HTP

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{{#ev:youtube| RLB0rLyIelo }} Dr. Chute on Phenotyping Christopher G. Chute, M.D., Dr. P.H., SHARPn Principal Investigator; Professor of Medical Informatics and Associate Professor of Epidemiology at Mayo Clinic College of Medicine; Dr. Chute discusses phenotype characteristics for identifying patient cohorts (clinical trials, clinical decision support, quality numerator/denominators, etc).

Resources

Releases - Download, install, configure, and use the software produced.

High-throughput Phenotyping Releases

Presentations - Presentations made or found during the coarse of this grant that are relevant to this project.

Documents - Documents created by or used by this project.

References - Additional resources relevant to this project.

Introduction to High-throughput Phenotyping

Phenotyping is identifying a set of characteristics of about a patient, such as:

  • A diagnosis
  • Demographics
  • A set of lab results

A well‐defined phenotype will produce a group of patients who might be eligible for a clinical study or a program to support high‐risk patients.

While originally for application of research cohorts from EMR's, this project has obvious extensions to clinical trial eligibility, clinical decision support and has relevance to quality metrics (numerator and denominator constitute phenotypes). High-throughput Phenotyping (HTP) leverages high-throughput computational technologies to derive efficient use of health information data. The field currently has barriers in technological research and tool generation.

SHARPn HTP Project

The SHARPn HTP project will allow clinicians and investigators to identify patients from their EHR data.

The project is developing:

  • Phenotyping processes
  • Algorithms for specific diseases
  • Tools to incorporate data from multiple sites
Phenoportal

Is a collaboration with the National Quality Forum (NQF) and investigation of the Measure Authoring Toolkit (MAT) and contribution to the library from the Electronic Medical Records and Genomics (eMERGE) Network with disease co-hort algorithms.

Offers Search and visualization

   Ability to do keyword-based searches foravailable algorithms
   Navigate a hierarchy of phenotypes
   Visualize the algorithm logic flow
   Download human readable version (MS word)

This is being implemented by the HTP Tech Enablers team.

Project Team

Thanks yous go to the High-throughput Phenotyping team.

Aims
  • To provide EMR derived phenotyping processes, algorithms and technical tools (widgets) for preparation of transportability evaluation across multiple sites. This project is cross cutting amongst all Area 4 projects. This project is a consumer of data normalization and Natural Language Processing; it provides outputs to scalability and evaluation. Will work collaboratively with Data Quality.
  • The approach to HTP will include;
    • Identify phenotypes of interest from a clinically studied/evaluated pool
    • Identify and extract necessary data elements and categories
    • Obtain clinician input where applicable
    • Obtain IRB approvals
    • Gain access to clinical data
    • Analyze the process and collaborate with data quality
    • Develop and document methods for extracting on phenotypes
    • Define technical requirements for a UIMA component
  • HTP Tech Enablers will create an application to implement the aims of this project group.