Importance

Electronic health record (EHR) clinical decision support (CDS) tools can provide evidence-based feedback at the point of care to reduce low-value imaging. Success of these tools has been limited partly due to lack of engagement by busy clinicians.

Objective

Measure the impact of a time-saving quality improvement intervention to increase engagement with a CDS tool for low back pain imaging ordering.

Design, setting and participants

We conducted a quasi-experimental difference-in-differences analysis at (BLINDED), examining back pain imaging orders from 29 May 2015 to 07 January 2016. The intervention site was (BLINDED) Emergency Medicine/Urgent Care Center (n=5736) and control sites included all other (BLINDED) hospitals and clinics (n=1621). In May 2015, the Department of Health Services installed a CDS tool that triggered a survey when clinicians ordered an imaging test, generating an 'appropriateness score' based on the American College of Radiology guidelines. Clinicians often bypassed the tool, resulting in 'unscored' tests.

Intervention

To increase clinician engagement with the tool and decrease the rate of unscored imaging tests, a new policy was implemented at the intervention site on 15 August 2015. If clinicians completed the CDS survey and scored an appropriateness score >3, they could forego a previously mandatory telephone call for pre-imaging utilisation review with the radiology department.

Main outcomes and measures

We used EHR data to measure pre-post-intervention differences in: (1) percentage of unscored tests and (2) percentage of tests with high appropriateness scores (>7).

Results

Percentage of unscored tests decreased from 69.4% to 10.4% at the intervention site and from 50.6% to 34.8% at the control sites (between-group difference: -23.3%, p<0.001). Percentage of high scoring tests increased from 26.5% to 75.0% at the intervention site and from 17.2% to 22.7% at the control sites (between-group difference: 19%, p<0.001).

Conclusion

Workflow time-saving interventions may increase physician engagement with CDS tools and have potential to improve practice patterns.

Objectives

Physician pay-for-performance (P4P) programs frequently target inappropriate antibiotics. Yet little is known about P4P programs' effects on antibiotic prescribing among safety-net populations at risk for unintended harms from reducing care. We evaluated effects of P4P-motivated interventions to reduce antibiotic prescriptions for safety-net patients with acute respiratory tract infections (ARTIs).

Study design

Interrupted time series.

Methods

A nonrandomized intervention (5/28/2015-2/1/2018) was conducted at 2 large academic safety-net hospitals: Los Angeles County+University of Southern California (LAC+USC) and Olive View-UCLA (OV-UCLA). In response to California's 2016 P4P program to reduce antibiotics for acute bronchitis, 5 staggered Choosing Wisely-based interventions were launched in combination: audit and feedback, clinician education, suggested alternatives, procalcitonin, and public commitment. We also assessed 5 unintended effects: reductions in Healthcare Effectiveness Data and Information Set (HEDIS)-appropriate prescribing, diagnosis shifting, substituting antibiotics with steroids, increasing antibiotics for ARTIs not penalized by the P4P program, and inappropriate withholding of antibiotics.

Results

Among 3583 consecutive patients with ARTIs, mean antibiotic prescribing rates for ARTIs decreased from 35.9% to 22.9% (odds ratio [OR], 0.60; 95% CI, 0.39-0.93) at LAC+USC and from 48.7% to 27.3% (OR, 0.81; 95% CI, 0.70-0.93) at OV-UCLA after the intervention. HEDIS-inappropriate prescribing rates decreased from 28.9% to 19.7% (OR, 0.69; 95% CI, 0.39-1.21) at LAC+USC and from 40.9% to 12.5% (OR, 0.72; 95% CI, 0.59-0.88) at OV-UCLA. There was no evidence of unintended consequences.

Conclusions

These real-world multicomponent interventions responding to P4P incentives were associated with substantial reductions in antibiotic prescriptions for ARTIs in 2 safety-net health systems without unintended harms.

BACKGROUND: Heart transplantation provides a significant improvement in survival and quality of life for patients with end-stage heart disease, however many recipients experience different levels of graft rejection that can be associated with significant morbidities and mortality. Current clinical standard-of-care for the evaluation of heart transplant acute rejection (AR) consists of routine endomyocardial biopsy (EMB) followed by visual assessment by histopathology for immune infiltration and cardiomyocyte damage. We assessed whether the sensitivity and/or specificity of this process could be improved upon by adding RNA sequencing (RNA-seq) of EMBs coupled with histopathological interpretation. METHODS: Up to 6 standard-of-care, or for-cause EMBs, were collected from 26 heart transplant recipients from the prospective observational Clinical Trials of Transplantation (CTOT)-03 study, during the first 12-months post-transplant and subjected to RNA-seq (n = 125 EMBs total). Differential expression and random-forest-based machine learning were applied to develop signatures for classification and prognostication. RESULTS: Leveraging the unique longitudinal nature of this study, we show that transcriptional hallmarks for significant rejection events occur months before the actual event and are not visible using traditional histopathology. Using this information, we identified a prognostic signature for 0R/1R biopsies that with 90% accuracy can predict whether the next biopsy will be 2R/3R. CONCLUSIONS: RNA-seq-based molecular characterization of EMBs shows significant promise for the early detection of cardiac allograft rejection.

Autoreactive memory T lymphocytes are implicated in the pathogenesis of autoimmune diseases. Here we demonstrate that disease-associated autoreactive T cells from patients with type-1 diabetes mellitus or rheumatoid arthritis (RA) are mainly CD4+ CCR7- CD45RA- effector memory T cells (T(EM) cells) with elevated Kv1.3 potassium channel expression. In contrast, T cells with other antigen specificities from these patients, or autoreactive T cells from healthy individuals and disease controls, express low levels of Kv1.3 and are predominantly naïve or central-memory (T(CM)) cells. In T(EM) cells, Kv1.3 traffics to the immunological synapse during antigen presentation where it colocalizes with Kvbeta2, SAP97, ZIP, p56(lck), and CD4. Although Kv1.3 inhibitors [ShK(L5)-amide (SL5) and PAP1] do not prevent immunological synapse formation, they suppress Ca2+-signaling, cytokine production, and proliferation of autoantigen-specific T(EM) cells at pharmacologically relevant concentrations while sparing other classes of T cells. Kv1.3 inhibitors ameliorate pristane-induced arthritis in rats and reduce the incidence of experimental autoimmune diabetes in diabetes-prone (DP-BB/W) rats. Repeated dosing with Kv1.3 inhibitors in rats has not revealed systemic toxicity. Further development of Kv1.3 blockers for autoimmune disease therapy is warranted.

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.