A novel decision algorithm for reducing medication errors in CPOE systems

The projection of the U.S. national healthcare expenditure in year 2019 is $4.7 trillion. Medical errors are part of this increasing healthcare costs because they cause tens of thousands of deaths in the U.S. hospitals each year, more than major diseases such as AIDS, breast cancer combined to highway accidents (Chiang S. Jao, Daniel B. Hirer) [10]. Based on a research published in the British Medical Journal (BMJ) in 2016 and conducted by (Michael Daniel & Martin A Makary) [98], Medical error is ranked the third cause of death in the US. With the advancement in technology, we have seen during the last years, important improvements in the design as well as the use of electronic health records (EHRs), Computerized Physician Order Entry (CPOE), and Clinical Decision-Support Systems (CDSS), and Diagnosis Decision-Support Systems (DDSS) to improve the quality of health care delivery; progress have been made but challenges remain. Medication errors can be: •Wrong drug, •Wrong dose, •Wrong route, •Wrong patient, •Bad combination, •Bad reaction to list a few, and are found at every stage from prescription and administration of drugs to monitoring. They hurt about 1.5 million people, and cost billions of dollars each year according to the Institute of Medicine of the National Academies. Medication errors can happen anywhere, from Doctors offices to hospitals, and pharmacies and your home. Sound-Alike / Look-Alike also known as drug name errors, are the most common causes of medication errors, they originate from poor communication between health care providers, poor communication between patients and their providers. To reduce the likelihood of ham related to medications and Adverse Drug Events (ADEs), many interventions have been attempted including notably: The US Food and Drug Administration (FDA), government legislation, policy makers, drug utilization reviews, health professionals, and patients education, all of this with limited success. The aim of this dissertation is to evaluate medication errors related to Sound-Alike drug names, and to propose a new approach of preventing them by "Embedding the Novel Decision Algorithm" coupled with "Confused Drug Names, Generic and Brand drug names" and "Doses" within a computerized provider order entry (CPOE) during the drug prescribing process.Ph.D.Includes bibliographical referencesby Samuel Imb