How Big Data and Business Intelligence are Spurring Evidence-Based Healthcare

December 8, 2014 Ben Kerschberg


Big data is revolutionizing nearly every modern industry. We now speak of data in term of petabytes (10 to the power of 15  bytes) and exabytes (10 to the power of 21  bytes). Almost all of the world’s data was created in the past two years, and only one half of one percent (0.5%) has been analyzed. Moreover, data is now primarily unstructured, which means that it cannot fit into databases of the past that consist of rows and columns. Big data comes from sources such as Word documents, Powerpoint slides, telecommunications records, electronic health records (“EHRs”), machine-based sensors (such as the one in your new car that senses when your brake pads need replacing), to the wave of sensors around the world that comprise the so-called Internet of Things.

As our ability to dig into big data has grown by leaps and bounds, data science has become the development of the day, with analytics a critical element under that umbrella. Analytics consists of complex algorithms run against streams of big data in order to infer actionable business intelligence at an unprecedented pace, often in real time.

The healthcare industry provides a very clear illustration of how unwieldy big data can be, and how important it is to lasso it. I wrote a piece in 2012 arguing that health care and big data are joined at the hip. Two years later, this has never been more true. EHRs have been in play for over a decade, with strong mandates from the Affordable Care Act generally, and the Health Information Technology for Economic Health Act (“HITECH Act”) specifically, to make EHRs the de facto standard for medical records.

Yet even in November 2014, the big data revolution in healthcare–an industry well known to be a technological laggard–is still finding its legs. For healthcare stakeholders–for example, a hospital or insurer–this means that the potential to infer business intelligence within one’s enterprise remains largely unclaimed. these parties are used to working in silos rather following overarching industry protocols. The reason is simple enough: Few such protocols even exist.

Harvesting Big Data for Healthcare

Healthcare stakeholders are slowly beginning to embrace the concept of evidence-based medicine, providing what we hope is the best patient care based on scientific evidence. Think of what IBM Watson is doing in the field. Or how major universities use data science to rethink the way they approach medical processes that save lives.

But there is much more to the story. The use of big data must balance what is (i) good for industry and (ii) the benefit to the patient. These can be–but most often are not–the same. A careful balance involves a comparison of healthcare cost and patient outcomes. McKinsey & Company published an excellent study (note: automatic PDF download) that focuses on big data and “new value pathways” in healthcare. They discuss the risks of big data, such as data privacy and HIPAA concerns. They assert that “fundamental changes need to occur within the healthcare system for stakeholders to capture big data’s full potential.”

McKinsey’s “new value proposition” is based on a continuous feedback loop amongst five elements:

  1. right consumer living;
  2. right care;
  3. right provider;
  4. right value; and
  5. right innovation.

Patients’ consumer living habits are the most important of these five when it comes to managing their own treatment. Companies such as Eliza Corporation are at the forefront of techniques such as Healthcare Engagement Management, which gathers big data from its clients and provides it to patients (e.g., a reminder to have a blood test) as is appropriate. The positive compliance results are amazing, providing significant returns on investment. McKinsey writes: patients must make “lifestyle choices that help them remain healthy, such as proper diet and exercise.” Seems simple enough, but most of us come up short in both categories.

The second element in the “new value proposition” is the right care — the appropriate care at the right time at very point of contact (“POC”). Delivering the right care requires transparency between each actor at each POC to avoid duplication, and to make sure that present and future administration of care is consistent with past treatments, sometimes only hours prior. Such information is then transmitted to a caregiver’s tablet, where he and all other actors, including the patient, may access it, often in the cloud depending on the technology implementation.

Measuring the right value is critical to establishing proper metrics in healthcare both systemically and at the individual level. According to McKinsey, systems benefits flow when there is a voluntary and synergistic transfer of information between patient and caregiver. Achieving that is not as easy as it sounds. They write that measuring value may take the form of “ensuring cost-effectiveness of care, such as trying provider reimbursement to patient outcomes, or eliminating fraud, waste, and abuse in the system.”

The combination of big data and analytics is essential to spur industry innovation, especially in the advancement of medicine and increasing research and development. Innovation requires the ability to examine all sorts of data, from EHRs to blood tests, to data in a 10-year old database that consists of rows and columns — i.e. both unstructured and structured data.


The challenge for the healthcare industry is to sip from the big data firehose in meaningful ways. The days of taking on faith that we are receiving the best possible healthcare are over. We try to get the best possible care, often locally and regionally. Few of us can afford to go to the Cleveland Clinic for heart surgery.  McKinsey posits five “rights” that stem from the use of big data. The reality is that most of us will get “wrong” those elements within our control. We’re human. What we all should be able to count on is the fact that metrics both inside (e.g., pulse and oxygen saturation) and outside (e.g., dialysis compliance) a caregiver’s four walls will provide the right care. Pay attention to what empirical data tells you and your doctors. Ask lots of questions–they may save your life–and be sure you have access to the same real-time data as your caregiver.

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