Systematic Review on Statistical Process Control: an Experience Report

Background: A systematic review is a rigorous method for assessing and aggregating research results. Unlike an ordinary literature review consisting of an annotated bibliography, a systematic review analyzes existing literature with reference to specific research questions on a topic of interest. Objective: Statistical Process Control (SPC) is a well established technique in manufacturing contexts that only recently has been used in software production. Software production is unlike manufacturing because it is human rather than machine-intensive, and results in the production of single one-off items. It is therefore pertinent to assess how successful SPC is in the context of software production. These considerations have therefore motivated us to define and carry out a systematic review to assess whether SPC is being used effectively and correctly by software practitioners. Method: A protocol has been defined, according to the systematic literature review process, it was revised and refined by the authors. At the current time, the review is being carried out. Results: We report our considerations and preliminary results in defining and carrying out a systematic review on SPC, and how graduate students have been included in the review process of a first set of the papers. Conclusions: Our first results and impressions are positive. Also, involving graduate students has been a successful experience

A Review on the Contribution of Emergency Department Simulation Studies in Reducing Wait Time

Background: Because of the important role of hospital emergency departments (EDs) in providing urgent care, EDs face a constantly large demand that often results in long wait times. Objective: To review and analyze the existing literature in ED simulation modeling and its contribution in reducing patient wait time. Methods: A literature review was conducted on simulation modeling in EDs. Results: A total of 41 articles have met the inclusion criteria. The papers were categorized based on their motivations, modeling techniques, data collection processes, patient classification, recommendations, and implementation statuses. Real impact is seldom measured; only four papers (~10%) have reported the implementation of their recommended changes in the real world. Conclusion: The reported implementations contributed significantly to wait time reduction, but the proportion of simulation studies that are implemented is too low to conclude causality. Researchers should budget resources to implement their simulation recommendations in order to measure their impact on patient wait time