Tag Archives: Computable phenotypes

Collaboratory phenotypes paper published in eGEMs special issue

Clinical trials, Data standards, Electronic health records, Learning health systems, Research tools, , , ,

A recently published special issue of eGEMs explores strategic uses of evidence to transform healthcare delivery systems. In A Framework to Support the Sharing and Re-Use of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications, Rachel Richesson and Michelle Smerek of the NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core, along with coauthor C. Blake Cameron, envision an infrastructure that facilitates re-use of computable phenotypes in a learning healthcare system.

The authors elaborate on four required components of the framework:

  • Searchable libraries of explicitly defined phenotype definitions
  • Knowledge bases with information and methods
  • Tools to identify, evaluate, and implement existing phenotype definitions
  • Motivated users and stakeholders

Read the entire eGEMs open access publication here. eGEMs (Generating Evidence & Methods to improve patient outcomes), a product of AcademyHealth’s Electronic Data Methods (EDM) Forum, is a peer-reviewed, open access journal that seeks to accelerate research and quality improvement using electronic health data.

Related resources:

You can find extensive information on computable phenotypes in the Living Textbook chapter and in Tools for Research.

New Living Textbook Chapter – Electronic Health Records-Based Phenotyping

Comparative Effectiveness Research, Pragmatic clinical trials, , , ,

A new Living Textbook topic chapter, “Electronic Health Records-Based Phenotyping,” has just been published. The chapter defines computable phenotypes and describes their role in data queries of electronic health records as part of pragmatic clinical trials. A main focus of the chapter is outlining mechanisms for identifying and evaluating phenotype definitions, with particular emphasis on standardization efforts of the NIH Collaboratory, including the Table 1 Project. Also included are links to recommended phenotype definitions from the Collaboratory Phenotypes, Data Standards, and Data Quality Core.