scholarly journals From Concepts and Codes to Healthcare Quality Measurement: Understanding Variations in Value Set Vocabularies for a Statin Therapy Clinical Quality Measure

Author(s):  
Raja A. Cholan ◽  
Nicole G. Weiskopf ◽  
Doug L. Rhoton ◽  
Bhavaya Sachdeva ◽  
Nicholas V. Colin ◽  
...  
BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043759
Author(s):  
Claire Barber ◽  
Diane Lacaille ◽  
Marc Hall ◽  
Victoria Bohm ◽  
Linda C Li ◽  
...  

ObjectivesTo obtain stakeholder perspectives to inform the development and implementation of a rheumatoid arthritis (RA) healthcare quality measurement framework.DesignQualitative study using thematic analysis of focus groups and interviews.SettingArthritis stakeholders from across Canada including healthcare providers, persons living with RA, clinic managers and policy leaders were recruited for the focus groups and interviews.ParticipantsFifty-four stakeholders from nine provinces.InterventionsQualitative researchers led each focus group/interview using a semistructured guide; the digitally recorded data were transcribed verbatim. Two teams of two coders independently analysed the transcripts using thematic analysis.ResultsPerspectives on the use of different types of measurement frameworks in healthcare were obtained. In particular, stakeholders advocated for the use of existing healthcare frameworks over frameworks developed in the business world and adapted for healthcare. Persons living with RA were less familiar with specific measurement frameworks, however, they had used existing online public forums for rating their experience and quality of healthcare provided. They viewed a standardised framework as potentially useful for assisting with monitoring the care provided to them individually. Nine guiding principles for framework development and 13 measurement themes were identified. Perceived barriers identified included access to data and concerns about how measures in the framework were developed and used. Effective approaches to framework implementation included having sound knowledge translation strategies and involving stakeholders throughout the measurement development and reporting process. Clinical models of care and health policies conducive to outcome measurement were highlighted as drivers of successful measurement initiatives.ConclusionThese important perspectives will be used to inform a healthcare quality measurement framework for RA.


2015 ◽  
Vol 588 ◽  
pp. 012027 ◽  
Author(s):  
F Pecoraro ◽  
D Luzi ◽  
M Cesarelli ◽  
F Clemente

2017 ◽  
pp. 1-6
Author(s):  
Richard Mansour ◽  
Samip Master

Purpose Quality measurement and improvement is a focus of ASCO. In the era of electronic health records (EHRs), computerized order entry, and medication administration records, quality monitoring can be an automated process. The EHR data are usually stored within tables in a relational database management system. ASCO Quality Oncology Practice Initiative measure NHL78a (hepatitis B virus antigen test and hepatitis B core antibody test within 3 months before initiation of obinutuzumab, ofatumumab, or rituximab for patients with non-Hodgkin lymphoma) presents an opportunity for automation of a quality measure using existing data in the EHR. Methods We used a locally developed Structured Query Language (SQL) language procedure in the Microsoft SQL Query Manager to access the EPIC CLARITY database. Access to the relational database management system of the EHR permits rapid case identification (the denominator set) of the unique ID of all of the patients who have received one of the target medications (ie, obinutuzumab, ofatumumab, or rituximab). Then, we went through a six-step process to find the number of patients who passed or failed the quality measure. Results When the final SQL procedure executes, it takes < 5 seconds to see the result set for a 12-month period. The procedure can be changed to incorporate a desired date range. Once the SQL procedure is created, there is essentially no labor and low costs to run the procedure at specific time intervals. Conclusion Our method of quality measurement using EHRs is cost effective, fast, and precise, and can be reproduced at other centers.


2018 ◽  
Vol 09 (02) ◽  
pp. 422-431 ◽  
Author(s):  
John D'Amore ◽  
Chun Li ◽  
Laura McCrary ◽  
Jonathan Niloff ◽  
Dean Sittig ◽  
...  

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.


Medical Care ◽  
2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Carlos A.Q. Santos ◽  
Craig Conover ◽  
Nadine Shehab ◽  
Andrew I. Geller ◽  
Yannis S. Guerra ◽  
...  

2020 ◽  
Vol 11 (01) ◽  
pp. 023-033
Author(s):  
Robert C. McClure ◽  
Caroline L. Macumber ◽  
Julia L. Skapik ◽  
Anne Marie Smith

Abstract Background Electronic clinical quality measures (eCQMs) seek to quantify the adherence of health care to evidence-based standards. This requires a high level of consistency to reduce the effort of data collection and ensure comparisons are valid. Yet, there is considerable variability in local data capture, in the use of data standards and in implemented documentation processes, so organizations struggle to implement quality measures and extract data reliably for comparison across patients, providers, and systems. Objective In this paper, we discuss opportunities for harmonization within and across eCQMs; specifically, at the level of the measure concept, the logical clauses or phrases, the data elements, and the codes and value sets. Methods The authors, experts in measure development, quality assurance, standards and implementation, reviewed measure structure and content to describe the state of the art for measure analysis and harmonization. Our review resulted in the identification of four measure component levels for harmonization. We provide examples for harmonization of each of the four measure components based on experience with current quality measurement programs including the Centers for Medicare and Medicaid Services eCQM programs. Results In general, there are significant issues with lack of harmonization across measure concepts, logical phrases, and data elements. This magnifies implementation problems, confuses users, and requires more elaborate data mapping and maintenance. Conclusion Comparisons using semantically equivalent data are needed to accurately measure performance and reduce workflow interruptions with the aim of reducing evidence-based care gaps. It comes as no surprise that electronic health record designed for purposes other than quality improvement and used within a fragmented care delivery system would benefit greatly from common data representation, measure harmony, and consistency. We suggest that by enabling measure authors and implementers to deliver consistent electronic quality measure content in four key areas; the industry can improve quality measurement.


2021 ◽  
Vol 19 (3) ◽  
pp. 207-211
Author(s):  
Robert L. Phillips ◽  
Lars Peterson ◽  
Ted E. Palen ◽  
Scott A. Fields ◽  
Michael L. Parchman ◽  
...  

Medical Care ◽  
1997 ◽  
Vol 35 (6) ◽  
pp. 539-552 ◽  
Author(s):  
Marguerite V.B. Dresser ◽  
Lisa Feingold ◽  
Susan L. Rosenkranz ◽  
Kathryn L. Coltin

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