Designing out Medical Error: An Interdisciplinary Approach to the Design of Healthcare Equipment

Medical error is an internationally recognised problem, with major financial and human costs (Gray; 2003, de Vries; et.al. 2008). The design of hospital equipment, devices and environments can contribute to the problem. Clinical staff often have to cope with confusing interfaces and equipment, making their tasks difficult and potentially dangerous. There are calls to rethink the approach to design in healthcare. Design should acknowledge the real world issues users face in the hospital environment. A collaborative approach is required to understand these issues, (Karsh & Scanlon, 2007). This paper outlines the methodologies used in two interdisciplinary case study projects, revealing the importance of a clear set of working methods and detailing the approach taken at each point. The resulting designs aim to better support healthcare processes, reducing the instance of medical error and ultimately saving lives

Modelling the expected net benefits of interventions to reduce the burden of medication errors

Objectives: The aim of this study is to estimate the potential costs and benefits of three key interventions (computerized physician order entry [CPOE], additional ward pharmacists and bar coding) to help prioritize research to reduce medication errors. Methods: A generic model structure was developed to describe the incidence and impacts of medication errors in hospitals. The model follows pathways from medication error points at alternative stages of the medication pathway through to the outcomes of undetected errors. The model was populated from a systematic review of the medication errors literature combined with novel probabilistic calibration methods. Cost ranges were applied to the interventions, the treatment of preventable adverse drug events (pADEs), and the value of the health lost as a result of an ADE. Results: The model predicts annual health service costs of between £0.3 million and £1 million for the treatment of pADEs in a 400-bed acute hospital in the UK. Including only health service costs, it is uncertain whether any of the three interventions will produce positive net benefits, particularly if high intervention costs are assumed. When the monetary value of lost health is included, all three interventions have a high probability of producing positive net benefits with a mean estimate of around £31.5 million for CPOE over a five-year time horizon. Conclusions: The results identify the potential cost-effectiveness of interventions aimed at medication errors, as well as identifying key drivers of cost-effectiveness that should be specifically addressed in the design of primary evaluations of medication error interventions

A prospective hazard and improvement analytic approach to predicting the effectiveness of medication error interventions

Medication errors are an important problem for the UK National Health Service (NHS). The aim of this study was to implement a novel quantitative modelling method to predict rates of preventable adverse drug events (ADEs) and identify interventions with the greatest potential for reducing the burden of medication errors in secondary care. A generic model structure was developed to describe the medication process in secondary care. The model followed pathways from error points through to the outcomes of undetected errors. The model was populated using quantitative estimates and calibration methods to describe the incidence and impacts of medication errors. The effectiveness of potential interventions was estimated by describing the impact of the interventions at different stages of the medication process. The model predicts the range of preventable adverse drug events that occur annually in a 400-bed hospital in the UK to be between 200 and 700. Of the interventions evaluated, computerised physician order entry systems and increased numbers of ward pharmacists are predicted to have the greatest impact on the number of preventable ADEs. The analysis provides a relative analysis of the interventions, and indicates priorities for research allocation decisions. The model highlights the complexity of the relationship between medication errors and adverse events, and the extreme attention to detail required in the development of interventions, and in their evaluation