Our goal for this Learning Healthcare System Demonstration Project is to reduce the rate of
inpatient hypoglycemia. Hypoglycemia can result in longer lengths of stay and increased
morbidity and mortality (ie falls and cardiovascular or cerebral events).
The group at Washington University (WSL) developed a predictive hypoglycemia risk score.
Using current glucose, body weight, creatinine clearance, insulin type and dosing, and oral
diabetic therapy, they identified patients at high risk for hypoglycemia and then provided
in-person education to the providers of these patients. This resulted in a 68% reduction in
severe hypoglycemia (blood glucose < 40 mg/dL). This approach required significant personnel
hours and is difficult to replicate in other systems.
We will implement an EHR-based intervention at UCSF to predict which patients are at high
risk of inpatient hypoglycemia and take action to prevent the hypoglycemic event. In real
time, all adult (non OB) patients with a glucose < 90, and a high risk of future hypoglycemia
(based on the WSL formula) will be identified. Patients will be randomly assigned to
intervention or no intervention (current standard care). The intervention will consist of an
automated provider alert with recommendations on what adjustments could be made to avoid a
potentially serious hypoglycemic event.
The outcomes that will be measured include: 1) reductions in serious hypoglycemic events, 2)
monitor the changes made by providers as a result of alerts in order to study provider
behavior and identify future areas of intervention, and 3) provider satisfaction with the