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There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter (“Protected Health Information filter”). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods.

Qualified Clinical Data Registries (QCDRs) have proliferated across many medical specialties, facilitating quality measure performance monitoring and reporting in programs like the CMS Merit-based Incentive Payment System. Many of these QCDRs offer web-based, clinician-facing dashboards to support quality improvement. However, it is unknown whether engagement with such dashboards is associated with improvements in quality of care. We investigated the cross-sectional relationship between engagement with a QCDR dashboard and quality measure performance. Data derived from a rheumatology QCDR (\"Rheumatology Informatics System for Effectiveness [RISE]\") and audit log data from the dashboard (exposure) and Merit-based Incentive Payment System submission data (outcome) from 2020-2022 were included. Among practices participating in RISE, we assessed aggregated engagement with the QCDR dashboard and quality performance for 8 rheumatology-specific measures at the practice level. For each measure, the binomial generalized linear model was used to examine the association between dashboard engagement and measure performance, adjusting for EHR vendor, study year, and clustering at the practice level to account for repeated measures. Two types of engagement were analyzed: (1) measure-specific (interactions with patient-level information for a particular measure) and (2) global (interactions with any feature of the dashboard, classified into 4 profiles). Linear trends between the level of dashboard engagement and performance were also tested in the global analysis. In total, 211 practices were included in the study; over half were single-specialty practices. During their first year in the study, 65% of the practices had \"most\" or \"moderate\" levels of global engagement. In measure-specific analyses, we observed a positive but nonsignificant association of each individual and \"any\" actions with performance on 6-8 measures. However, having a ≥90th percentile number of drill-down views on 1 measure (rheumatoid arthritis (RA) periodic disease activity assessment) was statistically significant (odds ratio [OR] 2.3, 95% CI 1.2-4.3). In global analyses, we observed a similar pattern, where practices \"most\" engaged with the dashboard had higher odds of better performance compared to those with \"none.\" In total, 4 measures (osteoporosis screening, RA functional status assessment, RA periodic disease activity assessment, and gout serum urate target) had a statistically significant association with engagement and exhibited a \"dose-response\" relationship (P=.004, .02, <.001, and .04, respectively, for trend). Practices with \"any\" global engagement had higher performance on 6 out of 8 measures, again, with RA periodic disease activity assessment being statistically significant (OR 2.9, 95% CI 1.3-6.6). We found that higher levels of engagement were associated with higher performance on some, but not all, rheumatology-specific quality measures. Additional work is needed to understand whether the dashboard facilitates quality improvement or is merely a marker for high-performing practices.

Rheumatic diseases are a group of chronic conditions that are associated with significant morbidity, impaired physical function, psychosocial stress, and cost to the healthcare system. Peer support interventions have been shown to have a positive impact on health outcomes in several chronic conditions, but no review has specifically assessed the impact of peer support on rheumatic conditions. The aim of this narrative literature review was to understand how peer support has been applied in the field of rheumatology, with a specific focus on the impact of observational and randomized studies of direct peer support interventions on various outcome measures across rheumatic conditions. We also examined studies exploring patient attitudes and preferences toward peer support. The majority of studies included focused on peer support in rheumatoid arthritis and systemic lupus erythematosus. Generally, patients across the spectrum of rheumatic disease perceive peer support as a useful tool. Peer support interventions, while highly variable, were generally associated with positive impacts on health-related quality of life metrics (both perceived and measured), although these differences were not always statistically significant. Important limitations include variability in study design, selection bias among study participants, and short follow-up periods across most peer support interventions.

The SARS-CoV-2 global pandemic resulted in major disruptions to medical care. We aimed to understand changes in outpatient care delivery and use of telemedicine in U.S. rheumatology practices during this period. Rheumatology Informatics System Effectiveness (RISE) is a national, EHR-enabled registry that passively collects data on all patients seen by participating practices. Included practices were required to have been participating in RISE from January 2019 through August 2020 (N = 213). We compared total visit counts and telemedicine visits during March–August 2020 to March–August 2019 and stratified by locations in states with shelter-in-place (SIP) orders. We assessed characteristics of patients within each practice, including primary rheumatic diagnosis and disease activity scores, where available. We included 213 practices with 945,160 patients. Overall, we found visit counts decreased by 10.9% (from 1,302,455 to 1,161,051) between March and August 2020 compared to 2019; this drop was most dramatic during the month of April (− 22.3%). Telemedicine visits increased from 0% to a mean of 12.1%. Practices in SIP states had more dramatic decreases in visits, (11.5% vs. 5.3%). We found no major differences in primary diagnoses or disease activity across the two periods. We detected a meaningful decrease in rheumatology visits in March–August 2020 during the SARS-CoV-2 global pandemic compared to the year prior with a concomitant increase in the use of telemedicine. Future work should address possible adverse consequences to patient outcomes due to decreased contact with clinicians.

Background Behçet’s disease (BD), a chronic systemic vasculitis, has distinct geographical and ethnic variation. Data regarding the epidemiology of patients with BD in the U.S. are limited; therefore, we sought to describe BD patient characteristics and medication use in the U.S., and compared them with data from patients from endemic regions. Methods We conducted a cross-sectional study using data from the RISE registry (2014–2018). Patients aged ≥ 18 years with BD were included. Sociodemographic and treatment information was extracted. We compared patients from the RISE registry to data from other published studies of patients with BD from endemic areas. Results One thousand three hundred twenty-three subjects with BD from the RISE registry were included. Mean age was 48.7 ± 16.3 years, female to male ratio was 3.8:1, and 66.7% were White. The most frequently used medications included glucocorticoids (67.6%) and colchicine (55.0%). Infliximab and adalimumab were the most used biologics (14.5% and 14.1%, respectively); 3.2% of patients used apremilast. The RISE registry had more women (79.3%), and patients were older compared to previously published BD studies from endemic areas. Methotrexate and TNFi were more commonly reported in RISE (21.8% and 29.4%) compared to studies from Egypt and Turkey. Colchicine, cyclosporine, and cyclophosphamide were more commonly used in cohorts from Egypt, Turkey, and Iran. Conclusions Findings from the largest BD dataset in the U.S. suggest that BD patients are predominantly female. Further research is needed to explore the reasons for the higher prevalence of BD among women in the U.S. and its possible impact on disease severity and management.

Background Outcome measures are crucial to support a treat-to-target approach to rheumatoid arthritis (RA) care, yet their integration into clinical practice remains inconsistent. We developed an Electronic Heath Record-integrated, patient-facing side-car application to display RA outcomes (disease activity, functional status, pain scores), medications, and lab results during clinical visits (“RA PRO Dashboard”). The study aimed to evaluate patient perceptions and attitudes towards the implementation of a novel patient-facing dashboard during clinical visits using a mixed-methods approach. Methods RA patients whose clinicians used the dashboard at least once during their clinical visit were invited to complete a survey regarding its usefulness in care. We also conducted semi-structured interviews with a subset of patients to assess their perceptions of the dashboard. The interviews were transcribed verbatim and analyzed thematically using deductive and inductive techniques. Emerging themes and subthemes were organized into four domains of the Ecological Model of Health. Results Out of 173 survey respondents, 79% were interested in seeing the dashboard again at a future visit, 71% felt it improved their understanding of their disease, and 65% believed it helped with decision-making about their RA care. Many patients reported that the dashboard helped them discuss their RA symptoms (76%) and medications (72%) with their clinician. Interviews with 29 RA patients revealed 10 key themes: the dashboard was perceived as a valuable visual tool that improved patients’ understanding of RA outcome measures, enhanced their involvement in care, and increased their trust in clinicians and the clinic. Common reported limitations included concerns about reliability of RA outcome questionnaires for some RA patients and inconsistent collection and explanation of these measures by clinicians. Conclusions In both the quantitative and qualitative components of the study, patients reported that the dashboard improved their understanding of their RA, enhanced patient-clinician communication, supported shared decision-making, and increased patient engagement in care. These findings support the use of dashboards or similar data visualization tools in RA care and can be used in future interventions to address challenges in data collection and patient education.

ObjectiveAccurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient identification. We investigated the current performance of International Classification of Diseases (ICD) codes, 9th and 10th editions (ICD9/10), for identifying prevalent LN, and developed scoring systems to increase identification of LN that are adaptable to settings with and without LN ICD codes.MethodsTraining and test sets derived from EHR data from a large health system. An external set comprised data from the EHR of a second large health system. Adults with ICD9/10 codes for SLE were included. LN cases were ascertained through manual chart reviews conducted by rheumatologists. Two definitions of LN were used: strict (definite LN) and inclusive (definite, potential or diagnostic uncertainty). Gradient boosting models including structured EHR fields were used for predictor selection. Two logistic regression-based scoring systems were developed (‘LN-Code’ included LN ICD codes and ‘LN-No Code’ did not), calibrated and validated using standard performance metrics.ResultsA total of 4152 patients from University of California San Francisco Medical Center and 370 patients from Zuckerberg San Francisco General Hospital and Trauma Center met the eligibility criteria. Mean age was 50 years, 87% were female. LN diagnosis codes demonstrated low sensitivity (43–73%) but high specificity (92–97%). LN-Code achieved an area under the curve (AUC) of 0.93 and a sensitivity of 0.88 for identifying LN using the inclusive definition. LN-No Code reached an AUC of 0.91 and a sensitivity of 0.95 (0.97 for the strict definition). Both scoring systems had good external validity, calibration and performance across racial and ethnic groups.ConclusionsThis study quantified the underutilisation of LN diagnosis codes in EHRs and introduced two adaptable scoring systems to enhance LN identification. Further validation in diverse healthcare settings is essential to ensure their broader applicability.

Background Rheumatoid arthritis (RA) impacts quality of life causing disability and increased mortality. Treatment decisions are complex and require individualization. Shared decision making (SDM) is the first principle of RA treat-to-target guidelines, but uptake is suboptimal. We aim to evaluate the effectiveness of a multicomponent SDM intervention on RA disease activity and explore the early implementation of the intervention within three geographically diverse rheumatology services. Methods The RAiSeD trial uses a stepped-wedge, cluster-randomized trial design at three U.S. Veterans Health Administration rheumatology clinics, targeted to enroll more than 400 patients and over 45 clinicians. The multicomponent SDM intervention consists of three parts: (1) rheumatology clinician training and a pocket card on SDM and fostering choice awareness (“acknowledging when there is more than one sensible option available to address a patient’s situation”), (2) RA patient activation using the AskShareKnow questions, and (3) a point-of-care decision aid (RA Choice) and medication summary guide. We will conduct a mixed-methods outcomes and process evaluation. Outcomes will be evaluated during a pre-intervention (usual care) and intervention period. The primary outcome is disease activity as measured by the validated Clinical Disease Activity Index (CDAI), with secondary outcomes of RA knowledge and medication adherence. SDM will be measured by two brief, validated patient-reported measures. A subgroup of clinic visits will be audio-recorded and clinicians’ efforts to involve patients in SDM will be assessed. The implementation process will be evaluated using stakeholder interviews and field notes at each of the three sites. Discussion This study is the first multi-site trial of a multicomponent intervention to facilitate SDM among veterans with RA. We expect to improve uptake of SDM across geographically distinct rheumatology clinics and hypothesize that patients exposed to the interventions will have a greater decrease in disease activity and an increase in knowledge of RA medications compared to usual care. Insights gained from this study will inform broader dissemination and implementation of SDM across VA rheumatology clinics and beyond, with the goal of improving quality of care for all persons with RA. Trial registration ClinicalTrials.gov NCT05530694. Registered on September 7, 2022.

Experts have noted a concerning gap between clinical natural language processing (NLP) research and real-world applications, such as clinical decision support. To help address this gap, in this viewpoint, we enumerate a set of practical considerations for developing an NLP system to support real-world clinical needs and improve health outcomes. They include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems, and (3) the feasibility of implementation and continued monitoring. These considerations are intended to benefit the design of future clinical NLP projects and can be applied across a variety of settings, including large health systems or smaller clinical practices that have adopted electronic medical records in the United States and globally.

Background Improving shared decision-making using a treat-to-target approach, including the use of clinical outcome measures, is important to providing high quality care for rheumatoid arthritis (RA). We developed an Electronic Health Record (EHR) integrated, patient-facing sidecar dashboard application that displays RA outcomes, medications, and lab results for use during clinical visits (“RA PRO dashboard”). The purpose of this study was to assess clinician perceptions and experiences using the dashboard in a university rheumatology clinic. Methods We conducted focus group (FG) discussions with clinicians who had access to the dashboard as part of a randomized, stepped-wedge pragmatic trial. FGs explored clinician perceptions towards the usability, acceptability, and usefulness of the dashboard. FG data were analyzed thematically using deductive and inductive techniques; generated themes were categorized into the domains of the Technology Acceptance Model (TAM). Results 3 FG discussions were conducted with a total of 13 clinicians. Overall, clinicians were enthusiastic about the dashboard and expressed the usefulness of visualizing RA outcome trajectories in a graphical format for motivating patients, enhancing patient understanding of their RA outcomes, and improving communication about medications. Major themes that emerged from the FG analysis as barriers to using the dashboard included inconsistent collection of RA outcomes leading to sparse data in the dashboard and concerns about explaining RA outcomes, especially to patients with fibromyalgia. Other challenges included time constraints and technical difficulties refreshing the dashboard to display real-time data. Methods for integrating the dashboard into the visit varied: some clinicians used the dashboard at the beginning of the visit as they documented RA outcomes; others used it at the end to justify changes to therapy; and a few shared it only with stable patients. Conclusions The study provides valuable insights into clinicians’ perceptions and experiences with the RA PRO dashboard. The dashboard showed promise in enhancing patient-clinician communication, shared decision-making, and overall acceptance among clinicians. Addressing challenges related to data collection, education, and tailoring dashboard use to specific patient populations will be crucial for maximizing its potential impact on RA care. Further research and ongoing improvements in dashboard design and implementation are warranted to ensure its successful integration into routine clinical practice.