Imagine this: you go to the movies but instead of seeing an entire film, you only see a few pictures projected on the screen – there’s no dialogue, no drama, no context. Would the movie make sense? Likely not, because there were important pieces of missing information and until you fill in those gaps you have an incomplete picture.
This is exactly how many physicians have to make clinical decisions – they have incomplete pictures of their patients’ health and are being asked to make informed decisions based on those snippets of information. The complete picture lies within Big Data.
Providers have successfully gathered an abundance of patient information – stacks of historical data from medical charts, X-rays and lab results. But it doesn’t tell the doctor what’s happening with a patient when they are plugged into a monitor at a hospital.
When a patient is being monitored, a nurse will typically make rounds every 4-5 hours or so to check the patient’s vital signs and document their results. After several rounds, when enough readings are plotted on a chart, the results begin to tell a story about how the person is responding to treatment. If the information were to be plotted as points on a chart, they could paint a very smooth, steady picture. But that picture could be misleading.
Between each vital signs check, there could be vast changes in the patient’s physiology that went unrecorded. For all we know, in between those nurse visits, the patient could have undergone a physiological shift, but the doctor is never seeing it and, therefore, cannot take it into account when trying to improve the course of treatment. When this real-time data is discarded, so too is the opportunity to improve the precision and confidence in the care we are delivering. By incorporating this previously undocumented information, we would gain a much richer perspective and likely come up with a very different definition of what is “normal” for that patient.
Fortunately, we’ve found new ways to capture some data and are constantly working on algorithms to make sense of it all. Technologies like early warning scoring systems capture minor fluctuations in vital signs long before a patient begins to show signs of deterioration. The system can send out an alert well in advance of a medical escalation, drastically improving the level of care and outcomes for the patients. Hospitals see the benefits in reduced lengths of stay and lower readmission rates.
In the next round, we need to harness the power of Big Data in the realm of predictive analytics for entire populations. For example, if a doctor begins to witness a similar set of symptoms across a number of patients, they can begin to track trends and monitor the progression of a condition across a population to draw more informed conclusions about the likely cause of an illness. This could potentially lead to earlier intervention and much faster treatments and responses.
The benefits don’t stop at the hospital and doctor’s office. With advanced data collection and analytics tools, pharmaceutical companies can begin to develop drugs that treat conditions at a much more targeted level. Payers will also have more certainty of outcomes, allowing them to evaluate cost of care with a new lens.
Along with the infinite benefits, there are also several barriers – patient privacy being one of the most critical ones. But to move the needle on improving patient outcomes, embracing and sharing Big Data technologies is inevitable. Also critical is the need for a cooperative endeavor across all stakeholders – from providers, to payers and even pharma, where financial incentives are aligned to the goal to improve the future of healthcare across the care continuum. To date there’s been no perfect model, but there are lessons to be learned from early adopters and Accountable Care Organizations (ACOs) are providing the best pathway to a more collaborative model of care.
But in the meantime, the effort to use Big Data is going to help show doctors the complete patient “movie,” not only improving outcomes but also increasing access to quality affordable care. For generations, doctors have been practicing the “art of medicine,” treating patient after patient based on what they’ve seen before and making calculated assumptions that if a course of treatment worked for one patient with similar symptoms in the past, it will likely work again. As new technology allows clinicians to see new layers of real-time patient data, that “art” is slowly being replaced by science. Through complex analysis of physiological information that was previously never captured, doctors can gain a clearer picture of a patient’s health, and more importantly, execute faster, more accurate clinical decisions.
