Abstract
Along with surgery and chemotherapy, radiation therapy (RT) is a very effective method to treat cancer. The
process is safety-critical, involving complex machines, human operators and software. A proactive hazard
analysis to predict what can go wrong in the process is therefore crucial. Failure Modes and Effect Analysis
(FMEA) is one of the methods widely used for risk assessment in healthcare. Unfortunately, the available
resources and FMEA expertise strongly vary across different RT organizations worldwide. This paper
describes i-SART, an interactive web-application that aims to close the gap by bringing together best practices
in conducting a sound RT-FMEA. Central is a database that at present contains approximately 420 FMs
collected from existing risk assessments and cleaned from ambiguities and duplicates using NLP techniques.
Innovative is that the database is designed to grow, due to both user input and generative AI algorithms. This
is work in progress. First experiments demonstrated that using machine learning in building i-START is
beneficial. However, further efforts will be needed to search for better solutions that do not require human
judgment for validation. We expect to release soon a prototype of i-SART that hopefully will contribute to
the global implementation and promotion of safe RT practices.
process is safety-critical, involving complex machines, human operators and software. A proactive hazard
analysis to predict what can go wrong in the process is therefore crucial. Failure Modes and Effect Analysis
(FMEA) is one of the methods widely used for risk assessment in healthcare. Unfortunately, the available
resources and FMEA expertise strongly vary across different RT organizations worldwide. This paper
describes i-SART, an interactive web-application that aims to close the gap by bringing together best practices
in conducting a sound RT-FMEA. Central is a database that at present contains approximately 420 FMs
collected from existing risk assessments and cleaned from ambiguities and duplicates using NLP techniques.
Innovative is that the database is designed to grow, due to both user input and generative AI algorithms. This
is work in progress. First experiments demonstrated that using machine learning in building i-START is
beneficial. However, further efforts will be needed to search for better solutions that do not require human
judgment for validation. We expect to release soon a prototype of i-SART that hopefully will contribute to
the global implementation and promotion of safe RT practices.
Original language | English |
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Pages | 420-427 |
Number of pages | 7 |
Publication status | Published - 2024 |
Event | HEALTHINF 2024 - 17th International Conference on Health Informatics - Rome, Italy Duration: 21 Feb 2024 → 23 Feb 2024 |
Conference
Conference | HEALTHINF 2024 - 17th International Conference on Health Informatics |
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Country/Territory | Italy |
City | Rome |
Period | 21/02/24 → 23/02/24 |