Implementation of Lean and Six Sigma Methodologies to Improve the Operations and Efficiencies of an Inpatient Pharmacy

Reducing overhead costs and eliminating process waste are important aspects of any successful organization. Hospital processes are frequently interconnected, with many exchanges of both materials and information between departments. The Inpatient Pharmacy, a key component of any hospital, has hundreds of interactions between departments on any given day, and many processes see the pharmacy acting as producers, consumers, and transporters of goods throughout the hospital. Such a varied role provides broad opportunities for process improvement. Within the broader manufacturing world, the philosophies of Lean and Six Sigma have helped many companies increase their process efficiencies, reduce costs, and increase quality. The end goal of this project is to improve the workflow within the Inpatient Pharmacy process through Lean Six Sigma techniques and provide pharmacies’ teams with the tools necessary to enact improvement projects in the future. A study was enacted which observed worker movements within the pharmacy and highlighted future improvement projects. This study identified the highest frequency destinations for pharmacy technicians, and the most frequently traveled routes between workstations. An adjusted facility layout was proposed and adopted which reduces non-valued added movement by at least 15%

Optimal acceleration thresholds for non-holonomic agents

Finding optimal trajectories for non-accelerating, non-holonomic agents is a well-understood problem. However, in video games, robotics, and crowd simulations non-holonomic agents start and stop frequently. With the vision of improving crowd simulation, we find optimal paths for virtual agents accelerating from a standstill. These paths are designed for the “ideal”, initial stage of planning when obstacles are ignored. We analytically derive paths and arrival times using arbitrary acceleration angle thresholds. We use these paths and arrival times to find an agent’s optimal ideal path. We then numerically calculate the decision surface that can be used by an application at run-time to quickly choose the optimal path. Finally, we use quantitative error analysis to validate the accuracy of our approach

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions

A Semi-Automated Technique for Transcribing Accurate Crowd Motions

We present a novel technique for transcribing crowds in video scenes that allows extracting the positions of moving objects in video frames. The technique can be used as a more precise alternative to image processing methods, such as background-removal or automated pedestrian detection based on feature extraction and classification. By manually projecting pedestrian actors on a two-dimensional plane and translating screen coordinates to absolute real-world positions using the cross ratio, we provide highly accurate and complete results at the cost of increased processing time. We are able to completely avoid most errors found in other automated annotation techniques, resulting from sources such as noise, occlusion, shadows, view angle or the density of pedestrians. It is further possible to process scenes that are difficult or impossible to transcribe by automated image processing methods, such as low-contrast or low-light environments. We validate our model by comparing it to the results of both background-removal and feature extraction and classification in a variety of scenes