The volumes of patient data healthcare organizations create should help them face their challenges in today’s value-based, population-health-focused environment. But to be able to use that data effectively, healthcare organizations need to take a critical look and ask the selfish question: What’s in it for me?
Data can inform innovation in a few ways. We spend a lot of time poring through clinical data in the electronic health record (EHR) to gain insight into the comorbidities of our patients with chronic illnesses. That’s a start. You cannot manage the costs associated with treating patients if you don’t know which patients created them.
But there are other sources of data that might help inform the work an organization is doing. We often look at data in terms of what we see in that one system without thinking how to connect the dots with other relevant data. Would it make sense to look at all your patients with diabetes
and connect their clinical data with your revenue cycle data? Are there patterns by insurer? Are there patterns of care that are problematic for denials or readmissions? Those are simple questions that may or may not be answered
within one application.
We are creating multiple data repositories on a routine basis. Usually, we can connect them with each other in a meaningful way and answer questions that previously may have left us making educated guesses. If we need to dig deeper into a management challenge, let’s ask: “Who is collecting the data I need?” and “What’s in that data for me?” From an informatics perspective, looking beyond the EHR is the most difficult challenge we face. If you have an ACO contract, connecting claims with clinical data from the EHR is becoming an everyday task. Connecting the dots is fairly easy. Finding the data to inform innovation is the tricky part.
Beyond the silos
So as healthcare leaders look beyond the “silos” of data, they should think about the challenges their organizations face in day-to-day operations. Think beyond the ACO and population health questions alone. It seems fee-for-service payment rates are being reduced with each contract renewal. Healthcare leaders can look at ways to find the operational efficiencies their organizations need to remain viable in the face of declining payment rates, or they can look to connect utilization data with skill mix.
It could be that adding an employee with a slightly lower skill mix, such as an additional nurse aide in a patient care unit reduces readmissions because the patient is better prepared for discharge. Yes, hours-per-patient day may creep up a bit, but there may be a downstream payoff. Although salaries may increase and management- engineered staffing standards may need a revisit, the savings might be worth it. I remember many a conversation with a department manager where the plea for an increase in a staffing standard was backed with only an anecdotal promise of improvement. Mining the data and making connections between clinical and business data might help healthcare organizations figure out
a more optimal and innovative way to staff patient-care areas.
Data also can be useful in helping organizations discern ROI. As we seek innovation, we may sometimes need to know what kind of ROI to expect. The returns may not be clear or immediate, but data can help us define what the return should be and perhaps how much there is.
We just have to look at the bigger picture and who else is involved. It seems most everyone is gathering operational data. A healthcare organization may have multiple vendors creating multiple useful and relevant data repositories. It’s important to ask what is in the data, wherever the data is gathered, and then use it. In so doing, an organization can get away from trying to drive innovation by anecdote. Instead, it can drive innovation from data that proves, for example, the majority of congestive heart failure readmissions come from one health plan or one part of the organization’s service area. Knowing the problem and the specifics of the challenge can help an organization ask the question and then propose answers.
But not all data can contribute to innovation. Healthcare leaders should remember the old adage in the early days of computers, “garbage in, garbage out.” If you use bad or incomplete data, you will likely get bad, incomplete or misleading results that can lead to bad decisions. Organizations should use data from established applications (such as payer claims or patient accounting)to seed innovation efforts. Most of those applications structure the data in a reliable, accurate way.
In today’s healthcare environment, where data is plentiful, using that data to inform innovation can bring much greater success than can brainstorming alone.
