Leveraging Generative AI in Enhancing Product Owner Responsibilities in the Post-Market Phase of Medical Device Software
Etim, Victoria Bassey (2024)
2024
Master's Programme in Human-Technology Interaction
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2024-11-15
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202410289538
https://urn.fi/URN:NBN:fi:tuni-202410289538
Tiivistelmä
Patients can die from using a medical device if it is not closely monitored after its release into the market. Post-market surveillance (PMS) was introduced by regulators as a requirement for medical device manufacturers to continuously monitor the performance and safety of their devices while they are in the market. However, medical device manufacturers face several challenges in the PMS phase of their devices. But it remained unclear what these challenges are and little research has explored the challenges that device manufacturers face in post-market surveillance.
Therefore, the aim of this thesis is to identify the activities carried out by device manufacturers in the post-market surveillance phase and understand the challenges they encounter while monitoring the devices in the market. This thesis also aims to map post-market surveillance activities to scrum product owner responsibilities and suggest ways of using generative AI to simplify the post-market surveillance process.
Semi-structured interviews were conducted with four industry professionals who currently have devices in post-market surveillance phase. The study was focused on the European Union with all participants from the European Union. The data collected from these interviews were thematically analysed using Claude 3.5 Sonnet with structured prompts to reveal themes.
The study revealed some key challenges in post-market surveillance, including data management and feedback analysis. Currently, there are complexities in managing large amounts of data such as customer feedback, generated during post-market surveillance. These challenges hinder the efficiency of a feedback-driven decision-making process which is crucial for continuous improvement of medical devices. Another crucial challenge was with limited resources, particularly for smaller medical device manufacturers. The inability of manufacturers to sometimes conduct post-market clinical follow-up studies based on customer feedback could hinder the product’s improvement, market expansion, and affect the long-term success of the device. It was also found that generative AI can be potentially used to automate initial feedback processing which could significantly improve the efficiency of categorising and prioritizing feedback.
This thesis confirms that generative AI has the potential to improve post-market surveillance of medical devices and offers insights to medical device manufacturers, product owners, project management office roles, regulatory bodies, and AI developers on the application of generative AI in post-market surveillance. Some main limitations of this study include its small sample size of four, its limited scope to the EU, and it only presents a theoretical model.
Therefore, the aim of this thesis is to identify the activities carried out by device manufacturers in the post-market surveillance phase and understand the challenges they encounter while monitoring the devices in the market. This thesis also aims to map post-market surveillance activities to scrum product owner responsibilities and suggest ways of using generative AI to simplify the post-market surveillance process.
Semi-structured interviews were conducted with four industry professionals who currently have devices in post-market surveillance phase. The study was focused on the European Union with all participants from the European Union. The data collected from these interviews were thematically analysed using Claude 3.5 Sonnet with structured prompts to reveal themes.
The study revealed some key challenges in post-market surveillance, including data management and feedback analysis. Currently, there are complexities in managing large amounts of data such as customer feedback, generated during post-market surveillance. These challenges hinder the efficiency of a feedback-driven decision-making process which is crucial for continuous improvement of medical devices. Another crucial challenge was with limited resources, particularly for smaller medical device manufacturers. The inability of manufacturers to sometimes conduct post-market clinical follow-up studies based on customer feedback could hinder the product’s improvement, market expansion, and affect the long-term success of the device. It was also found that generative AI can be potentially used to automate initial feedback processing which could significantly improve the efficiency of categorising and prioritizing feedback.
This thesis confirms that generative AI has the potential to improve post-market surveillance of medical devices and offers insights to medical device manufacturers, product owners, project management office roles, regulatory bodies, and AI developers on the application of generative AI in post-market surveillance. Some main limitations of this study include its small sample size of four, its limited scope to the EU, and it only presents a theoretical model.