Real-world evidence from interdisciplinary teams: The Cleveland Institute for Computational Biology and The Book of OHDSI

The Cleveland Institute for Computational Biology (CICB) is pleased to announce that it has been instrumental in helping to create The Book of OHDSI, 1 st Edition , the first textbook describing the Observational Health Data Sciences and Informatics (OHDSI) community, the OHDSI (OMOP) data standards used to extract and organize clinical data from EHRs, and the OHDSI open-source clinical analytics tools. The book is available on and can also be downloaded for free at:

OHDSI is a multi-stakeholder, interdisciplinary collaborative that brings out the value of health data through large-scale analytics. The CICB hosted the annual OHDSI Face-to-Face meeting June 2-4, 2019 at the Wolstein Research Building for the purpose of pushing this book to completion. A short video summary of this work can be seen here:

After this meeting, work continued throughout the summer, and, on September 16, 2019 at the annual OHDSI Symposium in North Bethesda (attended by over 500 people from around the world representing academic medical centers, clinicians, statisticians, programmers, epidemiologists, public health agencies, big-data companies, health policy experts, government agencies, pharmaceutical companies, and insurers) the competed version of the book was introduced.  The CICB is extremely proud to announce that they represent 17 of the 56 collaborators/authors from around the world!

The book is organized into 5 major sections: 

  1. The OHDSI Community
  2. Uniform data representation
  3. Data analytics
  4. Evidence Quality
  5. OHDSI Studies 

The book also describes three main use cases for OHDSI (OMOP) data marts: clinical characterization , population-level estimation , and patient-level prediction , as well as the analytical open-source tools used to support these activities.

Importantly, the book serves to formalize the mission of OHDSI to improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care. OHDSI (OMOP) clinical data marts support the creation of other tools to analyze clinical data locally as well as across federated networks.  The greater Cleveland medical and academic community is uniquely positioned to be at the forefront of this relatively new field where data standardization, harmonization, and sharing for regional population health will improve the lives of the Northeast Ohio community and beyond.