Uploaded June 2025 | Updated September 2026, 1 week ago
#datascience #centralizedmonitoring #clinicaltrials #technologyinpharma #pharmatransformation
In this episode, Jennifer Krohn, Associate Director of Risk-Based Quality Management at Gilead Sciences, shares how centralized monitoring is transforming clinical trial oversight, improving data quality, participant safety, and trial efficiency. From statistical tools and open-source innovation to CRA training and AI advancements, Jenn shares what it takes to detect critical data signals earlier, ensure trial integrity, and foster cross-functional collaboration in pharma through the open-source community.
Materials shared in the episode:
PHUSE RBQM Working Group: https://advance.hub.phuse.global/wiki/spaces/WEL/pages/26804599/Risk+Based+Quality+Management
RBQM Education Project: https://advance.hub.phuse.global/wiki/spaces/WEL/pages/36864002/End+to+End+RBQM+Education
PHUSE CM White Papers:
- Centralized Monitoring: Exploring the Considerations and Challenges of Implementation
phuse.s3.eu-central-1.amazonaws.com/Deliverables/Risk+Based+Quality+Management/WP-073.pdf
- Can the Value of Centralized Monitoring be Quantified
phuse.s3.eu-central-1.amazonaws.com/Deliverables/Risk+Based+Quality+Management/WP-075.pdf
Jennifer Krohn
linkedin.com/in/jenn-krohn
Nat Chrzanowska
linkedin.com/in/nat-chrzanowska
__________________________________________
More about Appsilon: ► appsilon.com
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► appsilon.com/contact-us
We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.
► appsilon.com/services/data-dashboards
We design and implement Statistical Computing Environment for R and Python for efficient data analysis:
► appsilon.com/services/sce
We design, implement, and optimise data analysis environments so you can focus focus on insights and innovation.
► appsilon.com/services/platform
Where Technology Meets Science podcast is available on all podcasting platforms:
► Spotify: open.spotify.com/show/6MckhYFZCwGU4Op6P09foR
► Apple Podcasts: apple.co/3CJDdVy
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► appsilon.com/blog
LinkedIn: linkedin.com/company/appsilon
_________________________________________
00:00 – Introduction to the episode and our guest
2:26 – Goals of centralized monitoring within risk-based quality
4:29 – Examples of signals detected by central monitoring
6:32 – Traditional data review & central monitoring
7:46 – Evolution and challenges of technology in central monitoring
12:13 – Gaps in CRA training and root cause analysis
18:30 – Why companies should implement central monitoring and considerations
21:25 – Personal challenges in building centralized monitoring
22:47 – How technology improves central monitoring effectiveness
25:40 – Challenges of implementing AI models
29:40 – In-house vs. outsourced vs. open-source tools
31:01 – Impact of PHUSE working groups and key projects
36:51 – Timeline for webinars and trainings
37:29 – Unsolved problems in the field
40:45 – Outro and closing remarks
#datascience #centralizedmonitoring #clinicaltrials #technologyinpharma #pharmatransformation
In this episode, Jennifer Krohn, Associate Director of Risk-Based Quality Management at Gilead Sciences, shares how centralized monitoring is transforming clinical trial oversight, improving data quality, participant safety, and trial efficiency. From statistical tools and open-source innovation to CRA training and AI advancements, Jenn shares what it takes to detect critical data signals earlier, ensure trial integrity, and foster cross-functional collaboration in pharma through the open-source community.
Materials shared in the episode:
PHUSE RBQM Working Group: https://advance.hub.phuse.global/wiki/spaces/WEL/pages/26804599/Risk+Based+Quality+Management
RBQM Education Project: https://advance.hub.phuse.global/wiki/spaces/WEL/pages/36864002/End+to+End+RBQM+Education
PHUSE CM White Papers:
- Centralized Monitoring: Exploring the Considerations and Challenges of Implementation
phuse.s3.eu-central-1.amazonaws.com/Deliverables/Risk+Based+Quality+Management/WP-073.pdf
- Can the Value of Centralized Monitoring be Quantified
phuse.s3.eu-central-1.amazonaws.com/Deliverables/Risk+Based+Quality+Management/WP-075.pdf
Jennifer Krohn
linkedin.com/in/jenn-krohn
Nat Chrzanowska
linkedin.com/in/nat-chrzanowska
__________________________________________
More about Appsilon: ► appsilon.com
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► appsilon.com/contact-us
We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.
► appsilon.com/services/data-dashboards
We design and implement Statistical Computing Environment for R and Python for efficient data analysis:
► appsilon.com/services/sce
We design, implement, and optimise data analysis environments so you can focus focus on insights and innovation.
► appsilon.com/services/platform
Where Technology Meets Science podcast is available on all podcasting platforms:
► Spotify: open.spotify.com/show/6MckhYFZCwGU4Op6P09foR
► Apple Podcasts: apple.co/3CJDdVy
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► appsilon.com/blog
LinkedIn: linkedin.com/company/appsilon
_________________________________________
00:00 – Introduction to the episode and our guest
2:26 – Goals of centralized monitoring within risk-based quality
4:29 – Examples of signals detected by central monitoring
6:32 – Traditional data review & central monitoring
7:46 – Evolution and challenges of technology in central monitoring
12:13 – Gaps in CRA training and root cause analysis
18:30 – Why companies should implement central monitoring and considerations
21:25 – Personal challenges in building centralized monitoring
22:47 – How technology improves central monitoring effectiveness
25:40 – Challenges of implementing AI models
29:40 – In-house vs. outsourced vs. open-source tools
31:01 – Impact of PHUSE working groups and key projects
36:51 – Timeline for webinars and trainings
37:29 – Unsolved problems in the field
40:45 – Outro and closing remarks
