What drives the 'August effect'?: an observational study of the effect of junior doctor changeover on out of hours work

Objective: To investigate whether measurements of junior doctor on-call workload and performance can clarify the mechanisms underlying the increase in morbidity and mortality seen after junior doctor changeover: the ‘August effect’. Design: Quantitative retrospective observational study of routinely collected data on junior doctor workload. Setting: Two large teaching hospitals in England. Participants: Task level data from a wireless out of hours system (n = 29,885 requests) used by medical staff, nurses, and allied health professionals. Main outcome measures: Number and type of tasks requested by nurses, time to completion of tasks by junior doctors. Results: There was no overall change in the number of tasks requested by nurses out of hours around the August changeover (median requests per hour 15 before and 14 after, p = 0.46). However, the number of tasks classified as urgent was greater (p = 0.016) equating to five more urgent tasks per day. After changeover, doctors took less time to complete tasks overall due to a reduction in time taken for routine tasks (median 74 vs. 66 min; p = 3.9 × 10−9). Conclusion: This study suggests that the ‘August effect’ is not due to new junior doctors completing tasks more slowly or having a greater workload. Further studies are required to investigate the causes of the increased number of urgent tasks seen, but likely factors are errors, omissions, and poor prioritization. Thus, improved training and quality control has the potential to address this increased duration of unresolved patient risk. The study also highlights the potential of newer technologies to facilitate quantitative study of clinical activity

Early In-Hospital Mortality following Trainee Doctors' First Day at Work

BACKGROUND:There is a commonly held assumption that early August is an unsafe period to be admitted to hospital in England, as newly qualified doctors start work in NHS hospitals on the first Wednesday of August. We investigate whether in-hospital mortality is higher in the week following the first Wednesday in August than in the previous week. METHODOLOGY:A retrospective study in England using administrative hospital admissions data. Two retrospective cohorts of all emergency patients admitted on the last Wednesday in July and the first Wednesday in August for 2000 to 2008, each followed up for one week. PRINCIPAL FINDINGS:The odds of death for patients admitted on the first Wednesday in August was 6% higher (OR 1.06, 95% CI 1.00 to 1.15, p=0.05) after controlling for year, gender, age, socio-economic deprivation and co-morbidity. When subdivided into medical, surgical and neoplasm admissions, medical admissions admitted on the first Wednesday in August had an 8% (OR 1.08, 95% CI 1.01 to 1.16, p=0.03) higher odds of death. In 2007 and 2008, when the system for junior doctors' job applications changed, patients admitted on Wednesday August 1(st) had 8% higher adjusted odds of death than those admitted the previous Wednesday, but this was not statistically significant (OR 1.08, 95% CI 0.95 to 1.23, p=0.24). CONCLUSIONS:We found evidence that patients admitted on the first Wednesday in August have a higher early death rate in English hospitals compared with patients admitted on the previous Wednesday. This was higher for patients admitted with a medical primary diagnosis

An evaluation of POSSUM and P-POSSUM scoring in predicting post-operative mortality in a level 1 critical care setting

Background POSSUM and P-POSSUM are used in the assessment of outcomes in surgical patients. Neither scoring systems’ accuracy has been established where a level 1 critical care facility (level 1 care ward) is available for perioperative care. We compared POSSUM and P-POSSUM predicted with observed mortality on a level 1 care ward. Methods A prospective, observational study was performed between May 2000 and June 2008. POSSUM and P-POSSUM scores were calculated for all postoperative patients who were admitted to the level 1 care ward. Data for post-operative mortality were obtained from hospital records for 2552 episodes of patient care. Observed vs expected mortality was compared using receiver operating characteristic (ROC) curves and the goodness of fit assessed using the Hosmer-Lemeshow equation. Results ROC curves show good discriminative ability between survivors and non-survivors for POSSUM and P-POSSUM. Physiological score had far higher discrimination than operative score. Both models showed poor calibration and poor goodness of fit (Hosmer-Lemeshow). Observed to expected (O:E) mortality ratio for POSSUM and P-POSSUM indicated significantly fewer than expected deaths in all deciles of risk. Conclusions Our data suggest a 30-60% reduction in O:E mortality. We suggest that the use of POSSUM models to predict mortality in patients admitted to level 1 care ward is inappropriate or that a recalibration of POSSUM is required to make it useful in a level 1 care ward setting