Additional Resources

Using Electronic Health Record Data in Pragmatic Clinical Trials

Section 9

Additional Resources


Rachel Richesson, MS, PhD, MPH

Richard Platt, MD, MSc

Gregory Simon, MD, MPH

Lesley Curtis, PhD

Reesa Laws, BS

Adrian Hernandez, MD, MSH

Jon Puro, MPA-HA

Doug Zatzick, MD

Erik van Eaton, MD, FACS

Vincent Mor, PhD


Contributing Editor

Karen Staman, MS

Resource Description
The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program OHDSI is a multi-stakeholder, interdisciplinary collaborative designed to bring out the value of health data through large-scale analytics. This network is experienced in the use of clinical data for research and brings in-depth understanding of clinical and administrative data from many different organizations with rigorous observational research methods. They are developing and sharing tools for assessing data quality, transforming data to reference data standards, and for visualization and analysis of data distributed across many organizations. Findings and recommendations from a recent workshop describe the significant challenges and recommendations to enable the use of data from electronic health records and other non-traditional data sources to inform the development and evaluation of medications.

User’s Guide to Computable Phenotypes
This document provides a practical framework that will help physicians, clinical researchers and informaticians evaluate published phenotype algorithms for re-use in
various purposes. The framework is divided into three phases, aligned with expected user roles:overall assessment, clinical validation, and technical review.
Key Issues in
Extracting Usable Data from Electronic Health Records for Pragmatic Clinical Trials
A working document from the NIH Collaboratory Biostatistics/Study Design
Principles and Practice of Clinical Research This book provides input from experts at the NIH on the principles and practice of clinical research.
Phenotyping Tools
Common Conditions


ADAPTABLE Tools for using patient-reported outcomes
ADAPTABLE Supplement Report: Patient-Reported Health Data and Metadata Standards in the ADAPTABLE Study Summary of patient-reported health data and metadata standards for the ADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness) trial
LOINC ADAPTABLE patient-reported outcome set LOINC (Logical Observation Identifiers Names and Codes) provides re-usable standards clinical information in electronic reports.
Reference material for the patient-reported item set from ADAPTABLE in LOINC version 2.64 A GitHub repository for reference materials and slides that were used in the development of the ADAPTABLE item set
June 7, 2018 NIH Releases First Strategic Plan for Data Science
Grand Rounds
June 29, 2018 Policy & Priorities: Rethinking University Research with State Data (Aaron McKethan, PhD)
April 18, 2018 OHDSI: Drawing Reproducible Conclusions from Observational Clinical Data (George Hripcsak, MD, MS)
April 27, 2018 Expanding Use of Real-World Evidence: A National Academies Workshop Series (Greg Simon, MD)
March 23, 2018 Data Science in the Era of Data Ubiquity (Robert Califf, MD)
December 1, 2017 Providing a Shared Repository of Detailed Clinical Models for All of Health and Healthcare (Stanley Huff, MD)
October 20, 2017 Automated Public Health Surveillance Using Electronic Health Record Data (Michael Klompas, MD)
July 3, 2018 Policy & Priorities: Rethinking University Research with State Data (Aaron McKethan, PhD)
April 9, 2018 Data Science in the Era of Data Ubiquity (Robert Califf, MD)




Version History

November 30, 2018: Added resources as part of annual update (changes made by K. Staman).

Published August 25, 2017


Richesson R, Platt P, Simon G, et al. Using Electronic Health Record Data in Pragmatic Clinical Trials: Additional Resources. In: Rethinking Clinical Trials: A Living Textbook of Pragmatic Clinical Trials. Bethesda, MD: NIH Health Care Systems Research Collaboratory. Available at: Updated November 30, 2018. DOI: 10.28929/038.