The Impact of an Electronic Medication Management System on Medication Deviations on Admission and Discharge from Hospital.

Medication errors at transition of care remain a concerning issue. In recent times, the use of integrated electronic medication management systems (EMMS) has caused a reduction in medication errors, but its effectiveness in reducing medication deviations at transition of care has not been studied in hospital-wide settings in Australia. The aim of this study is to assess medication deviations, such as omissions and mismatches, pre-EMMS and post-EMMS implementation at transition of care across a hospital. In this study, patient records were reviewed retrospectively to identify medication deviations (medication omissions and medication mismatches) at admission and discharge from hospital. A total of 400 patient records were reviewed (200 patients in the pre-EMMS and 200 patients in the post-EMMS group). Out of 400 patients, 112 in the pre-EMMS group and 134 patients in post-EMMS group met the inclusion criteria and were included in the analysis. A total of 105 out of 246 patients (42.7%) had any medication deviations on their medications. In the pre-EMMS group, 59 out of 112 (52.7%) patients had any deviations on their medications compared to 46 out of 134 patients (34.3%) from the post-EMMS group (p = 0.004). The proportion of patients with medication omitted from inpatient orders was 36.6% in the pre-EMMS cohort vs. 22.4% in the post-EMMS cohort (p = 0.014). Additionally, the proportion of patients with mismatches in medications on the inpatient charts compared to their medication history was 4.5% in the pre-EMMS group compared to 0% in the post-EMMS group (p = 0.019). Similarly, the proportion of patients with medications omitted from their discharge summary was 23.2% in the pre-EMMS group vs. 12.7% in the post-EMMS group (p = 0.03). Our study demonstrates a reduction in medication deviations after the implementation of the EMMS in hospital settings.

Impact of emergency department discharge stream short stay unit performance and hospital bed occupancy rates on access and patient flowmeasures: A single site study.

OBJECTIVE: The ED discharge stream short stay units (EDSSUs) aim to facilitate patient flows through EDs. We investigate the relationship between EDSSU census and hospital bed occupancy rates (BORs) on National Emergency Access Target (NEAT) performance and did-not-wait (DNW) rates at a tertiary metropolitan adult ED in Sydney, Australia. METHODS: We collated data for all ED presentations between 1 January 2012 and 31 December 2014. Daily ED, EDSSU census and ED-accessible hospital BORs were tabulated with daily ED NEAT performance and DNW rates. Non-parametric regression analyses was conducted on cohorts of appropriate, inappropriate, successful and failed EDSSU admissions based on local admission policies and BOR for NEAT and DNW outcomes. RESULTS: Among all presentations (n = 192 506) during the study period, 43.8% of patients were admitted in hospital including 10.4% for EDSSU (n = 20 081). Analyses reveal modest positive correlation of EDSSU admissions with NEAT performance (τ = 0.35, P < 0.001) and weak negative correlation with DNW rates (τ = -0.29, P < 0.001). These associations were more pronounced on days when BOR >100% (τ = 0.39 and τ = -0.36, P < 0.001). BOR of >100% were associated with reduced EDSSU admits, NEAT performance and increased DNW rates (P < 0.001). Appropriate EDSSU admissions had shorter EDSSU length of stay than inappropriate EDSSU admissions (350 vs 557 min, median difference -158 min, P < 0.001). CONCLUSION: Appropriate use of EDSSU provides effective conduit for ongoing patients' management beyond mandated timelines. Health systems should focus on reducing hospital BORs to mitigate exclusive ED pressure to deliver NEAT performance targets.

Systemic inflammatory response syndrome-based severe sepsis screening algorithms in emergency department patients with suspected sepsis.

OBJECTIVE: Systemic inflammatory response syndrome (SIRS)-based severe sepsis screening algorithms have been utilised in stratification and initiation of early broad spectrum antibiotics for patients presenting to EDs with suspected sepsis. We aimed to investigate the performance of some of these algorithms on a cohort of suspected sepsis patients. METHODS: We conducted a retrospective analysis on an ED-based prospective sepsis registry at a tertiary Sydney hospital, Australia. Definitions for sepsis were based on the 2012 Surviving Sepsis Campaign guidelines. Numerical values for SIRS criteria and ED investigation results were recorded at the trigger of sepsis pathway on the registry. Performance of specific SIRS-based screening algorithms at sites from USA, Canada, UK, Australia and Ireland health institutions were investigated. RESULTS: Severe sepsis screening algorithms' performance was measured on 747 patients presenting with suspected sepsis (401 with severe sepsis, prevalence 53.7%). Sensitivity and specificity of algorithms to flag severe sepsis ranged from 20.2% (95% CI 16.4-24.5%) to 82.3% (95% CI 78.2-85.9%) and 57.8% (95% CI 52.4-63.1%) to 94.8% (95% CI 91.9-96.9%), respectively. Variations in SIRS values between uncomplicated and severe sepsis cohorts were only minor, except a higher mean lactate (>1.6 mmol/L, P < 0.01). CONCLUSIONS: We found the Ireland and JFK Medical Center sepsis algorithms performed modestly in stratifying suspected sepsis patients into high-risk groups. Algorithms with lactate levels thresholds of >2 mmol/L rather than >4 mmol/L performed better. ED sepsis registry-based characterisation of patients may help further refine sepsis definitions of the future.

The emergency department prediction of disposition (EPOD) study.

BACKGROUND: Emergency departments (ED) continue to evolve models of care and streaming as interventions to tackle the effects of access block and overcrowding. Tertiary ED may be able to design patient-flow based on predicted dispositions in the department. Segregating discharge-stream patients may help develop patient-flows within the department, which is less affected by availability of beds in a hospital. We aim to determine if triage nurses and ED doctors can predict disposition outcomes early in the patient journey and thus lead to successful streaming of patients in the ED. METHODS: During this study, triage nurses and ED doctors anonymously predicted disposition outcomes for patients presenting to triage after their brief assessments. Patient disposition at the 24-h post ED presentation was considered as the actual outcome and compared against predicted outcomes. RESULTS: Triage nurses were able to predict actual discharges of 445 patients out of 490 patients with a positive predictive value (PPV) of 90.8% (95% CI 87.8-93.2%). ED registrars were able to predict actual discharges of 85 patients out of 93 patients with PPV of 91.4% (95% CI 83.3-95.9%). ED consultants were able to predict actual discharges of 111 patients out of 118 patients with PPV 94.1% (95% CI 87.7-97.4%). PPVs for admission among ED consultants, ED registrars and Triage nurses were 59.7%, 54.4% and 48.5% respectively. CONCLUSIONS: Triage nurses, ED consultants and ED registrars are able to predict a patient's discharge disposition at triage with high levels of confidence. Triage nurses, ED consultants, and ED registrars can predict patients who are likely to be admitted with equal ability. This data may be used to develop specific admission and discharge streams based on early decision-making in EDs by triage nurses, ED registrars or ED consultants.