Health Information Technology Enablement of Quality Measurement: Health Information Technology Expert Panel (District of Columbia)

Project Details - Ended

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Summary:

This project built on prior work that the National Quality Forum (NQF) completed under direction of the first Health Information Technology Expert Panel (HITEP-I). The report produced by HITEP-I recommended a set of 84 high-priority performance measures to enhance capabilities for the electronic capture of data for quality measurement. In this project, NQF reconvened the panel (HITEP-II) to gather, synthesize, and refine clinical workflow maps, focusing on care processes related to the previously prioritized set of measures. The project consisted of two primary workgroups, the Quality Data Set Workgroup and the Workflow Workgroup.

The main objectives of the project were to:

  • Represent quality data requirements (concepts, data types, data elements, code sets) unambiguously and specifically.
  • Determine mechanisms and opportunities within quality data management workflows for identifying patients who are eligible for inclusion in measure populations, gathering performance measurement data, and providing clinical decision support to optimize performance in targeted areas.

A quality data set (QDS) framework was developed.  The QDS is a classification system by which measure developers can offer and refine definitions.  The framework developed contains three levels of information: standard elements, quality data elements, and data flow attributes. Once fully developed, the QDS would be a centralized repository of quality data requirements and data definitions used by multiple stakeholders to develop, specify, and use quality measures. The QDS aims to provide direction to measure developers, electronic health record vendors, and other stakeholders on how to define quality terminology without ambiguity.

The QDS framework is also intended to represent clinical and administrative information required to calculate quality measures. These elements will be used to construct, with measure-related logic, numerators and denominators. Because of the importance of the QDS as a framework from which electronic health record developers can extract data for performance measurement, adherence to the QDS will likely become a requirement for NQF endorsement. The final report, titled Health Information Technology Automation of Quality Measurement: Quality Data Set and Data Flow and the HITEP II policy brief have been published and may be found in the News and Publications Section below.

Health Information Technology Enablement of Quality Measurement: Health Information Technology Expert Panel - 2010

Summary Highlights



Target Population: General

Summary: The project built on prior work the National Quality Forum (NQF) completed under direction of the first Health Information Technology Expert Panel (HITEP-I). The earlier HITEP-I report recommended 11 data categories and 39 data types, for a set of 84 high-priority performance measures to enhance capabilities for the electronic capture of data for quality measurement. Through this project, NQF reconvened the panel (HITEP-II) to gather, synthesize, and refine clinical workflow maps, focusing on care processes related to the previously prioritized set of measures. The project consisted of two primary workgroups:

  • The Quality Data Set Workgroup helped guide the development of recommendations to further quality measurement using health information technology, and used the panel’s expertise to develop materials concerning health information technology standards, capabilities, and quality measurement.
  • The Workflow Workgroup defined the workflow to manage electronic health information to enable expected patient care while decreasing the information seeking and documentation burden for individual clinicians. The work product defines the qualitative and quantitative aspects of data by examining quality data flow maps. Such quality data management addresses three requirements for data: the authoritative source, the methods for attribution of accountability at the source of the data, and the method of transit.
Project Objectives:
  • Represent quality data requirements (concepts, data types, data elements, and code sets) unambiguously and specifically. (Achieved)
  • Determine mechanisms and opportunities within quality data management workflows for identifying patients who are eligible for inclusion in measure populations, gathering performance measurement data, and providing clinical decision support to optimize performance in targeted areas. (Achieved)

2010 Activities: The project objectives listed above were completed in 2009, ahead of schedule. The final report, titled Health Information Technology Automation of Quality Measurement: Quality Data Set and Data Flow and the HITEP II policy brief have since been published.

Impact and Findings: A draft quality data set (QDS) was developed that could be used nationwide to support automated quality measurement. The QDS framework contains three levels of information: standard elements, quality data elements, and data flow attributes. The QDS is a classification system by which measure developers can offer and refine definitions. Once fully developed, the QDS would be a centralized repository of quality data requirements (such as concepts, data types, data elements, and code lists) and data definitions used by multiple stakeholders to develop, specify, and use quality measures. The QDS aims to provide direction to measure developers, electronic health record vendors, and other stakeholders on how to define quality terminology without ambiguity.

The QDS framework is also intended to represent clinical and administrative information required to calculate quality measures. These elements will be used to construct, with measure-related logic, numerators and denominators. Because of the importance of the QDS as a framework from which electronic health record developers can extract data for performance measurement, adherence to the QDS will likely become a requirement for NQF endorsement.

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Synthesis and Dissemination

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