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Using a stakeholder-engaged approach to develop and validate electronic clinical quality measures.

Jill Boylston Herndon1, Krishna Aravamudhan2, Ronald L Stephenson3

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Summary

Stakeholder engagement successfully developed and validated two oral health electronic clinical quality measures. This collaborative approach ensured feasibility, reliability, and validity, leading to published measures.

Keywords:
electronic health recordshealth care quality indicatorsmeaningful usequality measure developmentquality measure testing

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Area of Science:

  • Health Services Research
  • Quality Improvement Science
  • Dental Informatics

Background:

  • Developing robust electronic clinical quality measures (eCQMs) is crucial for improving oral healthcare.
  • Previous efforts may have lacked comprehensive stakeholder input, potentially impacting measure utility and adoption.

Purpose of the Study:

  • To detail the stakeholder-engaged methodology for creating and validating two oral health eCQMs.
  • To demonstrate a process for ensuring the feasibility, reliability, and validity of eCQMs.

Main Methods:

  • Engaged diverse stakeholders throughout the measure development lifecycle, from conception to testing.
  • Assessed data element feasibility via interviews and utilized synthetic patient datasets for implementation testing across electronic health record (EHR) systems.
  • Validated measure implementation using automated EHR data reporting compared to manual record reviews, employing the kappa statistic.

Main Results:

  • A dedicated stakeholder workgroup guided all development and testing phases.
  • All critical data elements passed feasibility assessments, and measure implementation was proven feasible in EHRs.
  • High reliability and validity were confirmed, with kappa statistics indicating "almost perfect" to "substantial" agreement, and the measures were published.

Conclusions:

  • Stakeholder engagement is vital for successful eCQM development and testing.
  • Early and continuous stakeholder involvement mitigates risks to feasibility, reliability, and validity, optimizing resource allocation.