Reducing care home falls: a real-world data validation of a multifactorial falls-intervention digital application

Abstract

Abstract Background One in three adults over the age of 65 and one in two adults over the age of 80 will experience a fall a year. Falls account for a considerable cost burden for the National Health Services. Preventing falls in elderly care homes is a significant public health policy goal in the United Kingdom. The 2004 National Institute for Health and Care Excellence Clinical Guideline (CG21) recommends risk detection and multifactorial fall prevention interventions. Digital technology allows individualised monitoring and interventions. However, there is no certainty of the impact of multifactorial interventions on the rate of falls. Methods A mixed methods Real-World Validation incorporating a retrospective multi-centre case–control study using real-world data and qualitative study to assess the effectiveness of a falls prevention application in 32 care homes in the Northwest of England. The study aims to assess if a multifactorial fall-prevention digital App reduces falls and injurious falls in care homes. The primary outcome measures were the rate of patient falls per 1000 occupied bed days in care homes for 12 months. A digital multifactorial risk assessment and a tailored fall prevention plan linking each risk factor with the appropriate preventive interventions were implemented/reviewed monthly. For the intervention group two datasets were used. The first set was data recorded in the App on falls and resulting injury levels, multifactorial risk assessments, and number of falls. Sociodemographic variables (gender and age) of care homes residents were also collected for this group. Data for the first twelve months of use of the intervention were collected for early adopter intervention homes. Less than twelve months data was obtainable from care home adopting the intervention later in the study. The second dataset was constituted by intervention and comparable control anonymised data extracted from the care home residents' registries from Borough 1 Council and Borough 2 Clinical Commissioning Group, including quantitative data on the number of falls, number of injurious falls, and outcomes, with emergency room and hospital records for Borough 2. For the qualitative study, twelve video interviews conducted by Safe Steps were analysed thematically to identify user perceptions of various aspects of the App including need, development, implementation, use and benefits. Results The secondary outcome was the rate of injurious falls per 1000 occupied bed days. There were 2.23 fewer falls per 1000 occupied bed days in the Intervention group (M = 6.46, SD = 3.65) compared with Control (M = 8.69, SD = 6.38) (t(2.67) = -2.686, p = 0.008). The intervention had 3.5 fewer low harm injurious falls ratio per 1000 occupied bed days (M = 3.14, SD = 4.08) (M = 6.64, SD = 6.22) (t(144) = -3588, p < 0.01). There were significant differences between Intervention and Control on injurious falls resulting in ambulance calls (t(31.18) = -3.09, p = 0.04); and patients arriving at Accident & Emergency (t(17.25) = -3.71, p = 0.002). Thematic analysis of the video interviews identified the following six themes: Alleviation of staff workload; the impact of falls on both the individual and on the health care system; achievement of health outcome benefits, including reduced hospital visits for falls and improved quality of life for the patients living in care homes; the improvement over paper-based risk assessments for staff; the uniqueness of the person-centred approach of the App; and the ability of the approach to track patients across boundaries in the health and social care system. Conclusions In this real-world validation, the implementation of a multifactorial fall-prevention digital app was associated with a significant reduction in falls and injurious falls, and was perceived to be highly beneficial by care home residents, staff, management and care commissioners where the approach was implemented.

Keywords

• Real-world data
• Falls-intervention
• Digital application
• Care home
• Northwest England