jung in park, uci school of nursing researcher

In this unprecedented pandemic, quickly and accurately triaging high-risk COVID-19 patients is crucial for emergency care providers.

However, a lack of time and resources makes this a challenge.

Assessing patients with the most critical risk for complications of the disease isn’t always straightforward or simple. Dozens of factors can come into play that can affect patient outcomes.

UCI School of Nursing faculty Jung In Park, PhD, RN, is part of a team that is working on an innovative solution: a Vulnerability Scoring System (VSS).

Thanks to a research database of thousands of patients across the UC healthcare systems who have tested for COVID-19, triaging future patients may soon become easier, faster and more accurate.

Vulnerability scoring tool based on patient data

The team is currently using prospectively acquired demographic, clinical, laboratory and radiologic data obtained from UCI Medical Center, to develop a predictive tool for COVID-19 patients using deep learning approach.

The team is planning to validate this tool using the COVID database from the UC healthcare system.

Park and the team will eventually use this valuable information to build a Vulnerability Scoring System (VSS), a web-based tool that will help clinicians quickly assess patients and determine who needs critical care soonest.

COVID treatment depends on many factors

The application, when live, will update in real-time as new data comes in.

There are more than 70 factors that come into play when determining how to treat a COVID-19 patient, including:

  • Chest X-ray data
  • Family history
  • Medical history
  • Comorbidities, such as diabetes or cancer
  • Lab tests

“It’s hard for clinicians on the front lines to sift through all that information to determine which patient has the priority,” Park says.

A proactive effort to avert tragedy

With the outbreak expected to persist, this project is part of a proactive effort to provide a more accurate, effective decision-support tool in real-time for COVID-19 patient care, Park says.

“We have witnessed tragedies in this country. We need to be better prepared for our patients and for a second wave of COVID-19.”

A huge collaboration

Park and her colleagues are in phase one of their project. During this period, they are developing and validating the VSS using deep learning.

In the second phase, Park will use the data to develop the application.

The project is a massive collaboration involving pathology, radiology, nursing and informatics, regulatory affairs, public health, biological sciences, medicine and computer sciences.

After validating the VSS, the team wants to launch the application as soon as possible.