The advanced data analytics train is increasing speed. Why you’d be smart to jump aboard.


Data is king right now, especially within the healthcare and life sciences sectors.

Massive pools of data allow us to make informed, patient-centered decisions as we gain increasing clinical insights – and that’s great.This data boom has been a long time coming, with the new focus on precision medicine, the increasing consumerization of genomics, and a healthcare system that leans heavily on prevention-based models of care.Additionally, healthcare reimbursement models are changing and leaning more heavily on data-centric care models, especially meaningful use and pay-for-performance models emerge as critical factors on today’s healthcare environment. Data plays a crucial role for Providers and Payers to make informed decisions by gaining insights from clinical and other data repositories. Why are we talking about this now? Healthcare reimbursement models are changing at rapid speed. I believe that we will soon be forced to turn vast amount of data into actionable items, likely in the coming year. It is vitally important for life science and healthcare organizations to develop new analytics systems and infrastructure capabilities beyond traditional methods for managing mass quantities of data effectively. If we don’t, we risk losing millions of dollars in revenue and profits. 

 Who benefits from Advanced Data Analytics?The answer there is pretty simple: everyone, especially life science and health care organizations. The train is already moving, and if you don’t jump aboard now, you’ll likely get left behind.

  • Healthcare Payers: Advanced data analytics systems help to analyze patient characteristics, and the cost and outcomes to care, as well as to identify the most efficient methods of diagnosis and treatment. Implementing new systems will also help payers identify, predict, and minimize fraud by checking the accuracy and consistency of plans ahead of time.
  • Medical Devices & Wearables: There are a myriad of opportunities for payers and providers when devices are tied to data analytics systems. These devices can capture and analyze lots of fast-moving data in real-time, enabling payers to monitor adherence to drugs and to monitor safety. Eventually, we’ll be able to track trends that lead to population wellness benefits, too!
  • Evidence Based Medicine:  One of the chief complaints from providers about today’s current digital health solutions is they are not built with the providers’ needs incorporated. Indeed, the AMA has gone as far as to say many of today’s digital health offerings are “quackery”.  While such dire evaluations could cause some to worry, I feel we are headed towards a bright future. One where we will be able to use historical data to personalize medical care by predicting and estimating developments or outcomes for each patient. This could save time and resources for hospitals.
  • Genomics: Advanced data analytics also have the added benefit of making genomic analyses available for consumers within the regular medical care decision process. Growing patient medical records will help us execute gene sequencing more efficiently and cost effectively as well.
  • Population Health: This kind of data collection could drastically improve the health of populations by improving public health surveillance (for example, analyzing disease patterns and tracking disease outbreaks). Soon, we may be able to predict virus evolution, leading to more accurately targeted seasonal vaccines, all because of better data that is more actionable. 

 Still, there are many challenges around data strategy and governance.Large sets of health data are a tempting target for cyber attackers. With this increasing focus on large sets of data also comes questions about governing data repositories with appropriate tools, infrastructures, and techniques. We garner the true benefits from Advanced analytics in life sciences and healthcare only when we address concerns such as guaranteeing privacy, safeguarding security, establishing standards and governance, and continually improving the tools and technologies need to be resolved in a compliant and cost-effective manner and that’s where I’d like to dive into the details a bit.Most importantly, questions about security loom heavy when building data infrastructures, as attackers have many opportunities now to steal data virtually, or to alter information used by business units, if that data is not protected effectively. At a big picture level, while organizations cannot afford to ignore Big Data capabilities, there must be appropriate security measures put in place. There’s a fine balance between creating safeguards for corporate data, while still maintaining optimal user experience.So as you begin to develop tools for managing data, it’s important to consider these tips for building a big data system with strong governance, security, infrastructure, and workflow effectiveness: 

1.  Prevent Hacking by Keeping These Rules in Mind:
Transparency & Visibility: no one should be able to cover their tracks
Accountability: every action should be attributable to its owner
Privacy: security should be afforded without giving up confidential information
Scalability: the tool and its security system must be able to scale to trillions of digital assets
Portability: security must move with the data, wherever the data goes
Permanence: security must not be ephemeral; it must exist as long as the data exists, and ideally longer
2. Monitor Consistently for Both Security and Workflow Effectiveness.
Effective monitoring is the only way you can reliably detect unauthorized and unwanted activities within your system. And once you’re logging everything, you’ll need to frequently review, analyze, and audit those logs for unauthorized activities as well. Consistent monitoring also allows for the detection of problems beyond security, so agile pivots and measured improvements are possible.
3. Analyze and Audit Consistently for Sensitive Information
Unfortunately, even if you have all of the above nailed, there’s still a problem that many people overlook: Advanced analytics is most likely a single platform from which many people access different data-derived information, but some information should not be shown to some people in some contexts.
4. Remember Identity Management
It helps to retain a singular focus on the perimeter model of security by building the perimeter around the people accessing sensitive data, as opposed to the physical locations that contain the data.
5. Consider Encryption
Using encryption or other ways of removing the sensitivity from data fields without negating their usefulness is also key. Encryption can be a very useful masking or obfuscation strategy.

The boom in advanced data analytics is coming.It’s basically already here. And as I said, I believe that the time for this work is now. We need to build more secure and streamlined advanced analytical systems in the coming year. If we don’t, we risk losing millions of dollars in revenue and profits… so let’s get to it!


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