Newswise — Columbus, OH. Challenged with cutting expenditures while delivering better care within the world’s most expensive healthcare system, U.S. hospitals are increasingly turning to time-motion studies (TMS) - a technique that reveals how inefficiencies and irregularities in workflow impact costs and patient outcomes.
Despite the introduction of dozens of TMS software programs in the last decade, no single platform has gained traction, primarily because many were developed for individual projects with limited features that offered little benefit over the traditional TMS method of capturing data with a stopwatch, pen and paper.
“Issues in existing TMS software programs make the data from them questionable, difficult to analyze and impossible to compare across institutions,” said clinical workflow expert Marcelo Lopetgui, MD, MS in The Ohio State University College of Medicine Department of Biomedical Informatics. “TMS are such powerful tools for guiding resource and training decisions, but the current software really holds back their potential for providing insights into healthcare delivery problems.”
Stunned at how an efficiency-driven process had become so inefficient, Lopetegui, a physician-turned -biomedical informatics researcher, decided TMS software needed more than an upgrade, but a complete overhaul that would change the way it looked, functioned and performed.
Having honed his programming skills developing websites for friends in medical school, Lopetegui developed a software platform called TimeCaT (Time Capture Tool) that solves existing software problems with mobility, user interface, and data collection, analysis and validation – areas that had never before been standardized in TMS, ultimately threatening the accuracy and usefulness of previous studies. “If you conduct a study on how long ICU staff takes to sanitize their hands between patients in order to help reduce infection rates and your observers are clocking the same activity from different start points – the data will be wrong. It sounds simple, but if resources are allocated or changes made based on inaccurate information, you haven’t solved the problem and have potentially made it worse.”
With an open access, web-based platform, TimeCaT has relied on user feedback to help the program evolve and improve. Since 2010 the team has been releasing new versions of TimeCaT to what they call the “TimeCAT community,” a consortium of approximately 50 users spanning ten major universities on three continents.
TimeCaT’s supporters continue to grow as Lopetegui and others have given presentations and published studies about the software’s use in areas ranging from ambulatory care to emergency medicine, proving that TimeCaT is flexible enough to apply to a diversity of clinical settings.
Department of Biomedical Informatics Chair, Philip R. O. Payne, PhD, who recruited Lopetegui from Chile and was his post-doc mentor, says that TimeCat’s greatest accomplishments have been to raise the bar on what scientists can expect from TMS, and offer reliable data that can shape and improve medicine worldwide.
“TimeCaT is allowing us to systematically and rigorously collect data on how people perform their jobs while interacting with technology, their environment and their coworkers in a way that wasn’t possible before,” said Payne, who is also the inaugural Director of the Data Analytics Collaborative, which is part of the Discovery Themes initiative at Ohio State. “It represents the best of what biomedical informatics has to offer: a human factors approach to making sense out of massive amounts of data in order to improve the delivery of safe and cost effective health care.”Making TMS, technology work harder – and smarter Lopetegui’s first step to improve TMS software was to make the data collection process easier and less error-prone. He made TimeCaT a web-based platform that was able to work on any internet capable device, harnessing mobile and touch-screen technology that lets observers keep their eyes on the activity.
He included other features that many digital TMS systems don’t have, such as a simple graphical user interface, the ability to correct an order collection error in the field, automated time stamps to make workflow analysis more accurate, and cloud-based data collection that allows off-site researchers to track incoming data from around the world in real time.
Another major flaw TimeCaT addresses is observer validation, which among TMS is often challenging. TimeCaT is programmed with one of the first-ever inter-observer validation algorithms. The tool allows researchers to perform a test run of their study to gauge the accuracy of the eyewitnesses and to conduct on going validity tests throughout the data collection.
Lopetegui was also the first to try to introduce a standard taxonomy, or language, to TMS, that would prompt researchers to use a common set of terms to describe tasks, which enables scientists – for the first time – to accurately pool data from multiple studies.
“The action of “hand-washing” has literally been described a dozen different ways,” said Lopetegui. “On the website researchers can find out what terms have already been used, or share their own for others to use. This and other features make it possible for researches to reliably aggregate and compare data across thousands of study locations."
Lopetegui says that the team will continue to roll out new versions of TimeCaT with expanded features, many of which have been suggested and then tested by the community – which has grown beyond the healthcare realm.
“We have had a professional soccer team use TimeCaT to see if they could improve their game,” said Lopetegui. “I’m not sure if it worked for them, but it was the perfect validation for me that we had created something truly accessible, functional and adaptable.”
TimeCaT was developed with support from Ohio State’s Center for Clinical and Translational Science (CCTS). Collaborators include Philip Payne, PhD,FACMI Professor and Chair, Department of Biomedical Informatics and Director of the Data Science Cluster for the Ohio State CCTS; Po-Yin Yen, RN, PhD, Assistant Research Professor of Biomedical Informatics; Albert Lai, PhD, Assistant Professor of Biomedical Informatics Peter Embi, MD, MS, Associate Professor and Vice-Chair, Department of Biomedical Informatics and Chief Research informatics officer for Ohio State’s Wexner Medical Center.
For more information about educational opportunities, research projects and funding opportunities within the Department of Biomedical Informatics, please contact [email protected]
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The Ohio State University Center for Clinical and Translational Science (CCTS) is funded by the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program (UL1TR001070, KL2TR001068, TL1TR001069) The CTSA program is led by the NIH’s National Center for Advancing Translational Sciences (NCATS). The content of this release is solely the responsibility of the CCTS and does not necessarily represent the official views of the NIH.
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