There has never been a more exciting time in healthcare IT than now. While there continues to be significant challenges with EHR implementation, interoperability, regulatory requirements, security and privacy, we are starting to see tangible value coming from our IT investments. With our transformational experiences at UCHealth's hospitals in Colorado, and through hundreds of conversations with peer CIOs, innovation partners and other thought leaders, I believe we have a set of three core principles that could shape how we use healthcare data and impact the next generation of healthcare IT and operational intelligence.
Our biggest challenge is not finding new or different sources of data; it is making sense of the data already being collected within our EHR.
Let's look at the digital world around us:
How many of you hear “I can't get the data”? I would argue that this is not the real problem. There is no shortage of data and no shortage of the ability to get data. The fundamental issue is that there is so much data and so little time to analyze it. What you are really hearing is “I need help interpreting the data and need the analysis to inform what I should do to make positive change.” Our operational and clinical peers simply do not have the time and patience to sift through vast amounts of data to find what they want and what they need. Data are no longer feeding information and knowledge. The volume of data is actually threatening information and knowledge because of our inability to make sense out of it.
Quite simply, we need to tap into the information already in front of us, already in our EHRs, and make it more predictive and prescriptive. The data explosion isn't going to cease. If we don't start using the data differently, we will fail to deliver on the real promise of healthcare IT.
Yesterday's tools cannot make sense of today's data.
We all have plenty of tools, from the EHR itself to patient-tracking systems to imaging systems to, more fundamentally, spreadsheets, reports, dashboards and visualization capabilities. These tools work well for transactional activities and deriving simple answers to simple questions. For transformational activities, we need sophisticated data science that can dig deep into vast amounts of data and uncover meaningful information.
Let's take the example of operating room utilization. Whether you have four or 104 operating rooms, ORs are some of the most expensive healthcare real estate in our health systems and also some of the hardest to ensure effective utilization. Based on tracking what surgeries are scheduled every day and EHR case log data, reports and business intelligence tools can easily show core metrics such as room utilization, block utilization, first case starts and delays. This is important for showing what happened, but none of these tools are telling us why it happened and, more importantly, how we can make changes.
Why is Dr. Jones' block utilization 62%? What can he/we do to improve it? Should he schedule more cases? Make his projections for case length tighter? Reduce turnover time? These are the types of questions for which OR managers desperately want answers, and the best EHR, case log data and dashboards simply cannot provide those answers.
To get more prescriptive actions, we need sophisticated data science techniques, techniques that go deep into the data, understand patterns, and uncover insights. We need sophisticated statistical modeling and machine learning that can actually understand and make sense of data, not just query information and present it.
We have the data, and we need to recognize that the standard tools we have used over the past several years work well for what they were meant to do. The “why” and “how” requires a deeper, contextual understanding of data and require different tools.
Use of advanced, enabling technology and prescriptive intelligence will transform healthcare.
Think about Uber, Amazon, Netflix, Apple, Google and other favorite tools you probably use every day. These organizations have creatively and innovatively used technology to make our lives easier. Technology is the enabler; these organizations have harnessed it and differentiated themselves from their competitors by how they allow us to use technology. Technology is rebooting industries.
We should be looking at healthcare the same way. Instead of focusing on the complexities of healthcare and asking why we have what we have, we should be asking ourselves, how we can make a difference? Many healthcare organizations across the country are already transforming their operations with cutting-edge data mining systems, enabling advanced prescriptive intelligence. They are using these systems to improve quality, improve safety, improve the patient experience, and with that, are starting to differentiate their operations from their competitors.
Organizations remain cautious about technology. They're asking hundreds of questions. But they're also asking a more important question: “why can't...?” Why can't we leverage data to lower wait times? Why can't we use data to make better scheduling decisions? Why can't we anticipate our staffing needs? Why can't we make our physicians more proactive and productive? Why can't we make the most of our resources? Why can't we leverage data to improve operations?
Some thoughts on the “how”
Some organizations are attempting to add data science capabilities to their business intelligence workforce but are quickly realizing that it's a daunting task. At UCHealth, like many other systems nationwide, we have brilliant physicians, excellent clinical and administrative leaders, good process-improvement capabilities and a good IT foundation. But, even with all of that capability in place, we have come to realize that we lack the resources, expertise and vision to execute on this next generation of artificial intelligence and machine learning. What we have done is partner with innovative healthcare organizations that have hard-to find and even harder to retain skills like good data scientists. My advice: Find a good partner and work together to get answers better and faster. If you can build this capability within your own team, great. Otherwise, find a good partner and leverage the collaboration to help you get to the answers better and faster. Use your existing data, truly understand what your current tools can and can't do, and take ownership to leverage technology and data science to transform your organization.
I think our time is now. The good news is we all have invested in a lot of data creation engines already. You have an EHR, you have device integration capabilities, and you have reports, dashboards and other intelligence tools. Let's use them for what they were built to do, but also recognize that they may not be able to do what we need to do next. Let's recognize that this is not about technology implementation, this is about technology enablement. We have the potential and the ability to use next-generation capabilities to create next-generation healthcare processes. Let's stop looking in the rear-view mirror and start looking forward.
