate on cutting-edge research — and shoot for the moon.
This was the last installment in the Big Data Analytics Series by Privacy Analytics. The previous installments:
- Turn Data Assets into Business Opportunity Under CCPADecember 19, 2019
- How does risk-based anonymization work?December 18, 2019
- Why should I use Expert Determination over Safe Harbor?December 18, 2019
- What do I need to know about GDPR, HIPAA and CCPA to meet our regulatory and privacy obligations?December 18, 2019
- Should we invest in building our own de-identification capability?December 17, 2019
- GDPR and The Future of Clinical Trials Data SharingMarch 18, 2019
- Advancing Principled Data Practices in Support of Emerging TechnologiesMarch 15, 2019
- “Zero Risk Does Not Exist”February 7, 2019
- Is Anonymization Possible with Current Technologies?January 9, 2019
- Comparing the benefits of pseudonymisation and anonymisation under the GDPRDecember 20, 2018