You're heavily involved in how doctors and nurses interact with electronic health records at Michigan Medicine. What’s been your biggest challenge? Your biggest success?
One of the biggest challenges faced by nurses and physicians is that very discipline has its own language and standards. Over time, this led to a separation of documentation which made it difficult to build on each other as a team. But as a nurse informaticist, I’ve learned both clinical and IT language and built a bridge between the two. This has saved wasted time spent on repetition of documentation, closed gaps among teams, improved the interoperability of the health information system, improved the efficiency of the treatment teams, and standardized terminology across disciplined. All of these outcomes combined make for a much better patient experience.
You're currently working with the MCIRCC Data Science team on technology that could potentially predict when patients will "crash." Can you give us some background on the project?
For a while now, I’ve heard clinicians discuss the need for predictive tools and how it could revolutionize healthcare. However, they didn’t seem to understand the difference between prediction and diagnostic tools. In my mind, true prediction is when you are able to forecast what direction the patient is heading in.
With that being said, this type of innovation requires smart technology, skilled people, and defined process. At MCIRCC, we have all three, the cornerstone being the skilled staff in the Ideation Lab.
Where do you think data science and medicine will be in five or ten years? Do you think predictive analytics will be bedside by then?
I believe data science will be used more in healthcare to improve patient flow, reduce patient readmission, predict disease outbreaks, and utilized across all stages of care: emergency room, operation room, and even in the field of combat. Data will come directly from the human body as opposed to having to wait for manual documentation. This will give us a better understanding of the body’s behavior – not only the current state, but also what lies ahead – so we can change our workflow from treating patients to preventing illnesses in the first place.
As for being bedside, I remember learning about the human genome before it was decoded. The same will happen to the data science and predictive tools. Not only will it be at the patient bedside, but it will impact the entire workflow of patient care.
You recently had twins (congratulations)! Do you have any funny stories about adding two more little ones to your family?
We had a system where I would take care of the boy if he was crying at night, and she would take care of the girl. In the first two weeks, you couldn’t tell them apart. Sometimes I would swap their blankets (pink and blue), to confuse my wife so I could sleep a little longer. It didn’t last long, of course!