Development and application of Breadth-Depth-Context (BDC), a conceptual framework for measuring technology engagement with a qualified clinical data registry

In the 2015 Medicare Access and CHIP Reauthorization Act, the Centers for Medicare and Medicaid Services (CMS) began the transition to a value-based payment system through implementation of the Quality Payment Program (QPP) ([1][1]). Representing a paradigm shift away from traditional volume-based

FRI0524 THE ACR'S RHEUMATOLOGY INFORMATICS SYSTEM FOR EFFECTIVENESS (RISE) REGISTRY SUPPORTS SMALL RHEUMATOLOGY PRACTICES FOR FEDERAL QUALITY REPORTING PROGRAM

BackgroundRoutine collection of disease activity (DA) and patient-reported outcomes (PROs) in rheumatoid arthritis (RA) are nationally endorsed quality measures and critical components of a treat-to-target approach. However, little is known about the role electronic health record (EHR) systems play in facilitating performance on these measures.ObjectiveUsing the American College Rheumatology’s (ACR’s) RISE registry, we analyzed the relationship between EHR system and performance on DA and functional status (FS) quality measures.MethodsWe analyzed data collected in 2018 from practices enrolled in RISE. We assessed practice-level performance on quality measures that require DA and FS documentation. Multivariable linear regression and zero-inflated negative binomial models were used to examine the independent effect of EHR system on practice-level quality measure performance, adjusting for practice characteristics and patient case-mix.ResultsIn total, 220 included practices cared for 314,793 patients with RA. NextGen was the most commonly used EHR system (34.1%). We found wide variation in performance on DA and FS quality measures by EHR system (median 30.1, IQR 0-74.8, and median 9.0, IQR 0-74.2), respectively). Even after adjustment, NextGen practices performed significantly better than Allscripts on the DA measure (51.4% vs 5.0%; P<.05) and significantly better than eClinicalWorks and eMDs on the FS measure (49.3% vs 29.0% and 10.9%; P<.05).ConclusionsPerformance on national RA quality measures was associated with the EHR system, even after adjusting for practice and patient characteristics. These findings suggest that future efforts to improve quality of care in RA should focus not only on provider performance reporting but also on developing and implementing rheumatology-specific standards across EHRs.

We aimed to develop readily measurable digital quality measure statements for clinical care in systemic lupus erythematosus (SLE) using a multistep process guided by consensus methods. Using a modified Delphi process, an American College of Rheumatology (ACR) workgroup of SLE experts reviewed all North American and European guidelines from 2000 to 2020 on treatment, monitoring, and phenotyping of patients with lupus. Workgroup members extracted quality constructs from guidelines, rated these by importance and feasibility, and generated evidence-based quality measure statements. The ACR Rheumatology Informatics System for Effectiveness (RISE) Registry was queried for measurement data availability. In 3 consecutive Delphi sessions, a multidisciplinary Delphi panel voted on the importance and feasibility of each statement. Proposed measures with consensus on feasibility and importance were ranked to identify the top 3 measures. Review of guidelines and distillation of 57 quality constructs resulted in 15 quality measure statements. Among these, 5 met high consensus for importance and feasibility, including 2 on treatment and 3 on laboratory monitoring measures. The 3 highest-ranked statements were recommended for further measure specification as SLE digital quality measures: 1) hydroxychloroquine use, 2) limiting glucocorticoid use >7.5 mg/day to <6 months, and 3) end-organ monitoring of kidney function and urine protein excretion at least every 6 months. The Delphi process selected 3 quality measures for SLE care on hydroxychloroquine, glucocorticoid reduction, and kidney monitoring. Next, measures will undergo specification and validity testing in RISE and US rheumatology practices as the foundation for national implementation and use in quality improvement programs.

Sarcoidosis is often treated with glucocorticoids, although the use of biologics is growing. Prescribing patterns for biologics for patients with sarcoidosis in US rheumatology practices have never been examined. Given that there are no steroid-sparing US Food and Drug Administration-approved therapies for sarcoidosis, we sought to characterize the real-world treatment of sarcoidosis and to assess practice-level variation in prescribing patterns. We conducted an observational study of patients with sarcoidosis using data from the Rheumatology Informatics System for Effectiveness (RISE) registry (2014-2018). The RISE registry represents an estimated 32% of the US clinical rheumatology workforce. Adult patients with ≥2 codes for sarcoidosis ≥30 days apart were included. We examined sarcoidosis-specific medication use at any time during the study period. Data were analyzed at the practice level. A total of 3,276 patients with sarcoidosis from 184 practices were included. Of those patients, 75.1% were women, with a mean age of 59.0 ± 12.5 years; 48.3% were White and 27.6% were Black. Overall, 59.3% of patients were prescribed glucocorticoids, and 24.7% received prolonged glucocorticoid therapy (≥10 mg/day for ≥90 days). In all, 12.1% received a biologic or targeted synthetic disease-modifying antirheumatic drug (tsDMARD), most commonly tumor necrosis factor inhibitors. There was wide practice-level variation among 31 practices with ≥30 patients with sarcoidosis; biologic use ranged from 15.6% to 69.2%. Infliximab represented the most common biologic prescribed. In a large sample of US rheumatology practices, 12.1% of patients with sarcoidosis received biologics or tsDMARDs. We found high variability in biologic use across practices. The significant use of long-term glucocorticoids suggests unmet therapeutic needs in this patient population.

Patients with rheumatoid arthritis (RA) who also have diabetes mellitus (DM) might have worse clinical outcomes and adverse events compared to patients with RA who do not have DM. We evaluated the effects of DM on Health Assessment Questionnaire (HAQ) changes and outpatient infection rates in patients with RA. Using the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) electronic health record-based registry, we identified patients with RA who had ≥ 1 rheumatologist visit with a HAQ measured in 2016 (index visit), ≥ 1 previous visit, and a subsequent outcome visit with the same HAQ measured at 12 months (± 3 months). We identified DM by diagnosis codes, medications, or laboratory values. Outpatient infection was defined by diagnosis codes or antiinfective medications. We calculated mean HAQ change and incidence rate (IR) of outpatient infections among patients with and without DM. Generalized linear models and Cox regression were used to calculate the adjusted mean HAQ change and HRs. We identified 3853 RA patients with DM and 18,487 without DM. The mean HAQ change between index and outcome visit among patients with DM was 0.03 and without DM was 0.002 (P < 0.01). We identified 761 outpatient infections for patients with DM with an IR of 22.6 (95% CI 21.0-24.2) per 100 person-years and 3239 among patients without DM with an IR of 19.8 (95% CI 19.1-20.5). The adjusted HR of outpatient infections among patients with DM was 0.99 (95% CI 0.91-1.07), compared to patients without DM. Patients with RA with concomitant DM had greater worsening, or less improvement, in their functional status, suggesting additional interventions may be needed for RA patients with DM to optimize treatment and management of other comorbidities.

Quality & Regulatory| July 2021 Groups and Individuals to See Increased Data Auditing Matthew T. Popovich, PhD; Matthew T. Popovich, PhD Search for other works by this author on: This Site PubMed Google Scholar DeLaine Schmitz, MSHL DeLaine Schmitz, MSHL Search for other works by this author on: This Site PubMed Google Scholar ASA Monitor July 2021, Vol. 85, 37–38. https://doi.org/10.1097/01.ASM.0000758380.94963.28 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share MailTo Twitter LinkedIn Cite Icon Cite Get Permissions Search Site Citation Matthew T. Popovich, DeLaine Schmitz; Groups and Individuals to See Increased Data Auditing. ASA Monitor 2021; 85:37–38 doi: https://doi.org/10.1097/01.ASM.0000758380.94963.28 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsASA Monitor Search Advanced Search Topics: aqi nacor, mips, quality indicators, quality payment program, registries, surgical procedures, operative, outcome measures, pain management As anesthesiologists and their groups settle in for another year of the Merit-based Incentive Payment System (MIPS), a lesser-known requirement for validating measure and improvement activity reporting is emerging as an added burden – audits. Individuals and groups may have seen this as part of their 2020 participation in the Anesthesia Quality Institute National Anesthesia Clinical Outcomes Registry (AQI NACOR) Qualified Registry or Qualified Clinical Data Registry (QCDR), as part of a different MIPS registry, or from a direct notification from CMS. Regardless, individuals and groups should make sure that their quality measure data is complete, documented, and readily available should they encounter an audit. Unfortunately, the added emphasis on auditing may surprise some who have viewed MIPS reporting as another “check-the-box” sort of program. CMS requires that individuals and groups maintain documentation related to their MIPS data for a minimum of six years. At any time within that six-year... You do not currently have access to this content.

Fulfill MIPS Requirements With QOPI Reporting Registry, a Qualified Clinical Data Registry (QCDR) Brought to You by ASCO and ASTRO

Clinical registries are increasingly used as national performance measurement platforms. In 2018, nearly 70 percent of the more than 50 specialty society registries in the United States were used by the Centers for Medicare & Medicaid Services (CMS) to measure the quality of clinical care. Private payers and evaluating organizations also use or desire to use registry information to inform quality improvement programs and value-based payment models.The requirements for an entity to become a CMS Qualified Clinical Data Registry (QCDR) constitute a minimum set of standards for the purpose of reporting to the CMS Quality Payment Program. Models and frameworks exist that can help classify registries by purpose and use, and maturity models are available for evaluating health IT systems generally. However, there is currently no framework that describes the capability that should be expected from a registry at different phases of its development and maturity.In response, the National Quality Registry Network (NQRN) has developed a registry maturational framework. The framework models early, intermediate and mature development phases, the capabilities anticipated during these phases and 17 domains across which registry programs support those capabilities. The framework was developed and refined by NQRN registry stewards, users and other stakeholders between 2013–2018. It is intended to be used as a developmental guide or for registry evaluation. The successful use of registry information to execute value-based payment models is a critical need in U.S. health care. The NQRN framework can help ensure that our national system of registries is rising to the occasion.

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.

ABSTRACTBased on a quantitative content analysis of political actors’ Facebook posts (N = 1915), this study investigates profile-level and post-level drivers of user engagement (comments, likes, and shares) by employing a multilevel approach. For the first time in extant research, we also examine the factors that drive political actors to react to user comments. Findings indicate that the number of followers, the use of an official fan profile, and party vote share were negatively related to political actors’ reactions to user comments. Furthermore, party profiles were least successful in stimulating user engagement. On the post level, we found that reasoning, post length, and references to competitive political actors have the potential to increase different types of user engagement. Negative, but not positive tonality increased user engagement and positive emotional expressions had a stronger effect on user engagement than negative emotions. Furthermore, humorous posts were more likely to be commented, liked, or shared, while mobilization cues had predominantly negative effects on user engagement.

This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work aims to enhance the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations that are aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-aware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

The United States Food and Drug Administration will consider the expansion of coverage indications for some drugs and devices based on real-world data. Real-world data accrual in patient registries has historically been via manual data entry from the medical chart at a time distant from patient care, which is fraught with systematic error. The efficient automated transmission of data directly from electronic health records is replacing this labor-intensive paradigm. However, real-world data collection is unfamiliar. The potential sources of bias arising from the source of data and data accrual, documentation, and aggregation have not been well defined. Furthermore, the technological aspects of data acquisition and transmission are less transparent. We explore opportunities for harnessing direct-from-electronic health record registry reporting and propose the ABCs of Registries (Analysis of Bias Criteria of Registries), which are an evaluation framework for publications to minimize potential bias of real-world data obtained directly from an electronic health record method. These standards are based on a point-of-care data documentation process using a common definitional framework and data dictionaries. By way of example, we describe a wound registry obtained directly from electronic health records. This qualified clinical data registry minimizes bias by ensuring complete and accurate point-of-care data capture, standardizes usual care linked to quality reporting, and prevents post-hoc vetting of outcomes. The resulting data are of high quality and integrity and can be used for comparative effectiveness research in wound care. In this way, the effort needed to succeed with the Quality Payment Program is leveraged to obtain the real-world data needed for comparative effectiveness research.

According to the existing classification chronic migraine (CM) is a primary headache that occurs on 15 or more days per month for more than 3 months and has the features of migraine on at least 8 days per month. In Europe, CM prevalence ranges from 2.7% to 4.7%. CM is an extremely disabling disorder associated with significant functional impairment. The 2010 Global Burden of Disease Survey conducted by the WHO listed migraine as the 7th cause of disability in the world, responsible for 2.9% of all years of life lost to disability. Unfortunately, however, in its current definition CM includes subgroups of patients with very different levels of severity and outcome. Failing to recognize them entails highly negative repercussions on research and clinical practice, with inadequate patient management and lack of cost-effectiveness. Therefore, it is essential that these subgroups of subjects be clearly identified to optimize clinical management, rationalize the allocation of economic resources, provide specific clinical and health care procedures, and clarify pathogenetic mechanisms. The clinical registry will include the data of all patients with CM, aged 18 or over, seen at the Headache Centres of Parma, Bologna, Rome, and Milan. The diagnosis of CM will be made by a headache specialist based on the diagnostic criteria of the ICHD-III beta (2013) and on the CM classification proposed by Manzoni et al[1]. The principal aim of the clinical registry will be to identify still undefined subgroups of subjects with CM through a specially designed clinical registry to be applied in a large number of outpatients. The registry will make it possible to collect: personal and social patient data, as well as data about their physiological conditions and non-essential habits; clinical features of headache (before and after its evolution to chronicity); any factors concomitant with the headache's evolution to chronicity (i.e., medication overuse, life events, hypertension); comorbidity and injuries; headache-related disability (MIDAS, WHO-DAS-II, MSQ); quality of life (SF36); symptomatic and preventive treatments; medication overuse; visits and hospitalizations for CM; and recognized sickness and invalidity allowances. The creation of a large-scale registry will make it possible to identify specific CM (i.e., refractory CM) subgroups, based on clinical and biological features. It will also make it possible to identify areas with lack of or inadequate health care provision and waste of resources (i.e., useless examinations or treatments), eventually helping to improve CM management and ensure health care procedures that are more appropriate.

Using the International Classification of Functioning, Disability and Health Framework to Achieve Interprofessional Functional Outcomes for Young Children: A Speech-Language Pathology Perspective