Automated tool monitors patient health after transplant surgery

blue background with computer algorithm pieces

by Ryn Thorn

Monitoring patients’ health after an organ transplant is vital, but the process can be challenging, laborious, and slow.

An interdisciplinary team at MUSC headed by Professor and Vice Chair of Surgery for Innovation, Joseph Scalea, M.D., has developed a tool that condenses weeks of work required to monitor patients after transplant into just seconds.

Scalea and his team designed an algorithmic tool that uses a variety of information to assess patients’ risk of negative outcomes after transplant. The algorithm factors in everything from lab values like organ function tests to social factors like health appointment attendance.

“If a patient is identified by these variables as having a potentially challenging outcome, we hope to engage the patient around that data immediately,” said Scalea. “That way they're not waiting two or three weeks to see the doctor to learn information that is now several weeks old.”

Normally, a team of practitioners manually review and monitor charts, test results, and other relevant information about their transplant patients after surgery in order to determine their potential for negative outcomes. But by using a tool which can quickly identify and stratify levels of risk, the process becomes much more efficient.

“We now use this tool every week to review patients who've had transplants,” said Scalea. “Instead of taking weeks and weeks for our nursing colleagues to go through each individual chart, we're able to do that work in a matter of seconds, saving hundreds of hours per year.” 

Growing needs

As the need for and access to organ transplants of all types grows, so does the need to monitor patients after transplant surgery. 

“As we do more transplants, trying to rapidly and thoughtfully stay on top of each of their different outcomes is challenging,” said Scalea.

Various patient factors are monitored after they undergo their transplant procedure. Factors such as white blood cell count, electrolyte levels, vital signs, and immune function must be gathered and assessed by nurses and doctors. Patients’ demographics as well as information from their follow-up visits with their care providers must also be taken into account.

“Based on our estimates, about 700,000 data points per year need to be evaluated just to manage our kidney transplant group of post-op patients,” said Scalea.

Shifting through this data costs nurses and doctors hours of time per week that could otherwise be spent face-to-face with patients. Scalea and his colleagues hope to cut down on inefficiencies like this in order to provide better patient care.

According to a 2019 article in the Journal of the American Medical Association, inefficiencies in healthcare are estimated to cost almost $1 trillion per year. Likewise, inefficiencies may also compromise the quality and uniformity of care. Automating the process of examining post-op data for transplant patients is just one way of making health care more efficient.  

It adds consistency from patient to patient,” said Scalea. “And so we make sure that everybody's getting not only the same treatment, but getting the best treatment.”

Scalea also observed that as healthcare providers carry out daily tasks that could otherwise be automated, they are more likely to burn out. More transplants means more of these tasks, which could lead to more burnout.

In fact, healthcare worker burnout is an issue that the US Department of Health and Human Services lists as one of the agency’s top priorities. Scalea believes that automating tasks such as aggregating post-op monitoring data may slightly lessen the chance of burnout.

Meeting a need with technology

Scalea and his colleagues first thought up the idea for an automated post-transplant monitoring process after conferring to assess transplant patients’ risks in weekly meetings. 

They noticed that certain factors regularly came up across higher risk patients, and many patients seemed to be at high risk for transplant failure for similar reasons. This put the idea in their minds of automating this assessment process.

To develop an automated assessment tool, Scalea worked with an interdisciplinary team that included practitioners from MUSC’s Surgical Innovation Center, frontline nurse coordinators, and software developers from MUSC’s Biomedical Informatics Center came together to develop the tool. 

The team noted clinically meaningful factors and surveyed other groups to solicit feedback about which types of information are most important in determining post-op transplant risk. 

After interviewing over 50 people in the transplant field and holding a series of informal focus groups, Scalea and his team identified factors for their algorithm. “From there, we hammered out a set of requirements and tested different versions of our algorithm to ensure accuracy,” he said.

Though Scalea and his team do not yet have hard data comparing their algorithm to the manual process, they are optimistic.

“We see this as a game changing technology for outpatient management in complex surgery,” Scalea said.