Toward a computational model and decision support system for reducing errors in pharmaceutical packaging

Abstract

The US Institute of Medicine reports that one medication error occurs per patient per day in hospital care, and other studies indicate that medication administration errors attributed to packaging and/or labeling confusion can be as high as 33%. While many engineered products have identifiable features that help establish commonality and differentiation within a product family, vital features of consumable products such as medications are often not readily apparent in their physical form. As a result, caregivers must rely on the labeling and packaging to effectively determine the contents. Adverse Drug Events (ADEs) are the most common category of medical errors and include wrong drug, wrong dose, wrong route of administration, and wrong patient. It is estimated that in the US each year, medication errors harm at least 1.5 million people, resulting in 106,000 deaths. Computational models and associated decision support systems have the potential to improve pharmaceutical delivery safety through informed design of packaging features and enhanced situational awareness and decision-making during drug identification and administration. Past research has led to the formulation of measures for representing the degree of commonality and differentiation of packaging features in pharmaceutical families or versus look-alike drugs. Preliminary studies have validated these measures of feature prominence based on feature size and location. This paper describes a study using eye tracking to evaluate gaze patterns and further validate these measures. The results support the measures and indicate that increased commonality of features results in shorter reaction times, but also shorter fixation times. These results have implications in the formulation of a resulting decision support system.