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Exploring Collaboration with Industry and the Physicist in the Clinic

S Tripathi1*, F Rath2*, M Herman3*, B Thomadsen4*, (1) Marshfield Clinic, Marshfield, WI, (2) University of Wisconsin Madison, Madison, WI, (3) Mayo Clinic, Rochester, MN, (4) University of Wisconsin, Madison, WI


(Tuesday, 7/31/2018) 11:00 AM - 12:15 PM

Room: Room 209

For a modern day radiotherapy department, the practice of effective risk-assessment and incidence reporting and learning for improvement of quality and safety is paramount. Towards this end, the tools that are needed by a clinical physicist include Process Mapping, Failure Mode Effects Analysis, Fault Tree Analysis, and other approaches to prevent and address potential failures. We address incidence learning analytics in this context and focus on the following areas:

Effective incidence learning and risk assessment - while recording and reporting occurrences is the first step, analysis of a large pool of data from participating clinics can help identify equipment limits and relevant human factors, thereby pointing the way to improvements in a quality management program and improve its workflow. When addressed as a part of standardized care, it can improve the delivery of safe and high quality treatments across our clinics and hospitals.

Learning analytics and incidence learning – combining data mining with learning analytics is a growing area of research that is relevant to incidence learning. In this context, learning analytics can recognize patterns in recorded incidences and extract useful information for quality management.

Tools for improvement of quality and safety - Software tools can automate the incidence learning process, making it simpler to adopt, enhancing usability, and thereby increasing acceptance by the clinical community. Universal adoption of this tool is also necessary to render this effort to be effective. Creation of such software tools, however, will require a massive collaboration of resources and expertise amongst industry, clinical physicists and human factors engineers.

Learning Objectives:
1. To understand the concepts of effective risk-assessment and incidence learning

2. To learn about tools available for improvement of quality and safety

3. To learn how learning analytics relates to the analytics of incidence learning.



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