James Lind

Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block

To investigate the effect of hospital occupancy levels on inpatient and ED patient flow parameters, and to simulate the impact of shifting discharge timing on occupancy levels.

The National Emergency Access Target (NEAT) and the 4-hour rule: time to review the target.

Objective: We explored the relationship between the National Emergency Access Target (NEAT) compliance rate, defined as the proportion of patients admitted or discharged from emergency departments (EDs) within 4 hours of presentation, and the risk‐adjusted in‐hospital mortality of patients admitted to hospital acutely from EDs.

How would Australian hospital staff react to an avian influenza admission, or an influenza pandemic?

Objective:  To estimate the expected staff absentee rates and work attitudes in an Australian tertiary hospital workforce in two hypothetical scenarios: (i) a single admission of avian influenza; and (ii) multiple admissions of human pandemic influenza.

New emergency department quality measure: from access block to National Emergency Access Target compliance.

The study aims to investigate the effect of time of day and ED occupancy on the ability of EDs to admit or discharge patients within 4 h in accordance with the National Emergency Access Target (NEAT), and to compare this with corresponding levels of access block, the measure for ED performance before NEAT.

Regression forecasting of patient admission data

Forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. This paper describes our work in analyzing patient admission data and forecasting this data using regression techniques. Five years of Emergency Department admissions data were obtained from two hospitals with different demographic techniques. Forecasts made from regression models were compared with observed admission data over a 6-month horizon. The best method was linear regression using 11 dummy variables to model monthly variation (MAPE=1.79%). Similar performance was achieved with a 2-year average, supporting further investigation at finer time scales.

Time based clustering for analyzing acute hospital patient flow

This paper describes a novel approach employing time based clustering of health data for visualization and analysis of patient flow. Clustering inpatient and emergency department patient episodes into hourly slots based on recorded timestamps, and then grouping them on required parameters, the technique provides a powerful tool for visualizing and analyzing interactions and interdependencies between hospital patient flow parameters. To demonstrate the efficacy of the approach, we employ time based clustering to address some typical patient flow related queries and discuss the findings.

Emergency care workload units: A novel tool to compare emergency department activity

Introduction:  Funding bodies have traditionally used attendance figures as a way of determining the allocation of funding for resources in the EDs. Using attendance figures only might not accurately reflect the funding and resources required. The need to create an easily implemented tool to compare workload and resources required was identified. Using the Australasian Triage Scale, a tool was developed to estimate staffing requirements and resource use within each ED. This, although currently not validated, provides a promising start in finding a way to accurately determine ED workload.

Impact of influenza across 27 public emergency departments in Australia: a 5-year descriptive study

Objective To describe the incidence, characteristics and outcomes of patients with influenza-like symptoms presenting to 27 public hospital emergency departments (EDs) in Queensland, Australia. Methods A descriptive retrospective study covering 5 years (2005–9) of historical data from 27 hospital EDs was undertaken. State-wide hospital ED Information System data were analysed. Annual comparisons between influenza and non-influenza cases were made across the southern hemisphere influenza season (June–September) each year. Results Influenza-related presentations increased significantly over the 5 years from 3.4% in 2005 to 9.4% in 2009, reflecting a 276% relative increase. Differences over time regarding characteristics of patients with influenza-like symptoms, based on the influenza season, occurred for admission rate (decreased over time from 28% in 2005 to 18% in 2009), length of stay (decreased over time from a median of 210 min in 2005 to 164 min in 2009) and access block (increased over time from 33% to 41%). Also, every year there was a significantly (p<0.001) higher percentage of access block in the influenza cohort than in the non-influenza cohort. Conclusions Although there was a large increase over time in influenza-related ED presentations, most patients were discharged home from the ED. Special consideration of health service delivery management (eg, establishing an ‘influenza clinic border protection and public rollout of vaccination, beginning with those most at risk’) for this group of patients is warranted but requires evaluation. These results may inform planning for service delivery models during the influenza season.

Expanding emergency department capacity: a multisite study

OBJECTIVES The aims of the present study were to identify predictors of admission and describe outcomes for patients who arrived via ambulance to three Australian public emergency departments (EDs), before and after the opening of 41 additional ED beds within the area. METHODS The present study was a retrospective comparative cohort study using deterministically linked health data collected between 3 September 2006 and 2 September 2008. Data included ambulance offload delay, time to see doctor, ED length of stay (LOS), admission requirement, access block, hospital LOS and in-hospital mortality. Logistic regression analysis was undertaken to identify predictors of hospital admission. RESULTS Almost one-third of all 286037 ED presentations were via ambulance (n=79196) and 40.3% required admission. After increasing emergency capacity, the only outcome measure to improve was in-hospital mortality. Ambulance offload delay, time to see doctor, ED LOS, admission requirement, access block and hospital LOS did not improve. Strong predictors of admission before and after increased capacity included age >65 years, Australian Triage Scale (ATS) Category 1-3, diagnoses of circulatory or respiratory conditions and ED LOS >4h. With additional capacity, the odds ratios for these predictors increased for age >65 years and ED LOS >4h, and decreased for ATS category and ED diagnoses. CONCLUSIONS Expanding ED capacity from 81 to 122 beds within a health service area impacted favourably on mortality outcomes, but not on time-related service outcomes such as ambulance offload time, time to see doctor and ED LOS. To improve all service outcomes, when altering (increasing or decreasing) ED bed numbers, the whole healthcare system needs to be considered.

The Implementation and Evaluation of the Patient Admission Prediction Tool: Assessing Its Impact on Decision-Making Strategies and Patient Flow Outcomes in 2 Australian Hospitals.

Study Objectives: To evaluate the implementation of a Patient Admission Prediction Tool (PAPT) in terms of patient flow outcomes and decision-making strategies. Methods: Setting: The PAPT was implemented in 2 Australian public teaching hospitals during October-December 2010 (hospital A) and October-December 2011 (hospital B). Design: A multisite prospective, comparative (before and after) design was used. Patient flow outcomes measured included access block and hospital occupancy. Daily and weekly data were collected from patient flow reports and routinely collected emergency department information by the site champion and researchers. Results: Daily decision-making strategies ranged from business as usual to use of overcensus beds. Weekly strategies included advanced approval to use of overcensus beds and prebooking nursing staff. These strategies resulted in improved weekend discharges to manage incoming demand for the following week. Following the introduction of the PAPT and workflow guidelines, patient access and hospital occupancy levels could be maintained despite increases in patient presentations (hospital A). Conclusions: The use of a PAPT, embedded in patient flow management processes and championed by a manager, can benefit bed and staff management. Further research that incorporates wider evaluation of the use of the tool at other sites is warranted.