Using Data-Driven Prognostic Algorithms for Completing Independent Failure Analysis
Independent Failure Analysis
Identification and Recovery
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AbstractCurrent failure analysis practices use diagnostic technology developed over the past 100 years of designing and manufacturing electrical and mechanical equipment to identify root cause of equipment failure requiring expertise with the equipment under analysis. If the equipment that failed had telemetry embedded, prognostic algorithms can be used to identify the deterministic behavior in completely normal appearing data from fully functional equipment used for identifying which equipment will fail within 1 year of use, can also identify when the presence of deterministic behavior was initiated for any equipment failure.
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Launch Vehicle and Satellite Independent Failure Analysis Using Telemetry Prognostic AlgorithmsLosik, Len; Failure Analysis (International Foundation for Telemetering, 2008-10)Unique vehicle designs encourage the use of the builder to complete its own failure analysis. Current failure analysis practices use telemetry and diagnostic technology developed over the past 100 years to identify root-cause. When telemetry isn't available speculation is used to create a list of prioritized, potential causes. Prognostic technology consists of generic algorithms that identify equipment that has failed and is going to fail while the equipment is still at the factory allowing the equipment to be repaired or replaced while it is still on the ground for any spacecraft, satellite, launch vehicle and missile.
Increasing Patient Satisfaction in a Rural Hospital Emergency Department: A Quality Improvement Project Using Failure Mode & Effects AnalysisGabriel, Alejandra (The University of Arizona., 2018)Over 59 million US residents live in rural areas where they cannot easily access healthcare services. Well-documented disparities between rural and urban healthcare access led the federal government to certify and financially support Critical Access Hospitals (CAHs), which offer rural healthcare services and 24/7 emergency care. Many CAHs are in dire financial distress, and some are looking to increase their patient population volume to improve financial health and ensure continued operations. It is a well-known business truism that satisfied customers are return customers. Today many patients’ first encounter with a hospital is with the emergency department (ED). Thus, it is likely that increasing patient satisfaction with their ED visits in a CAH can be expected to increase the chance that they will return for additional care. All hospitals engage in quality improvement (QI) activities. Many papers outline efforts by QI teams to implement one or a few predetermined interventions with mixed results. Because patients in an ED are subject to a variety of processes in the ED and other hospital departments, improving patient satisfaction in the ED demands a comprehensive approach. This paper focuses on the QI processes and tools used by the QI team in a CAH that developed a comprehensive list of (56) short- and long-term interventions to take place over five years to improve patient satisfaction in the ED. For this hospital, two aspects of the project deserve mention: 1. The use of Failure Mode and Effects Analysis (FMEA): The FMEA is a QI tool developed by the military to address complex problems. Although it has been adapted for use in healthcare QI, in the author’s experience, it has not always been fully implemented. The QI team completed a traditional, full, two-part FMEA. In completing both parts of a traditional FMEA, the team first identified and individually analyzed each known or potential failure in the care of an ED patient and potential interventions that could prevent each failure. Then, after careful analysis of all potential interventions, the QI team chose those most likely to succeed and began implementing a sequenced schedule of interrelated interventions deemed most likely to improve care and patient satisfaction. 2. Learner-Centered Teaching: QI projects typically use learner-centered teaching methods that, according to Social Cognitive Theory, improve participants’ general self-efficacy, which is the likelihood of choosing difficult problems to solve and persisting when faced with challenges. The hospital’s project team members’ self-efficacy scores increased after participating on the team. Post-project interviews with team members indicate they feel better equipped to solve other problems and have begun to plan other QI projects because they understand other areas’ processes, they know who should participate on projects, and they better understand QI processes and tools.