Workflow Mapping—A Q-Probes Study of Preanalytic Testing Processes: A College of American Pathologists Q-Probes Study of 35 Clinical Laboratories

Context.— Workflow mapping is a tool used to characterize operational processes throughout most industries and to identify non–value-added activities. Objective.— To develop a set of workflow mapping tools to compare the sequence and timing of activities, including waiting steps, used by clinical laboratories to process specimens during the preanalytic testing phase. Design.— Laboratories enrolled in this College of American Pathologists Q-Probes study created workflow maps detailing the steps they used to process specimens from the time of sample arrival in the laboratory to the time of sample delivery to chemistry analyzers. Enrollees recorded the sequence and types of steps involved in specimen processing and the time needed to complete each step. Results.— Institution average total specimen processing times (SPTs) and the number of steps required to prepare samples varied widely among institutions. Waiting steps, that is, steps requiring specimens to wait before advancing to the next process step, and specimen centrifugation consumed the greatest amount of processing times for both routine and STAT testing. Routine and STAT testing SPTs were shorter at institutions that used rapid centrifuges to prepare samples. Specimen processes requiring more sample waiting steps and computer entry steps had longer aggregate total process times than those with fewer such steps. Conclusions.— Aggregate specimen processing times may be shortened by reducing the number of steps involving sample waiting and computer entry activities. Rapid centrifugation is likely to reduce overall average institutional SPTs.

Improving efficiency of exam room use at a comprehensive cancer center.

307 Background: Efficient use of exam rooms is crucial to be able to accommodate increasing patient volumes. One workflow approach that takes patients into an exam room only when the provider is ready can free up space for other patient-provider pairs (co-rooming), in contrast to rooming the patient before knowing the provider’s readiness status (sequential-rooming). We conducted a pilot to implement co-rooming on the breast cancer patient floor at Dana-Farber Cancer Institute (DFCI) and to assess whether patient time alone in exam rooms is reduced. Methods: As part of the planning for the co-rooming pilot, root cause analysis, current state sequential-rooming workflow mapping, and proposed future state co-rooming workflow mapping were performed. In the pilot group, clinic assistants roomed a patient only if the provider verbally expressed readiness within 10 minutes. The real-time locating system (RTLS) database was leveraged to generate baseline and pilot data. For 5 providers, patient time alone in an exam room on 5 and 3 consecutive baseline (2/17/17 – 3/17/17) and pilot (3/24/17 – 4/7/17) Friday mornings, respectively, were analyzed using the XbarS statistical process control chart method. Results: At baseline, the mean patient time alone in an exam room was 14 minutes (range 0-86). During pilot mornings, the mean patient time alone was reduced to 4 minutes (range 0-20), which was statistically significantly lower than the baseline mornings. The table below provides the XbarS chart data for the mean time alone +/- 3-sigma. Conclusions: In our pilot, patient time alone in an exam room was significantly decreased by changing from a sequential-rooming process to a co-rooming process, with minimal disruption to workflow. Co-rooming can reduce inefficient exam room use. [Table: see text]

Bandwidth-Aware Scheduling of Workflow Application on Multiple Grid Sites

Bandwidth-aware workflow scheduling is required to improve the performance of a workflow application in a multisite Grid environment, as the data movement cost between two low-bandwidth sites can adversely affect the makespan of the application. Pegasus WMS, an open-source and freely available WMS, cannot fully utilize its workflow mapping capability due to unavailability of integration of any bandwidth monitoring infrastructure in it. This paper develops the integration of Network Weather Service (NWS) in Pegasus WMS to enable the bandwidth-aware mapping of scientific workflows. Our work demonstrates the applicability of the integration of NWS by making existing Heft site-selector of Pegasus WMS bandwidth aware. Furthermore, this paper proposes and implements a new workflow scheduling algorithm—Level based Highest Input and Processing Weight First. The results of the performed experiments indicate that the bandwidth-aware workflow scheduling algorithms perform better than bandwidth-unaware algorithms: Random and Heft of Pegasus WMS. Moreover, our proposed workflow scheduling algorithm performs better than the bandwidth-aware Heft algorithms. Thus, the proposed bandwidth-aware workflow scheduling enhances capability of Pegasus WMS and can increase performance of workflow applications.