Introduction to Software Services

Research IT team specializes in writing production software, and bringing our deep understanding of HIPAA requirements at Stanford. We coordinate with Stanford data hosting teams to support integrated systems so these solutions can run on secure hardware or Cloud infrastructure. If you are building systems that require storing PHI, then talk to us. We can advise on best practices, customize existing infrastucture solutions like REDCap or CHOIR, build custom software specific to your workflow, deploy and manage systems requiring long term support etc.

Research IT supports a variety of projects at SoM. Examples of these include Alliance Sleep Questionnaire, High Risk Infant Followup, Oncoshare, Stanford Cancer Institute Research Database, My Heart Counts, The Well Project, Apple Heart Study etc. 

Well for Life

Stanford WELL for Life wants to help you improve your health, wellness, and well-being, now and in the future. While much of Medicine is focused on diagnosing and treating diseases, Stanford WELL for Life provides a proactive approach for you to optimize your own well-being.


Launched in 2010, the OncoShare project was a groundbreaking collaboration between Stanford School of Medicine and Palo Alto Medical Foundation to support generation and analysis of sharable, anonymized datasets on breast cancer patients, based on a structured synopsis of their clinical treatment history.

Our Voice: Citizen Science for Health Equity

In the Our Voice approach, Citizen Scientists gather and analyze community data, then share their findings with decision makers to advocate for changes in the local environment, with the ultimate goal of advancing health equity. Core tools utilized by Our Voice are a mobile app and a web data portal/ Mobile app built on PhoneGap framework allowing for quick start-up and deployment on both the major mobile platforms (iOS & Android). Currently being piloted in a number of locations around the world including: Colombia, Israel, China, Taiwan, Chile, Brazil, New Zealand, U.K., Canada and many locales in the U.S.

Alliance Sleep Questionnaire

The ASQ was created as a tool to collect standard clinical information on all patients at the four AASR institutions (Harvard University, University of Pennsylvania, University of Wisconsin-Madison, St Luke's Hospital, and Stanford University) in order to facilitate research (especially in combination with biospecimen collection for genetic and biomarker studies) and to contribute to improved patient care.

Multicenter Perioperative Outcomes Group

The Multicenter Perioperative Outcomes Group (MPOG) was formed in 2008 to develop the necessary policies, procedures, and technical infrastructure required for multicenter perioperative outcomes research. Currently, MPOG is a consortium of 56 medical centers aggregating large volumes of observational inpatient electronic health record (EHR) data, patient reported outcomes, and long term administrative outcomes. MPOG has already extracted, transformed, and semantically mapped 6.3 million cases with 3.8 million distinct patient records from 32 different medical centers across the US and Europe using distinct EHR products. Anesthesia cases are extracted daily from adult Clarity that runs on Oracle. Data is copied from Oracle tables to SQL server schema once a month where it is reviewed, validated and attested by a clinician after which it gets uploaded to MPOG central housed at UMichigan Ann Harbor.

Apple Heart Study

The Apple Heart Study app uses data from Apple Watch to identify irregular heart rhythms, including those from potentially serious heart conditions such as atrial fibrillation. Apple is conducting this research study in collaboration with Stanford Medicine to improve the technology used to detect and analyze irregular heart rhythms, like atrial fibrillation - a leading cause of stroke.

Cancer Institute Research Database

In support of the Stanford Cancer Institute Research Database (SCIRDB), Research IT engineered a custom data transmission pipeline that transmits Clarity data from the clinical data warehouse into a separate database run by the Cancer Institute along with data from Cancer Genetics Research Database and the Pathology Core Tissue Bank.

CTSA Accrual to Clinical Trials

CTSA Accrual to Clinical Trials (CTSA ACT), was launched by developing a nationwide network of sites that share EHR data. Building on existing platforms and operating models to create a “federated” network with common standards, data terminology and shared resources, CTSA ACT investigators are focused on: Data harmonization (using the same term for the same type of data) across EHR platforms; Technical needs assessment and implementation; Regulatory approaches to ensure compliance with protocols for data access and participant contact; and Governance development to establish proper agreements among institutions. 

High Risk Infant Followup Program

Under the auspices of the California Children's Services Program in close collaboration of the California Perinatal Quality Care Collaborative, Research IT has implemented an online data reporting tool designed to capture all details of High Risk Infant Followup (HRIF) program clinic visits, known as the High Risk Infant Follow-up Quality of Care Initiative. Key features of the system include: a) Comprehensive data capture on all program visits, including the standard visit, which includes an interval medical assessment, neurological, neurosensory and developmental assessment, and a medical and special services review. b) Online reporting for administrative use and program oversight. c) Referral workflow support. d) A dynamically generated per-patient health audit report for quality improvement on a case by case basis.

MyHeart Counts

MyHeart Counts is a research app designed to study activity and heart health through your phone. It is also one of the largest cardiovascular research trials ever conducted. Stanford University scientists plan to use data gathered from app users to improve methods of preventing and treating heart disease.

Other example projects

Faculty labs supported by Research IT team

Nigam Shah Lab

Shah lab uses machine learning, text-mining, and prior knowledge in medical ontologies to enable the learning health system. Shah lab analyzes longitudinal EHR data, including unstructured data, to answer clinical questions, generate insights, and build predictive models. 

Tina Hernandez-Boussard Lab

A key focus of Hernandez-Boussard lab is the application of novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery.