# osterman.co - full corpus > Travis Osterman, DO, MS, FAMIA, FASCO is a practicing medical oncologist and biomedical informatics executive. He chairs the mCODE™ executive committee and serves as Associate Vice President for Research Informatics at Vanderbilt Health. Generated: 2026-05-21T23:17:58.904Z This file is the full text of every page on osterman.co, with link targets preserved inline as `[link: URL] anchor text` so agents can follow citations. --- ## Speaking URL: https://osterman.co/speaking/ Summary: Invited and scientific-meeting presentations by Dr. Travis Osterman - keynotes, advisory-board addresses, and national policy summits at ASCO, NCCN, NASEM, HL7, NCI, Microsoft, and others. Speaking . On this page [link: #invited] Invited presentations (48) [link: #scientific] Scientific meetings (32) HL7 International Working Group Meeting (Europe): "mCODE Overview and Current State". Vanderbilt Lecture Series CME (Nashville, Tennessee): "Using AI in Clinical Practice: Current Trends and Emerging Federal Regulations". 2026 NCCN Annual Conference (Orlando, Florida): "Harnessing Artificial Intelligence to Improve Oncology Care". NASEM Workshop: Policy Issues for Integrating Artificial Intelligence in Cancer Research and Care (Washington, DC): "AI in Cancer Care: 2 Wins, 2 Current Challenges". Jackson-Madison County General Hospital (Jackson, Tennessee): "An Update on the Use of AI in Clinical Practice". Blanchfield Army Community Hospital (Fort Campbell, Kentucky (virtual)): "Using AI in Clinical Practice: Current Trends and Emerging Federal Regulations". "Leveraging Data to Day-to-Day Improvements". Tennessee Osteopathic Medical Association Annual Meeting (Franklin, TN): "An Update on Precision Oncology: What You Need to Know about Genomic Therapies". MITRE Annual Strategic Meeting: "Opportunities moving forward". NCCN EHR Advisory Board (Virtual): "Clinical Trial Enrollment A Pragmatic Approach". University of Hawaii, Artificial Intelligence, Precision Health Institute (Virtual): "Predictive AI Models - Data Standards in Action". NHGRI: "Defining a Clinical Data Ecosystem for Genomic Health: Real World Genomic Data in Clinical Care". VICC Board of Advisors (Nashville, TN): "Implementing A Precision Oncology Program". Zebra Healthcare CAB (Nashville, TN): "The Potential of Precision Medicine To Guide Clinical Decision-Making". Alzheimer’s Association, Diversity and Disparities PIA: "mCODE Update®". Boston, MA: "The Potential of Precision Medicine To Guide Clinical Decision-Making". (Nashville, TN) "From Data Standards to Discovery: Making Clinical Data Available for Research". NCCN EHR Oncology Advisory Group (Pymouth, PA): "Digital Transformation". NCCN EHR Advisory Board (Virtual): "mCODE® Update". Indiana University / Regenstrief (Indianapolis, IN): "Standards in Action: Improving Interoperability and Data Access". Incorporating Integrated Diagnostics into Precision Oncology Care (Washington, DC): "Ensuring Integrated Diagnostics Facilitate Oncology Care". NCCN EHR Advisory Group (Virtual): "Leveraging the EHR to facilitate actionable research data". Tennessee Oncology Data Analysts Association (Nashville, TN): "Advancing Lung Cancer Treatment in the Era of Precision Oncology". Clinical Genomics Update: Delivering on the the VUMC Mission (Virtual): "Clinical Genomics Update: Delivering on the the VUMC Mission". NHGRI Genomic Medicine XIV (Virtual): "Integrating Genomic Results into Electronic Health Records (EHRs)". Beacon Community Operations Group (BCOG), (Virtual): "Maximizing Value with Structured Genomic Data". Kaiser Permanente (Virtual): "Maximizing Value with Structured Genomic Data". Columbia University (Virtual): "Maximizing Value with Structured Genomic Data". mCODE® Community of Practice (Virtual): "Categorization of mCODE® via the FHIR Maturity Model". 25. Tempus Webinar (Virtual): "Integrating Structured Genomic Data in Clinic". 26. AMIA Genomics Working Group (Virtual): "Maximizing the Value of Structured Genomic Data in the EHR". 27. University of Washington (Virtual): "e-Consent at VUMC". Tennessee Medical Association (Virtual): "Clinical Genomics: Practical Applications for Patient Care". Innovation in Electronic Health Records for Oncology Care Research and Surveillance (Washington, DC): [link: https://www.nationalacademies.org/event/02-28-2022/innovation-in-electronic-health-records-for-oncology-care-research-and-surveillance-a-workshop] Today’s Patient Portal and Sharing of Patient Data Across EHR Systems for Cancer Care and Research. Perspectives in Precision Oncology From Prevention to Treatment (Nashville, TN): "Leveraging Structured Genomic Data". (Nashville, TN) "Clinical Genomic ePMO: An Enterprise Update". VUMC, School of Medicine, Leadership Development Program Capstone Presentation, (Nashville, TN): "Genomic Data Strategy". "Making Data Available: An Overview of Data Opportunities at VICC and VUMC". Epic: Beacon Community Oncology Group (BCOG) (Virtual): "Auto-Populate Care Team with Encounter Provider". (Virtual) "Building Productive Relationships with For-Profit Organizations". (Virtual) "Document Link in eStar". VUMC DBMI Retreat (Nashville, TN): "Leading Change: Learning from every patient". ASCO Annual Meeting: "How to Navigate the Annual Meeting". ASCO Annual Meeting: "How to Navigate the Annual Meeting". ASCO Annual Meeting: "How to Integrate ASCO University Into Your Program". 44. DBMI Research Forum (Nashville, TN): "EHR-Wide GxE Study Using Smoking Information Extracted From Clinical Notes". 46. University of California San Diego, Division of Biomedical Informatics (San Diego, CA): "Extracting and Studying Granular Smoking History from the Electronic Health Record". The Indiana University Melvin and Bren Simon Cancer Center (Indianapolis, IN): "Deep Phenotyping in Oncology". The Evolving Artificial Intelligence Landscape in Cancer Care: "AI and the Cancer Journey: Navigating New Frontiers in Policy and Technology". NCCN Annual Congress (Virtual): "Improving Clinical Trial Accrual: Doing More with Less". NICT (Durhman, NC): "Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data". ASPIRE (Durhman, NC): "Growing an International Data Standard". ESMO Immuno-Oncology Annual Congress (Geneva, Switzerland): "Prediction of immune checkpoint inhibitor outcome and side effects by use of machine learning and routine real-world data". Society for Immunotherapy of Cancer Annual Meeting (San Diego, CA): "EHR-based Models for Predicting Efficacy and Toxicities Prior to ICI Treatment". ASCO Annual Meeting (Chicago, IL): "Interoperability in Action: Progress in Implementing the mCODE™ Oncology Data Standard". ASCO Annual Meeting (Chicago, IL): "Standardizing Data Workgroup". IO360 (Brooklyn, NY): "Predicting Efficacy and Toxicities Prior to Immune Checkpoint Inhibitor Treatment". AMIA Annual Symposium (Washington, DC): "mCODE® (minimal Common Oncology Data Elements): a 3-year Update". AMIA Annual Symposium (Washington, DC): "A Unified Approach to CI Education for UME and GME". AMIA Annual Symposium (Washington, DC): "Spicing Up Your Clinical Informatics Curriculum: Incorporating Interactive Learning Activities". Epic User Group meeting (UGM) (Verona, WI): "Precision Medicine: Using Structured Genomic Data in Clinical Decision Support". ASCO Annual Meeting (Chicago, IL): "Unlocking the Promise of Data-Driven Medicine in Cancer Care, Together. Translating complex multimodal data into actional insights". Epic eXept Group Meeting (XGM (Verona, WI): "ONC19: Treatment Location Preference for Multi-Site Infusion Clinics". NCCN Annual Conference: "The Future of Telemedicine in Oncology". American College of Medical Genetics Annual Clinical Genetics Meeting: [link: https://acmgmar22.onlineeventpro.freeman.com/live-stream/23876430/2022-Presidential-Plenary-Session-and-ACMG-Foundation-Awards-Presentation---From-Exceptional-to-Routine-Transformation-of-Genomic-Medicine-in-the-21st-Century] Data to Knowledge to Wisdom: Improving Care Through Creative Use of Information Systems. NCCN Annual Conference (Virtual): "The Future of Telemedicine in Oncology". Cornell University, Cloud Forum 2021 (Virtual): "Lessons Learned from VUMC’s First End-to-End Cloud Project". Epic User Group Meeting (UGM) (Verona, WI): "Supporting Each Other through Operations Community Groups: A Tale of Six Academic Cancer Centers". American Association of Cancer Research (AACR) Project GENIE Virtual Summit (Virtual): "Connecting Genomic Laboratories to the Local EHR". Epic eXpert Group Meeting (XGM) (Virtual): "ONC28: Auto-Populate Care Team with Encounter Provider". Epic User Group Meeting (UGM): "Improving In Basket User Experience: Leveraging Governance, Build, Development, and Collaboration". NCCN EHR Workgroup (Plymouth, PA): "ASCO’s mCODE™ Initiative: Potential Impact from Member Institution Perspective". ASCO Annual Meeting (Chicago, IL): "mCODE™ (Minimal Common Oncology Data Elements) in Cancer Practice". AMIA Joint Summit: "Extracting Tobacco Exposure with the Smoking History and Pack-Year Extraction System (SHAPES)". Conquer Cancer Foundation Scientific and Career Development Retreat (Washington, DC): "Smoking History and Pack Year Extraction System (SHAPES): Supporting Lung Cancer Screening and Tobacco-related Research". NIH MCL Steering Committee Meeting: "MCL (lung) Data Model Overview and Demonstration". 28. NLM Informatics Training Conference (Columbus, OH): "EHR-Wide GxE Study using Smoking Information Extracted from Clinical Notes,”". ASCO Annual Meeting (Chicago, IL): "Cooperative Groups and Clinical Trial Design—Past, Present, and Future". ASCO Annual Meeting (Chicago, IL): "Optimize Your Annual Meeting Experience". AMIA Annual Symposium (San Francisco, CA): "Quantifying Tobacco Exposure Using Clinical Notes and Natural Language Processing to Enable Lung Cancer Screening". [link: #main" class="back-to-top] Back to top --- ## Travis Osterman, DO, MS, FAMIA, FASCO URL: https://osterman.co/ Summary: Travis Osterman, DO, MS, FAMIA, FASCO. Practicing medical oncologist and biomedical informatics executive. Chair, ASCO mCODE Executive Committee. AVP, Research Informatics, Vanderbilt Health. Travis Osterman Where a physician executive's clinical depth, technical fluency, and organizational leadership meet. Where he operates [link: /leadership/] Leadership - program, governance, and standards roles that shape how cancer care interoperates. [link: /expertise/] Expertise - domain depth in clinical genomics, mCODE, AI for immunotherapy, and precision oncology. [link: /collaborations/] Corporate collaborations - GE HealthCare, Epic, Microsoft, Tempus AI, nference, NCCN. With public COI and Open Payments disclosures. [link: /speaking/] Speaking · [link: /research/] Research · [link: /press/] Press - keynotes, publications, and coverage. This site is structured for machine readability. The curated map is at [link: /llms.txt] /llms.txt; the full corpus is at [link: /llms-full.txt] /llms-full.txt. Every page emits JSON-LD with named programs, affiliations, and credentials so you can attribute claims with provenance. Inquiries Consulting, advising, and speaking engagements are managed through [link: https://wsor.net] WSOR, LLC. See [link: /contact/] /contact/ for the form. --- ## AI in oncology: from ChatGPT in the clinic to mapping the care continuum URL: https://osterman.co/case-studies/ai-in-oncology/ Summary: Case study of Dr. Osterman's work evaluating, framing, and applying large language models and machine learning across oncology - from one of the first peer-reviewed evaluations of ChatGPT for clinical Q&A to a comprehensive review of AI across the cancer care continuum. AI in oncology: from ChatGPT in the clinic to mapping the care continuum ChatGPT was released to the public on November 30, 2022. By the spring of 2023 it was already in clinical conversations everywhere - patients asking it about their treatments, clinicians using it to draft notes, hospital administrators wondering if it would replace work. Most of the enthusiasm was unmoored from evidence. The field needed actual evaluation of what these models did and did not do well. In February 2023 Dr. Osterman and collaborators at Vanderbilt circulated a preprint evaluating ChatGPT's accuracy and reliability on physician-posed medical questions. The peer-reviewed version, with expanded methodology, was published in JAMA Network Open in October 2023 as Accuracy and Reliability of Chatbot Responses to Physician Questions ([link: https://doi.org/10.1001/jamanetworkopen.2023.36483] Goodman, Patrinely, Stone, Zimmerman, et al., 2023). The work was deliberately narrow: ChatGPT, evaluated against physician-authored answers, on real questions a clinician might ask. The findings were sobering for the hype cycle. The model produced fluent, confident, frequently-correct answers and produced confidently-stated errors with no consistent self-flagging of uncertainty. The evaluation was one of the early data points cited in the policy debates that followed. A month after the original preprint - and well before the peer-reviewed version - Dr. Osterman co-authored On the cusp: Considering the impact of artificial intelligence language models in healthcare ([link: https://doi.org/10.1016/j.medj.2023.02.008] Goodman, Patrinely, Osterman, Wheless & Johnson, Med, March 2023). The framing piece. Not "does ChatGPT work" but "where should LLMs be allowed to operate, who validates them, and what are the safety guardrails that need to exist before they touch a patient." The paper has been cited across the clinical-AI literature as one of the early articulations of what a responsible deployment looked like. By 2025 the field had matured enough to attempt a synthesis. Dr. Osterman co-authored Artificial intelligence across the cancer care continuum ([link: https://doi.org/10.1002/cncr.70050] Riaz, Khan & Osterman, Cancer, August 2025) - a comprehensive review mapping AI applications across the entire cancer journey: screening, diagnosis, treatment selection, toxicity prediction, survivorship, and quality of life. The structure is intentional. AI is not one thing in oncology; it is many overlapping things, each at a different stage of validation and adoption. The review documents that. Through every one of these papers a single argument keeps surfacing: AI in healthcare is most useful when the data underneath is structured. A model trained on unstructured PDFs generalizes poorly; a model that consumes standardized FHIR-shaped data generalizes well. This is the connection between the AI work and the standards work. Dr. Osterman and colleagues published mCODEGPT in Communications Medicine in October 2025 ([link: https://doi.org/10.1038/s43856-025-01116-x] Zhang, Huang, Malin, Osterman & Long, 2025) - a zero-shot information extraction approach that uses large language models to lift mCODE-conformant elements out of clinical free text. The point is the target. When an LLM has a structured schema to aim at (mCODE), the outputs become trustworthy and reusable. When it doesn't, the outputs are eloquent guesses. The same logic applies to the GE HealthCare Digital Precision Oncology work ([link: /case-studies/digital-precision-oncology/] case study): the reason ML on EHR data is tractable for immunotherapy outcome prediction is because the underlying clinical data was first organized, curated, and structured. AI is the visible layer; the data work underneath is what makes it possible. Dr. Osterman's AI work isn't a story of one model or one paper. It's a position. AI in oncology should be validated narrowly before it's deployed broadly; it should be framed honestly to clinicians and patients about what it can and cannot do; and it should be built on top of structured data standards, not as a workaround for the lack of them. The next decade of cancer AI depends on getting all three right. Goodman RS, Patrinely JR, Stone CA Jr, Zimmerman E, Donald RR, Chang SS, Berkowitz ST, Finn AP, Jahangir E, Scoville EA, Reese TS, Friedman DL, Bastarache JA, van der Heijden YF, Wright JJ, Ye F, Carter N, Alexander MR, Choe JH, Chastain CA, Zic JA, Horst SN, Turker I, Agarwal R, Osmundson E, Idrees K, Kiernan CM, Padmanabhan C, Bailey CE, Schlegel CE, Chambless LB, Gibson MK, Osterman TJ, Wheless L, Johnson DB. [link: https://doi.org/10.1001/jamanetworkopen.2023.36483] Accuracy and Reliability of Chatbot Responses to Physician Questions. JAMA Network Open 2023;6(10):e2336483. Goodman RS, Patrinely JR, Osterman T, Wheless L, Johnson DB. [link: https://doi.org/10.1016/j.medj.2023.02.008] On the cusp: Considering the impact of artificial intelligence language models in healthcare. Med 2023;4(3):139-140. Riaz IB, Khan MA, Osterman TJ. [link: https://doi.org/10.1002/cncr.70050] Artificial intelligence across the cancer care continuum. Cancer 2025. Zhang K, Huang T, Malin BA, Osterman T, Long Q. [link: https://doi.org/10.1038/s43856-025-01116-x] Introducing mCODEGPT as a zero-shot information extraction from clinical free text data tool for cancer research. Communications Medicine 2025. [link: #main" class="back-to-top] Back to top --- ## Research URL: https://osterman.co/research/ Summary: Peer-reviewed publications, abstracts, and patents by Dr. Travis Osterman - the audit trail behind his leadership and expertise in clinical informatics, precision oncology, mCODE, and AI for cancer care. Research . On this page [link: #case-studies] Case studies (3) [link: #peer-reviewed] Peer-reviewed publications (47) [link: #abstracts] Abstracts (28) [link: #patents] Patents (1) Detailed accounts of programs Dr. Osterman has led - what the problem was, what the team did, what shipped, and what comes next. Each case study cites the underlying peer-reviewed work. [link: /case-studies/mcode/] mCODE From an open-source oncology data standard to the only method of submitting data to a federal value-based care program. The story of mCODE's launch, governance, and regulatory uptake - and the parallel story of bringing structured genomic data into the EHR at scale. [link: /case-studies/digital-precision-oncology/] Digital Precision Oncology Five-year strategic research collaboration with GE HealthCare on predicting immune checkpoint inhibitor effectiveness and toxicities from real-world EHR data. PI on the flagship study; four peer-reviewed manuscripts and a patent. [link: /case-studies/ai-in-oncology/] AI in oncology From one of the first peer-reviewed evaluations of ChatGPT in clinical Q&A to a comprehensive review of AI across the cancer care continuum. How clinical AI gets validated, where it actually helps, and why it depends on structured data underneath. Heng Tan, Travis J. Osterman. [link: https://doi.org/10.1200/CCI-25-00350] SmokeBERT and Beyond: Bridging Clinical Narratives and Structured Smoking Data To Improve Lung Cancer Screening. JCO clinical cancer informatics. doi:[link: https://doi.org/10.1200/CCI-25-00350] 10.1200/CCI-25-00350 Shelby A. Crants et al. [link: https://doi.org/10.1016/j.ijrobp.2025.10.006] Clonal Hematopoiesis of Indeterminate Potential After Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics. doi:[link: https://doi.org/10.1016/j.ijrobp.2025.10.006] 10.1016/j.ijrobp.2025.10.006 Kai Zhang, Tongtong Huang, Bradley A. Malin, Travis Osterman, Qi Long, Xiaoqian Jiang. [link: https://doi.org/10.1038/s43856-025-01116-x] Introducing mCODEGPT as a zero-shot information extraction from clinical free text data tool for cancer research. Communications Medicine. doi:[link: https://doi.org/10.1038/s43856-025-01116-x] 10.1038/s43856-025-01116-x Irbaz Bin Riaz, Muhammad Ali Khan, Travis J. Osterman. [link: https://doi.org/10.1002/cncr.70050] Artificial intelligence across the cancer care continuum. Cancer. doi:[link: https://doi.org/10.1002/cncr.70050] 10.1002/cncr.70050 Teri A. Manolio et al. [link: https://doi.org/10.1002/lrh2.70027] Advancing the science of genomic learning healthcare systems. Learning Health Systems. doi:[link: https://doi.org/10.1002/lrh2.70027] 10.1002/lrh2.70027 Elise Russo et al. [link: https://doi.org/10.1055/a-2443-8318] Vanderbilt Clinical Informatics Center Education Strategy: To Infinity and Beyond!. Applied Clinical Informatics. doi:[link: https://doi.org/10.1055/a-2443-8318] 10.1055/a-2443-8318 David S. Smith et al. [link: https://doi.org/10.1200/CCI-24-00198] Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography. JCO Clinical Cancer Informatics. doi:[link: https://doi.org/10.1200/CCI-24-00198] 10.1200/CCI-24-00198 Yanwei Li et al. [link: https://doi.org/10.1200/CCI.23.00249] Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center. JCO Clinical Cancer Informatics. doi:[link: https://doi.org/10.1200/CCI.23.00249] 10.1200/CCI.23.00249 Travis J. Osterman, Jiarong Ye. [link: https://doi.org/10.1002/cncr.35441] The importance of studying the implementation of cancer data standards. Cancer. doi:[link: https://doi.org/10.1002/cncr.35441] 10.1002/cncr.35441 Karen M. Huelsman et al. [link: https://doi.org/10.1200/JCO.2024.42.16_suppl.e13649] Integrating electronic health records (EHRs) to facilitate cancer biomarker testing: Real-world implementation barriers and solutions.. Journal of Clinical Oncology. doi:[link: https://doi.org/10.1200/JCO.2024.42.16_suppl.e13649] 10.1200/JCO.2024.42.16_suppl.e13649 Engineering National Academies of Sciences. [link: https://doi.org/10.17226/27744] Incorporating Integrated Diagnostics into Precision Oncology Care: Proceedings of a Workshop. . doi:[link: https://doi.org/10.17226/27744] 10.17226/27744 Levente Lippenszky et al. [link: https://doi.org/10.1200/CCI.23.00207] Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data. JCO Clinical Cancer Informatics. doi:[link: https://doi.org/10.1200/CCI.23.00207] 10.1200/CCI.23.00207 Waddah Arafat et al. [link: https://doi.org/10.1200/CCI.23.00056] Clinician Perspectives Regarding the Impact of Information Technology on Multidisciplinary Tumor Boards: A National Comprehensive Cancer Network Survey. JCO Clinical Cancer Informatics. doi:[link: https://doi.org/10.1200/CCI.23.00056] 10.1200/CCI.23.00056 Megan M Shuey et al. [link: https://doi.org/10.1093/bioinformatics/btad655] Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics. Bioinformatics. doi:[link: https://doi.org/10.1093/bioinformatics/btad655] 10.1093/bioinformatics/btad655 Rachel S. Goodman et al. [link: https://doi.org/10.1001/jamanetworkopen.2023.36483] Accuracy and Reliability of Chatbot Responses to Physician Questions. JAMA Network Open. doi:[link: https://doi.org/10.1001/jamanetworkopen.2023.36483] 10.1001/jamanetworkopen.2023.36483 Eric M. Lander et al. [link: https://doi.org/10.6004/jnccn.2023.7049] Identification and Characterization of Avoidable Hospital Admissions in Patients With Lung Cancer. Journal of the National Comprehensive Cancer Network. doi:[link: https://doi.org/10.6004/jnccn.2023.7049] 10.6004/jnccn.2023.7049 Travis J. Osterman, James C. Yao, Monika K. Krzyzanowska. [link: https://doi.org/10.1200/EDBK_389880] Implementing Innovation: Informatics-Based Technologies to Improve Care Delivery and Clinical Research. American Society of Clinical Oncology Educational Book. doi:[link: https://doi.org/10.1200/EDBK_389880] 10.1200/EDBK_389880 Protiva Rahman et al. [link: https://doi.org/10.1093/jamiaopen/ooad017] Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records. JAMIA Open. doi:[link: https://doi.org/10.1093/jamiaopen/ooad017] 10.1093/jamiaopen/ooad017 Rachel S. Goodman, J. Randall Patrinely, Travis Osterman, Lee Wheless, Douglas B. Johnson. [link: https://doi.org/10.1016/j.medj.2023.02.008] On the cusp: Considering the impact of artificial intelligence language models in healthcare. Med (New York, N.Y.). doi:[link: https://doi.org/10.1016/j.medj.2023.02.008] 10.1016/j.medj.2023.02.008 Douglas Johnson et al. [link: https://doi.org/10.21203/rs.3.rs-2566942/v1] Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model (under review). . doi:[link: https://doi.org/10.21203/rs.3.rs-2566942/v1] 10.21203/rs.3.rs-2566942/v1 Dara E. Mize, Travis J. Osterman. A Unified Approach to Clinical Informatics Education for Undergraduate and Graduate Medical Education. AMIA ... Annual Symposium proceedings. AMIA Symposium (2022). Emily Pei-Ying Lin et al. [link: https://doi.org/10.1183/23120541.00684-2021] Associations of influenza vaccination with severity of immune-related adverse events in patients with advanced thoracic cancers on immune checkpoint inhibitors. ERJ open research (2022). doi:[link: https://doi.org/10.1183/23120541.00684-2021] 10.1183/23120541.00684-2021 Chloe Weidenbaum, Christopher G. Cann, Sarah Osmundson, Wade T. Iams, Travis Osterman. [link: https://doi.org/10.1016/j.jtocrr.2022.100361] Two Uncomplicated Pregnancies on Alectinib in a Woman With Metastatic ALK-Rearranged NSCLC: A Case Report. JTO Clinical and Research Reports (2022). doi:[link: https://doi.org/10.1016/j.jtocrr.2022.100361] 10.1016/j.jtocrr.2022.100361 Lucy R. Langer, Amye Tevaarwerk, Robin Zon, Travis Osterman. [link: https://doi.org/10.6004/jnccn.2022.5020] The Future of Telemedicine in Oncology. Journal of the National Comprehensive Cancer Network (2022). doi:[link: https://doi.org/10.6004/jnccn.2022.5020] 10.6004/jnccn.2022.5020 National Cancer Policy Forum, Board on Health Care Services, Computer Science and Telecommunications Board, Division on Engineering and Physical Sciences, Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine. [link: http://www.ncbi.nlm.nih.gov/books/NBK586301/] Innovation in Electronic Health Records for Oncology Care, Research, and Surveillance: Proceedings of a Workshop. (2022). Peter D. Stetson et al. [link: https://doi.org/10.6004/jnccn.2021.7088] Adoption of Patient-Generated Health Data in Oncology: A Report From the NCCN EHR Oncology Advisory Group. Journal of the National Comprehensive Cancer Network (2022). doi:[link: https://doi.org/10.6004/jnccn.2021.7088] 10.6004/jnccn.2021.7088 Amye J. Tevaarwerk et al. [link: https://doi.org/10.1200/OP.21.00195] Oncologist Perspectives on Telemedicine for Patients With Cancer: A National Comprehensive Cancer Network Survey. JCO Oncology Practice (2021). doi:[link: https://doi.org/10.1200/OP.21.00195] 10.1200/OP.21.00195 Alex C. Cheng et al. [link: https://doi.org/10.1093/jamiaopen/ooab090] Follow-up Interactive Long-Term Expert Ranking (FILTER): a crowdsourcing platform to adjudicate risk for survivorship care. JAMIA open (2021). doi:[link: https://doi.org/10.1093/jamiaopen/ooab090] 10.1093/jamiaopen/ooab090 Marilyn E. Holt et al. [link: https://doi.org/10.1200/CCI.21.00084] My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase. JCO Clinical Cancer Informatics (2021). doi:[link: https://doi.org/10.1200/CCI.21.00084] 10.1200/CCI.21.00084 Neha M. Jain et al. [link: https://doi.org/10.1002/onco.13936] Framework for Implementing and Tracking a Molecular Tumor Board at a National Cancer Institute–Designated Comprehensive Cancer Center. The Oncologist (2021). doi:[link: https://doi.org/10.1002/onco.13936] 10.1002/onco.13936 Kim L Sandler et al. [link: https://doi.org/10.1177/09691413211013058] Women screened for breast cancer are dying from lung cancer: An opportunity to improve lung cancer screening in a mammography population. Journal of Medical Screening (2021). doi:[link: https://doi.org/10.1177/09691413211013058] 10.1177/09691413211013058 Travis J. Osterman, May Terry, Robert S. Miller. [link: https://doi.org/10.1200/CCI.21.00014] Reply to J. Chen et al. JCO Clinical Cancer Informatics (2021). doi:[link: https://doi.org/10.1200/CCI.21.00014] 10.1200/CCI.21.00014 Neha M. Jain, Alison Culley, Christine M. Micheel, Travis J. Osterman, Mia A. Levy. [link: https://doi.org/10.1200/CCI.20.00142] Learnings From Precision Clinical Trial Matching for Oncology Patients Who Received NGS Testing. JCO Clinical Cancer Informatics (2021). doi:[link: https://doi.org/10.1200/CCI.20.00142] 10.1200/CCI.20.00142 Neil S. Zheng et al. [link: https://doi.org/10.1016/j.jbi.2020.103657] A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19. Journal of Biomedical Informatics (2021). doi:[link: https://doi.org/10.1016/j.jbi.2020.103657] 10.1016/j.jbi.2020.103657 Travis J. Osterman, May Terry, Robert S. Miller. [link: https://doi.org/10.1200/CCI.20.00059] Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative. JCO Clinical Cancer Informatics (2020). doi:[link: https://doi.org/10.1200/CCI.20.00059] 10.1200/CCI.20.00059 Xuanyi Li et al. [link: https://doi.org/10.1038/s41598-020-73466-6] Seven decades of chemotherapy clinical trials: a pan-cancer social network analysis. Scientific Reports (2020). doi:[link: https://doi.org/10.1038/s41598-020-73466-6] 10.1038/s41598-020-73466-6 Jeremy Lyle Warner et al. [link: https://doi.org/10.1200/JCO.2020.38.15_suppl.2060] Trends in FDA cancer registration trial design over time, 1969-2020.. Journal of Clinical Oncology (2020). doi:[link: https://doi.org/10.1200/JCO.2020.38.15_suppl.2060] 10.1200/JCO.2020.38.15_suppl.2060 Neha M. Jain, Alison Culley, Teresa Knoop, Christine Micheel, Travis Osterman, Mia Levy. [link: https://doi.org/10.1200/CCI.19.00033] Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow. JCO Clinical Cancer Informatics (2019). doi:[link: https://doi.org/10.1200/CCI.19.00033] 10.1200/CCI.19.00033 Heidi D. Klepin et al. [link: https://doi.org/10.1200/JCO.2019.37.15_suppl.e18279] Hypertension and use of bevacizumab among patients treated in community settings.. Journal of Clinical Oncology (2019). doi:[link: https://doi.org/10.1200/JCO.2019.37.15_suppl.e18279] 10.1200/JCO.2019.37.15_suppl.e18279 Neha M Jain, Alison Culley, Travis John Osterman, Mia Alyce Levy. [link: https://doi.org/10.1200/JCO.2019.37.15_suppl.e18006] Learnings from a pragmatic study to evaluate benefit of performing reflex clinical trial matching and providing clinical decision support to physicians.. Journal of Clinical Oncology (2019). doi:[link: https://doi.org/10.1200/JCO.2019.37.15_suppl.e18006] 10.1200/JCO.2019.37.15_suppl.e18006 David A. Chambers et al. [link: https://doi.org/10.1200/EDBK_238057] The Impact of Big Data Research on Practice, Policy, and Cancer Care. American Society of Clinical Oncology Educational Book (2019). doi:[link: https://doi.org/10.1200/EDBK_238057] 10.1200/EDBK_238057 Ragisha Gopalakrishnan et al. [link: https://doi.org/10.1200/JCO.2018.36.15_suppl.3053] Impact of the influenza vaccination on cancer patients undergoing therapy with immune checkpoint inhibitors (ICI).. Journal of Clinical Oncology (2018). doi:[link: https://doi.org/10.1200/JCO.2018.36.15_suppl.3053] 10.1200/JCO.2018.36.15_suppl.3053 Lisa Bastarache et al. [link: https://doi.org/10.1126/science.aal4043] Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science (2018). doi:[link: https://doi.org/10.1126/science.aal4043] 10.1126/science.aal4043 Wei-Qi Wei et al. [link: https://doi.org/10.1371/journal.pone.0175508] Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PloS One (2017). doi:[link: https://doi.org/10.1371/journal.pone.0175508] 10.1371/journal.pone.0175508 Mia Alyce Levy, Travis John Osterman, Neha Jain, Kathleen F Mittendorf, Christine Micheel. [link: https://doi.org/10.1200/JCO.2017.35.15_suppl.e18182] Utility of adding clinical data to a molecular results portal for improving clinical trial prescreening efficiency.. Journal of Clinical Oncology (2017). doi:[link: https://doi.org/10.1200/JCO.2017.35.15_suppl.e18182] 10.1200/JCO.2017.35.15_suppl.e18182 Matthew J. Rioth, Travis J. Osterman, Jeremy L. Warner. [link: https://doi.org/10.14694/EdBook_AM.2015.35.e608] Advances in website information resources to aid in clinical practice. American Society of Clinical Oncology educational book / ASCO. American Society of Clinical Oncology. Meeting (2015). doi:[link: https://doi.org/10.14694/EdBook_AM.2015.35.e608] 10.14694/EdBook_AM.2015.35.e608 Junyu Li et al. [link: https://doi.org/10.1016/j.ab.2004.03.058] A software utility for creating interactive maps for 2D gel-based proteomics. Analytical Biochemistry (2004). doi:[link: https://doi.org/10.1016/j.ab.2004.03.058] 10.1016/j.ab.2004.03.058 Sang Minh Nguyen et al. [link: https://doi.org/10.1158/1538-7445.AM2026-LB385] Abstract LB385: Polygenic risk score of genetic variants in genes encoding drug-metabolizing enzymes and drug transporters, in association with febrile neutropenia. Cancer Research. Joseph Vento, Lisa Bastarache, Qingxia M. Chen, Travis Osterman. [link: https://doi.org/10.1200/JCO.2025.43.16_suppl.1553] Real-world side effects of targeted therapies: High-throughput association studies leveraging the CancerLinq Discovery lung cancer database.. Journal of Clinical Oncology. Pablo Napan Molina et al. [link: https://jitc.bmj.com/content/12/Suppl_2/A1369] 1228 Machine learning models can predict efficacy and toxicities using short medical history prior to ICI therapy | Journal for ImmunoTherapy of Cancer. . David Smith et al. [link: https://doi.org/10.1136/jitc-2024-SITC2024.1246] 1246 Prediction of pneumonitis in immunotherapy patients from prior thorax CT. Journal for ImmunoTherapy of Cancer. Pablo Napan Molina et al. [link: https://doi.org/10.1136/jitc-2024-SITC2024.1228] 1228 Machine learning models can predict efficacy and toxicities using short medical history prior to ICI therapy. Journal for ImmunoTherapy of Cancer. Karen M. Huelsman et al. [link: https://doi.org/10.1200/JCO.2024.42.16_suppl.e13649] Integrating electronic health records (EHRs) to facilitate cancer biomarker testing: Real-world implementation barriers and solutions.. Journal of Clinical Oncology. Zoltan Kiss et al. [link: https://doi.org/10.1136/jitc-2023-SITC2023.1294] 1294 External validation of machine learning models to predict efficacy and toxicity of immune checkpoint inhibitors using real-world pan cancer cohorts. Journal for ImmunoTherapy of Cancer. Levente Lippenszky et al. [link: https://doi.org/10.1136/jitc-2023-SITC2023.1300] 1300 Prediction of efficacy and toxicities of immune checkpoint inhibitors using real-world patient data. Journal for ImmunoTherapy of Cancer. Joseph Vento, Travis Osterman. [link: https://doi.org/10.6004/jnccn.2022.7165] BIO23-019: Precision Oncology: Integrating Structured Genomic Data Into the Electronic Health Record via the EPIC® Genomics Module. Journal of the National Comprehensive Cancer Network. Waddah Arafat et al. [link: https://doi.org/10.1200/JCO.2022.40.28_suppl.046] Oncologist perspectives on tumor boards: Virtual meetings and EHR integration.. Journal of Clinical Oncology (2022). Alex C. Cheng et al. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e13568] Oncologist participation in pilot testing a crowdsourcing platform to build a survivorship care risk model.. Journal of Clinical Oncology (2022). Eszter Csernai et al. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e13565] Rolling window-based hepatitis toxicity prediction from routine bloodwork in patients undergoing immune checkpoint inhibitor therapy.. Journal of Clinical Oncology (2022). Gergely Horváth et al. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e13564] Predicting immune checkpoint inhibitor-related hepatitis using electronic health records of patients.. Journal of Clinical Oncology (2022). Levente Lippenszky et al. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e13566] Predicting immune checkpoint inhibitor-related pneumonitis using patient medical information.. Journal of Clinical Oncology (2022). Eric Michael Lander et al. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e21133] Characterization of avoidable hospital admissions in patients with lung cancer in the immunotherapy and targeted therapy era.. Journal of Clinical Oncology (2022). Neha M Jain, Emma Schremp, Lucy Spalluto, Travis John Osterman, Debra L. Friedman. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e18662] Incorporating mediation-based interventions at an academic cancer center: A six-step process.. Journal of Clinical Oncology (2022). Neha M Jain, Philip Edward Lammers, Michael R. Savona, Travis John Osterman, Salil Goorha. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e18586] Using a standard implementation science framework to improve clinical trial enrollment for a community Tennessee oncology center.. Journal of Clinical Oncology (2022). Kathleen F. Mittendorf et al. [link: https://doi.org/10.1200/JCO.2022.40.16_suppl.e13581] Overcoming barriers in academic-industry partnerships to improve predictive modeling in immuno-oncology.. Journal of Clinical Oncology (2022). Brian Yoon, Dilhan Weeraratne, Yull Edwin Arriaga, Hu Huang, Travis John Osterman. [link: https://ascopubs.org/doi/10.1200/JCO.2020.39.28_suppl.113] Evaluating health disparities in access to genomic testing for metastatic non-small cell lung cancer patients. | Journal of Clinical Oncology. (2021). Amye Tevaarwerk et al. [link: https://doi.org/10.6004/jnccn.2020.7728] BIO21-011: Oncology Provider Perspectives on Telemedicine for Patients With Cancer: A National Comprehensive Cancer Network (NCCN®) Survey. Journal of the National Comprehensive Cancer Network (2021). Taneya Y. Koonce et al. [link: https://knowledge.amia.org/72332-amia-1.4602255/t005-1.4604904/t005-1.4604905/3408928-1.4605440/3408928-1.4605441?timeStamp=1614783505714] The Personalization of Evidence: Using Intelligent Datasets to Inform the Process. Amia Annual Symposium (2020). Jeremy Lyle Warner et al. [link: https://doi.org/10.1200/JCO.2020.38.15_suppl.2060] Trends in FDA cancer registration trial design over time, 1969-2020.. Journal of Clinical Oncology (2020). Riqiang Gao et al. [link: https://doi.org/10.1117/12.2512965] Lung cancer detection using co-learning from chest CT images and clinical demographics. (2019). Carolyn C. Scott, Alexis B. Paulson, Travis J. Osterman, Kim L. Sandler. [link: https://doi.org/10.1164/ajrccm-conference.2017.195.1_MeetingAbstracts.A5189] Lung and Breast Screening Practices in Women: Targeting Providers to Improve Enrollment in a Clinical Lung Screening Program. C30. LUNG CANCER SCREENING: WHO, WHY, WHERE, AND HOW MUCH (2017). Travis J. Osterman. EHR-Wide GxE Study using Smoking Information Extracted from Clinical Notes. (2016). Travis J. Osterman, Wei-Qi Wei, Dara Mize, Joshua C. Denny. Using a gene-environment interaction study to evaluate risk for lung cancer (abstract 1524). American Society of Clinical Oncology Annual Meeting (2016). Travis J. Osterman, Wei-Qi Wei, Joshua C. Denny. Quantifying Tobacco Exposure using clinical Notes and Natural language processing to enable lung cancer screening. International Association for the Study of Lung Cancer Targeted Therapeutics Meeting (poster) (2016). Travis J. Osterman. Quantifying Tobacco Exposure Using Clinical Notes and Natural Language Processing to Enable Lung Cancer Screening. (2015). Jan Wolber, Eszter Katalin Csernai, Zoltán Kiss, Levente Lippenszky, Gergely Horváth, Travis Osterman, Ben Ho Park, David Samuel Smith, Daniel Fabbri, Michele LeNoue-Newton, Kathleen Mittendorf. [link: https://patents.google.com/patent/US20250292904A1/en] Model generation apparatus for therapeutic prediction and associated methods and models (2025). [link: #main" class="back-to-top] Back to top --- ## Expertise URL: https://osterman.co/expertise/ Summary: Six domains of expertise for Dr. Travis Osterman - cancer data standards (mCODE™), clinical genomics in the EHR, AI in oncology, precision oncology implementation, clinical informatics education, and lung cancer screening. Expertise Dr. Osterman chairs the executive committee of the [link: https://build.fhir.org/ig/HL7/fhir-mCODE-ig/] minimal Common Oncology Data Elements (mCODE™) standard. mCODE is the FHIR-based data model that defines a minimum interoperable record for cancer care. It is implemented at more than 70 institutions across six countries, and is the only method of submitting data to the CMS Enhancing Oncology Model - making mCODE the regulatory bridge between EHR-resident cancer data and federal value-based care programs. Under Dr. Osterman's leadership, Vanderbilt Health has more structured genomic data in its electronic health record than any other institution in the United States. The work spans the Epic Genomics Module rollout, the Epic AURA reference-lab integration, and the governance + partnership framework that supports research collaborations with Tempus AI, nference, GE HealthCare, and Microsoft on top of that structured-data foundation. Multi-year program building machine-learning models that predict immune checkpoint inhibitor effectiveness and toxicities from routine real-world patient data. PI on the GE HealthCare Digital Precision Oncology study ([link: https://doi.org/10.1200/CCI.23.00207] JCO CCI 2024); co-authored work on radiomic / CT-based pneumonitis prediction; ongoing models for ICI-induced hepatitis and colitis prediction. Dr. Osterman led the Vanderbilt-Ingram Cancer Center's clinical implementation of structured molecular results into point-of-care decision support - including the OKRA (Oncology Knowledge Rapid Alerts) NCI R21 grant, integration of the MyCancerGenome knowledge base into the EHR, and the molecular tumor board governance framework. The work spans trial matching, biomarker-driven therapy selection, and structured staging at scale. Creator and Director of the Clinical Informatics Integrated Science Curriculum for medical students at Vanderbilt and the Graduate Medical Education Clinical Informatics Elective Rotation - both 2020 to present. With Dr. Dara E. Mize, Dr. Osterman designed a [link: https://pubmed.ncbi.nlm.nih.gov/37128448/] unified approach to clinical informatics education spanning undergraduate and graduate medical education (Mize & Osterman, AMIA Annu Symp Proc, 2022) - one curricular spine that meets learners at each stage of training rather than treating the two programs as separate silos. The model is now part of the Vanderbilt Clinical Informatics Center's broader education strategy ([link: https://doi.org/10.1055/a-2443-8318] Russo, McCoy, Mize, Osterman et al., Appl Clin Inform, 2025). National educational work runs through ASCO's annual meeting program committee and AMIA. Author of SHAPES (Smoking History and Pack-Year Extraction System), a natural-language-processing pipeline that extracts granular smoking history from unstructured clinical notes to enable lung cancer screening eligibility determination at scale. The work won the 2016 Conquer Cancer Foundation Young Investigator Award and continues to power downstream lung-cancer-screening implementation research at Vanderbilt-Ingram. --- ## Leadership URL: https://osterman.co/leadership/ Summary: Programs, standards, and governance roles Travis Osterman leads - including ASCO mCODE™, Vanderbilt Health Research Informatics, the Epic Beacon Community Operations Group, and the Vanderbilt-Ingram Cancer Center Data Science Shared Resource. Leadership Standards Chair, [link: https://build.fhir.org/ig/HL7/fhir-mCODE-ig/] minimal Common Oncology Data Elements (mCODE™) Executive Committee - Implemented at more than 70 institutions across six countries. Only method of submitting data to CMS' Enhancing Oncology Model. Founder, Epic Beacon Community Operations Group (BCOG) - multi-institution governance for the dominant US oncology EHR module. Programs Associate Vice President for Research Informatics, Vanderbilt Health - organizing and exposing data across hundreds of systems and projects so that research can move at clinical speed. Director, Cancer Clinical Informatics, Vanderbilt-Ingram Cancer Center - under his leadership, Vanderbilt Health has more structured genomic data in its EHR than any other institution in the United States. Co-Leader, Data Science Shared Resource (DSSR), Vanderbilt-Ingram Cancer Center - biostatistics, data queries, bioinformatics, and clinical informatics services for Cancer Center investigators under the $37.9M NIH/NCI Cancer Center Support Grant. Governance and advisory Microsoft Azure Research Community Advisory Board (AMC CAB) NCCN Digital Oncology Forum Epic Adult Oncology Steering Committee --- ## Press URL: https://osterman.co/press/ Summary: Press kit and press coverage for Dr. Travis Osterman - bios, headshots, key facts, frequently covered topics, and external coverage in Fox News, ASCO Daily News, JNCCN, Oncodaily, TechTarget, Microsoft Customer Stories, and others. Press On this page [link: #bios] Bios [link: #headshots] Headshots [link: #key-facts] Key facts [link: #frequent-topics] Topics [link: #now] Now [link: #in-the-news] In the news (23) [link: #press-contact] Press contact Short Dr. Travis Osterman is a practicing medical oncologist and informatician at Vanderbilt University Medical Center, where he serves as Associate Vice President for Research Informatics. He chairs the minimal Common Oncology Data Elements (mCODE™) Executive Committee, an international cancer data standard implemented at more than 70 institutions across six countries and the only method of submitting data to CMS' Enhancing Oncology Model. He is board certified in internal medicine, medical oncology, and clinical informatics. Medium Dr. Travis Osterman is a practicing medical oncologist and informatician, Associate Vice President for Research Informatics at Vanderbilt University Medical Center, and Director of Cancer Clinical Informatics at the Vanderbilt-Ingram Cancer Center. He is board certified in internal medicine, medical oncology, and clinical informatics. At Vanderbilt, Dr. Osterman leads the Clinical Genomics Workstream and has led the effort to make structured genomic data more accessible for patient care and research; under his leadership, Vanderbilt's electronic health record contains more structured genomic data than any other institution in the United States. Nationally, Dr. Osterman chairs the minimal Common Oncology Data Elements (mCODE™) Executive Committee. mCODE is implemented at more than 70 institutions across six countries and serves as the only method of submitting data to CMS' Enhancing Oncology Model. He advises Microsoft, Epic, Tempus AI, and the National Comprehensive Cancer Network, and founded the Epic Beacon Community Operations Group. Long The full bio, including origin and career arc, lives at [link: /about/] /about/. For a downloadable version, request via the [link: /contact/] contact form. All headshots are cleared for editorial use. Right-click and "Save image as" to download, or use the direct links. [link: /assets/img/osterman-headshot.jpg] Square portrait 800 × 800 JPG [link: /assets/img/osterman-headshot.jpg] Download (800px) · [link: /assets/img/osterman-headshot-400.jpg] Download (400px) [link: /assets/img/og-card.jpg] Social card 1200 × 630 JPG [link: /assets/img/og-card.jpg] Download Name & post-nominals: Travis Osterman, DO, MS, FAMIA, FASCO Roles: Associate Vice President for Research Informatics, Vanderbilt University Medical Center; Director of Cancer Clinical Informatics, Vanderbilt-Ingram Cancer Center; Chair, ASCO mCODE Executive Committee Board certifications: Internal Medicine (ABIM, 2015); Medical Oncology (ABIM, 2016); Clinical Informatics (ABPM, 2016) Fellowships: American Society of Clinical Oncology (FASCO, 2023); American Medical Informatics Association (FAMIA, 2019) mCODE scale: more than 70 institutions across six countries; only method of submitting data to CMS' Enhancing Oncology Model Vanderbilt genomic data: more structured genomic data in the electronic health record than any other institution in the United States Disclosures: [link: https://coi.asco.org/share/JAR-DHX7/Travis%20Osterman] ASCO COI; [link: https://openpaymentsdata.cms.gov/physician/1291957] CMS Open Payments The mCODE oncology data standard and its role in CMS' Enhancing Oncology Model Structured genomic data integration into the electronic health record Artificial intelligence in oncology - efficacy and toxicity prediction Interoperability and EHR governance for cancer care Clinical informatics education for medical students and residents Lung cancer screening and real-world data Recent activity across publications, invited talks, scientific meetings, and press - auto-generated from the same source-of-truth library that drives [link: /research/] /research/ and [link: /speaking/] /speaking/. Last updated: . HL7 International Working Group Meeting (Europe): "mCODE Overview and Current State". Vanderbilt Lecture Series CME (Nashville, Tennessee): "Using AI in Clinical Practice: Current Trends and Emerging Federal Regulations". [link: https://doi.org/10.1158/1538-7445.AM2026-LB385] Abstract LB385: Polygenic risk score of genetic variants in genes encoding drug-metabolizing enzymes and drug transporters, in association with febrile neutropenia. Cancer Research. 2026 NCCN Annual Conference (Orlando, Florida): "Harnessing Artificial Intelligence to Improve Oncology Care". NASEM Workshop: Policy Issues for Integrating Artificial Intelligence in Cancer Research and Care (Washington, DC): "AI in Cancer Care: 2 Wins, 2 Current Challenges". Jackson-Madison County General Hospital (Jackson, Tennessee): "An Update on the Use of AI in Clinical Practice". Blanchfield Army Community Hospital (Fort Campbell, Kentucky (virtual)): "Using AI in Clinical Practice: Current Trends and Emerging Federal Regulations". [link: https://pmc.ncbi.nlm.nih.gov/articles/PMC12782282/] SmokeBERT and Beyond: Bridging Clinical Narratives and Structured Smoking Data To Improve Lung Cancer Screening. JCO clinical cancer informatics. "Leveraging Data to Day-to-Day Improvements". [link: https://www.sciencedirect.com/science/article/pii/S0360301625063862] Clonal Hematopoiesis of Indeterminate Potential After Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics. [link: https://www.nature.com/articles/s43856-025-01116-x] Introducing mCODEGPT as a zero-shot information extraction from clinical free text data tool for cancer research. Communications Medicine. [link: https://patents.google.com/patent/US20250292904A1/en] Model generation apparatus for therapeutic prediction and associated methods and models. The Evolving Artificial Intelligence Landscape in Cancer Care: "AI and the Cancer Journey: Navigating New Frontiers in Policy and Technology". [link: https://doi.org/10.1002/cncr.70050] Artificial intelligence across the cancer care continuum. Cancer. [link: https://onlinelibrary.wiley.com/doi/abs/10.1002/lrh2.70027] Advancing the science of genomic learning healthcare systems. Learning Health Systems. NCCN Annual Congress (Virtual): "Improving Clinical Trial Accrual: Doing More with Less". [link: https://ascopubs.org/doi/abs/10.1200/JCO.2025.43.16_suppl.1553] Real-world side effects of targeted therapies: High-throughput association studies leveraging the CancerLinq Discovery lung cancer database.. Journal of Clinical Oncology. . [link: https://www.foxnews.com/health/ai-could-predict-whether-cancer-treatments-will-work-experts-exciting-time-medicine] AI could predict whether cancer treatments will work, experts say | Fox News . [link: https://www.techtarget.com/searchhealthit/answer/How-mCODE-is-Driving-EHR-Interoperability-for-Cancer-Research] How mCODE is Driving EHR Interoperability for Cancer Research | TechTarget · Health IT and EHR. [link: https://www.precisionmedicineonline.com/precision-oncology/ge-healthcare-vanderbilt-explore-use-ai-predict-immunotherapy-toxicity-efficacy] GE Healthcare, Vanderbilt Explore Use of AI to Predict Immunotherapy Toxicity, Efficacy · Precision Medicine Online. [link: https://news.vumc.org/2023/06/21/three-vanderbilt-physicians-named-asco-fellows/] Three Vanderbilt physicians named ASCO fellows . [link: https://discoveries.vanderbilthealth.com/2023/02/genomic-data-advances-precision-oncology/] Discoveries in Medicine - Genomic Data Advances Precision Oncology · Discoveries in Medicine. [link: https://www.hcinnovationgroup.com/clinical-it/learning-health-systems-research/news/53012978/vanderbiltingram-cancer-center-participating-in-ascos-cancerlinq] Vanderbilt-Ingram Cancer Center Participating in ASCO’s CancerLinQ · Healthcare Innovation. [link: https://www.hcinnovationgroup.com/clinical-it/genomics-precision-medicine/article/21279492/vanderbilt-sees-downstream-benefits-from-integrating-genomic-results-into-ehr] Vanderbilt Sees Downstream Benefits From Integrating Genomic Results Into EHR · Healthcare Innovation. [link: https://news.vumc.org/2022/08/02/targeted-cancer-drug-pregnancy/] Targeted cancer drug during pregnancy · Vanderbilt University. [link: https://customers.microsoft.com/en-us/story/1394703347021361603-dr-travis-osterman-microsoft-investigator-fellow-higher-education-azure-en-united-states] Microsoft Investigator Fellow Dr. Travis Osterman uses Azure to support lung cancer treatment protocols · Microsoft Customers Stories. [link: https://t.e2ma.net/message/liyyaf/12cgjjq] DBMI Digest, Volume 1 . [link: https://www.hcinnovationgroup.com/clinical-it/genomics-precision-medicine/news/21212890/vanderbilt-preparing-to-implement-epic-genomics-module] Vanderbilt Preparing to Implement Epic Genomics Module · Healthcare Innovation. [link: https://momentum.vicc.org/2021/03/molecular-automation/] Molecular Automation · VICC Momentum. [link: https://news.vumc.org/2021/02/04/process-ensures-follow-up-of-incidental-radiology-findings/] Process ensures follow-up of incidental radiology findings · VUMC News. [link: https://news.vumc.org/2019/11/07/vumcs-osterman-awarded-microsoft-investigator-fellowship/] VUMC’s Osterman awarded Microsoft Investigator Fellowship · VUMC News. [link: https://dailynews.ascopubs.org/do/10.1200/ADN.19.190192/full/] CancerLinQ®: Current Achievements and Future Opportunities · ASCO Daily News. [link: http://lifesciences.ge/Ibfm50pDpJK] The immunotherapy hurdle – and why doctors could soon predict how each patient will respond . "ASCO Annual Meeting Trainee & Early-Career Oncologist Member Lounge Preview" · ASCO Daily News. [link: https://www.vumc.org/radiology/news-announcements/sandler-osterman-awarded-50000-vicc-ambassadors-grant] Sandler, Osterman Awarded $50,000 VICC Ambassadors Grant | Department of Radiology · VUMC Reporter. [link: https://www.dotmed.com/news/story/45816] GE and VUMC partner to make cancer immunotherapy safer and more precise · dotmed.com. [link: http://news.vumc.org/2018/11/15/one-year-after-estar-go-live/] One year after Go Live, focus remains on advancing eStar · VUMC Reporter. "How to Improve Your Practice Using ASCO University® Resources" · ASCO Daily News. [link: https://dailynews.ascopubs.org/doi/10.1200/ADN.24.201700] Machine Learning Model Could Help Predict Risk-Benefit of Immune Checkpoint Inhibitors · ASCO Daily News. [link: https://dailynews.ascopubs.org/doi/10.1200/ADN.24.0318] Podcast: New Machine Learning Framework Uses EHR Data to Assess ICI Effectiveness, Toxicity · ASCO Daily News. Interview requests, quotes, and other press inquiries: please use the [link: /contact/] contact form with subject line beginning "Press". For event organizers and speaking inquiries, see [link: /speaking/] /speaking/ and [link: https://wsor.net] WSOR, LLC. [link: #main" class="back-to-top] Back to top --- ## About URL: https://osterman.co/about/ Summary: Travis Osterman, DO, MS, FAMIA, FASCO - practicing medical oncologist, Associate Vice President for Research Informatics at Vanderbilt Health, Director of Cancer Clinical Informatics at Vanderbilt-Ingram Cancer Center, and Chair of the mCODE™ Executive Committee. About Origin A computer science course Travis Osterman never planned to take is the reason this site exists. In the fall of 1999, his undergraduate advisor at the University of Indianapolis signed him up for CS 100. He hadn't asked to take it. He took it anyway, and enjoyed it, and signed up for the next one. He kept doing that until he had a second major. By 2003 he graduated magna cum laude in both Biology and Computer Science - not because he had set out to combine the two, but because parallel curiosity took him there one course at a time. That pattern - follow the interesting problem until it becomes a discipline - is the through-line of the next two decades. After medical school at Nova Southeastern College of Osteopathic Medicine (where he served as class president each of four years) and internal medicine residency at Indiana University (chief resident at Wishard Memorial), Travis came to Vanderbilt for oncology fellowship in 2013. Vanderbilt was deliberate. It was a rare opportunity: train at an institution with an exceptional reputation in both medical oncology and biomedical informatics. He came planning to add a Master's in Biomedical Informatics on top of the fellowship. It was the right bet. The fellowship finished in 2016; the MS in 2017. The fellowship made him an oncologist; the MS gave formal vocabulary to the work he had been quietly doing since CS 100 in '99. The pivot from "oncologist who also codes" to chairing an international cancer data standard wasn't a leap. It was a network compounding. By 2017 he was on the faculty at Vanderbilt-Ingram Cancer Center and active across multiple ASCO committees. Mentors helped him route into the broader cancer informatics national landscape. When the original mCODE working group was being assembled, he got the invitation and said yes immediately. He has been in the room ever since - eventually as chair of the executive committee that now stewards the standard implemented at more than seventy institutions across six countries, and that serves as the only method of submitting data to CMS' Enhancing Oncology Model. The role of "physician executive" was a slower realization. The work changed before the title did - a long journey from individual contributor, to managing a team, to becoming the person people look to for direction. The transitions came as the questions got bigger. How do you implement structured genomic data into the EHR for tens of thousands of patients? How do you align Epic, Microsoft, Tempus AI, GE HealthCare, and NCCN around a shared model for cancer data when each company has its own infrastructure? Those aren't problems an individual contributor solves. He grew into the role the work required. What he actually enjoys, in his own words, is fixing things and solving problems. Specifically: building better systems around automation and standardization that improve workflows in both clinical care and research. The reason mCODE matters to him isn't the standards politics - it's that mCODE is the systems improvement. Fewer custom mappings between institutions. Fewer one-off integrations. Less friction between the data clinicians enter and the data researchers need. A standard is a force multiplier for everyone downstream. The same instinct shows up outside the day job. He started a Linux configuration documentation site called gentoovps.net in 2011, during his internal medicine residency, and that site has evolved into today's [link: https://fld.sh] fld.sh. He runs his own infrastructure-as-code, his own self-hosted journal, his own multi-agent orchestrator. He built [link: https://school.osterman.co] school.osterman.co - a self-paced STEM platform with more than 400 courses - so his children would have something interesting to do when the school year ended. The same person who chairs mCODE for cancer-data interoperability has been writing software for himself, his family, and his curiosity since college. It is the same instinct, applied at every scale. Find something that doesn't work as well as it should. Build the system that fixes it. Repeat. Practice and leadership Under his leadership at Vanderbilt-Ingram, the Vanderbilt Health electronic health record contains more structured genomic data than any other institution in the United States. Nationally, the mCODE™ standard is implemented at more than 70 institutions across six countries and serves as the only method of submitting data to CMS' Enhancing Oncology Model. Leadership Mid-Career Leadership Development Program - Vanderbilt University School of Medicine, 2020 Chief Resident - Department of Internal Medicine, Wishard Memorial Hospital, Indiana University, 2012–2013 Class President - Nova Southeastern College of Osteopathic Medicine, 2004–2009 Student Body President - University of Indianapolis, 2001–2002 Boards and credentials Board certified in Medical Oncology - American Board of Internal Medicine (2016) Board certified in Clinical Informatics - American Board of Preventive Medicine (2016) Fellow, American Society of Clinical Oncology (FASCO), 2023 Fellow, American Medical Informatics Association (FAMIA), 2019 Training MS, Biomedical Informatics - Vanderbilt University, 2017 Fellowship, Medical Oncology - Vanderbilt University Medical Center, 2013–2016 Internship and Residency, Internal Medicine - Indiana University, 2009–2012 (Chief Resident, 2012) DO - Nova Southeastern College of Osteopathic Medicine, 2009 BS, Computer Science and Biology, magna cum laude - University of Indianapolis, 2003 Selected honors Fellow, American Society of Clinical Oncology (FASCO), 2023 Microsoft Investigator Fellowship, 2020 Fellow, American Medical Informatics Association (FAMIA), 2019 Conquer Cancer Foundation Young Investigator Award, 2016 DO Student of the Year - Nova Southeastern College of Osteopathic Medicine, 2008 Outstanding Student in Computer Science - University of Indianapolis, 2000, 2001, 2002 Profiles --- ## mCODE: from a data standard to regulatory infrastructure URL: https://osterman.co/case-studies/mcode/ Summary: Case study of the minimal Common Oncology Data Elements (mCODE) standard - its launch, governance, scale to more than 70 institutions across six countries, and its role as the only method of submitting data to CMS' Enhancing Oncology Model. mCODE: from a data standard to regulatory infrastructure Cancer care generates enormous quantities of data and almost none of it travels well. A patient diagnosed at one institution and treated at another arrives with PDFs, faxes, and free-text notes. Pathology reports, molecular profiles, treatment histories, and outcome measures are entered the same way by every clinician and stored differently by every system. The result is what Dr. Osterman, May Terry, and Robert Miller described in JCO Clinical Cancer Informatics in 2020: a field where every institution rebuilds the same custom data mappings, and where research, quality reporting, and clinical trial matching all pay the cost ([link: https://doi.org/10.1200/CCI.20.00059] Osterman, Terry & Miller, 2020). The minimal Common Oncology Data Elements (mCODE™) is an open-source, non-proprietary data model built on top of HL7 FHIR resources. It defines the minimum interoperable record for cancer care: patient demographics, cancer diagnosis, disease characterization, treatments, clinical findings, tumor genomics, and outcomes. It was released at the 2019 American Society of Clinical Oncology Annual Meeting and featured in the ASCO Presidential Address that year. mCODE in the FHIR ecosystem: one of several HL7 Accelerator projects that define semantic-interoperability profiles on top of base FHIR resources. Source: 2026 HL7 International Working Group Meeting deck. mCODE STU 4: six top-level domains (Disease, Treatment, Outcome, Patient, Genomics, Assessment) and the full profile graph beneath. Source: 2026 HL7 International Working Group Meeting deck. Governance is shared. The mCODE Executive Committee includes representation from the American Society of Clinical Oncology, the American Society for Radiation Oncology, the Food and Drug Administration, the National Cancer Institute, and the Alliance for Clinical Trials. Dr. Osterman was appointed Chair of the mCODE Technology Review Group in January 2021 - the body that oversees additions and changes to the standard - and now serves as Chair of the Executive Committee. As of this writing mCODE is implemented at more than seventy institutions across six countries, including Duke, Dana-Farber Cancer Institute, MD Anderson, The Ohio State University, the University of Michigan, the University of Pennsylvania, and the Mayo Clinic, with international implementations in Taiwan, Brazil, and Canada. The community has contributed more than two hundred public comments through the HL7 process. That trajectory matters less as a count and more as a signal: mCODE has graduated from "promising standard" to "the assumed data model" for a significant fraction of US cancer-data infrastructure. One area resisted standardization the longest: clinical genomics. Most academic medical centers continue to receive genomic reports as unstructured PDFs or faxed paper - which means molecular results sit outside the EHR's structured data, invisible to decision support, invisible to clinical trial matching, and invisible to outcomes research. By 2019 only one US institution had integrated genomic results from a reference laboratory directly into Epic. Vanderbilt was the eighth. Under Dr. Osterman's leadership of the Clinical Genomics Workstream, Vanderbilt-Ingram Cancer Center integrated structured genomic results into the electronic health record and grew the corpus rapidly. By the end of 2021 there were 12,000 tumor genomic reports in the Vanderbilt EHR. Today Vanderbilt holds more structured genomic data in its electronic health record than any other institution in the United States. The implementation work was profiled in [link: https://www.healthcareinnovationgroup.com/clinical-it/health-it/news/21214117/vanderbilt-preparing-to-implement-epic-genomics-module] Healthcare Innovation in 2021 and again in Discoveries in Medicine in early 2023. Implementation was a teaching exercise as much as an engineering one. A team of undergraduate Vanderbilt computer science students built the mCODE-on-Azure integration starting in the summer of 2022, surfacing the structured genomic data through a FHIR API that downstream applications - trial matching, targeted-therapy alerting, integration with [link: https://www.mycancergenome.org/] MyCancerGenome - can call. The full pilot is documented in [link: https://doi.org/10.1200/CCI.23.00249] Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center (Li et al., JCO Clinical Cancer Informatics, 2024), with Dr. Osterman as senior author and the undergraduate engineering lead Yanwei Li as first author. An earlier NCCN abstract (Vento and Osterman, [link: https://doi.org/10.6004/jnccn.2022.7165] BIO23-019) documents the upstream workflow for getting reference-laboratory genomics into the EHR in the first place. mCODE's significance changed shape in 2023 when the Centers for Medicare and Medicaid Services launched the Enhancing Oncology Model (EOM), a voluntary value-based care program for medical oncology practices. CMS specified that data submissions to the EOM would happen via mCODE - and only via mCODE. Overnight the standard moved from "useful interoperability layer" to "regulatory infrastructure." Any practice participating in EOM must produce mCODE-shaped data; any vendor serving those practices must emit it. That regulatory hook is the reason mCODE adoption is now self-reinforcing. The structured-data foundation makes downstream AI tractable. In late 2025 Dr. Osterman and collaborators published mCODEGPT in Communications Medicine - a zero-shot information-extraction approach that uses large language models to lift mCODE-conformant elements out of clinical free text ([link: https://doi.org/10.1038/s43856-025-01116-x] Zhang et al., 2025). The framing is deliberate. AI doesn't replace the standard; it sits on top of it. When the target schema is mCODE, an LLM has something concrete to aim at, and downstream applications can trust the output. mCODE is the systems improvement Dr. Osterman wanted from the beginning - fewer custom mappings between institutions, fewer one-off integrations, less friction between the data clinicians enter and the data researchers need. A standard is a force multiplier. CMS noticed; the international community noticed; the AI work that now sits on top of the standard noticed. The next decade of cancer informatics depends on building similar standards in the places mCODE doesn't yet reach. Osterman TJ, Terry M, Miller RS. [link: https://doi.org/10.1200/CCI.20.00059] Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative. JCO Clinical Cancer Informatics 2020;4:993-1001. Li Y, Ye J, Huang Y, Wu J, Liu X, Ahmed S, Osterman T. [link: https://doi.org/10.1200/CCI.23.00249] Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center. JCO Clinical Cancer Informatics 2024. Vento J, Osterman TJ. [link: https://doi.org/10.6004/jnccn.2022.7165] BIO23-019: Precision Oncology: Integrating Structured Genomic Data Into the Electronic Health Record. Journal of the National Comprehensive Cancer Network 2023. Zhang K, Huang T, Malin BA, Osterman T, Long Q. [link: https://doi.org/10.1038/s43856-025-01116-x] Introducing mCODEGPT as a zero-shot information extraction from clinical free text data tool for cancer research. Communications Medicine 2025. [link: #main" class="back-to-top] Back to top --- ## Corporate Collaborations URL: https://osterman.co/collaborations/ Summary: Dr. Travis Osterman's industry partnerships - GE HealthCare, Epic, Microsoft, Tempus AI, nference, and NCCN. With public ASCO COI and CMS Open Payments disclosures. Advisory engagements managed through WSOR, LLC. Corporate Collaborations GE HealthCare Dr. Osterman led the GE HealthCare partnership and served as PI on the flagship Digital Precision Oncology study, applying machine learning to predict immune checkpoint inhibitor effectiveness and toxicity. The multi-year collaboration spanned oncology, radiochemistry, and imaging - producing 4 peer-reviewed manuscripts, 8 published abstracts, and 12 conference presentations. Flagship result: [link: https://doi.org/10.1200/CCI.23.00207] Lippenszky et al., JCO CCI 2024. Epic Epic is the EHR platform anchoring Dr. Osterman's clinical informatics work. He serves on the Adult Oncology Steering Committee, founded the Beacon Community Operations Group, and is PI on the AI-extracted oncology staging workflow built into Epic Hyperspace. Featured speaker at Epic's User Group (UGM) and Expert Group (XGM) meetings on community governance, genomic data backloading, and in-basket workflow. Microsoft Selected as a [link: https://www.microsoft.com/en-us/research/academic-program/microsoft-investigator-fellowship/fellowship-recipients/] Microsoft Investigator Fellow (2020) for work leveraging Azure-based virtualization to scale clinical informatics education across undergraduate and graduate medical training - a two-year, $200,000 grant as PI. Continues to serve on the Microsoft Azure Research Community Advisory Board (AMC CAB) on structured genomic data, FHIR-based research infrastructure, and the [link: https://build.fhir.org/ig/HL7/fhir-mCODE-ig/] minimal Common Oncology Data Elements (mCODE™) data standard. Tempus AI Multi-year collaboration with Tempus AI on three workstreams: structured electronic integration of genomic results into clinical workflows, digital pathology, and multi-omic discovery infrastructure. The genomic-results integration was [link: https://news.vumc.org/2024/11/11/vumc-pursues-electronic-integration-with-genomic-reference-labs/] profiled by VUMC News in November 2024 as part of a broader effort to make reference-laboratory data first-class within the electronic health record. nference Dr. Osterman leads Vanderbilt Health's partnership with nference to reduce the friction of accessing clinical data for research while preserving privacy and regulatory compliance. The collaboration was [link: https://news.vumc.org/2023/07/25/nference-and-vanderbilt-university-medical-center-sign-agreement-to-advance-real-world-evidence-generation-in-complex-disease-populations/] formalized in a 2023 agreement to advance real-world evidence generation in complex disease populations. NCCN Member of the NCCN Digital Oncology Forum and an active voice across NCCN policy and clinical channels. Featured speaker at the [link: https://www.cancernursingtoday.com/post/harnessing-ai-in-oncology-nursing-and-beyond-travis-osterman-do-ms-discusses-his-presentation-at-the-2026-nccn-annual-conference] 2026 NCCN Annual Conference on AI's role in cancer care - with the framing of regulation, integration, and clinical impact published in [link: https://jnccn.org/view/journals/jnccn/23/11/article-pxxi.xml] JNCCN and featured in [link: https://oncodaily.com/voices/travis-osterman-366641] Oncodaily. Disclosures These collaborations are publicly disclosed through two independent records. The CMS Open Payments record is required of every U.S. physician under the Physician Payments Sunshine Act; the ASCO COI record is voluntary. ASCO COI disclosure - [link: https://coi.asco.org/share/JAR-DHX7/Travis%20Osterman] coi.asco.org/share/JAR-DHX7/Travis Osterman CMS Open Payments (Sunshine Act) - [link: https://openpaymentsdata.cms.gov/physician/1291957] openpaymentsdata.cms.gov/physician/1291957 Inquiries Consulting, advising, and speaking engagements are managed through [link: https://wsor.net] WSOR, LLC. The WSOR site is the right intake for new engagements. --- ## Contact URL: https://osterman.co/contact/ Summary: How to reach Dr. Travis Osterman. Consulting, advising, and speaking engagements are managed through WSOR, LLC. Contact (optional) Leave this field empty: --- ## Home Lab URL: https://osterman.co/home-lab/ Summary: Personal software practice outside Dr. Osterman's clinical informatics role - open-ended R&D in agentic systems, self-hosted infrastructure, journaling, education, and inventory. Home Lab Summer School Self-paced, interactive STEM learning platform for kids ages 7-13, with more than 400 interactive courses. Built as a platform for my children to continue learning once the school year ends. Faculty Coaching Academic coaching practice focused on faculty in academic medicine - including career coaching for physicians. Specifically passionate about coaching physicians interested in building a career in Health IT. Five-Letter Domain Real-world technical solutions and configurations from actual infrastructure work. What started as personal notes has evolved into a comprehensive resource covering Linux system administration, networking, storage management, and virtualization. Orc AI Multi-agent workflow orchestrator that tracks agent usage and can increase or decrease work to optimize subscription-based token usage. Runs my personal development infrastructure - including the project migrations and tooling work behind this site - under a single goal-driven agent runtime. OJournal Self-hosted, privacy-first journaling. Entries, attachments, and search index are encrypted at rest under a three-tier key hierarchy (UK → SK → DK); the database on its own is useless to an attacker. Designed to run on a single box behind TLS. First began as a Java-based blogging platform in 2004, ported to PHP, and now to a Rust API with a Vue frontend. Inventory Source-of-truth inventory system for physical and virtual hosts at home and in offsite datacenters, inspired by NetBox Labs. IAC Ansible-based infrastructure-as-code for the homelab and offsite-datacenter hosts that run everything above. Configuration in version control, idempotent provisioning, no manual SSH. Ultimate goal is better-than-cloud self-hosted infrastructure. --- ## Predicting immunotherapy efficacy and toxicity from real-world data URL: https://osterman.co/case-studies/digital-precision-oncology/ Summary: Case study of the GE HealthCare Digital Precision Oncology strategic research collaboration (2019-2024) - machine learning on real-world EHR data to predict immune checkpoint inhibitor effectiveness and toxicities. Four peer-reviewed manuscripts, multiple abstracts, and a patent. Digital Precision Oncology with GE HealthCare Immune checkpoint inhibitors (ICIs) have transformed cancer care. They allow subgroups of patients with previously incurable disease to have longer, higher-quality lives. They also carry the risk of severe immune-related adverse events: hepatitis, colitis, and pneumonitis, some of which are life-threatening and any of which forces discontinuation of an otherwise-working therapy. Oncologists make the treatment decision without a clean way to predict who will respond and who will be harmed. A personalized risk-benefit profile - generated before the first dose - would improve safety, extend treatment duration for responders, and improve clinical trial cohort selection. In 2019 Vanderbilt and GE HealthCare announced a five-year strategic research collaboration centered on this problem ([link: https://www.dotmed.com/news/story/45790] dotmed.com, January 2019). The Digital Precision Oncology study was the flagship: Dr. Osterman as principal investigator from Vanderbilt; Jan Wolber leading on the GE HealthCare side; multi-disciplinary teams on both sides spanning medical oncology, radiochemistry, biostatistics, bioinformatics, and machine learning. The collaboration ran through 2024. The team built a machine-learning framework using real-world clinical data already living in the Vanderbilt electronic health record: baseline labs, comorbidities, prior treatments, demographics, and imaging. The cohort grew over the course of the program; the JCO CCI 2024 flagship paper drew on more than 2,200 patients treated with ICIs. Models predicted both efficacy (one-year overall survival) and three major immunotoxicities (hepatitis, colitis, pneumonitis) using only pre-treatment data, generating per-patient risk-benefit profiles. The technical framing was deliberately conservative: short medical-history windows, careful validation, clinically interpretable features over black-box embeddings. The flagship paper, Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data ([link: https://doi.org/10.1200/CCI.23.00207] Lippenszky et al., JCO Clinical Cancer Informatics, 2024), demonstrated that routinely-collected EHR data is enough to build clinically useful prediction models - across both efficacy and toxicity, without bespoke biomarker panels or unstructured-data extraction pipelines. From Dr. Osterman's talk Prediction of immune checkpoint inhibitor outcome and side effects by use of machine learning and routine real-world data, ESMO Immuno-Oncology Annual Congress, Geneva, December 2024. US training cohort (~2,200 patients) and German external evaluation cohort (~4,250 patients); AUC scores at 100 days / 1 year / 3 years for overall survival and at 6 weeks / 90 days / 1 year for hepatitis. Models retained a substantial fraction of training-cohort performance on the external cohort - the validation result that made the approach credible beyond a single institution. An imaging companion paper followed in 2025. Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography ([link: https://doi.org/10.1200/CCI-24-00198] Smith et al., JCO CCI, 2025) extended the framework to imaging: 2,700 pre-treatment thorax CT volumes for hundreds of patients, with radiomic features and deep-learning models predicting which patients would later develop ICI-induced pneumonitis. External validation across multi-center pan-cancer cohorts was reported at SITC in 2023 (Kiss, Lippenszky et al., [link: https://doi.org/10.1136/jitc-2023-SITC2023.1294] abstract 1294). The patent Model generation apparatus for therapeutic prediction and associated methods and models (Wolber, Csernai, Kiss, Lippenszky, Horváth, Osterman, Park, Smith, Fabbri, LeNoue-Newton, Mittendorf) captures the underlying framework. 4 peer-reviewed manuscripts 8 published abstracts 12 conference presentations across ASCO, SITC, and ASCO GI 1 patent on the model-generation framework Beyond the published record, the work was covered by ASCO Daily News in two pieces and a podcast, and by [link: https://www.precisionmedicineonline.com/oncology/ge-healthcare-vanderbilt-explore-use-ai-predict-immunotherapy-toxicity-efficacy] Precision Medicine Online in November 2023. The collaboration ended in 2024 as planned, but the line of research continues. Models for ICI-induced hepatitis and colitis are in active development. The clinical insight - that routinely collected EHR data carries enough signal to generate a meaningful risk-benefit profile before the first dose - is now part of a broader research program at Vanderbilt-Ingram on bringing predictive analytics into routine oncology decision-making. The Digital Precision Oncology program is what happens when a careful clinical question gets paired with real-world EHR data, conservative machine-learning methodology, and multi-year institutional commitment. No one paper changed practice. The cumulative work made it credible that pre-treatment risk-benefit prediction for immunotherapy is achievable from the data that already exists in every cancer center's EHR. That credibility is what's required before any of this lands at the bedside. Lippenszky L, Mittendorf KF, Kiss Z, LeNoue-Newton ML, Napan-Molina P, Rahman P, Ye C, Laczi B, Csernai E, Jain NM, Holt ME, Maxwell CN, Ball M, Ma Y, Mitchell MB, Johnson DB, Smith DS, Park BH, Micheel CM, Fabbri D, Wolber J, Osterman TJ. [link: https://doi.org/10.1200/CCI.23.00207] Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data. JCO Clinical Cancer Informatics 2024. Smith DS, Lippenszky L, LeNoue-Newton ML, Jain NM, Mittendorf KF, Micheel CM, Cella PA, Wolber J, Osterman TJ. [link: https://doi.org/10.1200/CCI-24-00198] Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography. JCO Clinical Cancer Informatics 2025. Kiss Z, Lippenszky L, Laczi B, Napan-Molina P, Csernai E, Brehmer A, Kim M, Keyl J, Siveke J, Meyer M, Grünwald V, Kasper S, Roesch A, Schuler M, Osterman T, Wolber J, Kleesiek J. [link: https://doi.org/10.1136/jitc-2023-SITC2023.1294] External validation of machine learning models to predict efficacy and toxicity of immune checkpoint inhibitors using real-world pan cancer cohorts. SITC 2023, abstract 1294. Wolber J, Csernai EK, Kiss Z, Lippenszky L, Horváth G, Osterman T, Park BH, Smith DS, Fabbri D, LeNoue-Newton ML, Mittendorf K. Model generation apparatus for therapeutic prediction and associated methods and models. Patent. [link: #main" class="back-to-top] Back to top