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The Journey Newsletter (July 2025)
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ATLAS is an open source software tool for researchers to conduct scientific analyses on standardized observational data. Over the last month, leads of the ATLAS team highlighted various aspects of the tool and sought community input to develop a roadmap for future versions. Learn more about the tool in this newsletter. We also look at the #OHDSI2025 tutorials, June publications, and more updates in the latest newsletter. #JoinTheJourney
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Podcast: ATLAS, Open-Source, Symposia
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In the July 2025 On The Journey podcast, Patrick Ryan and Craig Sachson take a look at the ATLAS tool, how it impacts research, and its place within the overall OHDSI open-source environment. They also look ahead to the second half of 2025, which includes symposia across four continents. (If video doesn't appear, click 'View this email in your browser')
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Where Have We Been?
• ATLAS is an open-source, web-based tool that enables researchers to conduct scientific analyses on standardized observational health data. June community calls focused on both educating users about ATLAS and shaping its future roadmap. Watch the videos to learn more—and be sure to complete the surveys to help guide the next phase of ATLAS development.
• OHDSI recently joined Bluesky. You can now get updates on all community activities and see all global research through the #OHDSISocialShowcase on Bluesky.
Where Are We Now?
• Full registration is now open for the 2025 OHDSI Global Symposium, which will be held Oct. 7-9 at the Hyatt Regency Hotel in New Brunswick, NJ, USA. Information on the Day 1 tutorials is available later in this newsletter. The main conference will be held Oct. 8, and the agenda will be posted when available. The collaborator showcase will be held during the main conference; thank you to everybody who submitted their brief reports before the July 1 deadline.
• Cindy Cai, a 2024 Titan Award winner and co-lead of the Eyecare and Vision Research workgroup, announced a new network study to research whether semaglutide is associated with neovascular age-related macular degeneration. This study follows up on a recent JAMA Ophthalmology publication. If you are interested in collaborating or learning more, please join the Eyecare and Vision Research workgroup.
• The agenda for the 2025 OHDSI Europe Symposium (July 5-7) is available on its main page. The main conference will be held July 7, while tutorials and workshops are set for July 5-6. Registration is open and available on the homepage.
Where Are We Going?
• The 2025 UK Symposium will be held Sept. 26 at Wellcome HQ in London. Registration is open through Sept. 12. The #OHDSISocialShowcase is currently highlighting research from the 2024 UK Symposium. Please make sure you are following our LinkedIn, Twitter/X, Bluesky and Instagram feeds to learn more about the research happening in our community.
• The 2025 OHDSI Africa Symposium will be held Nov. 10-12 in Kampala, Uganda. The abstract submission deadline will be August 25. More details will be shared when available.
• The 2025 OHDSI Asia-Pacific (APAC) Symposium will be held Dec. 6-7 in Shanghai, China. More information will be shared when available.
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Introductory, Advanced Tutorials Provide Educational Opportunities at Global Symposium
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The 2025 Global Symposium will open with a day of tutorials (Oct. 7), providing opportunities for both OHDSI newcomers and veterans to learn more about the community and focused research areas. An introductory tutorial will be a standalone session during the morning, while the afternoon will include five advanced tutorials. Learn more about each below; you can sign up for specific tutorials during the symposium registration process.
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Morning Session
An Introduction to the Journey from Data to Evidence Using OHDSI
The journey from data to evidence can be challenging alone, but it is greatly enabled through community collaboration. In this half-day tutorial, we will introduce newcomers to OHDSI. Specifically, you will learn about the tools, practices, and open-science approach to evidence generation that the OHDSI community has developed and evolved over the past decade. Lead: Erica Voss
Afternoon Session
Developing and Evaluating Your Extract, Transform, Load (ETL) Process to the OMOP Common Data Model
In this tutorial, students will learn about the tools and practices developed by the OHDSI community to support the journey to establish and maintain an ETL to standardize your data to OMOP CDM and enable standardized evidence generation across a data network. Lead: Clair Blacketer
Using the OHDSI Standardized Vocabularies for Research
In this tutorial, students will learn how to take advantage of the OHDSI standardized vocabularies as an analytic tool to support your research, including searching for relevant clinical concepts, navigating concept relationships, creating concept sets and understanding source codes that map within these expressions. Students will also learn where the OHDSI standardized vocabularies are used throughout OHDSI’s standardized analytic tools. Lead: Anna Ostropolets
Clinical Characterization Applications to Generate Reliable Real-World Evidence
Clinical characterization—descriptive statistics to summarize disease natural history, treatment utilization, and outcome incidence—are at the heart of many real-world data applications, including study feasibility and quality improvement. In this tutorial, students will learn how to design and implement observational network studies for characterization, and how to apply tools and practices developed by the OHDSI community to ensure the evidence generated is reliable. Lead: Patrick Ryan
Population-Level Effect Estimation Applications to Generate Reliable Real-World Evidence
Population-level effect estimation—causal inference methods for comparative effectiveness and safety surveillance—enables researchers to understand how exposure to medical interventions are expected to impact health outcomes. In this tutorial, students will learn how to design causal inference studies and how to apply tools (such as CohortMethod) and practices (such as objective diagnostics) developed by the OHDSI community to ensure the evidence generated is reliable. Lead: George Hripcsak
Patient-Level Prediction Applications to Generate Reliable Real-World Evidence
Patient-level prediction—the use of machine learning to train, test, and apply predictive models for disease interception and precision medicine—offers the potential to personalize healthcare by enabling individualized risk prediction based on personal health history. In this tutorial, students will learn how to apply tools and practices developed by the OHDSI community, including the PatientLevelPrediction HADES R package, to design and implement network studies capable of learning and externally validating prediction models, and how to apply these models to your population. Lead: Jenna Reps
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ATLAS Deepdive: Learn About the Current Tool, Help Develop the Roadmap for Future Versions
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ATLAS is an open-source, web-based tool that enables researchers to conduct scientific analyses on standardized observational health data. Our June community calls focused on both educating users about ATLAS and shaping its future roadmap. Watch the videos below to learn more—and be sure to complete the surveys to help guide the next phase of ATLAS development.
ATLAS workgroup lead Christopher Knoll guided the community through the month, while tool collaborators Peter Hoffmann, Alexey Manoylenko, Richard Boyce and Konstantin Iaroshovets provided demos on various aspects of ATLAS, including data sources and vocabularies, concept sets and cohorts, and characterization, incidence and treatment pathways.
One of the most widely used research tools in the community, the ATLAS team is now considering future versions and is seeking global input. What tools are the most important to you? How often do you use them? This is your opportunity to have your voice heard as we develop the roadmap for future versions of ATLAS!
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Dr. Phan Thanh Phuc, a healthcare management and data science professional at the University Medical Center in Ho Chi Minh City, Viet Nam, holds a Ph.D. and MBA from Taipei Medical University (TMU), Taiwan. As a TMU researcher, he uses AI and the TMU Clinical Research Database to predict long-term complications in type 2 diabetes patients, such as cardiovascular diseases and cognitive impairment. His JMIR-published work highlights how electronic health records improve dementia risk prediction in diabetes.
Dr. Phuc’s research is significantly influenced by OHDSI, utilizing the OMOP Common Data Model (CDM) and OHDSI’s open-science approach to develop robust, reproducible patient-level prediction models and foster international collaborations. His expertise blends clinical research and AI, focusing on healthcare data science and Real-World Evidence (RWE) generation. His doctoral work involved an AI model to predict diabetes complications using large clinical databases like TMUCRD and OHDSI's CDM. He is enthusiastic about OHDSI's growth in the Asia-Pacific region, where OMOP CDM adoption is increasing for cross-border research.
In the latest edition of the collaborator spotlight, Dr. Phuc talks about his research in dementia prediction, how OHDSI impacts global collaboration, the community growth in the APAC region, and plenty more.
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Feng L, Wang W, Yin C, Li J, Zhang X, Chang X, Feng Z, Van Zandt M, You SC, Seager S, Reich C, Zhan S, Sun F, Wang G. Risk of Switch to Mania/Hypomania in Bipolar Depressive Patients Treated with Antidepressants: A Real-World Study. Health Data Sci. 2025 Jun 3;5:0209. doi: 10.34133/hds.0209. PMID: 40464055; PMCID: PMC12130621.
Bohn J, Gilbert JP, Knoll C, Kern DM, Ryan PB. Large-scale Empirical Identification of Candidate Comparators for Pharmacoepidemiological Studies. Drug Saf. 2025 Jun 4. doi: 10.1007/s40264-025-01569-y. Epub ahead of print. PMID: 40467833.
Zhang M, Shen P, Liu ZK, Van Zandt M, Li J, Li C, Sun YX, Xie JQ, Wan YUKFAI, George H, Chen Y, Lin HB, Zhan SY, Sun F. [Study of application of Common Data Model of Observational Medical Outcomes Partnership in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2025 May 10;46(5):907-913. Chinese. doi: 10.3760/cma.j.cn112338-20240924-00595. PMID: 40494801.
McCarthy ML, Bradenday J, Chen E, Zonfrillo MR, Sarkar IN. Reductions in Blood Lead Level Screening During Peak COVID-19 Restrictions and Beyond. Public Health Chall. 2025 Feb 25;4(1):e70021. doi: 10.1002/puh2.70021. PMID: 40496104; PMCID: PMC12039357.
Zelko JS, Manjourides J. A Generalized Tool to Assess Algorithmic Fairness in Disease Phenotype Definitions. AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:624-633. PMID: 40502243; PMCID: PMC12150753.
Kim S, Boo D, Yoo S, Kim B, Kim K, Kim K, Song E, Kim J, Ryoo HG, Paeng JC, Choi IY, Ko S, Yoo IR, Park RW, Lee HY. Secondary Cancer Risk in Breast Cancer with and without Radiotherapy: The Observational Health Data Sciences and Informatics (OHDSI) Cohort Study. Cancer Res Treat. 2025 Jun 5. doi: 10.4143/crt.2024.968. Epub ahead of print. PMID: 40506029.
Kim J, Kim JS, Lee JH, Kim MG, Kim T, Cho C, Park RW, Kim K. Pretrained patient trajectories for adverse drug event prediction using common data model-based electronic health records. Commun Med (Lond). 2025 Jun 13;5(1):232. doi: 10.1038/s43856-025-00914-7. PMID: 40514403; PMCID: PMC12166071.
López-Güell K, Català M, Dedman D, Duarte-Salles T, Kolde R, López-Blasco R, Martínez Á, Mercier G, Abellan A, Arinze JT, Burkard T, Burn E, Cuccu Z, Delmestri A, Delseny D, Khalid S, Kim C, Kim JW, Kostka K, Loste C, Mayer MA, Meléndez-Cardiel J, Mercadé-Besora N, Mosseveld M, Nishimura A, Nordeng HM, Oyinlola JO, Paredes R, Pérez-Crespo L, Pineda-Moncusí M, Ramírez-Anguita JM, Trinh NT, Uusküla A, Valdivieso B, Prieto-Alhambra D, Xie J, Mateu L, Jödicke AM. Clusters of post-acute COVID-19 symptoms: a latent class analysis across 9 databases and 7 countries. J Clin Epidemiol. 2025 Jun 13:111867. doi: 10.1016/j.jclinepi.2025.111867. Epub ahead of print. PMID: 40517846.
Babiak MC, Hula WD, Autenreith A, Nader MM, Hula SA, Swiderski A, Cavanaugh R, Nunn K, Johnson JP, Dickey MW. Interim Treatment Fidelity for a Randomized Controlled Comparative Effectiveness Trial of Two Variants of Semantic Feature Analysis Treatment for Aphasia. Am J Speech Lang Pathol. 2025 Jun 18:1-17. doi: 10.1044/2025_AJSLP-24-00331. Epub ahead of print. PMID: 40532091.
Noll R, Berger A, Facchinello C, Stratmann K, Schaaf J, Storf H. Enhancing diagnostic precision for rare diseases using case-based reasoning. J Am Med Inform Assoc. 2025 Jun 17:ocaf092. doi: 10.1093/jamia/ocaf092. Epub ahead of print. PMID: 40574694.
Saelmans A, Seinen T, Pera V, Markus AF, Fridgeirsson E, John LH, Schiphof-Godart L, Rijnbeek P, Reps J, Williams R. Implementation and Updating of Clinical Prediction Models: A Systematic Review. Mayo Clinic Proceedings: Digital Health. DOI: 10.1016/j.mcpdig.2025.100228. Vol 3. Issue 3.
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Week 1: Overview/Roadmap | First Survey (Christopher Knoll, Peter Hoffmann)
Week 2: Data Sources/Vocabularies (Christopher Knoll, Alexey Manoylenko)
Week 3: Concept Sets/Cohorts (Christopher Knoll, Richard Boyce)
Week 4: Characterization, Incidence, Treatment Pathways (Christopher Knoll)
Week 5: Technical and Administrative Capabilities (Christopher Knoll, Konstantin Iaroshovets)
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