Clinical Data Warehouse Reimagined

Fast forward another dozen years to 2016, the year SoM envisions STARR (STAnford medicine Research data Repository). By now, it has become clear that Hospital data is growing at a petascale rate (imaging, pathology, computer vision) and we need a brand new set of technologies and capabilities to push the new frontiers of AI in Medicine. 

Our first product offering was the second pillar of this data science platform, Nero, a HIPAA compliant research computing platform, that went live in summer of 2018. Nero is built and managed by our partners, Stanford Research Computing Center (SRCC). The SRCC team has in-depth expertise across the spectrum of HPC and data management services. The SRCC team develops, and manages the Nero platform and provides secure workspaces and research computing support to the users.  Nero has a private cloud infrastructure based on open source components hosted at Stanford Research Computing Facility (SRCF) with direct connectivity to a 100 gigabit network connection to available Research and Education Networks via CENIC’s California Research and Education Network. Nero also integrates with Google Cloud Platform (GCP). The Jupyter Hub interface provides a unified experience between on-premise and cloud experiences. Google services such as BigQuery are accessed from on-premise via APIs. 

Knowing that we were going to take on many data types in STARR, reaching petascale proportions, we were also keen to use a public Cloud to help us acquire the speed of experimentation. In particular, the data warehouse and related technologies are crucial to STARR. What we had seen in genomics was an unprecedented scalability and performance with BigQuery for queries spanning terabytes of genomics data. Downstream integration of BigQuery with a range of analytical tools is a big draw. Even better is a managed service that scales with our users, automagically. In addition BigQuery allows for cost sharing with end users. The storage costs are billed to the billing account attached to the project where data resides i.e., Research IT billing account pays for storage. The charges associated with BigQuery jobs (query) are billed to the billing account attached to the project from which user runs jobs (query), i.e., user pays for query. 

We continued borrowing from genomics when for our workflow orchestration framework, we adopted Common Workflow Language (CWL) and Cromwell workflow execution engine. Cromwell was selected due to its broad range of platform support, its maturity on the GCP, and its ease of deployment.  For genomics data, we had secured GCP to meet dbGaP requirements. For STARR (and Nero), we had to secure GCP to meet HIPAA requirements. Guided by ISO’s clear requirements, we partnered with Biarca for infrastructure security support, and with Atredis for penetration testing. 

Unlocking clinical text:

We collaborated closely with data scientist and Natural Language Processing (NLP) expert, Jose Posada, a member of Shah lab, to augment text processing features in STARR-OMOP. In particular, we have incorporated a text mining algorithm in our production pipeline for STARR-OMOP that was originally developed and used extensively by Shah lab scientists. The algorithms finds medical concepts, and can annotate whether the experience is happening to the patient (e.g. patient has diabetes), whether the experiencer is the patient (e.g. patient’s father has diabetes), whether the experience is current at the time of the patient’s visit (e.g. patient has no reported symptoms of diabetic retinopathy at this time). 

Our production pipeline processes 100 million clinical notes in our EHR using burst computing  to find 30 billion medical concepts. These 30 billion concepts and annotations are stored in NOTE_NLP table in OMOP and made searchable via BigQuery (Manuscript, Supplementary Section 7 for a slightly older result).

Jose, in collaboration with our engineering team, also developed an open-source clinical text de-identification pipeline, TiDE (Text DE-identification), that incorporates regular expression search, Name Entity Recognition and Hiding in Plain Sights (HIPS). Results show best-in-class precision and recall, but most importantly, the HIPS methodology brings state-of-the-art privacy protection (Manuscript, Supplementary Section 6).

TiDE enabled us to build the first pillar of our data science platform, a frequently updated pre-IRB non human subject dataset (STARR-OMOP-deid) accessible to any Stanford user with minimal paperwork (only a data use agreement needs to be signed) but under maximally secure conditions (the dataset is only accessible from a HIPAA compliant Nero Google account). The STARR-OMOP-deid dataset brings all the 100 million clinical notes and 30 billion standardized medical concepts within easy query access.

Several of our engineering team members have been with us since early days of STRIDE. Adoption of cloud has resulted in a significant change in what we do  and the services we bring to our research community.  It is essentially about the effort it takes to do something new and the effort it takes to sustain what we have built. The cost-benefit ratio of cloud is different, compared to on-premise, it is not about reducing cost, it is about creating value. 

Here are some of the key impact of cloud on our capabilities:

Bringing researcher to data:

The third pillar of our data science platform are a series of training and support initiatives, a tour de force effort from our R&D lead, Priya Desai. She joined our group as the product manager of STARR-OMOP. A data scientist by training, she focused from day one on the fundamental needs of a data scientist - data quality, data re-usability and data transparency. As her first commitment to the informatics community, she credited Nigam’s “BIOMEDIN 215: Data Science for Medicine” course. As part of the release, she generated product documentation. She also created python notebooks in Stanford gitlab that show users how to work with OMOP data model, the notebooks are written for SynPUF and seamlessly run on STARR-OMOP data. Nero team offers user support via emails, office hours, slack channel, and youtube videos. Priya’s team shares Nero office hours. Priya launched a companion Stanford slack channel to support the STARR user community on Nero.  Her team now offers additional ATLAS office hours in collaboration with Odysseus. She then proceeded to launch a new data science training program, a series of day long sessions that start with basic building blocks of doing data science and builds up to more complex skills using STARR clinical text. With COVID-19 shelter-in-place, the hands-on curriculum is being converted to a series of short videos on Stanford STARR youtube channel.  

1. Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study, Burn E., ..., Posada J. D., ..., Ryan P., Nature Communications volume 11, 2020 (link)
2. Heterogeneity and temporal variation in the management of COVID-19: a multinational drug utilization study including 71,921 hospitalized patients from China, South Korea, Spain, and the United States of America, Prats-Uribe, A., ..., Posada J.D., ..., Prieto-Alhambra, D.,  https://doi.org/10.1101/2020.09.15.20195545 (link)
3. Characteristics and outcomes of 627 044 COVID-19 patients with and without obesity in the United States, Spain, and the United Kingdom, Recalde M., ...,  Posada J.D., ..., Duarte-Salles T.,  https://doi.org/10.1101/2020.09.02.20185173 (link)
4. Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis, Tan E.H.,  ..., Posada J.D., ..., Prieto-Alhambra, D., doi: 10.1101/2020.11.24.20236802 (link)
5. Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 240,392 patients hospitalized with COVID-19 in the United States, Burn, E., ..., Posada J.D., ..., Duarte-Salles T.,  DOI: 10.1101/2020.11.25.20229088 (link)
6. Baseline characteristics, management, and outcomes of 55,270 children and adolescents diagnosed with COVID-19 and 1,952,693 with influenza in France, Germany, Spain, South Korea and the United States: an international network cohort study, Duarte-Salles T., ..., Posada J.D., ..., Prieto-Alhambra, D., DOI: 10.1101/2020.10.29.20222083 (link)
7. Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States, Golozar, A., ..., Posada J.D., ..., Prieto-Alhambra, D., doi: 10.1101/2020.10.25.20218875 (link)
8. “Clinical characteristics, symptoms, management and health outcomes in 8,598 pregnant women diagnosed with COVID-19 compared to 27,510 with seasonal influenza in France, Spain and the US: a network cohort analysis”, Lai, L. Y. H., ..., Posada J.D., ..., Prieto-Alhambra, D.,  doi: https://doi.org/10.1101/2020.10.13.20211821 (link)
9. An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza, Burns, E., ..., Posada J.D., ..., Ryans, P. , doi: 10.1101/2020.04.22.20074336 (link)