# A new paradigm for accelerating clinical data science at Stanford Medicine

arXiv: https://arxiv.org/abs/2003.10534 · HTML: https://mlsanity.com/paper/2003.10534/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents](https://mlsanity.com/paper/2501.14654/) (2025)
- [INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis](https://mlsanity.com/paper/2311.10798/) (2023)
- [PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments](https://mlsanity.com/paper/2605.02240/) (2026)
- [A comparison of approaches to improve worst-case predictive model performance over patient subpopulations](https://mlsanity.com/paper/2108.12250/) (2021)
- [DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record](https://mlsanity.com/paper/2303.06269/) (2023)
- [DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization](https://mlsanity.com/paper/2410.17035/) (2024)
- [Instability in clinical risk stratification models using deep learning](https://mlsanity.com/paper/2211.10828/) (2022)
- [Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection](https://mlsanity.com/paper/2209.09188/) (2022)
- [Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data](https://mlsanity.com/paper/2603.24903/) (2026)
- [SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification](https://mlsanity.com/paper/2605.03301/) (2026)
- [Pyclipse, a library for deidentification of free-text clinical notes](https://mlsanity.com/paper/2311.02748/) (2023)
- [Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs](https://mlsanity.com/paper/2503.07664/) (2025)
- [PLeDO: Pain Level Detection for Osteoarthritis from EMR Data](https://mlsanity.com/paper/2608.15719/) (2026)
- [Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss](https://mlsanity.com/paper/2608.17051/) (2026)
- [Learning Continuous Patient Trajectories from Electronic Health Records](https://mlsanity.com/paper/2609.36144/) (2026)
- [TIDE 2.0: an open, model-agnostic engine for keyed de-identification of clinical notes](https://mlsanity.com/paper/2610.07224/) (2026)

## 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.