OBJECTIVE: To evaluate the incidence of duplicate medication orders before and after computerized provider order entry (CPOE) with clinical decision support (CDS) implementation and identify contributing factors. DESIGN: CPOE with duplicate medication order alerts was implemented in a 400-bed Northeastern US community tertiary care teaching hospital. In a pre-implementation post-implementation design, trained nurses used chart review, computer-generated reports of medication orders, provider alerts, and staff reports to identify medication errors in two intensive care units (ICUs). MEASUREMENT: Medication error data were adjudicated by a physician and a human factors engineer for error stage and type. A qualitative analysis of duplicate medication ordering errors was performed to identify contributing factors. RESULTS: Data were collected for 4147 patient-days pre-implementation and 4013 patient-days post-implementation. Duplicate medication ordering errors increased after CPOE implementation (pre: 48 errors, 2.6% total; post: 167 errors, 8.1% total; p<0.0001). Most post-implementation duplicate orders were either for the identical order or the same medication. Contributing factors included: (1) provider ordering practices and computer availability, for example, two orders placed within minutes by different providers on rounds; (2) communication and hand-offs, for example, duplicate orders around shift change; (3) CDS and medication database design, for example confusing alert content, high false-positive alert rate, and CDS algorithms missing true duplicates; (4) CPOE data display, for example, difficulty reviewing existing orders; and (5) local CDS design, for example, medications in order sets defaulted as ordered. CONCLUSIONS: Duplicate medication order errors increased with CPOE and CDS implementation. Many work system factors, including the CPOE, CDS, and medication database design, contributed to their occurrence.

A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.

A form of adaptive model-based control is proposed and experimentally evaluated. An adaptive model-based neural network controller (AMBNNC) uses multilayer perceptron artificial neural networks to estimate the payload during high-speed manipulator motion. The payload estimate adapts the feedforward compensator to unmodeled system dynamics and payload variation. The neural nets are trained through repetitive training on trajectory tracking error data. The AMBNNC was experimentally evaluated on the third link of a PUMA-560 manipulator. Tracking performance was evaluated for a wide range of payload and trajectory conditions and compared to a nonadaptive model-based controller. The superior tracking accuracy of the AMBNNC demonstrates the potential of the technique.>

The wavefront solver is a type of linear equation solver that is suitable for solving the system of linear equations that arises in many finite-element applications. A new version of the wavefront solver was recently introduced into the ANSYS® program that uses a rank-n update. The rank-n update has properties that allow it to make very efficient use of a hierarchical memory structure. In addition, the rank-n update can exploit both vector and parallel processing to increase its performance. We discuss several general techniques for tuning the rank-n update, using the IBM ES/3090 VF as an example of a computer that incorporates hierarchical memory and vector and parallel processing capabilities. We then report the performance of the tuned rank-n update, both in isolation and in the context of ANSYS jobs.