The feasibility and accuracy of evaluating lipid management performance metrics using an electronic health record

Danford, Christopher P.; Navar-Boggan, Ann Marie; Stafford, Judy; McCarver, Catherine; Peterson, Eric D.; Wang, Tracy Y.

doi:10.1016/j.ahj.2013.07.024

Cited by 8 publications

(9 citation statements)

References 30 publications

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“…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%

“…18 In other words, in cases where clinicians documented processes of care more commonly in unstructured formats, such as clinical notes, sensitivity was severely compromised. 16,19,21 This was particularly true for care that is almost entirely documented in narrative form, such as psychological screening and counseling. 22 Across studies, initial work on eCQMs demonstrated that 39% to 65% of required data for existing measures resides in physician notes or medication administration records outside the EHR, suggesting that automated quality reporting based on the use of structured data is limited without systems to draw on these additional data sources.…”

Section: Introductionmentioning

confidence: 99%

“…These measures were based on LDL-C measurements and statin therapy and the data was extracted from laboratory and medication lists rather than from unstructured data. 19 …”

Section: Introductionmentioning

confidence: 99%

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

Tonner¹,

Schmajuk

Yazdany³

2017

Current Opinion in Rheumatology

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tonner¹,

Schmajuk

Yazdany³

2017

Current Opinion in Rheumatology

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“…Similarly, evaluating performance through the electronic health record data alone may misrepresent the actual quality of care provided by the practice. 58 Despite our attempt to design an evaluation set that can be feasibly implemented, however, limitations may include lack of staff trained to implement the multiple methods, the burden of data collection, and the unpredictable consequences of change. The contextually comprehensive approach we describe may provide insights into some of the more fundamental changes that are needed to drive transformation toward the joy and enhanced quality outcomes and satisfaction that many are hoping to foster in the health care experienced by patients, clinicians, and staff.…”

Section: Discussionmentioning

confidence: 99%

Recommendations for a Mixed Methods Approach to Evaluating the Patient-Centered Medical Home

Goldman

Parker

Brown

et al. 2015

The Annals of Family Medicine

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PURPOSE There is a strong push in the United States to evaluate whether the patient-centered medical home (PCMH) model produces desired results. The explanatory and contextually based questions of how and why PCMH succeeds in different practice settings are often neglected. We report the development of a comprehensive, mixed qualitative-quantitative evaluation set for researchers, policy makers, and clinician groups. METHODSTo develop an evaluation set, the Brown Primary Care Transformation Initiative convened a multidisciplinary group of PCMH experts, reviewed the PCMH literature and evaluation strategies, developed key domains for evaluation, and selected or created methods and measures for inclusion. RESULTSThe measures and methods in the evaluation set (survey instruments, PCMH meta-measures, patient outcomes, quality measures, qualitative interviews, participant observation, and process evaluation) are meant to be used together. PCMH evaluation must be sufficiently comprehensive to assess and explain both the context of transformation in different primary care practices and the experiences of diverse stakeholders. In addition to commonly assessed patient outcomes, quality, and cost, it is critical to include PCMH components integral to practice culture transformation: patient and family centeredness, authentic patient activation, mutual trust among practice employees and patients, and transparency, joy, and collaboration in delivering and receiving care in a changing environment. CONCLUSIONSThis evaluation set offers a comprehensive methodology to enable understanding of how PCMH transformation occurs in different practice settings. This approach can foster insights about how transformation affects critical outcomes to achieve meaningful, patient-centered, high-quality, and cost-effective sustainable change among diverse primary care practices.

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“…Indeed, querying population‐level EHR data has the potential to be a highly efficient way for practices and insurers to gauge performance and adherence to performance measures. Nevertheless, recent studies suggest that EHR data query may have limited fidelity compared with manual chart review 7, 8. For this reason, it is critical that providers, healthcare institutions, and policy makers understand the limitations and common errors that result from trying to measure quality performance through EHR data query 9.…”

mentioning

confidence: 99%

Errors in Electronic Health Record–Based Data Query of Statin Prescriptions in Patients With Coronary Artery Disease in a Large, Academic, Multispecialty Clinic Practice

Shin

Ochuko

Bhatt

et al. 2018

JAHA

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BackgroundWith the recent implementation of the Medicare Quality Payment Program, providers face increasing accountability for delivering high‐quality care. Such pay‐for‐performance programs aim to leverage systematic data captured by electronic health record (EHR) systems to measure performance; however, the fidelity of EHR query for assessing performance has not been validated compared with manual chart review. We sought to determine whether our institution's methodology of EHR query could accurately identify cases in which providers failed to prescribe statins for eligible patients with coronary artery disease.Methods and ResultsA total of 9459 patients with coronary artery disease were seen at least twice at the Emory Clinic between July 2014 and June 2015, of whom 1338 (14.1%, 95% confidence interval 13.5–14.9%) had no statin prescription or exemption per EHR query. A total of 120 patient cases were randomly selected and reviewed by 2 physicians for further adjudication. Of the 120 cases initially classified as statin prescription failures, only 21 (17.5%; 95% confidence interval, 11.7–25.3%) represented true failure following physician review.ConclusionsSole reliance on EHR data query to measure quality metrics may lead to significant errors in assessing provider performance. Institutions should be cognizant of these potential sources of error, provide support to medical providers, and form collaborative data management teams to promote and improve meaningful use of EHRs. We propose actionable steps to improve the accuracy of EHR data query that require hypothesis testing and prospective validation in future studies.

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The feasibility and accuracy of evaluating lipid management performance metrics using an electronic health record

Cited by 8 publications

References 30 publications

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

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

Recommendations for a Mixed Methods Approach to Evaluating the Patient-Centered Medical Home

Errors in Electronic Health Record–Based Data Query of Statin Prescriptions in Patients With Coronary Artery Disease in a Large, Academic, Multispecialty Clinic Practice

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