It appears that in busy interrupt-driven clinical environments, clinicians reduce the time they spend on clinical tasks if they experience interruptions, and may delay or fail to return to a significant portion of interrupted tasks. Task shortening may occur because interrupted tasks are truncated to 'catch up' for lost time, which may have significant implications for patient safety.

ObjectiveTo quantify and compare the time doctors and nurses spent on direct patient care, medication-related tasks, and interactions before and after electronic medication management system (eMMS) introduction.MethodsControlled pre–post, time and motion study of 129 doctors and nurses for 633.2 h on four wards in a 400-bed hospital in Sydney, Australia. We measured changes in proportions of time on tasks and interactions by period, intervention/control group, and profession.ResultseMMS was associated with no significant change in proportions of time spent on direct care or medication-related tasks relative to control wards. In the post-period control ward, doctors spent 19.7% (2 h/10 h shift) of their time on direct care and 7.4% (44.4 min/10 h shift) on medication tasks, compared to intervention ward doctors (25.7% (2.6 h/shift; p=0.08) and 8.5% (51 min/shift; p=0.40), respectively). Control ward nurses in the post-period spent 22.1% (1.9 h/8.5 h shift) of their time on direct care and 23.7% on medication tasks compared to intervention ward nurses (26.1% (2.2 h/shift; p=0.23) and 22.6% (1.9 h/shift; p=0.28), respectively). We found intervention ward doctors spent less time alone (p=0.0003) and more time with other doctors (p=0.003) and patients (p=0.009). Nurses on the intervention wards spent less time with doctors following eMMS introduction (p=0.0001).ConclusionseMMS introduction did not result in redistribution of time away from direct care or towards medication tasks. Work patterns observed on these intervention wards were associated with previously reported significant reductions in prescribing error rates relative to the control wards.

The elderly population had the highest rate of ED attendances. The use of diverse diagnosis classifications and source information systems may present problems with further analysis. Patterns and characteristics of ED presentations in NSW were broadly consistent with those reported in other states in Australia.

BackgroundDisposition decisions are critical to the functioning of Emergency Departments. The objectives of the present study were to derive and internally validate a prediction model for inpatient admission from the Emergency Department to assist with triage, patient flow and clinical decision making.MethodsThis was a retrospective analysis of State-wide Emergency Department data in New South Wales, Australia. Adult patients (age ≥ 16 years) were included if they presented to a Level five or six (tertiary level) Emergency Department in New South Wales, Australia between 2013 and 2014. The outcome of interest was in-patient admission from the Emergency Department. This included all admissions to short stay and medical assessment units and being transferred out to another hospital. Analyses were performed using logistic regression. Discrimination was assessed using area under curve and derived risk scores were plotted to assess calibration.Results1,721,294 presentations from twenty three Level five or six hospitals were analysed. Of these 49.38% were male and the mean (sd) age was 49.85 years (22.13). Level 6 hospitals accounted for 47.70% of cases and 40.74% of cases were classified as an in-patient admission based on their mode of separation. The final multivariable model including age, arrival by ambulance, triage category, previous admission and presenting problem had an AUC of 0.82 (95% CI 0.81, 0.82).ConclusionBy deriving and internally validating a risk score model to predict the need for in-patient admission based on basic demographic and triage characteristics, patient flow in ED, clinical decision making and overall quality of care may be improved. Further studies are now required to establish clinical effectiveness of this risk score model.