Healthcare, like many industries, is in the midst of a paradigm shift, says Chris Donovan, Executive Director of Enterprise Information Management & Analytics for the Cleveland Clinic. "Historically, healthcare was really about intervention, and about taking care of you when you were sick and getting you better."
That type of care depends a lot on treatments like surgery, and as the largest single-site OR in the world, the Cleveland Clinic is good at that kind of care, says Donovan. But there's a shift occurring.
What’s happening in healthcare now is a focus on preventative care. "How can we move away from just taking care of you when you show up as an individual patient in the ER or the doctor’s office, to looking at a population of patients and thinking about how to prevent people from getting sick in the first place," says Donovan.
As you can imagine, this type of transformation requires analytics.
"Now we have to know, within the population we’re trying to take care of, who are the highest-risk patients, and how best to intervene to drive a better outcome," he says.
That requires gathering a whole bunch of social data, demographic data, economic data. For some populations, sending an email works better, but for others a phone call or an in-home visit with a nurse will be more effective.
"We need to be able to do predictive models, to run clustering algorithms to understand how patients are connected to each other, and to run machine learning models so we can do this at scale," explains Donovan.
One example Donovan used is a change in post-surgery care for knee replacement patients. Using analytics, Cleveland Clinic developed a model to identify which patients would recover successfully at home instead of requiring post-acute care in a facility for all patients.
"We put the propensity score right into the clinical workflow, so when the doctor is determining the care path for the patient, they saw that score and had the conversation with the patient." The program was successful at taking patient needs into account and driving costs down, says Donovan.
Donovan spoke during the opening session of Analytics Experience in Washington DC on a panel, along with SAS CEO Jim Goodnight, SAS CTO Oliver Schabenberger and Shawn Hushman, Vice President of Decision Sciences and Valuations for Cox Automotive.
Hushman's industry is also experiencing transformation. For example, his company has experienced a significant transition from print to digital for its Autotrader and Kelley Blue Book brands. This change has put Cox Automotive in a constant state of evolution in the way they capture, present, and analyze information, and how they organize around that information, explained Hushman.
"Amidst that change, how do we continue to be relevant to the consumer? How do we bring the ability to process data to enhance the consumer experience? Analytics is core to who we are in answering those questions," says Hushman.
"We have an amazing amount of data — which is a key asset for Cox Automotive, but that has also been one of our most challenging problems," says Hushman. "There isn’t an easy button to effectively leverage the data to drive the right decisions. This is something we have to build from the foundation of data intelligence."
Hushman describes data intelligence as the process of moving from specific analytics projects to building analytics frameworks. Instead of just building churn or retention models, he asks analysts to pull back and look at the original problems: dealers need more customers, the Web site needs to answer customer's questions — and then solve for that problem.
This type of thinking is essential for new services like "instant cash offers" that simplify the trade-in process to a single click online.
"We now process petabytes of behavior information across our brands, which we leverage to build better consumer experiences, increase our vehicle valuation accuracy, provide consumers with relevant content, and expand our brand to address unmet needs."
Heather Lowe and Lane Whatley also contributed to this post.
