Electronic Clinical Quality Measures Research Articles

Identifying and Managing Malnourished Hospitalized Patients Utilizing the Malnutrition Quality Improvement Initiative: The UPMC Experience

Registered dietitian nutritionists at University of Pittsburgh Medical Center, a group of 40 academic, community, and specialty hospitals in Pittsburgh, PA, recognized the need to improve the identification and management of malnourished adult patients at their institutions. It was decided to pilot the Malnutrition Quality Improvement Initiative (MQii) at two institutions within their health care system. The MQii is based on the dual-pronged approach of malnutrition-focused electronic clinical quality measures and a quality improvement toolkit (MQii Toolkit), to help identify and manage malnourished adult patients. The quality improvement implementation focused on hospital-wide adoption of the Nutrition Focused Physical Examination (NFPE). The MQii team was guided by the malnutrition electronic clinical quality measures focused on completing a nutrition assessment (the NFPE) within 24 hours of identification of malnutrition risk and ensuring documentation of a malnutrition diagnosis when it was identified. Performance on both measures improved significantly (P<0.01). Performance on appropriate timing of nutrition risk screening improved slightly, and there was almost perfect compliance for completion of nutrition care plans in the presence of malnutrition. Overall, the performance data demonstrated the effectiveness of using the MQii to improve the nutrition processes and the ability to implement NFPE into the process of malnutrition identification. Funding/SupportPublication of this supplement was supported by Abbott. The Academy of Nutrition and Dietetics does not receive funding for the MQii. Avalere Health's work to support the MQii was funded by Abbott.

Enhancing Identification and Management of Hospitalized Patients Who Are Malnourished: A Pilot Evaluation of Electronic Quality Improvement Measures

Malnutrition in hospitalized patients has long been recognized as a contributor to poor patient outcomes; malnutrition often leads to higher costs of care. Thus, it is important to improve the identification of patients who are at risk for malnutrition or already malnourished and to initiate treatment to optimize outcomes. The Malnutrition Quality Improvement Initiative (MQii) is based on a dual-pronged approach consisting of a set of four electronic clinical quality measures and a Quality Improvement Toolkit that support delivery of high-quality malnutrition care by clinicians including nurses, registered dietitian nutritionists, and physicians. A large pilot hospital validated the four malnutrition electronic clinical quality measures (screening for nutrition risk, assessment, care plan, diagnosis), demonstrating their value in support of continuous quality improvement for hospital-based malnutrition care with the ultimate goal of better patient outcomes while reducing health care costs. Funding/SupportPublication of this supplement was supported by Abbott. The Academy of Nutrition and Dietetics does not receive funding for the MQii. Avalere Health's work to support the MQii was funded by Abbott.

Engagement of the Academy’s Dietetic Practice Groups with MQii

Raise awareness of Malnutrition Quality Improvement Initiative (MQii) efforts and recruitment in the Learning Collaborative among the Academy’s Dietetic Practice Groups (DPGs). As efforts of the Malnutrition Quality Improvement Initiative (MQii) were underway it became apparent to MQii Partners, Academy of Nutrition and Dietetics and Avalere Health, that engagement with the Academy’s DPG Leaders is critical in order to advance objectives of the initiative which include:Advance adoption of malnutrition electronic clinical quality measures (eCQMs);Improve effectiveness and timeliness of malnutrition care through an interdisciplinary toolkit; andSupport availability of tools that can be integrated into EHR systems to improve care quality. Three major strategies were identified and operationalized to advance the noted objectives. First, a Lunch and Learn event was held for DPG Leaders at the Food & Nutrition Conference & Expo™ (FNCE) 2017 to brief them on current MQii activities. Second, a series of MQii follow-up webinars with DPG leaders got underway in order to provide MQii updates and provide opportunity for attendees to ask questions. Lastly, a Malnutrition Quality Improvement survey was deployed to 26 DPG memberships to gain member interest level with participation in the MQii Learning Collaborative. Over 100 participants attended the MQii Lunch and Learn event at FNCE Chicago 2017. Attendees received a customized malnutrition communication packet to share with their DPG membership focusing on how to get started using malnutrition eCQMs, obtaining C-suite and clinical care team support, and overcoming barriers. Four separate DPG engagement webinars occurred from February to March 2018. During the webinars DPG leaders were updated on MQii progress and engaged in a discussion to share their insights on where their memberships have opportunities to be involved with MQii. The Malnutrition Quality Improvement survey indicated that 20% of respondents were interested in participating in the MQii Learning Collaborative. DPGs play a unique role of facilitating dietetic practitioners to connect with fellow like-minded colleagues. Targeting the 26 DPGs allowed the Academy to significantly increase recruitment efforts for the MQii Learning Collaborative. A total of 119 adult in-patient acute-care hospitals expressed interest in joining the MQii Learning Collaborative.

The Malnutrition Quality Improvement Initiative: A Multiyear Partnership Transforms Care

The Academy of Nutrition and Dietetics, representing credentialed nutrition and dietetics practitioners—registered dietitian nutritionists (RDNs) and nutrition and dietetics technicians, registered, and students and interns and professionals holding nutrition and dietetics undergraduate and advanced degrees—and Avalere Health, a Washington, DC–based strategic advisory services firm, have led the charge in closing malnutrition gaps with the Malnutrition Quality Improvement Initiative (MQii), a national nutrition-focused quality improvement initiative. The initiative’s journey from 2013-2019 utilized technical advisors and stakeholders to improve care and outcomes for hospitalized adults age 65 and older with a series of innovations. These innovations include the development of the first malnutrition electronic clinical quality measures (eCQMs) and a complementary interdisciplinary quality improvement toolkit and establishing the first nutrition-focused national Learning Collaborative. MQii’s vision for future directions and applications in 2020 and beyond will explore partnerships to include the malnutrition eCQM in available clinical data registries. Qualified Clinical Data Registries will provide a pathway for collecting nutrition data relevant to RDNs because as of 2020, payments for Medicare Part B nutrition services and quality improvement are available for eligible RDNs participating in the Centers for Medicare and Medicaid Services Quality Payment Program. The MQii Toolkit’s technical specification manuals, data dictionaries, and implementation guides will help RDNs integrate the malnutrition quality measures into existing electronic health records and lead nutrition data collection and analysis. RDNs’ continued advancement with information technology leaders to incorporate terminology and clinical standards into electronic health record platforms will provide for malnutrition data transfer across care settings. Funding/SupportPublication of this supplement was supported by Abbott. The Academy of Nutrition and Dietetics does not receive funding for the MQii. Avalere Health's work to support the MQii was funded by Abbott.

The Importance of Nutrition Post-Discharge Interventions

To discuss the benefits of utilizing a quality improvement process to identify gaps and opportunities in an established malnutrition initiative. Malnutrition initiatives were implemented to improve the quality of malnutrition care, standardize practices, and increase awareness of the importance of the care of malnourished patients. Health care is always changing, and established initiatives or programs need to be evaluated often to stay relevant to current trends. The Malnutrition Quality Improvement Initiative (MQii) Toolkit was used to review current practices and ensure key points were in place including the electronic clinical quality measures, establishing key stakeholders on the interdisciplinary team, and education. The Malnutrition Care Assessment and Decision Tool was used to review current malnutrition initiatives across several hospitals in the health care system. The tool identified the discharge planning process as an area for improvement and further investigation. A process and data collection were developed that focused on all patients diagnosed by the registered dietitian nutritionist (RDN) with severe malnutrition in a two month period. Their charts were reviewed by an RDN and included collection of prescribed RDN nutrition recommendations, discharge location, and information obtained through follow-up phone calls that focused on compliance with RDN recommendations. Follow-up phone calls were performed on only those patients discharged home. Preliminary data indicates favorable and unanticipated findings. Patients called via the protocol established were well versed on the recommendations from the inpatient RDN and the vast majority were following instructions based on nutrition prescription. Of those who could not verbalize recommendations were able to reference written discharge instructions that were entered into the medical record by the RDN and reviewed by the discharging nurse prior to returning home. What was surprising was the number of patients that did not meet criteria for follow-up calls. Patients discharged to short/long term care facilities, skilled nursing facilities or other institutions comprised the majority of the Severe Malnutrition diagnosis list. Using the Malnutrition Care Assessment and Decision Tool provided by the MQii was helpful in highlighting opportunities to improve the discharge piece of malnutrition initiatives. The process of nutrition assessment with RDN-driven patient education regarding diet modifications and oral nutrition supplement use, followed by RN reinstruction and written guidelines provided at discharge, is helpful to aid in memory retention of nutrition recommendations. Patients tend to understand the importance of diet and supplements and follow nutrition prescriptions. The majority of severely malnourished patients are not discharged home but are discharged to other facilities requiring a deeper dive into these discharge processes as they comprise a greater proportion of malnourished patients.

Primary Care Practices’ Ability to Report Electronic Clinical Quality Measures in the EvidenceNOW Southwest Initiative to Improve Heart Health

The capability and capacity of primary care practices to report electronic clinical quality measures (eCQMs) are questionable. To determine how quickly primary care practices can report eCQMs and the practice characteristics associated with faster reporting. This quality improvement study examined an initiative (EvidenceNOW Southwest) to enhance primary care practices' ability to adopt evidence-based cardiovascular care approaches: aspirin prescribing, blood pressure control, cholesterol management, and smoking cessation (ABCS). A total of 211 primary care practices in Colorado and New Mexico participating in EvidenceNOW Southwest between February 2015 and December 2017 were included. Practices were instructed on eCQM specifications that could be produced by an electronic health record, a registry, or a third-party platform. Practices received 9 months of support from a practice facilitator, a clinical health information technology advisor, and the research team. Practices were instructed to report their baseline ABCS eCQMs as soon as possible. The main outcome was time to report the ABCS eCQMs. Cox proportional hazards models were used to examine practice characteristics associated with time to reporting. Practices were predominantly clinician owned (48%) and in urban or suburban areas (71%). Practices required a median (interquartile range) of 8.2 (4.6-11.9) months to report any ABCS eCQM. Time to report differed by eCQM: practices reported blood pressure management the fastest (median [interquartile range], 7.8 [3.5-10.4] months) and cholesterol management the slowest (median [interquartile range], 10.5 [6.6 to >12] months) (log-rank P < .001). In multivariable models, the blood pressure eCQM was reported more quickly by practices that participated in accountable care organizations (hazard ratio [HR], 1.88; 95% CI, 1.40-2.53; P < .001) or participated in a quality demonstration program (HR, 1.58; 95% CI, 1.14-2.18; P = .006). The cholesterol eCQM was reported more quickly by practices that used clinical guidelines for cardiovascular disease management (HR, 1.35; 95% CI, 1.18-1.53; P < .001). Compared with Federally Qualified Health Centers, hospital-owned practices had greater ability to report blood pressure eCQMs (HR, 2.66; 95% CI, 95% CI, 1.73-4.09; P < .001), and clinician-owned practices had less ability to report cholesterol eCQMs (HR, 0.52; 95% CI, 0.35-0.76; P < .001). In this study, time to report eCQMs varied by measure and practice type, with very few practices reporting quickly. Practices took longer to report a new cholesterol measure than other measures. Programs that require eCQM reporting should consider the time and effort practices must exert to produce reports. Practices may benefit from additional support to succeed in new programs that require eCQM reporting.

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets.

BackgroundDefining clinical phenotypes from electronic health record (EHR)–derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely grained clinical terminology—either native SNOMED CT or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does insuring that their contents accurately represent the clinically intended condition.ObjectiveThe goal of the research was to compare an intensional (concept hierarchy-based) versus extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT–encoded data from EHRs by evaluating value set conciseness, time to create, and completeness.MethodsStarting from published Centers for Medicare and Medicaid Services (CMS) high-priority eCQMs, we selected 10 clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (1) VSAC-downloaded list-based (extensional) value sets, (2) corresponding hierarchy-based intensional value sets for the same conditions, and (3) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional versus intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians.ResultsThe 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 versus 78 concepts to define and 5 versus 37 minutes to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms.ConclusionsIn the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to include and exclude, and streamline broad EHR implementation of condition-specific decision support promoting guideline adherence for patient benefit.

Assessment and Stratification of High-Impact Data Elements in Electronic Clinical Quality Measures: A Joint Data Quality Initiative Between CancerLinQ® and Cancer Treatment Centers of America.

A joint data quality initiative between the Cancer Treatment Centers of America and the ASCO big data health technology platform CancerLinQ® was initiated to document and codify the steps taken to evaluate, stratify, and determine the potential effect of data elements used for electronic clinical quality measures as captured within structured fields in electronic health records. The processes involved the identification of clinical concepts required in measure population criteria and then to map these to the corresponding components of the CancerLinQ data model. A quantitative assessment of mappings between electronic clinical quality measure clinical concepts and attributes from the CancerLinQ clinical database was performed. In parallel, a qualitative analysis of high-impact data elements from the Cancer Treatment Centers of America clinical measures was made using local, expert consensus. An impact assessment was derived using a count of the data elements across measures and the specific population criteria affected. A list of putative high-impact data elements can provide guidance for clinicians to facilitate specific data element capture related to quality metrics in an electronic environment.

Performance on Electronic Clinical Quality Measures in the Comprehensive Primary Care Initiative.

Electronic clinical quality measures (eCQMs) that capture data from electronic health records promise accurate and timely measurement, but their use has been limited in payment. The Comprehensive Primary Care initiative sponsored by the Centers for Medicare & Medicaid Services used eCQMs as part of a shared savings incentive. To assess performance, the authors developed benchmarks for 11 measures and compared performance of initiative and benchmark practices. Initiative practices outperformed benchmark practices on 8 measures in 2015 (between 6.3 and 17.7 percentage points) and 9 measures in 2016 (between 1.7 and 20 percentage points). Initiative practices improved significantly on 7 measures from 2015 to 2016 (between 3.3 and 8.6 percentage points). For 3 measures, the improvement was greater than benchmark practices that reported the same measures in a 2-year period (between 1 and 8.9 percentage points). The authors conclude that eCQMs can be used for payment.

Understanding the Impact of Variations in Measurement Period Reporting for Electronic Clinical Quality Measures

Objective:To understand the impact of varying measurement period on the calculation of electronic Clinical Quality Measures (eCQMs).Background:eCQMs have increased in importance in value-based programs, but accurate and timely measurement has been slow. This has required flexibility in key measure characteristics, including measurement period, the timeframe the measurement covers. The effects of variable measurement periods on accuracy and variability are not clear.Methods:209 practices were asked to extract and submit four eCQMs from their Electronic Health Records on a quarterly basis using a 12-month measurement period. Quarterly submissions were collected via REDCap. The measurement periods of the survey data were categorized into non-standard (3, 6, 9 months and other) and standard periods (12 months). For comparison, patient-level data from three clinics were collected and calculated in an eCQM registry to measure the impact of varying measurement periods. We assessed the central tendency, shape of the distributions, and variability across the four measures. Analysis of variance (ANOVA) was conducted to analyze the differences among standard and non-standard measurement period means, and variation among these groups.Results:Of 209 practices, 191 (91 percent) submitted data over three quarters. Of the 546 total submissions, 173 had non-standard measurement periods. Differences between measures with standard versus non-standard periods ranged from –3.3 percent to 14.2 percent between clinics (p < .05 for 3 of 4), using the patient-level data yielded deltas of –1.6 percent to 0.6 percent when comparing non-standard and standard periods.Conclusion:Variations in measurement periods were associated with variation in performance between clinics for 3 of the 4 eCQMs, but did not have significant differences when calculated within clinics. Variations from standard measurement periods may reflect poor data quality and accuracy.

Early Implementation of Malnutrition Clinical Quality Metrics to Identify Institutional Performance Improvement Needs

The Centers for Medicare and Medicaid Services (CMS) is part of the federal government and the largest health care payer in the United States. 1 About CMS. Centers for Medicare and Medicaid Services website. https://www.cms.gov/About-CMS/About-CMS.html. Accessed January 3, 2018. Google Scholar The CMS writes regulations to ensure they only pay for high-quality, safe health care. To better measure quality in the U.S. health care system, the CMS developed electronic clinical quality measures (eCQMs), which became available for voluntary use in 2013. 2 eCQMs. eCQI Resource Center. The Office of the National Coordinator of Health Information Technology, Centers for Medicare and Medicaid Services. https://ecqi.healthit.gov/ecqms. Accessed January 3, 2018. Google Scholar Effective calendar year (CY) 2016, hospitals are required to electronically report clinical quality measures (CQMs) as a part of hospital inpatient quality reporting (IQR) and the electronic health record (EHR) incentive programs (more commonly known as “meaningful use” of the EHR). Hospitals that successfully submit eCQM data will fulfill the Medicare requirements for both programs with one submission. The CMS approves which eCQMs are accepted for any given CY, as well as the minimum number on which a hospital must report. Starting in CY 2017, there were 15 available eCQMs from which to choose, and hospitals were required to report on a minimum of eight. This has changed to four for CY 2018. 3 Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the LongTerm Care Hospital Prospective Payment System and Policy Changes and Fiscal Year 2018 Rates; Quality Reporting Requirements for Specific Providers; Medicare and Medicaid Electronic Health Record (EHR) Incentive Program Requirements for Eligible Hospitals, Critical Access Hospitals, and Eligible Professionals; Provider-Based Status of Indian Health Service and Tribal Facilities and Organizations; Costs Reporting and Provider Requirements; Agreement Termination Notices. Fed Regist. 2017; 82 (Updated August 14, 2017. Accessed January 3, 2018): 38001https://www.gpo.gov/fdsys/pkg/FR-2017-08-14/pdf/2017-16434.pdf Google Scholar

Development of the American College of Rheumatology Electronic Clinical Quality Measures for Gout.

Electronic clinical quality measures (eCQMs) are increasingly used by health registries and third parties to evaluate and improve the quality of health care. To complete these eCQMs, data are extracted from electronic health records (EHRs). The treatment of gout has been an area identified with gaps in quality of care. On behalf of the American College of Rheumatology (ACR), we sought to develop and test eCQMs to evaluate gout care. Drawing from the 2012 ACR gout guidelines, a working group developed candidate gout process measures that were evaluated by an interdisciplinary panel of health care stakeholders, the ACR Quality Measures Subcommittee (QMS), and ultimately the ACR Board of Directors for formal validity testing. For each of the selected gout eCQMs, 3 clinical sites using different EHR systems tested the scientific feasibility and validity of the measures. Measures appropriate for accountability were presented for national endorsement. Of the 10 proposed eCQMs, 4 were endorsed by the ACR QMS, 3 were incorporated into the ACR's Rheumatology Informatics System for Effectiveness (RISE) Registry, and 2 were endorsed by the National Quality Forum. The 3 eCQMs incorporated into RISE (evaluating indications for urate-lowering therapy [ULT]), monitoring serum urate, and treat-to-target outcome) demonstrated high validity and reliability. Proportions of patients passing these 3 eCQMs in RISE and at the 3 clinical testing sites ranged between 32% and 58%, indicating significant room for improvement in care. Three eCQMs have been validated and implemented into RISE. Two of these measures (evaluating indications for ULT and monitoring serum urate) are available for use in federal quality reporting programs. Performance on these measures suggests there is significant room for improvement in the management of gout.

Primary Care Practices' Abilities And Challenges In Using Electronic Health Record Data For Quality Improvement.

Federal value-based payment programs require primary care practices to conduct quality improvement activities, informed by the electronic reports on clinical quality measures that their electronic health records (EHRs) generate. To determine whether EHRs produce reports adequate to the task, we examined survey responses from 1,492 practices across twelve states, supplemented with qualitative data. Meaningful-use participation, which requires the use of a federally certified EHR, was associated with the ability to generate reports-but the reports did not necessarily support quality improvement initiatives. Practices reported numerous challenges in generating adequate reports, such as difficulty manipulating and aligning measurement time frames with quality improvement needs, lack of functionality for generating reports on electronic clinical quality measures at different levels, discordance between clinical guidelines and measures available in reports, questionable data quality, and vendors that were unreceptive to changing EHR configuration beyond federal requirements. The current state of EHR measurement functionality may be insufficient to support federal initiatives that tie payment to clinical quality measures.

Circling the Dragon-Taming Health IT Systems to Measure What Matters (TH367)

•Identify the challenges hospice and palliative care programs face when trying to use electronic health records and other IT systems to improve quality and meet requirements of quality payment programs.•Describe the mission and evolution of the AAHPM/HPNA Measuring What Matters Electronic Clinical Quality Measures (eCQMs) Working Group.•Describe the eCQM Group's progress to date on developing specifications for Measuring What Matters measures as electronic Clinical Quality Measures that can be integrated into electronic health records and clinical data registries and outline the work group's future priorities. In this team presentation, leaders from AAHPM and HPNA will showcase the work of the Measuring What Matters Electronic Clinical Quality Measures (eCQMs) Working Group. They will identify the challenges hospice and palliative care programs face when trying to use electronic health records (EHRs) and other IT systems to measure and improve quality. They will discuss how electronic systems have helped or presented challenges in meeting requirements of quality payment programs and describe how the eCQM Group is tackling them. In particular, presenters will explain why electronic clinical quality measures are necessary for interoperability and integration into a myriad of electronic health records and clinical data registries. Discussion may focus on what a data sheet looks like, how to engage with an EHR vendor, etc. They will highlight the committee's work to date to harness IT systems to measure what matters to patients and families living with serious illness. They will also outline the work group's future priorities and provide ample time for audience questions and comments.

The Hyperbaric Oxygen Therapy Registry: Driving quality and demonstrating compliance

To provide an update on the status of provider participation in the US Wound Registry (USWR) and its specialty registry the Hyperbaric Oxygen Therapy Registry (HBOTR), which provide much-needed national benchmarking and quality measurement services for hyperbaric medicine. Providers can meet many requirements of the Merit-Based Incentive Payment System (MIPS) and simultaneously participate in the HBOTR by transmitting Continuity of Care Documents (CCDs) directly from their certified electronic health record (EHR) or by reporting hyperbaric quality measures, the specifications for which are available free of charge for download from the registry website as electronic clinical quality measures for installation into any certified EHR. Computerized systems parse the structured data transmitted to the USWR. Patients undergoing hyperbaric oxygen (HBO₂) therapy are allocated to the HBOTR and stored in that specialty registry database. The data can be queried for benchmarking, quality reporting, public policy, or specialized data projects. Since January 2012, 917,758 clinic visits have captured the data of 199,158 patients in the USWR, 3,697 of whom underwent HBO₂ therapy. Among 27,404 patients with 62,843 diabetic foot ulcers (DFUs) captured, 9,908 DFUs (15.7%) were treated with HBO2 therapy. Between January 2016 and September 2018, the benchmark rate for the 1,000 DFUs treated with HBO₂ was 7.3%, with an average of 28 treatments per patient. There are 2,100 providers who report data to the USWR by transmitting CCDs from their EHR and 688 who submit quality measure data, 300 (43.6%) of whom transmit HBO₂ quality data.

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

This article reviews the evolution of quality measurement in rheumatology, highlighting new health-information technology infrastructure and standards that are enabling unprecedented innovation in this field. Spurred by landmark legislation that ties physician payment to value, the widespread use of electronic health records, 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 electronic health records. At the same time, qualified clinical data registries, like the American College of Rheumatology's Rheumatology Informatics System for Effectiveness registry, are enabling large-scale implementation of eCQMs across national electronic health record networks with real-time performance feedback to clinicians. Although 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. 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.

Development of the American College of Rheumatology's Rheumatoid Arthritis Electronic Clinical Quality Measures.

Electronic clinical quality measures (eCQMs) rely on computer algorithms to extract data from electronic health records (EHRs). On behalf of the American College of Rheumatology (ACR), we sought to develop and test eCQMs for rheumatoid arthritis (RA). Drawing from published ACR guidelines, a working group developed candidate RA process measures and subsequently assessed face validity through an interdisciplinary panel of health care stakeholders. A public comment period followed. Measures that passed these levels of review were electronically specified using the quality data model, which provides standard nomenclature for data elements (category, datatype, and value sets) obtained through an EHR. For each eCQM, 3 clinical sites using different EHR systems tested the scientific feasibility and validity of measures. Measures appropriate for accountability were presented for national endorsement. Expert panel validity ratings were high for all measures (median 8-9 of 9). Health system performance on the eCQMs was 53.6% for RA disease activity assessment, 69.1% for functional status assessment, 93.1% for disease-modifying antirheumatic drug (DMARD) use, and 72.8% for tuberculosis screening. Kappa statistics, which evaluated whether the eCQM validly captured data obtained from manual EHR chart review, demonstrated moderate to substantial agreement (0.54 for functional status assessment, 0.73 for tuberculosis screening, 0.84 for disease activity, and 0.85 for DMARD use). Four eCQMs for RA have achieved national endorsement and are recommended for use in federal quality reporting programs. Implementation and further refinement of these measures is ongoing in the ACR's registry, the Rheumatology Informatics System for Effectiveness (RISE).

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

To describe the stakeholder-engaged processes used to develop, specify, and validate 2 oral health care electronic clinical quality measures. A broad range of stakeholders were engaged from conception through testing to develop measures and test feasibility, reliability, and validity following National Quality Forum guidance. We assessed data element feasibility through semistructured interviews with key stakeholders using a National Quality Forum-recommended scorecard. We created test datasets of synthetic patients to test measure implementation feasibility and reliability within and across electronic health record (EHR) systems. We validated implementation with automated reporting of EHR clinical data against manual record reviews, using the kappa statistic. A stakeholder workgroup was formed and guided all development and testing processes. All critical data elements passed feasibility testing. Four test datasets, representing 577 synthetic patients, were developed and implemented within EHR vendors' software, demonstrating measure implementation feasibility. Measure reliability and validity were established through implementation at clinical practice sites, with kappa statistic values in the "almost perfect" agreement range of 0.80-0.99 for all but 1 measure component, which demonstrated "substantial" agreement. The 2 validated measures were published in the United States Health Information Knowledgebase. The stakeholder-engaged processes used in this study facilitated a successful measure development and testing cycle. Engaging stakeholders early and throughout development and testing promotes early identification of and attention to potential threats to feasibility, reliability, and validity, thereby averting significant resource investments that are unlikely to be fruitful.

Methods for Developing the American College of Rheumatology's Electronic Clinical Quality Measures.

Methods for Developing the American College of Rheumatology's Electronic Clinical Quality Measures.

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution

The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.