Top 10 Tips on Safety From the Air Medical Transport Industry.

The air medical transport industry places a high value on developing and maintaining a culture of safety due to the higher risk nature of its operations. The dynamic nature of response and transport, inherent risks involved with flight, lack of supporting resources, weather conditions, and austere nature of the transport environment are all factors that highlight the need for enhanced safety. As such, the air medical transport industry has developed a robust and unique approach to provider and patient safety involving many tactics not otherwise used in other areas of health care. This article describes some of the unique safety features and approaches that are commonplace in the air medical transport industry and proposes a means for these initiatives to other areas of the health care system.

Introducing a clinically effective allied health rapid discharge team within a complex aged subacute in-patient cohort on a cost recovery basis: the Supported Patient centred Early Discharge (SPeED) initiative.

This case study reports the outcomes of an early supported discharge program. This model of care was trialled after Victoria introduced subacute weighted inlier equivalent separations funding to subacute in-patients in 2016. An allied health team (Supported Patient centred Early Discharge (SPeED)) managed patients suitable for assessment, intervention and early supported discharge (ESD). The SPeED cohort was compared to a matched historical control. Data included no advantage financially (NAF) days, length of stay (LOS), functional independence measure (FIM) scores and 30-day readmission rates. Staff and patient experiences were collected through surveys and call-back data. Regression analysis compared quantitative data, whereas a broad thematic approach compared qualitative data. There were no differences between the study cohort and historical control in age or sex (P>0.05). The SPeED cohort had lower median NAF days (F=-21.38; 95% confidence interval (CI) -37.70, -15.00; P<0.001), shorter LOS (F)=4.65; 95% CI -0.41, -0.02; P=0.034), fewer readmissions within 30 days (odds ratio 0.14; 95% CI -0.03, 0.68; P=0.014) and greater change in FIM scores during admission (F=4.20; 95% CI 0.16, 10.74; P=0.044). Staff morale was high in recognition of improved patient care. Patient satisfaction remained positive across the SPeED cohort and historical control group. The introduction of a dedicated allied health ESD team within a geriatric evaluation and management population is effective and enhances patient outcomes.

Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single-site validation of a decision tree algorithm.

OBJECTIVES: Health services have an imperative to reduce prolonged patient length of stay (LOS) in ED. Our objective is to develop and validate an accurate prediction model for patient LOS in ED greater than 4 hours using a data mining technique. METHODS: Data were collected from a regional Australian public hospital for all ED presentations between 1 January 2016 and 31 December 2017. A decision tree algorithm was built to predict patients with an ED LOS >4 hours. A total of 33 attributes were analysed. The performance of the final model was internally validated. Clinically relevant patterns from the model were analysed. RESULTS: The accuracy of the model was 85%. We identified that patients at our site who were at high risk of ED LOS >4 hours were those who were waiting in ED for a medical consultation, or those who were waiting for a urology, surgical, orthopaedic or paediatric consultation if the request for consultation occurred more than 2 hours after the patient was first seen by an ED doctor. CONCLUSION: This model performed very well in predicting ED LOS >4 hours for each individual patient and demonstrated a number of clinically relevant patterns. Identifying patterns that influence ED LOS is important for health managers in order to develop and implement interventions targeted at those clinical scenarios. Future work should look at the utility of displaying individual patient risk of ED LOS >4 hours using this model in real-time at the point-of-care.

Four-item fall risk screening tool for subacute and residential aged care: The first step in fall prevention.

AIM: To report the reliability, accuracy and compliance of a brief fall risk screening tool in subacute and residential aged care. METHOD: A 9-item tool, developed by expert and literature review, was administered to 291 persons admitted to subacute and residential aged care at Peninsula Health (PH) Victoria, Australia. Items were analysed for their ability to predict falls and the four strongest incorporated into a screening tool. Reliability was assessed on six nurses. RESULTS: Most predictive items were recent falls (0.82), psychological status (0.55), medications (0.46) and cognition (0.41) chi(2) (4, n= 291) = 89.89, P < 0.0001. The final 4-item tool (PH-FRAT) provides 80% accuracy (sensitivity(ER) 70.2%, specificity(ER) 68.8%) and high reliability (ICC = 0.79). The PH-FRAT is now used in 50 local subacute and residential facilities. CONCLUSION: The 4-item PH-FRAT is a popular, moderately predictive, reliable and brief method of screening fall risk in subacute and residential aged care.