The Value of Big Data: From Bench to Bedside to the Bottom Line

Anil Jain
Anil Jain

Guest post by Anil Jain, MD, FACP, senior vice president and chief medical officer, Explorys, and consulting staff, Department of Internal Medicine, Cleveland Clinic.

Despite advances in medical education, the proliferation of medical journals and the speed of light retrieval of information on the Internet, the lag time between when researchers identify life-saving clinical interventions and when they are put into practice ranges from 10 to 25 years, averaging 17 years. This lag time between the discovery at the “bench” and its practice at the “bedside” is even more startling when you consider the impact of care at the “bedside” to the “bottom-line.”  This “bottom-line” has become increasingly important with the formation of accountable care organizations (ACOs) that aim to reward provider organizations and payers that meet the “triple-aim”:  high-quality care for the population, high-quality care for the patient, at the most affordable cost. Unfortunately, current practices at the “bedside” reportedly generate approximately $700 billion in care that isn’t necessary and may even be potentially harmful to the “bottom-line.” Moreover, despite healthcare expenditures of 17 percent of our GDP, the U.S. lags behind most industrial nations when looking at composite measures of healthcare quality.

With the increasing use of health information technology and data we should be able to shorten the time between “bench” to “bedside” and improve the “bottom line.”

Big Data

“Big data” is data that is of high volume, variety and of sufficient velocity that is not amenable to traditional data storage and analysis tools.  This “big data” is most typically generated from health systems’ electronic health records (EHRs), laboratory, radiology, financial and billing systems, personal health records, biometrics and smart devices.  In addition, patients today are oftentimes utilizing various mobile health and wellness apps and wearable devices which also collect a plethora of data, which only adds to the complexity.

The Bench

The aggregation of de-identified medical information across millions of health records from varying venues of care facilitating a longitudinal view of a person can be incredibly beneficial for researchers focused on net new knowledge discovery. For data from disparate health systems to be aggregated, it is vital that it is standardized and that subjects across health systems can be matched. This harmonization of disparate data coupled with the appropriate analytics software is critical to identify patterns in the data.

In this setting, the larger the data set, the more likely that a signal can be detected through the noise, even in the rarest of conditions. Fortunately, many hypotheses can be conceived and tested through appropriate analytics within this real-world data set in a much more cost-effective manner than conducting full-scale clinical trials. Furthermore, if a signal is detected or a pattern is found, researchers can then design a more focused explanatory or pragmatic clinical trial to prospectively test the hypotheses. For example, over the past few years within the Explorys network, more than a dozen peer-reviewed abstracts and publications have been generated by leveraging a de-identified data set comprised of nearly 48 million subjects, searchable by a specialized browser-based analysis and query application.

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Healthcare Big Data Defined: Improving Care, Coordination and Coding

Lance Speck
Lance Speck

Lance Speck, general manager of Actian cloud and healthcare, speaks here about healthcare big data and how it can be used in healthcare to improve processes from care coordination to coding for ICD-10. In his day job, he is focused on delivering healthcare solutions to help payers and providers address an estimated $450 billion annual opportunity created through data analytics, ranging from fraud analytics to patient re-admission reduction to staff optimization to accountable care reporting and clinical auto-coding. For more than 20 years, Lance has served in a variety of management, sales and product roles in the software industry including a decade focused on SaaS, cloud and healthcare.

How can big data analytics improve patient care?

According to a recent PwC survey, 95 percent of healthcare CEOs are exploring better ways of using and managing big data; however, only 36 percent have made any headway in getting to grips with big data.  All agree that big data analytics has the potential to improve the quality and cost of care, but many are still struggling with finding the right ways to infuse analytics into everyday operations. Assuming they realize that they already have access to the data, what do they do with it? What are the areas that will have the biggest impact? Where do they start?

Start with the basics. Organizations should focus in infusing big data analytics where a big impact can be recognized. They should ask themselves:

Very early in the process, organizations should address how they plan to incorporate big data into the everyday workflow of clinicians, financial staff and other healthcare stakeholders for organizations to:

How can healthcare providers transition to ICD-10 as simply as possible?

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