Evaluating and Predicting Patient Safety for Medical Devices with Integral Information Technology

Human errors in medical device use account for a large portion of medical errors. Most of these errors are due to inappropriate designs for user interactions, rather than mechanical failures. Evaluating and predicting patient safety in medical device use is critical for developing interventions to reduce such errors either by redesigning the devices or, if redesign is not an option, by training the users on the identified trouble spots in the devices. We developed two methods for evaluating and predicting patient safety in medical devices with integral information technology, then applied and tested them on several infusion pumps. The first method is a modified discount-usability method called heuristic evaluation. The method was used to evaluate and compare the safety of two 1channel volumetric infusion pumps. The results show that heuristic evaluation, when modified for medical devices, is a useful, efficient, and low-cost method for evaluating patient safety features of medical devices through the identification of usability problems and their severities. The second method is an extended hierarchical task analysis (EHTA), devised to predict medical errors in medical device use. EHTA divides the task space between the external world of the device interface and the internal cognitive world of the user, allowing for descriptive predictions of potential user errors at the human device level. Its use is demonstrated in the analysis of two infusion pumps. The estimates of the likelihood of user errors with the two pumps are consistent with the corresponding reported use errors in the Federal Drug Administration (FDA)’s Manufacturer and User Device Experience (MAUDE) database, thus demonstrating the usefulness of this tool for predicting medical device use errors. Introduction Since the medical error report from the Institute of Medicine in 2000 and subsequent increased funding for research on medical errors from U.S. government and private institutions, many studies on medical errors have been conducted and reported, including many from the medical informatics and cognitive science communities. 3 Our research has focused on developing methods for evaluating and predicting medical errors in medical device use from the perspectives of cognitive science and user interface design. Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.