Hope modified the association between distress and incidence of self-perceived medical errors among practicing physicians: prospective cohort study.

The presence of hope has been found to influence an individual's ability to cope with stressful situations. The objective of this study is to evaluate the relationship between medical errors, hope and burnout among practicing physicians using validated metrics. Prospective cohort study was conducted among hospital based physicians practicing in Japan (N = 836). Measures included the validated Burnout Scale, self-assessment of medical errors and Herth Hope Index (HHI). The main outcome measure was the frequency of self-perceived medical errors, and Poisson regression analysis was used to evaluate the association between hope and medical error. A total of 361 errors were reported in 836 physician-years. We observed a significant association between hope and self-report of medical errors. Compared with the lowest tertile category of HHI, incidence rate ratios (IRRs) of self-perceived medical errors of physicians in the highest category were 0.44 (95%CI, 0.34 to 0.58) and 0.54 (95%CI, 0.42 to 0.70) respectively, for the 2(nd) and 3(rd) tertile. In stratified analysis by hope score, among physicians with a low hope score, those who experienced higher burnout reported higher incidence of errors; physicians with high hope scores did not report high incidences of errors, even if they experienced high burnout. Self-perceived medical errors showed a strong association with physicians' hope, and hope modified the association between physicians' burnout and self-perceived medical errors

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454