Researcher develops digital tool to improve geriatric rehabilitation admissions
Decisions on whether to admit older adults at Karin Grech Hospital often made without a standardised assessment tool
Francesca Muscat was recently awarded a PhD by the University of Malta for her doctoral research focusing on the development and digitisation of an assessment process for the rehabilitation potential of older adults.
Muscat’s research culminated in the development of a pioneering digital assessment tool that could reshape how older adults are screened for rehabilitation services.
Titled ‘Development and digitisation of an assessment process for the rehabilitation potential of the older adult’, her research was conducted jointly with the Faculty of Health Sciences and the Faculty of Information and Communication Technology (FICT). It was inspired by first-hand clinical experience at Karin Grech Hospital, Malta’s only state-run rehabilitation facility, where admission decisions for older adults are often made without a standardised assessment tool.
The resulting system, named TERESA Patient Assessment, was developed through a highly-consultative process. Numerous interviews were conducted with clinicians, alongside usability and feasibility studies. The process also included collaborating with an expert panel made up of professionals working at Karin Grech Hospital to ensure local relevance.
Seven variables proved to significantly affect whether a patient was admitted to inpatient rehabilitation or not
Starting with 83 possible variables, known as potential predictors, Muscat refined her model through extensive data collection and statistical analysis based on 250 real-life patient cases. This led to the creation of a clinical prediction model built on seven core indicators of rehabilitation potential.
Out of these variables, seven proved to significantly affect whether a patient was admitted to inpatient rehabilitation or not. These included cases where the cause of admission was a fall; when the patient enjoyed good cognitive abilities and high levels of mobility prior to the incident; and when the patient lived with other older adults of similar age prior to admission.
Conversely, patients who smoked or could independently transfer from lying to sitting decreased the likelihood of acceptance to rehabilitation.
Additionally, if relatives were withdrawing support and opting for long-term care placement, the chances of admission to inpatient rehabilitation further decreased. These seven predictors were then used to build an algorithm that generates a probability score for rehabilitation benefit.
The tool, designed to support rather than replace clinical judgement, offers clinicians a data-informed guide during patient evaluations. Internal testing showed an accuracy rate of 76%, suggesting strong potential for wider application in healthcare settings.
Muscat’s PhD, which took three years to complete, was funded by the Tertiary Education Scholarship Scheme (TESS).
The research was supervised by Stephen Lungaro Mifsud and co-supervised by Conrad Attard.
Muscat is now pursuing further research and publication in the field, with plans to scale the model in collaboration with both public and private healthcare providers.
She is currently pursuing a career with the Caremalta Group.