“It’s not about the size of the data, it’s what we do with it”- Elizabeth McGlynn, Kaiser Permanente
A crowded room of attendees chewed on this quote and many, many more during a session titled “Using Big Data to Advance Healthcare” at AcademyHealth’s 2014 National Health Policy Conference in February. What follows is a summary of the session.
The session featured an all-star panel of data users, holders and advocates and dove into hot topics like reengineering EMRs and incorporating microbiome information into the research repertoire.
Dwayne Spradlin moderated the day’s session and led by framing the pressing “need to put data to work." In a world of data overload, Dwayne posited that health and health care information is the most useful of all. If so, the good news is there's plenty of it: claims data, cost data, clinical data, research data, vital statistics, environmental data, social data, person-level data, etc. Unfortunately, much of this data is locked in silos and not easily available for useful consumption.
He then spoke about “big data” and what it really means. In short, it’s a “buzzword” describing a massive volume of unstructured data. He referenced the McKinsey & Company study articulating that the benefits of big data are plenty, but, he noted, the elephant in the room is how we realistically transform big data into it’s full potential – to ultimately improve health and health care.
Elizabeth McGlynn, Kaiser Permanente, cautioned the audience that decisions around big data should be made using a “value” lens – we shouldn’t do something because it’s new; rather, it must have a value proposition to be worthwhile. Related to value, she articulated promising uses of big data such as care delivery (personalized medicine), operations, public health, and research.
She stated that EMRs of tomorrow will include big data (and more complete data), offer global data access for researchers, and provide real-time decision support. In closing, she noted that the “future of big data lies in its ability to support the safest, highest quality, most individualized care without constraint of borders and boundaries.”
Gregory Moore, Geisinger Health System, echoed the need to put big data into action. He highlighted four enablers to using big data. First, effective use relies on the talent (e.g., clinical leadership, data scientists). Well-versed personnel are crucial to maximizing the full potential of data. Then, proper tools must be in place (e.g., functioning data architecture, meaningful clinical rules.) Next, a process must be implemented to validate the quality of data (e.g., data scrubbing.) Lastly, patients must be engaged and activated in order for maximize the benefits of big data usage and integration into the health and healthcare ecosystem.
He went on to provide the audience a brief peek into Geisinger’s efforts using big data streams to conduct real-time modeling. Right now, Geisinger has produced automated data models of emergency rooms, everything from clinicians to hospital beds. Researchers then alter ratios of these resources (e.g., removing or adding hospital beds) to test its efforts on care delivery, resource allocation and health outcomes. This is a real-world example of systems level data in action.
Lastly, Larry Smarr, California Institute for Telecommunications and Information Technology, spoke about his in-depth experience observing, measuring, and evaluating nearly all aspects of his health (e.g., what nutrients he requires, what happens when he sleeps, etc.) through the use of gadgets (e.g., heart rate monitors). He has recorded tens of billions of data, all of which are not included in traditional medical records.
He stated that current medical care is only treating 10% of the patient’s makeup, as the remainder is the composed of the microbiome. Though the NIH has funded work in this area (Human Microbiome Project), Larry believes that more needs to be done to quantify how changes in a person’s microbiome relate to health, and how this data should be used in health care delivery. This is yet another example of the potential of big data.
The session wrapped with a rapid series of audience questions. The tone of the room was energized from start to finish, and as the session formally ended, the dialogue, thought-provoking discussion, and lively debate continued into the hallway.