Reduced Time to Admit Emergency Department Patients to Inpatient Beds Using Outflow Barrier Analysis and Process Improvement

Objective: Because admitted emergency department (ED) patients waiting for an inpatient bed contribute to dangerous ED crowding, we conducted a patient flow investigation to discover and solve outflow delays. After solution implementation, we measured whether the time admitted ED patients waited to leave the ED was reduced. Methods: In June 2022, a team using Lean Healthcare methodologies identified flow delays and underlying barriers in a Midwest, mid-sized hospital. We calculated barriers’ magnitudes of burden by the frequency of involvement in delays. During October–December 2022, solutions targeting barriers were implemented. In October 2023, we tested whether waiting time, defined as daily median time in minutes from admission disposition to departure (ADtoD), declined by conducting independent sample, single-tailed t-test comparing pre- to post-intervention time periods, January 1–September 30, 2022 (273 days) to January 1–September 30, 2023 (273 days). Additionally, we regressed ADtoD onto pre-/post period while controlling for ED volume (total daily admissions and ED daily encounters) and hospital occupancy. A run chart analysis of monthly median ADtoD assessed improvement sustainability. Results: Process mapping revealed that three departments (ED, environmental services [EVS], and transport services) co-produced the outflow of admitted ED patients wherein 18 delays were identified. The EVS-clinical care collaboration failures explained 61% (11/18) of delays. Technology contributed to 78% (14/18) of delays primarily because staff’s technology did not display needed information, a condition we coined “digital blindness.” Comparing pre- and post-intervention days (3,144 patients admitted pre-intervention and 3,256 patients post), the median minutes a patient waited (ADtoD) significantly decreased (96.4 to 87.1 minutes, P = 0.04), even while daily ED encounter volume significantly increased (110.7 to 117.3 encounters per day, P < 0.001). After controlling in regression for other factors associated with waiting, the intervention reduced ADtoD by 12.7 minutes per patient (standard error 5.10, P = 0.01; 95% confidence interval −22.7, −2.7). We estimate that the intervention translated to ED staff avoiding 689 hours of admitted patient boarding over nine months (ADtoD coefficient [−12.7 minutes] multiplied by post-intervention ED admissions [3,256] and divided by 60). Run chart analysis substantiated the intervention’s sustainability over nine months. Conclusion: After systemwide patient flow investigation, solutions resolving digital blindness and environmental services-clinical care collaboration failures significantly reduced ED admitted patient boarding.&nbsp

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Targeting Mast Cells in Allergic Disease: Current Therapies and Drug Repurposing

The incidence of allergic disease has grown tremendously in the past three generations. While current treatments are effective for some, there is considerable unmet need. Mast cells are critical effectors of allergic inflammation. Their secreted mediators and the receptors for these mediators have long been the target of allergy therapy. Recent drugs have moved a step earlier in mast cell activation, blocking IgE, IL-4, and IL-13 interactions with their receptors. In this review, we summarize the latest therapies targeting mast cells as well as new drugs in clinical trials. In addition, we offer support for repurposing FDA-approved drugs to target mast cells in new ways. With a multitude of highly selective drugs available for cancer, autoimmunity, and metabolic disorders, drug repurposing offers optimism for the future of allergy therapy

Additional file 28 of Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Additional file 28: Table S18. Sex-participation association of the variants with significant sex-specific lipid results

Additional file 17 of Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Additional file 17: Table S9. PheWAS UKB-MVP meta-analysis results for each index lipid variant at Bonferroni threshold for multiple testing

Additional file 5 of Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Additional file 5: Table S4. Frequency of lipid-related publications for the PoPS+ prioritized genes