Welcome to OHDSI! - Please introduce yourself

General
1022

Wow, this is amazing. I only just stumbled into this and it is incredible. In many ways I don’t know where to start, I have some specific questions, but not sure I even have the right vocabulary to search for them. In due time. Meanwhile as suggested a bit about me, and why I’m interested.

I am the CEO and Co-founder of Semantic Arts, a consulting firm that specializes in helping enterprises on their journey to harmonizing all the data they manage through semantics and knowledge graphs. We have been focusing on this exclusively for 25 years. Only a very few of our projects to date have been in healthcare, but the approach we use seems to work in all the domains we’ve tried it in.

As I said I have some specific things I’m looking for, but being an ontologist and semanticist, I can get very excited about things like the thread I just saw on What is a phenotype in the context of observational research? Great stuff. I’m sure I’m just at the tip of the iceberg here.

Meanwhile it looks like we are about to start a project with a Clinical Research Organization, and one of their interests is targeting hospitals based on whether they think a given patient population will be conducive to a given trial. At one point a couple of years ago talking to some ontologists at Montefiore, I recall them saying they were able to get anonymized (aggregated really) patient profiles by hospital, and I thought it was through OHDSI, or maybe it was through some other means, and they were using OMOP from you guys.

But if this rings a bell and you can point me in a right direction I’d appreciate it.

1023

@mkumba:

Welcome to the family.

To your question: Hospitals notoriously do not like to be treated like data vending machines, handing out information about their data. It’s a lot of work for them, and they never know how those data could be used for non-intended purposes. What you want to do is to propose a specific study and then go around. @clairblacketer can help you with her Research Network, and @agolozar if that is in cancer. Good luck.

1024

Hello all! I recently started in a new position as an Analytics Engineering at Fred Hutch Cancer Center in Seattle, where I will be doing ETL work to implement the OMOP CDM, beginning with transformation from our Epic clinical data within our Databricks workspace. Over the past few weeks I’ve joined for the Getting Starting, Databricks, and dbt workgroup calls and a community calls and appreciated all the help I’ve already received from the community. You may see me posting questions in this forum around ETL, Databricks, dbt vs SQLMesh, and more. Thanks!

1025

Hi everyone!

My name is Pablo Pérez López, and I’m a fifth-year medical student at the Autonomous University of Madrid in Spain. I’m deeply interested in the intersection of clinical medicine and data science, and I’m very excited to learn more about OHDSI and get involved as a medical student.

My long-term goal is to become a physician-scientist in the field of Cardiology, with a strong focus on leveraging real-world data to better understand risk factors, predict clinical outcomes, and ultimately develop personalized models of care for patients. Looking forward to connecting and collaborating with you all!

1026

Hello! My name is Atticus. I am a pre-medical student working on research pertaining to the recurrence of diagnoses. I am new to OHDSI and the OMOP CDM.

Previously I had a career in economics and consulting. A little later in life I decided to transition careers and apply to medical school. I took the MCAT recently, and am in the process of applying to schools. In the meantime I am getting involved with a lab on campus because clinical research has always been an interest of mine.

To be honest I am learning so much from the deep wealth of knowledge shared by you all in YouTube videos, GitHub repositories, the book of OHDSI, and of course the OHDSI website. However I still have so many questions as I dive into this world and I’m thankful this forum exists.

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1027

Hi All!
I’m Beth Menefee. I am completing my Master’s in Artificial Intelligence at the end of August. I currently work for the Federal Reserve, but have become very passionate about healthcare AI as I have gone through my coursework. I would love to keep learning more about healthcare AI and work on getting into the field in the future. I have built a PCOS detection model based on data from Kaggle, and am planning to do a second detection model using ultrasound images, also on Kaggle. I look forward to learning more in this group. Feel free to connect with me and if you have any advice, I’m all ears!

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1028

Hi everyone,

I’m Kenny Hombroeckx. I have a background in theoretical physics and began my career as a programmer. I’ve always had a strong interest in biomedical science, which led me to pursue a minor in biomedical physics during my studies. This program emphasized applying mathematical principles from physics to biomedical challenges, including modeling biological systems and exploring medical imaging technologies.

After graduating, I was offered a position as a consultant programmer. I started as an employee, and five years ago, I transitioned to self-employment by setting up my own company. Since then, I’ve been working as a freelance programmer. My main motivation for this shift was to gain more flexibility in my schedule, which allowed me to pursue a master’s program in Systems Biology at Ghent University in Belgium. Interestingly, my freelance journey began during the COVID-19 pandemic—I had only spent eight days at my new office before the lockdowns began. That period rekindled my passion for biomedical science, and I began diving back into the literature.

I’m currently about halfway through the master’s program and have completed courses in clinical study design, epidemiology, mathematical optimization, and a broad range of statistical methods.

I’m eager to combine my programming expertise with my renewed interest in medical science. A colleague recently introduced me to OHDSI, and I was immediately intrigued. They encouraged me to join the community and introduce myself.

To help lay the groundwork for a future transition into biomedical science, I hope to contribute to OHDSI. I plan to publish a separate post proposing the addition of support for debiased causal machine learning estimators such as AIPW and TMLE, as well as double robust approaches like the R-learner and DR-learner. I encountered these methods in a recent course and, after reviewing the OHDSI documentation, it appears they are not yet part of the existing toolkit (or at least I was not able to find a mention of these methods).

I’ll make a more detailed case for these methods in the follow-up post. Although causal machine learning is not a new field, it has recently gained momentum—particularly in economics, and increasingly in biomedical research. These techniques offer model-agnostic, bias-resistant approaches for estimating causal treatment effects. They are especially valuable for observational data but also offer advantages when applied to randomized controlled trials.

Nice to meet you all. I look forward to learning from and collaborating with you—and hopefully meeting some of you at an upcoming conference.

Kind regards,
Kenny

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1029

Hi all!

I’m Yurri, a Senior Data Engineer from Life Line Screening, passionate about growing digital, unified healthcare. I’ve been familiar with OHDSI for a few years and am now actively building an open-source product that combines Microsoft Azure, FHIR, and OMOP.

If you have questions or need help with Big Data, SQL, Python, or Azure, feel free to reach out - I’m always happy to help! Additionally, I am actively seeking a working group to join that utilizes my expertise.

Let’s make healthcare (and our planet) better together :muscle:
BR, Yurri

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1030

Hello!
I’m Paul Wright, a research fellow at King’s College London. I am working on transforming large, real-world medical imaging data to OMOP. I am using the proposed tables in the Park 2024 paper and I would be interesting in comparing notes with anyone else doing the same, especially is this seems to be a relatively new territory, especially for real-world data.

1031

Hi Paul,
I’m Chan (Seng Chan You) from Yonsei University, Seoul, Korea.

Together with colleagues from Johns Hopkins, we’ve been working on implementing OMOP extensions for medical imaging data. We recently published a study in JAMIA that may be of interest to you :https://doi.org/10.1093/jamia/ocaf091.

We also have an active Medical Imaging CDM Working Group that meets regularly. You’re more than welcome to join our discussions.

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1032

Hi! I’m Haymanot and I am masters in data science (public health focus) candidate at the Addis Ababa University, Ethiopia :ethiopia:. I have a background in medicine and I am interested in applying data science methods in medicine and public health.

I am really excited to have found the OHDSI community and hope to contribute and learn here. :smiley:

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1033

Hi, I’m Khaled, and I am a lecturer in pharmacy practice and outcomes research in Saudi Arabia. My research focuses on health outcomes using real-world data to enhance medication utilization, evaluate safety, and address disparities in mental health treatment, particularly concerning adult ADHD with co-occurring psychiatric conditions.

My work highlights the importance of health literacy for patients and providers, noting that stigma, lack of awareness, and inconsistent guidelines affect care.

I focus on revealing prescription patterns and bias, stressing the need for standardized practices based on solid policies and research.

I joined the OHDSI community to deepen my understanding of the OMOP Common Data Model, which I used in projects like the All of Us research initiative. I want to connect with professionals in real-world evidence and population health for collaboration.

Beyond my professional life, I enjoy road trips, hiking, and camping.

I look forward to potential collaborations.

1034

Hi! I’m Tyrus and I am working towards a master in Health Science Informatics at Johns Hopkins. I currently work on provisioning data for research projects focused on liver disease. I am excited to learn from the OHDSI community and how to apply it to liver transplant research.

1035

Hi everyone! I’m Justin (Jiajun) Wu, a Senior Business Intelligence develoer working with clinical data. I’m diving deeper into data analysis and health informatics, and I’m especially excited about OMOP/OHDSI and sharpening my SQL and ETL skills. I’m hoping to learn from the community and contribute back with practical cohort extractions and transparent workflows. when i’m not wrangling data, you’ll find me walking my german shepherd (Tiff), exploring new eats, and tinkering with dashboards :grinning:

1036

Good day OHDSI Community! My name is Andrew Benson and I am a pharmacist with 15 years of experience that includes clinical, informatics, administrative and leadership roles within a large global healthcare system. I am currently enrolled in the Health Sciences Informatics Master’s program at Johns Hopkins University where I am learning more about the OMOP common data model and the real-life capabilities of the fantastic OHDSI Community!

I hope to have an opportunity to participate in meaningful observational research in various medication management topics that can lead to true impacts to how EHRs and Clinical Decision Support tool’s function.

Thank you!

1037

Hi all, I am Kerri Wizner, an occupational public health professional in Denver, Colorado. I have an MPH in epidemiology from Tulane and am currently a DrPH health informatics student at Johns Hopkins. I am taking an OHDSI class at Hopkins as part of my degree program.
My research focuses on supporting working-age people and implementing data-driven or evidence-based medical guidance. I am especially interested in ensuring that research becomes practice by making study results applicable for employers, disability case managers, and occupational medicine clinicians.
Looking forward to learning more about OMOP.
Cheers.

1038

Hello all! My name is Nazia Qureshi, and I am currently a doctoral student in Health Sciences Informatics at Johns Hopkins University. I have a background in pharmacological chemistry and public health/epidemiology and was formerly the epidemiologist for a carceral health system. I was introduced to the OMOP common data model and the OHDSI community through a class. I am interested in maximizing the use of EHR data, both structured and unstructured, to make better clinical decisions and improve patient outcomes. I am eager to learn more about all the exciting and impactful research opportunities that exist within the OHDSI community.

1039

Hi everyone! It’s great to see you all on the discussion forum. My name is Yuanji Han, and I am a master’s student in the Health Science Informatics program at Johns Hopkins University. Prior to this, I worked as a research assistant, where I trained statistical prediction models for biological age and also conducted surveys on healthcare systems. Through working with OMOP/OHDSI, I gained a deeper understanding of how potential connections across different medical terms can be established, and how grouping them helps reveal relationships between different diseases.

1040

Hello everyone! I am Rahul Gorijavolu, a MD/MSE candidate at Johns Hopkins University School of Medicine and Whiting School of Engineering. I am taking a class that teaches us about OHDSI and OMOP as a component of my degree. My research interests are in health AI, algorithmic bias/fairness, and AI governance. After medical school, I hope to pursue a career in surgery.

Looking forward to learning more about OMOP!

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1041

Hi everyone! My name is Grace Lyons, and I’m a Master’s student at Johns Hopkins University studying Applied Health Informatics. I currently work in federal healthcare consulting, where I support a variety of digital health projects. Through my coursework at JHU, I’ve begun exploring the OMOP Common Data Model and am excited to continue learning from the OHDSI community. I look forward to applying these insights to my work and contributing to the advancement of digital health.