The application of an artificial intelligence tool to improve diabetic ketoacidosis treatment security in the emergency department: a quasi-experimental study

Abstract

BACKGROUND Diabetic ketoacidosis (DKA) is one of the most serious diabetes complications. It is characterized by hyperglycemia, anion gap metabolic acidosis and increased total body ketone concentration. DKA is the most common cause of death in youth type 1 diabetic patients and an important cause of morbimortality in type 2 diabetic patients. Its management is complex, however, if it is well performed DKA has a good prognosis. Lately, the need to standardize DKA treatment has become an important issue in our Department. OBJECTIVE Our main objective is to decrease hospital length of stay of DKA patients treated in Hospital Universitari Josep Trueta’s Emergency Department (ED) with the application of a computer decision support and electronic order sets (CDS&EOS) to minimize security errors. DESIGN Quasi-experimental study designed as a before-and-after evaluation of the application of a CDS&EOS in ED’s SILICON® to standardize DKA management. PARTICIPANTS A consecutive non-probabilistic model will be used to select DKA patients aged 18 years old or older treated in Hospital Universitari Josep Trueta’s ED. METHODS

The study will include 134 participants in total, 67 for each group (pre and post- intervention). Each sample will be selected in an 18-month period, with a washout

period between them. Data will be collected prospectively between May 2021 and October 2024. The association between the independent and dependent variables will be adjusted to avoid possible confounding factors