Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System

Phipps, Michael; Fahner, Jeff; Sager, Danielle; Coffing, Jessica; Maryfield, Bailey; Williams, Linda S.

doi:10.1007/s11606-015-3562-5

Cited by 6 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…26 In the Veterans Health Administration integrated health system, the final version of a Meaningful Use program eCQM regarding stroke had nearly 90% accuracy when local algorithms were iteratively improved by examining patterns of misclassifications. 20 These studies illustrate that although significant challenges exist with eCQM implementation, mature data systems that iteratively examine and address measure misclassifications can achieve high levels of accuracy. However, this customization is often time-consuming and requires specialized informatics expertise.…”

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

A new era of quality measurement in rheumatology: electronic clinical quality measures and national registries

Tonner¹,

Schmajuk

Yazdany³

2017

Current Opinion in Rheumatology

View full text Add to dashboard Cite

Purpose of Review We review the evolution of quality measurement in rheumatology, highlighting new health-information technology infrastructure and standards that are enabling unprecedented innovation in this field. Recent Findings Spurred by landmark legislation that ties physician payment to value, the widespread use of electronic health records (EHRs), and standards such as the Quality Data Model, quality measurement in rheumatology is rapidly evolving. Rather than relying on retrospective assessments of care gathered through administrative claims or manual chart abstraction, new electronic clinical quality measures (eCQMs) allow automated data capture from EHRs. At the same time, Qualified Clinical Data Registries, like the American College of Rheumatology’s RISE registry, are enabling large-scale implementation of eCQMs across national EHR networks with real-time performance feedback to clinicians. While successful examples of eCQM development and implementation in rheumatology and other fields exist, there also remain challenges, such as lack of health system data interoperability and problems with measure accuracy. Summary Quality measurement and improvement is increasingly an essential component of rheumatology practice. Advances in health information technology are likely to continue to make implementation of eCQMs easier and measurement more clinically meaningful and accurate in coming years.

show abstract

Section: Introductionmentioning

confidence: 96%

“…Studies examined the feasibility and accuracy of eCQMs in pediatrics, 16 asthma, 17 cancer, 17,18 diabetes, 17 coronary heart disease, 19 cardiovascular disease, 20,21 and in primary care and preventive health screening. 17,21 Not surprisingly, many of these studies reported challenges with data extraction and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A new era of quality measurement in rheumatology: electronic clinical quality measures and national registries

Tonner¹,

Schmajuk

Yazdany³

2017

Current Opinion in Rheumatology

View full text Add to dashboard Cite

show abstract

“…Phipps and colleagues developed and validated inpatient stroke electronic clinical quality measures that are part of the Meaningful Use (MU) program and VA efforts to improve inpatient stroke care. 1 The authors found that stroke MU indicators can be accurately generated from existing VA data in its electronic health record (EHR) system (nearly a 90 % match to chart review), but accuracy decreased slightly when data from the VA's national-level corporate data warehouse (CDW) was used rather than more complete local data sources. The authors also found that a relatively small number of error types are responsible for a large number of the observed mismatches, suggesting specific areas in which EHR developers and informaticians could improve the accuracy of the electronic record for generating MU measures.…”

mentioning

confidence: 99%

The Next Generation of Clinical Performance Measures

Atkins

2016

J GEN INTERN MED

View full text Add to dashboard Cite

M any are familiar with the adage that Byou can't improve what you can't measure.^Adopting clinical performance measures was an important part of the VA transformation of the 1990s, and measuring clinical performance has become an integral part of current efforts to drive improvement under Medicare, the Affordable Care Act (ACA), and in programs run by private payers. Measuring clinical performance, however, is a health system intervention, and as such, needs to be carefully examined. This includes how measures are created, how they are implemented, and the evidence for their potential benefits and harms. The landscape of healthcare has also changed substantially since the advent of the performance measurement movement, and it is time to update our approach to measuring quality. Rather than setting a single standard, identifying a random number of patients to whom the measure should apply, and conducting manual chart reviews at a single point in time, we need to be assessing quality for the entire population, including the more complex patients. A world with electronic health records and a much richer array of administrative and clinical data allows us to update our approach to include measures that consider population health, episodes of care, changes over time, and individual circumstances. The Department of Veterans Affairs (VA), which operates the largest integrated healthcare system in the United States, is a key contributor to the emerging knowledge base regarding how best to create and implement performance measures.Funded by the VA's Health Services Research and Development (HSR&D) Service, this Journal of General Internal Medicine (JGIM) Supplement is the product of an HSR&D-sponsored state-of-the-art (SOTA) conference titled BNext-Generation Clinical Performance Measures: Patient-Centered, Clinically Meaningful, and High Value,^held in 2014. After the conference, a Call for Papers was issued, and 14 manuscripts were submitted to JGIM. After rigorous JGIM peer review, ten articles were accepted for publication in this special JGIM Supplement. Published papers discuss empirical research on the effects of performance measurement on improvements in clinical care, as well as on unintended outcomes (e.g., inappropriate treatment or over-treatment). Papers also describe new methods and methodological challenges in the selection and creation of performance measures that incorporate measures of benefit and harm, value, or patient preferences, as well as research on the implementation of performance measures that address human factors, incentives and facilitators, barriers, and expected and unintended consequences. USING VA'S ELECTRONIC HEALTH RECORD SYSTEM TO MEASURE QUALITYThree papers examined the ability to use VA electronic data to measure clinical quality in specific clinical areas. Phipps and colleagues developed and validated inpatient stroke electronic clinical quality measures that are part of the Meaningful Use (MU) program and VA efforts to improve inpatient stroke care. 1 The authors found that stro...

show abstract

“…There have been relatively few studies on the accuracy of eCQMs. Therefore, the report by Phipps 8 and colleagues in this issue of JGIM offers encouraging results that such measures can be implemented and will yield accurate results. However, this study was conducted only within VA hospitals using a single EHR, and there may be differential accuracy of eCQMs across different EHRs.…”

Section: Advance Measurement Of Care Processesmentioning

confidence: 95%