Elizabeth Meyers began to see the opportunities — and challenges — of introducing digital technology into the healthcare system when she was a surgical nurse in the U.S. Army Nurse Corps more than a decade ago.
“An electronic documentation system was coming in, and nurses were getting more worried about the computers than the patients,” she recalled. “It wasn’t working, and it didn’t seem the developers had ever been in an operating room.”
That frustration led Meyers on a different path, “from scrubbing up to geeking out,” as she puts it. She didn’t leave nursing, but the experience did make her wonder how data could be pulled back out of these systems to improve patient care. That curiosity led to a master’s degree in technology management and a move from the Army to a series of ever more responsible jobs in nursing and data management.
Now, as a nurse executive and healthcare analytics strategy director for business applications company Infor, Meyers is working to improve patient care by melding data science with hospital management.
“I’m most definitely still a nurse,” says Meyers, “I just care for people in a different way.”
In this interview, Meyers discusses why changing healthcare is particularly hard, what’s driving digital transformation now, and what impact these changes could have on patients.
Technology has been part of hospitals for decades. Supply chain software is common in many industries — in automotive for example. Why makes it so challenging to bring to healthcare?
We must remember that healthcare is about an individual provider caring for an individual patient. And every single one of our patients is different. That’s the difference between healthcare and manufacturing. I love that the Toyota production system says, “There’s one best way to do things at any point in time.” But, the best way change can happen is to introduce new information. This has been adopted as a learning for healthcare systems.
Let’s use an example of a health system with 50 orthopedic surgeons. There’s not enough evidence out there to say, “This is the very best way to do a particular surgery every time,” especially when it comes down to my specific 102-year-old grandma, who maybe had three fractures before, or my 17-year-old son, who is athletic, or could have other medical complications. That’s where expertise comes back in. That’s why a doctor goes to college for eight years, plus for orthopedics, five years of residency, or even longer to learn specialty surgeries.
What are the drivers for change here?
Most hospital systems now have electronic health records. Twenty years ago, we weren’t collecting all of that data, and it was harder for us to prescribe best practices. When I worked at a small critical access hospital in 2005, the whole surgery system — the system for ordering supplies and then documenting what supplies were used on the patient — was a manual process. When it’s manual, that means data mining is really difficult. Now, that data is collected and stored electronically, so we’ll really be able to see, end to end, everything that was given to a particular patient. That will give us the evidence, allow us to do deeper analytics — and that, in turn, means both a broad view of the best practices for everyone and the ability to zero in on the most effective treatment for each individual.
What role is Infor playing in all of this?
It starts with a common platform, so all the information can be shared. Infor is uniquely positioned because operational data resides in a system, and we’re experts in operational data and systems. We can access clinical data in real time with our software. Electronic health record companies and clinical data warehouses have been really struggling with interoperability and to wield that data to improve decision making because healthcare data is so complex. Infor’s take is to store it at the most granular level and then leverage the advanced technologies instead of traditional data warehousing techniques.
What is the impact on patient care?
Our customers today are using Infor software to feed data, in real-time, into clinical decision support systems. They leverage the information received from the pharmacy, from the laboratories, and from electronic health records, while also connecting to the patient monitoring system. Then they use these data feeds to create an algorithm that makes recommendations. You can use this, for example, to find early indicators of blood stream infections. Instead of checking through ten screens to get a picture of a patient’s status, an automated system flags the problem right away in real time.
