Improving fall-risk screening tools for elderly patients in the emergency department

By Daniel Wickins, AusHSI PhD Scholar

My clinical journey with falls goes back to 2001, when I commenced my career as a physiotherapist in Southern NSW. I have always worked in musculoskeletal clinics and the emergency department (ED), but a few years ago I sought a new challenge, completing a degree in Mathematics and Statistics. This opened my eyes to the power of data analysis to generate insights from routinely collected data and support decisions about patient care. Considering the frequency of falls in my own clinics, I have long been aware of their burden on health services.

Falls affect one in three adults over 65 annually, and one in two over the age of 85. Crucially, falls can rob people of their independence and confidence, impacting on their fitness, general health and wellbeing. Assessment and management of falls patients arriving in the ED at hospital is extremely variable. Peak body guidelines recommend that older patients presenting to the ED be screened for risk factors related to falls, and be referred for more comprehensive management where warranted. However, knowledge of these guidelines remains very low among the ED workforce.

My PhD research aims to investigate factors affecting the use of fall-risk screening tools in the ED and their impact on patient outcomes. My research will explore how aspects relating to workforce, data and workflow influence patient care. For example, whether integrating fall-risk screening into ED workflows via electronic medical records affects the identification of patients at high risk of future falls.

My first study reviewed published evidence for the impact of ED fall-risk screening tools on future falls risk. My key findings were that predicting future falls from the ED with existing tools has shown mixed results, but accuracy may increase when incorporating data from the electronic medical record.

My second study will conduct a qualitative analysis of ED staff attitudes and behaviours towards fall-risk screening in the ED. The existing literature only involves a limited number of workforce surveys, so my research will provide a much deeper analysis of the barriers and enablers to implementing fall-risk screening at the health workforce level.

My third study will use longitudinal data over a five-year period, linking Emergency and Public Hospital databases and applying statistical techniques to identify hidden groups among patients more associated with future falls. This identification aims to help ED clinicians better prioritise patients for screening when workforce resources are limited. Hospitals could then recommend these patients for fall prevention programs.

My final study will use an emerging statistical method known as a ‘target trial’ to explore whether introducing a decision support system for falls risk will lead to more referrals from the ED to fall-prevention services. A target trial is a relatively new statistical approach that uses existing data to simulate the effects of hypothetical interventions of short and long-term patient outcomes. Target trials are increasingly being used to generate initial evidence to identify promising interventions to test in a clinical trial.

Giving decision-makers in health services the tools to translate research into policy can be complex, but the opportunity to improve outcomes for the patient and the health service is what drives me to keep asking the important research questions.