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A new paradigm for accelerating clinical data science at Stanford Medicine

arXiv 2003.10534 ↗First posted 17 Mar 2020cs.CY cs.DB cs.IRcited by 16

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Abstract (arXiv)

Stanford Medicine is building a new data platform for our academic research community to do better clinical data science. Hospitals have a large amount of patient data and researchers have demonstrated the ability to reuse that data and AI approaches to derive novel insights, support patient care, and improve care quality. However, the traditional data warehouse and Honest Broker approaches that are in current use, are not scalable. We are establishing a new secure Big Data platform that aims to reduce time to access and analyze data. In this platform, data is anonymized to preserve patient data privacy and made available preparatory to Institutional Review Board (IRB) submission. Furthermore, the data is standardized such that analysis done at Stanford can be replicated elsewhere using the same analytical code and clinical concepts. Finally, the analytics data warehouse integrates with a secure data science computational facility to support large scale data analytics. The ecosystem is designed to bring the modern data science community to highly sensitive clinical data in a secure and collaborative big data analytics environment with a goal to enable bigger, better and faster science.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Mar 2020.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 1; 2025: 2; 2026: 6.

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  1. 2023EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Modelscited by 101
  2. 2025MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agentscited by 56
  3. 2023INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosiscited by 24
  4. 2026PhysicianBench: Evaluating LLM Agents in Real-World EHR Environmentscited by 19
  5. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12
  6. 2023DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical recordcited by 5
  7. 2024DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymizationcited by 4
  8. 2022Instability in clinical risk stratification models using deep learningcited by 3
  9. 2022Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selectioncited by 1
  10. 2023Pyclipse, a library for deidentification of free-text clinical notescited by 0
  11. 2025Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRscited by 0
  12. 2026Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Datacited by 0
4 more
  1. 2026SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identificationcited by 0
  2. 2026PLeDO: Pain Level Detection for Osteoarthritis from EMR Datacited by 0
  3. 2026Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Misscited by 0
  4. 2026Learning Continuous Patient Trajectories from Electronic Health Recordscited by 0