The idea that there could be undiscovered insights residing in large data sets that can be used to drive innovation is fascinating. That’s why Big Data has so many people excited, including myself. In healthcare, Big Data is essentially a core component of population health management, a strategy that is helping healthcare organizations transform to value-based care. Although most healthcare organizations are still in the early stages of both strategies, there is no question that Big Data and population health will play major roles in shaping the future of healthcare. Let’s explore this issue further to better understand what steps healthcare organizations can take to realize the full promise of Big Data.
Getting to Big Data
The traditional healthcare organization is evolving beyond the walls of the hospital. Providers must now coordinate care across a broader health network that can include a large number of clinical partners often using a variety of technology systems. Although it can be a challenge to bring data together, it is the first required step towards gathering large quantities of data. It is also important to consider that clinical data is only the starting point. Where Big Data in healthcare starts to get really innovative is when clinical data is combined with other types of information such as claims, financial, lab, pharmacy, unstructured data (e.g. clinical notes), etc. Providers that can establish the processes and tools to automate the aggregation and normalization of data regardless of the system or structure they reside in will be set up for Big Data success.
From Data to Insights
After an organization achieves data aggregation, it should celebrate because that is a major accomplishment. However, that is only step one in the journey because the data has to be turned into insights that can drive improvement. One of the most powerful uses of healthcare data is in predictive modeling, which helps providers understand their patient populations and anticipate where they can have the highest return on intervention. For example, providers can measure patient motivation, and use it to predict which patients will follow physician instructions and benefit most from targeted interventions. They can also predict which patients will become the sickest next, i.e. those that are most likely to transition from low to high clinical and financial risk over the next 12 months, so they can intervene proactively. As risk has shifted from payers to providers, this ability to understand the risk in a population has become essential.
From Insights to Action
The last, but most important, step is to surface insights and information within clinician workflows in real-time so that they can take action and improve outcomes. For example, a physician can see gaps in care for patients and close those gaps while still in their presence. A care manager can be alerted when a high-risk patient is admitted to the emergency department to ensure proper transition of care. A clinical analyst can monitor patient data on a large number of patients who have had catheters inserted, and alert a nurse when the data shows that a patient is showing signs of a potential infection.
Most exciting is that all of the above use cases are not hypothetical or years away from becoming reality. They are all possible with today’s health information technology. These innovations stemming from Big Data are starting to take hold, but it will take time for them to truly become the norm in healthcare. There is no doubt, however, that momentum is building for both Big Data as well as population health, and that they will play leading roles in healthcare’s future.