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Implementation of an Intelligent Orderset in Treating Hyperkalemia

Author: James Schlebus

Primary Advisor: Dean Sittig, PhD

Committee Members: M. Sriram Iyengar, PhD

Masters thesis, The University of Texas Health Science Center School of Health Information Sciences at Houston.

Abstract:

An Electronic Medical Record (EMR) system provides countless benefits to clinicians in performing their daily tasks. These systems allow providers to track data over time, monitor patient's on-going conditions, review trends in data (e.g. labs), compare patient statistics against published metrics, and improve the quality of care through decision support and ease of access to pertinent procedures and medication orders presented via the EMR interface. Ordersets are one of the features of an EMR, if done correctly, that can greatly improve the quality of care for patients, help guide clinicians in their treatment plans and speed up access to orders in the system. Ordersets, however, must be carefully designed to prevent contributing to ordering errors.

Hyperkalemia is a fairly common problem in hospitalized patients which can prove to be fatal if left untreated. The rates of hyperkalemic incidences range from 1-10%, and result from such conditions as decreased renal excretion of potassium (this accounts for almost 80% of hyperkalemia in patients), increased potassium intake, thrombocytosis, and hemolysis to name a few (Schafers, Nauheim, Vijayan & Tobin, 2012). Hyperkalemia is characteristically asymptomatic, but does impair normal cardiac conditions. In addition, while treating hyperkalemia, there are increased chances of the patient developing hypoglycemia because of insulin and dextrose administered while stabilizing hyperkalemic patients.

What follows is a "how to" guide for orderset development, maintenance and review guided by best practices and evidence-based research with an emphasis on treating hyperkalemia and preventing related hypoglycemia.