Real World Data, Digital Health, & AI

Investigator

Temor Dourandish, MBBS, MSc

• University of Minnesota Medical School

The Project

TCRTC is working with CLHSS to optimize thresholds for identifying shock using non-invasive blood pressure monitoring in the setting of prehospital transport for trauma. Identifying most predictors of in-hospital mortality in patients with pre-hospital hypotensive shock will improve pre-hospital triage & decision-making, optimize resuscitation strategies, guide in-hospital management, and might refine shock definitions to better reflect patient outcomes.

Investigator

Matt Bahr

• Fairview Health Services

The Problem

Trauma is the fourth leading cause of death for Americans (Gaillard et al, 1990). Rib fractures are sustained in nearly 15% of trauma patients, associated with significant morbidity and mortality (Sharma et al, 2008). Adherence with evidence-based rib fracture practice significantly reduces morbidity and mortality. However, despite best practice treatment algorithms for rib fracture patients and other diseases, adherence is poor.

Our Solution

In 2020, Fairview Health Services developed and deployed a comprehensive clinical decision support system to help providers and nurses at the point of care to improve the adherence with best practice for patients with rib fracture. Deployed across multiple U.S. Midwest trauma centers, this system includes in-hospital decision support and post-discharge remote patient monitoring. A prospective evaluation showed that using the system improved patient outcomes, including better survival rates, shorter hospital stays, and fewer unplanned ICU transfers.  

Unfortunately, adoption of the system has been inconsistent, peaking at 76% and now stabilizing at around 50%. Given this, there is a need for artificial intelligence-based approaches which can identify patients with rib fractures on admission by “interpreting” patient x-ray images and medical records. Such a system could improve utilization of decision support systems to approach 100%, facilitating maximum use of evidence-based practice and reducing healthcare disparities.

Status: in progress

Investigator

Mike Usher, MD, PhD

• Fairview Health Services

The Problem

Sepsis is the body’s uncontrolled response to an infection, leading to organ dysfunction (Singer et al, 2016). It can progress quickly and has a high mortality rate. Early identification and prompt intervention are crucial to saving lives and reducing morbidity.

Our Solution

A cognitive computing model can provide clinical decision support, helping clinicians identify patients who might need to be screened for sepsis. This model can assist clinicians in earlier identification of sepsis, helping them intervene sooner. It can be used directly by on‐site clinicians or as part of a remote patient monitoring program.

Dr. Usher and the CLHSS team built on previous work from the University of Michigan to create a predictive model that triggers a sepsis score for patients in the Fairview Health Services emergency department one hour and six hours after admission. The model uses data such as vitals, labs, medications prescribed, and the patient’s chief complaint in the emergency department. Overall the model has performed better than similar sepsis prediction models in the literature.

Status: in progress