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

Quality Measures in Systemic Sclerosis

Quality improvement is an emerging field, that applies principles of improvement science and utilizes measurement methods with the aim of improving patient care. Systemic sclerosis (SSc) is a systemic autoimmune rheumatic disease associated with increased healthcare burden, cost, morbidity, and mortality. Gaps in delivering care to patients with SSc have been consistently observed. In this article, we introduce the discipline of quality improvement and its use of quality measures. We summarize and comparatively evaluate three sets of quality measures that have been proposed to evaluate the quality of care of patients with SSc. Finally, we highlight the areas of unmet needs and indicate future directions for quality improvement and quality measures in SSc.

A Systematic Review of Electronic Medical Record Driven Quality Measurement and Feedback Systems

Historically, quality measurement analyses utilize manual chart abstraction from data collected primarily for administrative purposes. These methods are resource-intensive, time-delayed, and often lack clinical relevance. Electronic Medical Records (EMRs) have increased data availability and opportunities for quality measurement. However, little is known about the effectiveness of Measurement Feedback Systems (MFSs) in utilizing EMR data. This study explores the effectiveness and characteristics of EMR-enabled MFSs in tertiary care. The search strategy guided by the PICO Framework was executed in four databases. Two reviewers screened abstracts and manuscripts. Data on effect and intervention characteristics were extracted using a tailored version of the Cochrane EPOC abstraction tool. Due to study heterogeneity, a narrative synthesis was conducted and reported according to PRISMA guidelines. A total of 14 unique MFS studies were extracted and synthesized, of which 12 had positive effects on outcomes. Findings indicate that quality measurement using EMR data is feasible in certain contexts and successful MFSs often incorporated electronic feedback methods, supported by clinical leadership and action planning. EMR-enabled MFSs have the potential to reduce the burden of data collection for quality measurement but further research is needed to evaluate EMR-enabled MFSs to translate and scale findings to broader implementation contexts.

Performance of a rule-based semi-automated method to optimize chart abstraction for surveillance imaging among patients treated for non-small cell lung cancer

Background

We aim to develop and test performance of a semi-automated method (computerized query combined with manual review) for chart abstraction in the identification and characterization of surveillance radiology imaging for post-treatment non-small cell lung cancer patients.

Methods

A gold standard dataset consisting of 3011 radiology reports from 361 lung cancer patients treated at the Veterans Health Administration from 2008 to 2016 was manually created by an abstractor coding image type, image indication, and image findings. Computerized queries using a text search tool were performed to code reports. The primary endpoint of query performance was evaluated by sensitivity, positive predictive value (PPV), and F1 score. The secondary endpoint of efficiency compared semi-automated abstraction time to manual abstraction time using a separate dataset and the Wilcoxon rank-sum test.

Results

Query for image type demonstrated the highest sensitivity of 85%, PPV 95%, and F1 score 0.90. Query for image indication demonstrated sensitivity 72%, PPV 70%, and F1 score 0.71. The image findings queries ranged from sensitivity 75–85%, PPV 23–25%, and F1 score 0.36–0.37. Semi-automated abstraction with our best performing query (image type) improved abstraction times by 68% per patient compared to manual abstraction alone (from median 21.5 min (interquartile range 16.0) to 6.9 min (interquartile range 9.5), p < 0.005).

Conclusions

Semi-automated abstraction using the best performing query of image type improved abstraction efficiency while preserving data accuracy. The computerized query acts as a pre-processing tool for manual abstraction by restricting effort to relevant images. Determining image indication and findings requires the addition of manual review for a semi-automatic abstraction approach in order to ensure data accuracy.

Identification of Five Quality Needs for Rheumatology (Text Analysis and Literature Review)

Background: While the use of the term "quality" in industry relates to the basic idea of making processes measurable and standardizing processes, medicine focuses on achieving health goals that go far beyond the mere implementation of diagnostic and therapeutic processes. However, the quality management systems used are often simple, self-created concepts that concentrate on administrative processes without considering the quality of the results, which is essential for the patient. For several rheumatic diseases, both outcome and treatment goals have been defined. This work summarizes current mainstreams of strategies with published quality efforts in rheumatology. Methods: PubMed, Cochrane Library, and Web of Science were used to search for studies, and additional manual searches were carried out. Screening and content evaluation were carried out using the PRISMA-P 2015 checklist. After duplicate search in the Endnote reference management software (version X9.1), the software Rayyan QCRI (https://rayyan.qcri.org) was applied to check for pre-defined inclusion and exclusion criteria. Abstracts and full texts were screened and rated using Voyant Tools (https://voyant-tools.org/). Key issues were identified using the collocate analysis. Results: The number of selected publications was small but specific (14 relevant correlations with coefficients >0.8). Using trend analysis, 15 publications with relative frequency of keywords >0.0125 were used for content analysis, revealing 5 quality needs. The treat to target (T2T) initiative was identified as fundamental paradigm. Outcome parameters required for T2T also allow quality assessments in routine clinical work. Quality care by multidisciplinary teams also focusing on polypharmacy and other quality aspects become essential, A global software platform to assess quality aspects is missing. Such an approach requires reporting of multiple outcome parameters according to evidence-based clinical guidelines and recommendations for the different rheumatic diseases. All health aspects defined by the WHO (physical, mental, and social health) have to be integrated into the management of rheumatic patients. Conclusion: For the future, quality projects need goals defined by T2T based initiatives in routine clinical work, secondary quality goals include multidisciplinary cooperation and reduction of polypharmacy. Quality indicators and standards in different health systems will provide new information to optimize patients' care in different health systems.

Quality of Neurologic Care in the United States: Initial Report From the Axon Registry

OBJECTIVE

To provide the initial description of the quality of outpatient US neurologic care as collected and reported in the Axon Registry.

METHODS

We describe characteristics of registry participants and the performance of neurology providers on 20 of the 2019 Axon Registry quality measures. From the distribution of providers' scores on a quality measure, we calculate the median performance for each quality measure. We test for associations between quality measure performance, provider characteristics, and intrinsic measure parameters.

RESULTS

There were 948 neurology providers who contributed a total of 6,480 provider-metric observations. Overall, the average quality measure performance score at the provider level was 66 (median 77). At the measure level (n = 20), the average quality measure performance score was 53 (median 55) with a range of 2 to 100 (interquartile range 20-91). Measures with a lower-complexity category (e.g., discrete orders, singular concepts) or developed through the specialty's qualified clinical data registry pathway had higher performance distributions. There was no difference in performance between Merit-Based Incentive Payment System (MIPS) and non-MIPS providers. There was no association between quality measure performance and practice size, measure clinical topic/neurologic condition, or measure year of entry.

CONCLUSIONS

This cross-sectional assessment of quality measure performance in 2019 Axon Registry data demonstrates modest performance scores and considerable variability across measures and providers. More complex measures were associated with lower performance. These findings serve as a baseline assessment of quality of ambulatory neurologic care in the United States and provide insights into future measure design.

Digital health technologies: opportunities and challenges in rheumatology

The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learning. The increased availability of these technologies offers opportunities for improving important aspects of rheumatology, including access, outcomes, adherence and research. However, despite its growth in some areas, particularly with non-health-care consumers, digital health technology has not substantially changed the delivery of rheumatology care. This Review discusses key barriers and opportunities to improve application of digital health technologies in rheumatology. Key topics include smart design, voice enablement and the integration of electronic patient-reported outcomes. Smart design involves active engagement with the end users of the technologies, including patients and clinicians through focus groups, user testing sessions and prototype review. Voice enablement using voice assistants could be critical for enabling patients with hand arthritis to effectively use smartphone apps and might facilitate patient engagement with many technologies. Tracking many rheumatic diseases requires frequent monitoring of patient-reported outcomes. Current practice only collects this information sporadically, and rarely between visits. Digital health technology could enable patient-reported outcomes to inform appropriate timing of face-to-face visits and enable improved application of treat-to-target strategies. However, best practice standards for digital health technologies do not yet exist. To achieve the potential of digital health technology in rheumatology, rheumatology professionals will need to be more engaged upstream in the technology design process and provide leadership to effectively incorporate the new tools into clinical care.

Nurses' Attitudes Toward Electronic Clinical Quality Measures: A Descriptive Study

BACKGROUND

Electronic clinical quality measures (eCQMs) are a method that automatically extract data from electronic health records (EHRs) and compute and generate the results to report and track the quality of care and patient outcomes.

PURPOSE

The purpose of this study was to explore nurses' attitudes toward eCQMs and the factors influencing this attitude.

METHODS

A descriptive cross-sectional study was conducted using a closed-ended questions survey of 92 nurses in a teaching hospital.

RESULTS

The average score for nurses' attitudes toward eCQMs was 3.47 out of 4. Participants with a master's degree had more positive attitudes than those with a baccalaureate degree. Head nurses had more positive attitudes than staff nurses.

CONCLUSIONS

The nurses in the study hospital have a positive attitude toward eCQMs. Health care organizations should strengthen the attitudes of nurses toward eCQMs.

Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms

Objective

The accurate and efficient collection and documentation of disease activity measures (DAMs) is critical to improve clinical care and outcomes research in rheumatoid arthritis (RA). This study evaluated the performance of an automated process to extract DAMs from medical notes in the electronic health record (EHR).

Methods

An automated text processing system was developed to extract the Disease Activity Score for 28 joints (DAS28) and its clinical and laboratory elements from the Veterans Affairs EHR for patients enrolled in the Veterans Affairs Rheumatoid Arthritis (VARA) registry. After automated text processing derivation, data accuracy was assessed by comparing the automated text processing system and manual extraction with gold standard chart review in a separate validation phase.

Results

In the validation phase, 1569 notes from 596 patients at 3 sites were evaluated, with 75 (6%) notes detected only by automated text processing, 85 (5%) detected only by manual extraction, and 1408 (90%) detected by both methods. The accuracy of automated text processing ranged from 90.7% to 96.7% and the accuracy of manual extraction ranged from 91.3% to 95.0% for the different clinical and laboratory elements. The accuracy of the two methods to calculate the DAS28 was 78.1% for automated text processing and 78.3% for manual extraction.

Conclusion

The automated text processing approach is highly efficient and performed as well as the manual extraction approach. This advance has the potential for significant improvements in the collection, documentation, and extraction of these data to support clinical practice and outcomes research relevant to RA as well as the potential for broader application to other health conditions.

Unmet need in rheumatology: reports from the Targeted Therapies meeting 2019

Objectives

To detail the greatest areas of unmet scientific and clinical needs in rheumatology.

Methods

The 21st annual international Advances in Targeted Therapies meeting brought together more than 100 leading basic scientists and clinical researchers in rheumatology, immunology, epidemiology, molecular biology and other specialties. During the meeting, breakout sessions were convened, consisting of 5 disease-specific groups with 20–30 experts assigned to each group based on expertise. Specific groups included: rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, systemic lupus erythematosus and other systemic autoimmune rheumatic diseases. In each group, experts were asked to identify unmet clinical and translational research needs in general and then to prioritise and detail the most important specific needs within each disease area.

Results

Overarching themes across all disease states included the need to innovate clinical trial design with emphasis on studying patients with refractory disease, the development of trials that take into account disease endotypes and patients with overlapping inflammatory diseases, the need to better understand the prevalence and incidence of inflammatory diseases in developing regions of the world and ultimately to develop therapies that can cure inflammatory autoimmune diseases.

Conclusions

Unmet needs for new therapies and trial designs, particularly for those with treatment refractory disease, remain a top priority in rheumatology.

The development of quality indicators for systemic lupus erythematosus using electronic health data: A modified RAND appropriateness method

Objective: Quality indicators (QIs) are tools that standardize evaluations in terms of the minimum acceptable quality of care, presumably contributing for the better management of patients with systemic lupus erythematosus (SLE). This study aimed to develop QIs for SLE using electronic health data.Methods: The modified RAND/UCLA Appropriateness Method was used to develop the QIs. First, a literature review was conducted. Second, the candidate QI items that were available to be evaluated using the electronic health data were extracted. Third, the appropriateness of the items was assessed via rating rounds and panelists' discussions.Results: We found 3621 articles in the initial search. Finally, 34 studies were reviewed, from which 17 potential indicators were extracted as candidate QIs. Twelve indicators were selected as the final QI set through the process of appropriateness. The median appropriateness of these 12 indicators was at least 7.5, and all of them were without disagreement. The QI included assessment of disease activity, treatment of SLE, drug toxicity monitoring, treatment of glucocorticoid complications, and assessment of SLE complications.Conclusion: We formulated 12 QIs for the assessment of patients with SLE based on electronic medical data. Our QI set would be a practical tool as a quality measure.

Quality improvement initiatives in rheumatology: an integrative review of the last 5 years

PURPOSE OF REVIEW

We reviewed recent quality improvement initiatives in the field of rheumatology to identify common strategies and themes leading to measurable change.

RECENT FINDINGS

Efforts to improve quality of care in rheumatology have accelerated in the last 5 years. Most studies in this area have focused on interventions to improve process measures such as increasing the collection of patient-reported outcomes and vaccination rates, but some studies have examined interventions to improve health outcomes. Increasingly, researchers are studying electronic health record (EHR)-based interventions, such as standardized templates, flowsheets, best practice alerts and order sets. EHR-based interventions were most successful when reinforced with provider education, reminders and performance feedback. Most studies also redesigned workflows, distributing tasks among clinical staff. Given the common challenges and solutions facing rheumatology clinics under new value-based payment models, there are important opportunities to accelerate quality improvement by building on the successful efforts to date. Structured quality improvement models such as the learning collaborative may help to disseminate successful initiatives across practices.

SUMMARY

Review of recent quality improvement initiatives in rheumatology demonstrated common solutions, particularly involving leveraging health IT and workflow redesign.

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

Background

Defining 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.

Objective

The 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.

Methods

Starting 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.

Results

The 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.

Conclusions

In 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.