Use of Failure Modes Effects and Criticality Analysis to Improve Patient Safety

Author(s):  
Garill A. Coles ◽  
Jonathan Young

The Joint Commission for Accreditation of Healthcare Organizations recently approved revisions to their accreditation standards that are intended to support improvements in patient safety and reduce medical errors. Key among these is the requirement to perform a Failure Modes, Effects, and Criticality Analysis (FMECA) on one high-risk process each year and propose measures to address the most critical failures. Because FMECA was developed for other industries such as nuclear, aerospace, and chemical, some adaptation of its form and use is needed. The FMECA process is normally performed by analyzing each element of an engineered system as represented on a process flow diagram. Medical processes, in contrast, are usually defined procedurally. The key elements of a medical process are more likely to be actions than equipment and components. A community project was put together to develop and test the FMECA adaptation and had good results. This collaboration consisted of safety analysts at Pacific Northwest National Laboratory in Richland, Washington and the Quality and Performance Improvement managers of the three local hospitals. This paper describes this adaptation.

2020 ◽  
Vol 54 (6) ◽  
pp. 44-61
Author(s):  
Lindsay M. Sheridan ◽  
Raghavendra Krishnamurthy ◽  
Alicia M. Gorton ◽  
Will J. Shaw ◽  
Rob K. Newsom

AbstractThe offshore wind industry in the United States is gaining strong momentum to achieve sustainable energy goals, and the need for observations to provide resource characterization and model validation is greater than ever. Pacific Northwest National Laboratory (PNNL) operates two lidar buoys for the U.S. Department of Energy (DOE) in order to collect hub height wind data and associated meteorological and oceanographic information near the surface in areas of interest for offshore wind development. This work evaluates the performance of commonly used reanalysis products and spatial approximation techniques using lidar buoy observations off the coast of New Jersey and Virginia, USA. Reanalysis products are essential tools for setting performance expectations and quantifying the wind resource variability at a given site. Long-term accurate observations at typical wind turbine hub heights have been lacking at offshore locations. Using wind speed observations from both lidar buoy deployments, biases and degrees of correspondence for the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), ERA5, and the analysis system of the Rapid Refresh (RAP) are examined both at hub height and near the surface. Results provide insights on the performance and uncertainty of using reanalysis products for long-term wind resource characterization. A slow bias is seen across the reanalyses at both deployment sites. Bias magnitudes near the surface are on the order of 0.5 m s−1 greater than their hub height counterparts. RAP and ERA5 produce the highest correlations with the observations, around 0.9, followed by MERRA-2 and NARR.


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