Obamacare or Not, Analytics is the Future of Health Care Cost Reduction

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Ian Graham, Salient CRGT

Ian Graham, Salient CRGT

Ian Graham is a vice president at Salient CRGT.

With Congress deadlocked about the fate of the Affordable Care Act, one thing is still certain: The overall cost of health care in America is too high.

According to the U.S. Centers for Medicare and Medicaid Services, in 2015, U.S. health care spending grew 5.8 percent to reach $3.2 trillion, or $9,990 per person annually. And health care spending is predicted to increase at a rate higher than GDP through at least 2025.

Significant cost reduction will require transformational change to how we deliver care.  That transformation is best informed by comprehensive analysis of what works best – from patient triage and diagnosis, to targeted therapies and chronic disease management, to operational efficiencies. These are all areas that, through the use of advanced data analytics, can have a dramatic impact on health care costs while at the same time, increasing quality.

We are at an exceptional time in history where data analytics can enable health care to advance more rapidly than ever before.

So, what’s the delay?

With increasing advancements in technology and decreasing cost of storage and processing power, there has never been a better time for federal agencies and commercial organizations to tap into the potential of advanced analytics and move to launch enterprisewide adoption strategies.

Small steps have been taken, but a focus on the following data analytics practices will help push organizations into areas that will proactively cut the cost of health care.

  1. Predictive medicine: When using big data and machine learning for predictive analytics, as more data is accumulated (from electronic health records, wearables, etc.), systems become better at predicting the future based on what it has learned from the past. Physicians have been doing this for years using their own experience and education, but machine algorithms are becoming very accurate, as they can remember (and process) much more information. Instead of a doctor diagnosing only based on her experience, she can diagnose with the help of thousands or millions of experiences.
  2. Big data: Scientific advances are allowing us to capture how nature works – disease progression, the role of DNA and other health matters – as data. Big data analytics can facilitate the segmentation and analysis of large amounts of data through automated linking, visualization techniques and proven statistical algorithms.
  3. Precision medicine: This technique takes into account individual differences in people’s genes, microbiomes, environments and lifestyles. While precision medicine has been around for quite a while, advancements in data collection and analysis has allowed for more effective, targeted and cost-efficient treatments in many areas, from diabetes to allergies to cancer.
  4. Self-service analytics: Recent advancements in analytics has guided us to more business-led, self-service environments. Business-focused health care professionals now have the ability to perform queries and other data discovery techniques previously only in the hands of an IT shop. Today, analysts can use low-cost tools to explore their “democratized” data in a very iterative, visual and intuitive way.
  5. IoT analytics: The amazing rate of adoption in the internet of things has spurred the medical community to embrace it for health care treatment and prevention as well. For instance, remote monitoring through wearables can drastically reduce the cost of health care and actually increase the amount of monitoring that is possible versus a hospital stay. With rapidly decreasing costs of sensors, physicians can continuously monitor vitals such as blood pressure, body temperature, heart rate and breathing rate at a very low cost. In addition, the data collected can be used to help with the predictive aspects of health analytics.
  6. Data mining: To maximize legacy tools and approaches, the health industry has structured, categorized, formatted and developed ontologies for health data many times over. And this has greatly improved the ability to leverage data; however, the most complex nuances and lessons of health are still best captured in unstructured, narrative text. Through the use of data mining techniques, we now have the ability to accommodate and exploit clinical narratives and other descriptive, unstructured content to unlock insights from our EHRs and other unstructured content.

Analytics has the power to jumpstart the health care industry toward a more modern, data-driven environment. From precision telemedicine to value-based care, the vast amounts of health care data that can be unlocked with analytics holds enormous value in quicker, more informed decisions, enhanced services and lower costs – for everyone.

We’re on the doorstep of transforming our health care system, and analytics is one of the best tools to open the door and start the journey forward, regardless of the fate of Obamacare.

Read More:

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