Improving Initiation and Tracking of Research Projects at an Academic Health Center: A Case Study. (September 2017)

• Record Type: Journal Article
• Authors: Schmidt, Susanne; Goros, Martin; Parsons, Helen M.; Saygin, Can; Wan, Hung-Da; Shireman, Paula K.; Gelfond, Jonathan A. L.
• Is Part Of: Evaluation & the health professions. Volume 40:Number 3(2017:Sep.)
• Page Start: 372
• Page End: 379
• Publication Date: 2017-09
• Subjects: lean six sigma -- project tracking -- academic biostatistics units -- web-based dashboard -- R shiny; Medical care -- Evaluation -- Periodicals; Medicine -- Ability testing -- Periodicals; 362.105
• DOI: 10.1177/0163278716669793
• Languages: English
• ISSNs: 0163-2787

Abstract

Research service cores at academic health centers are important in driving translational advancements. Specifically, biostatistics and research design units provide services and training in data analytics, biostatistics, and study design. However, the increasing demand and complexity of assigning appropriate personnel to time-sensitive projects strains existing resources, potentially decreasing productivity and increasing costs. Improving processes for project initiation, assigning appropriate personnel, and tracking time-sensitive projects can eliminate bottlenecks and utilize resources more efficiently. In this case study, we describe our application of lean six sigma principles to our biostatistics unit to establish a systematic continual process improvement cycle for intake, allocation, and tracking of research design and data analysis projects. The define, measure, analyze, improve, and control methodology was used to guide the process improvement. Our goal was to assess and improve the efficiency and effectiveness of operations by objectively measuring outcomes, automating processes, and reducing bottlenecks. As a result, we developed a web-based dashboard application to capture, track, categorize, streamline, and automate project flow. Our workflow system resulted in improved transparency, efficiency, and workload allocation. Using the dashboard application, we reduced the average study intake time from 18 to 6 days, a 66.7% reduction over 12 months (January to December 2015).