Public Performance Metrics: Driving Physician Motivation and Performance

Introduction: As providers transition from “fee-for-service” to “pay-for-performance” models, focus has shifted to improving performance. This trend extends to the emergency department (ED) where visits continue to increase across the United States. Our objective was to determine whether displaying public performance metrics of physician triage data could drive intangible motivators and improve triage performance in the ED.Methods: This is a single institution, time-series performance study on a physician-in-triage system. Individual physician baseline metrics—number of patients triaged and dispositioned per shift—were obtained and prominently displayed with identifiable labels during each quarterly physician group meeting. Physicians were informed that metrics would be collected and displayed quarterly and that there would be no bonuses, punishments, or required training; physicians were essentially free to do as they wished. It was made explicit that the goal was to increase the number triaged, and while the number dispositioned would also be displayed, it would not be a focus, thereby acting as this study’s control. At the end of one year, we analyzed metrics.Results: The group’s average number of patients triaged per shift were as follows: Q1-29.2; Q2-31.9; Q3-34.4; Q4-36.5 (Q1 vs Q4, p < 0.00001). The average numbers of patients dispositioned per shift were Q1-16.4; Q2-17.8; Q3-16.9; Q4-15.3 (Q1 vs Q4, p = 0.14). The top 25% of Q1 performers increased their average numbers triaged from Q1-36.5 to Q4-40.3 (ie, a statistically insignificant increase of 3.8 patients per shift [p = 0.07]). The bottom 25% of Q1 performers, on the other hand, increased their averages from Q1-22.4 to Q4-34.5 (ie, a statistically significant increase of 12.2 patients per shift [p = 0.0013]).Conclusion: Public performance metrics can drive intangible motivators (eg, purpose, mastery, and peer pressure), which can be an effective, low-cost strategy to improve individual performance, achieve institutional goals, and thrive in the pay-for-performance era

The Frequency of Reevaluation or Peak Flow Meter Documentation in Acute Asthma Exacerbations in the Emergency Department: Are We Treating in Accordance with NIH/NAEPP Guidelines?

Objectives: To evaluate the frequency of peak expiratory flow rate (PEFR) measurement and clinical re-evaluation in the management of ED asthmatic patients. Methods: This was a retrospective chart review examining consecutive asthma patients who presented to the University of California Irvine ED between September 1, 2003 and December 31, 2003. Patients were excluded if they had a diagnosis of COPD, lung cancer, pneumonia, congestive heart failure, alpha 1 anti-trypsin deficiency or were under 5 years of age. Data collected included patient demographics, pulse oximetry reading(s), ED treatments rendered, and frequencies of PEFR measurement (pre and post therapy), of clinical re-evaluations in the ED, and of ED return visits. Results: Of the 122 ED visits from 111 patients, 11 (10%) patients returned during the 4 month study period, with 5 patients (4.5%) returning in less than 72 hours. Seven (6.0%) patients had PEFR done both pre and post treatment and 24 (20%) had one or more PEFR performed either before or after treatment. Only 61 (50%) of the visits had a documented clinical re-evaluation prior to disposition. Conclusions: Despite their documented role in asthma treatment algorithms, PEFR was performed infrequently and clinical re-evaluation was documented in only half of cases. Recommended algorithms for asthma management were not commonly followed in this academic ED

Echocardiography to Supplement Stress Electrocardiography in Emergency Department Chest Pain Patients

Introduction: Chest pain (CP) patients in the Emergency Department (ED) present a diagnostic dilemma, with a low prevalence of coronary disease but grave consequences with misdiagnosis. A common diagnostic strategy involves ED cardiac monitoring while excluding myocardial necrosis, followed by stress testing. We sought to describe the use of stress echocardiography (echo) at our institution, to identify cardiac pathology compared with stress electrocardiography (ECG) alone.Methods: Retrospective cohort study of 57 urban ED Chest Pain Unit (CPU) patients from 2002-2005 with stress testing suggesting ischemia. Our main descriptive outcome was proportion and type of discordant findings between stress ECG testing and stress echo. The secondary outcome was whether stress echo results appeared to change management.Results: Thirty-four of 57 patients [59.7%, 95% confidence interval (CI) 46.9-72.4%] had stress echo results discordant with stress ECG results. The most common discordance was an abnormal stress ECG with a normal stress echo (n=17/57, 29.8%, CI 17.9-41.7%), followed by normal stress ECG but with reversible regional wall-motion abnormality on stress echo (n = 10/57, 17.5%, CI 7.7-27.4%). The remaining seven patients (12.3%, CI 3.8-20.8%) had non-diagnostic stress ECG due to sub-maximal effort. Stress echo showed reversible wall-motion abnormality in two, and five were normal. Twenty-five of the 34 patients (73.5%, CI 56.8-85.4%) with discordant results had a different diagnostic strategy than predicted from their stress ECG alone.Conclusion: The addition of echo to stress ECG testing in ED CPU patients altered diagnosis in 34/57 (59.7%, CI 46.9-72.4%) patients, and appeared to change management in 25/57 (43.9%, CI 31.8-57.6%) patients. [West J Emerg Med. 2010; 11(4):379-383.

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A first update on mapping the human genetic architecture of COVID-19

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