User Error

Abstract: This study examines service failure and recovery in using technology-based self-service (TBSS) systems to determine the effects of a variety of relevant factors on negative customer/user attributions to the service provider, to employees who try to help in recovery, and to the technology itself, as well as the effects on customer/user satisfaction with the failure/recovery experience. The findings show that immediate recovery of TBSS failures reduces negative attributions and increases customer/user satisfaction with the experience, as does a low-anxiety environment around the kiosk. Technology error (as opposed to user error) decreases user satisfaction. Employee assistance decreases negative attributions to the employee but increases negative attribution to the technology. Some interactions were found among the experimental factors that are also meaningful.

Abstract: Glucose performance is reviewed in the context of total error, which includes error from all sources, not just analytical Many standards require less than 100% of results to be within specific tolerance limits Analytical error represents the difference between tested glucose and reference method glucose Medical errors include analytical errors whose magnitude is great enough to likely result in patient harm The 95% requirements of International Organization for Standardization 15197 and others make little sense, as up to 5% of results can be medically unacceptable The current American Diabetes Association standard lacks a specification for user error Error grids can meaningfully specify allowable glucose error Infrequently, glucose meters do not provide a glucose result; such an occurrence can be devastating when associated with a life-threatening event Nonreporting failures are ignored by standards Estimates of analytical error can be classified into the four following categories: imprecision, random patient interferences, protocol-independent bias, and protocol-dependent bias Methods to estimate total error are parametric, nonparametric, modeling, or direct The Westgard method underestimates total error by failing to account for random patient interferences Lawton’s method is a more complete model Bland–Altman, mountain plots, and error grids are direct methods and are easier to use as they do not require modeling Three types of protocols can be used to estimate glucose errors: method comparison, special studies and risk management, and monitoring performance of meters in the field Current standards for glucose meter performance are inadequate The level of performance required in regulatory standards should be based on clinical needs but can only deal with currently achievable performance Clinical standards state what is needed, whether it can be achieved or not Rational regulatory decisions about glucose monitors should be based on robust statistical analyses of performance

Abstract: Medical devices are essential tools for modern healthcare delivery. However, significant issues can arise if medical devices are designed for ‘work as imagined’ when this is misaligned with ‘work as done’. This problem can be compounded as the details of device design, in terms of usability and the way a device supports or changes working practices, often receives limited attention. The ways devices are designed and used affect patient safety and quality of care: inappropriate design can provoke user error, create system vulnerabilities and divert attention from other aspects of patient care. Current regulation involves a series of pre-market checks relating to device usability, but this assumes that devices are always used under the conditions and for the purposes intended (i.e. work as imagined); there are many reasons for devices being used in ways other than those assumed at development time. Greater attention needs to be paid to learning points in actual use and user experience (i.e. work as done). This needs to inform manufacturers’ designs, management procurement decisions and local decisions about how devices are used in practice to achieve co-adaptation; without these, we foster risks and inefficiencies in healthcare.

Abstract: A computer-based method and apparatus for auditing electronic information, most often a database. A database auditor of the present invention conducts an audit as specified by a user-defined project. The project indicates focus groups, filters, skews and whether to count blank entries. The database auditor selects a sample representative of the view of the database described by the project. It presents the sample to the user in a standardized set of reports or on-line forms. The user then determines the number errors contained in the sample and communicates these data to the database auditor. The database auditor uses the error data to calculate the accuracy of the database, as well as the accuracies of the individual fields and focus groups, and to presents these accuracies to the user. Finally, the database auditor charts areas of accuracy and inaccuracy by field and focus group and indicates which inaccuracies are due to process errors and which are due to user errors. The indication of whether the source of inaccuracy is inherent in the process (i.e., a process error) or caused by human negligence (i.e., a user error) enables the user to efficiently and effectively correct database inaccuracies.