A Simple Incident Learning System for Radiation Oncology in a Community Hospital

Hossain, Murshed; Papalia, Nathan M.; Stoffel, Thomas J.; Carpenter, Halee E.; Sharis, Christine

doi:10.1016/j.jacr.2017.01.055

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Incident learning systems (ILS) reduce the risk of potential patient harm by identifying opportunities to improve a system (C. Kim et al, 2017). ILS have been implemented in radiation oncology in a wide range of different sized hospitals, including large academic centers and community hospitals (Hossain, Papalia, Stoffel, Carpenter, & Sharis, 2017;Novak et al, 2014;Rhieu et al, 2019). In a national effort, the American Association of Physicists in Medicine and the American Society for Radiation Oncology partnered to develop a comprehensive ILS for radiation oncology departments (American Society for Radiation Oncology, 2014).…”

Section: Introductionmentioning

confidence: 99%

Using Incident Reports to Advance Patient Safety and System Resilience in Radiation Oncology Treatment Planning

DiCostanzo

Patterson

2021

Proceedings of the International Symposium on Human Factors and

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We used incident reports to improve treatment planning in radiation oncology to advance patient safety. First, a cluster of related reports enabled discovery of communication issues for setup instructions stemming from a text box with a limited character size. A redesigned process used structured data and image formats to reduce the proportion of inaccurate setup instructions. An upward trend in inaccurate setup instructions identified that some rotating staff were unaware of which equipment to use. Implementing default disease site setups ensured that when a patient was to commence treatment for a specific disease site, it was known which immobilizing equipment should be utilized as the basis before customization. Second, inappropriate plans were identified for proactive improvement. The process resilience was enhanced with targeted validations of generated treatment plans. The automated checks incorporated data automatically pulled from the electronic health record. Useful methods included clustering reports into categories to proactively see patterns in reported issues, collaborating with clinical experts from different perspectives, surveillance monitoring to identify new risks as conditions change, improving communications across disciplines through health information technology, and increasing system resilience with targeted validation of known gaps and variation in plans. The implementation of ILS frameworks, careful review of events and categorization, which culminate with interventions targeting the most frequent or highest severity incidents reported, increased patient safety within our department.

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