Sometime in the nineteenth century, medicine went from being an art to a science with the industrial use of the printing press. For the first time, there was a way to aggregate information and share best practices among physicians. It was a huge leap forward. Similarly, today’s health analytics technologies offer the opportunity for yet another leap forward in evidence-based care.
In the information-intensive healthcare industry, we are currently drowning in data, yet thirsting for insight. The irony of evidence-based care is that we often aren’t using the best evidence at our disposal. Is evidence from a trial performed in another country 10 years ago relevant to your patient in surgery today? What if instead—or in addition—you could look at how similar patients in your region have responded to a certain treatment over the last year?
That sort of data is in the electronic medical record (EMR) systems now in place in many health organizations, but it’s often left untapped. There is also loads of valuable operational data in our existing systems that can be used to better align resources with needs. Health analytics offer the opportunity to unlock the potential of the data that’s all around us. And the tools available today make it possible for everyone in the organization to gain timely, actionable insight.
While analytics technologies—also known as business intelligence—are mainstream in other industries, they’re relatively underutilized in healthcare. I think that’s because the role of analytics in our industry is poorly understood.
So let’s strip the jargon, and see what analytics look like in health. Here are a few real-world scenarios:
Decision support: At Alice Springs Hospital, patient information in clinical systems is used to provide prompts, alerts, and visual feedback to support safe, efficient care. Performance management: Clinical scorecards and executive dashboards show providers and executives at Austin Health how the hospital is managing patient load in near-real time, allowing bottlenecks to be addressed quickly. Predictive analytics: Royal Adelaide Hospital identifies patterns in patient volumes across days (weekday to weekend variance), weeks, seasons (flu season, holidays), and years so it can staff effectively and plan service expansion appropriately. Clinical outcomes: Ajou University Medical Center uses a clinical data warehouse for faster, easier analysis of medical information in its EMR and clinical trials research, and it has expanded its research exchange with other institutions—all to support better, evidence-based care. Activity-based funding: Djerriwarrh Health Services improves reporting accuracy and analysis to increase reimbursements and better manage resources.
The drive to implement EMR systems has been to increase care quality, safety, and efficiency. To truly achieve those goals, we need to stop thinking about the information we capture in these systems as a byproduct of the activity we do. Rather, this data is a strategic asset that we can take advantage of to improve outcomes and drive operational efficiencies. As such, every EMR project should be coupled with a health analytics initiative.
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