Automating risk stratification for geriatric syndromes in the emergency department

Enhancing healthcare access for an older population: The age-friendly emergency department

Healthcare systems face significant challenges in meeting the unique needs of older adults, particularly in the acute setting. Age-friendly healthcare is a comprehensive approach using the 4Ms framework-what matters, medications, mentation, and mobility-to ensure that healthcare settings are responsive to the needs of older patients. The Age-Friendly Emergency Department (AFED) is a crucial component of a holistic age-friendly health system. Our objective is to provide an overview of the AFED model, its core principles, and the benefits to older adults and healthcare clinicians. The AFED optimizes the delivery of emergency care by integrating age-specific considerations into various aspects of (1) ED physical infrastructure, (2) clinical care policies, and (3) care transitions. Physical infrastructure incorporates environmental modifications to enhance patient safety, including adequate lighting, nonslip flooring, and devices for sensory and ambulatory impairment. Clinical care policies address the physiological, cognitive, and psychosocial needs of older adults while preserving focus on emergency issues. Care transitions include communication and involving community partners and case management services. The AFED prioritizes collaboration between interdisciplinary team members (ED clinicians, geriatric specialists, nurses, physical/occupational therapists, and social workers). By adopting an age-friendly approach, EDs have the potential to improve patient-centered outcomes, reduce adverse events and hospitalizations, and enhance functional recovery. Moreover, healthcare clinicians benefit from the AFED model through increased satisfaction, multidisciplinary support, and enhanced training in geriatric care. Policymakers, healthcare administrators, and clinicians must collaborate to standardize guidelines, address barriers to AFEDs, and promote the adoption of age-friendly practices in the ED.

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Introduction

Novel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population.

Materials and methods

The study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups.

Results and conclusion

The study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

Recruiting patients for falls prevention in the emergency department - worth the challenge

BACKGROUND

Severe falls escalate the risk of future falls and functional decline as indicated by recent global guidelines. To establish effective falls prevention, individuals at highest risk must be thoroughly studied and, therefore, successfully recruited.

OBJECTIVE

Recruiting from an emergency department (ED) may mitigate common selection biases, such as overrepresentation of individuals with a higher social status and healthier lifestyle. However, this approach presents unique challenges due to ED-specific conditions. Hence, we present the successes and challenges of an ED-based recruitment for an observational study.

METHODS

The SeFallED study targets older adults aged ≥60 years, who present to either of two hospitals in Oldenburg after a fall without subsequent admission. A study nurse addressed individuals in the EDs. Subsequently, potential participants were contacted by phone to arrange a home visit for obtaining written consent. Data of participants were compared with total admissions during the recruitment period to determine recruitment rate and compare patients' characteristics.

RESULTS

Over 1.500 individuals met the inclusion criteria. Of these, 288 participants were successfully recruited. Most patients presented to the ED outside of the study team's working hours, and some opted not to participate (main reason: too unwell (40%)). Compared to working hours, a participant was recruited every 14 h. Comparing characteristics, a trend towards better health and younger age was observed.

CONCLUSION

ED-based recruitment offers the opportunity to include more diverse individuals in falls prevention. To achieve adequate sample sizes, flexibility in working days and hours of the research team are obligatory.

TRIAL REGISTRATION

DRKS00025949.