CC BY-NC-ND 4.0 · Appl Clin Inform 2018; 09(02): 422-431
DOI: 10.1055/s-0038-1656548
Research Article
Schattauer GmbH Stuttgart

Using Clinical Data Standards to Measure Quality: A New Approach

John D. D'Amore
1  Diameter Health, Inc., Farmington, Connecticut, United States
2  Boston University Metropolitan College, Boston University, Boston, Massachusetts, United States
,
Chun Li
1  Diameter Health, Inc., Farmington, Connecticut, United States
,
Laura McCrary
3  Kansas Health Information Network, Topeka, Kansas, United States
,
Jonathan M. Niloff
1  Diameter Health, Inc., Farmington, Connecticut, United States
,
Dean F. Sittig
4  School of Biomedical Informatics, University of Texas-Memorial Hermann Center for Healthcare Quality and Safety, University of Texas Health Science Center, Houston, Texas, United States
,
Allison B. McCoy
5  Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States
,
Adam Wright
6  Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
› Author Affiliations
Funding Support for this research was provided by the Kansas Health Information Network and Diameter Health, jointly donating time and resources to the research team.
Further Information

Publication History

12 January 2018

17 April 2018

Publication Date:
13 June 2018 (online)

  

Abstract

Background Value-based payment for care requires the consistent, objective calculation of care quality. Previous initiatives to calculate ambulatory quality measures have relied on billing data or individual electronic health records (EHRs) to calculate and report performance. New methods for quality measure calculation promoted by federal regulations allow qualified clinical data registries to report quality outcomes based on data aggregated across facilities and EHRs using interoperability standards.

Objective This research evaluates the use of clinical document interchange standards as the basis for quality measurement.

Methods Using data on 1,100 patients from 11 ambulatory care facilities and 5 different EHRs, challenges to quality measurement are identified and addressed for 17 certified quality measures.

Results Iterative solutions were identified for 14 measures that improved patient inclusion and measure calculation accuracy. Findings validate this approach to improving measure accuracy while maintaining measure certification.

Conclusion Organizations that report care quality should be aware of how identified issues affect quality measure selection and calculation. Quality measure authors should consider increasing real-world validation and the consistency of measure logic in respect to issues identified in this research.

Protection of Human and Animal Subjects

This study was approved by the Institutional Review Board for the University of Texas, Health Science Center, Committee for the Protection of Human Subjects. Technical and administrative safeguards were utilized to protect the privacy of such information throughout this research.