Michael Blackman on the role of EHRs in data accessibility and analytics
Michael Blackman, MD, is chief medical officer at McKesson Enterprise Information Solutions, McKesson Technology Solutions. An Internist, he previously served as the CMIO for Berkshire Healthcare Systems.
As a division of America’s oldest and largest healthcare services company, McKesson Technology Solutions (MTS) plays an integral role in healthcare with a 360-degree perspective to help organizations -- hospitals, physicians, pharmacies and payers – confidently manage their business today while charting a path toward better health. That includes offering industry-leading analytics solutions that provide a clear picture of the clinical, financial and operational health of an organization.
Q: What are the best strategies for optimizing quality measurement and reporting with IT systems?
A: We all realize that some of the quality measures can be a changing target. With the improving alignment between CMS and insurers around quality measures, it’s becoming more about looking holistically at slightly different requests in the same area.
The best strategy, as basic as it sounds, is communication to insure people know both what they want to report and how they want to report it. Healthcare organizations should be working with their vendor to define: What measures are we trying to meet? How should that information be captured? If these questions aren’t addressed up front, you’re setting yourself up for either making changes later or extra work to collect the desired data.
Another important consideration is how best to get the desired information into the system. When I’m out talking to hospitals, I’m commonly asked for guidance on defining the best type of person to handle this role. My response is to encourage them to ask themselves: who will the information come from? Don’t make staff input information they don’t know or where the purpose of doing so is not clear. If your staff does not see the value of the activity, not only will adoption rates will be low but the quality of the information is likely to be poor. When a busy clinician understands that getting the right data creates the opportunity to take better care of patients both now and in the future, the desired task is more likely to be addressed correctly.
Q: EHRs play such a crucial role in patient data analysis. How do we optimize EHRs to better meet provider needs and/or fit into their workflow?
A: That’s the million dollar question. The industry’s first-generation EHRs were traditionally sold to the IT side and didn’t provide enough focus on clinical workflow. Second-generation EHRs must fit into the clinician’s day-to-day activities and that requires insuring that the desired data capture flows, as much as possible, from work a clinician is already doing.
I liken it to purchasing an airline ticket or other service online. Any number of companies has made that process easy, and that’s where our industry needs to move. It’s not a choice anymore. While EHR users will still be educated users, the systems must be as intuitive as possible.
At McKesson, our strategy is to rollout changes progressively so that they can be absorbed with as little disruption as possible. However, there are incoming demands for changes across the enterprise and you must guard against unintended consequences. We want to involve all stakeholders in the process because change is going to occur and you’re better off being part of the discussion rather than having the change done to you.
Q: Many say that big data analytics in healthcare is still a long way away. Why and what can be done to speed things along?
A: With big data, you need to fully recognize the data going out is only as good as the data going in. In the span of a day in a busy clinical practice, staff members aren’t thinking about the steps they could take to collect better data. Until they understand the importance of this value-added task, as noted above, the full potential of big data won’t be realized.
Another aspect is normalizing the data with standards so information from various locations can be, as appropriate, shared or combined. There are regional variations in terminology in medicine that can create gaps where they simply shouldn’t be. For instance, even something simple like a hydrocodone/ acetaminophen combination product; while this drug can always be described using the generic name, it is commonly called Percocet in some regions and Roxicet in others. Depending on how this information is entered (coded vs. free text), an EHR might be unable to determine they’re the same drug, and that’s a gap in a patient record that shouldn’t exist.
Education, time, money and desire will accelerate the use of big data analytics as will the transformation of the industry itself. Insurers have always considered big data to be important, but providers haven’t seen the need. As we shift into a value-based and pay-for-care model, hospitals and providers will get on board. We’ve already seen this trend at McKesson with more than 1,000 organizations using our analytics solutions.
Q: What needs to be done for more effective use of analytics in 2015?
A: Again, we need to start by asking the right questions. Many interfaces capture quality measures but remain slow because they are capturing more data than needed. Rather than trying to do everything, we should be asking: what pieces of data do you need? Take physicians, for instance. A 2003 article in the American Journal of Public Health estimated that addressing all of the desired prevention issues for a typical Family Practice patient panel would take nearly eight hours a day. By leveraging clearly defined analytics, physicians can save time and still accomplish what’s most important.