Development of a fall registration prototype based on machine learning for institutionalized older adults
Abstract
Introduction. Falls in institutionalized older adults represent an underestimated public health problem associated with disability, dependence and mortality. In Chile, the absence of standardized records in long-term care facilities (LTCF) for older adults limits effective prevention. The objective of this study was to design a prototype of a digital fall registration system based on machine learning (ML) for its implementation in LTCF. Methodology. The double diamond design methodology was used in four phases: a) identifying stakeholders and gathering information through qualitative interviews; b) analyzing causes and prioritizing ideas with an Analytical Hierarchy Process (AHP) and Pugh matrices; c) conceptual design and generating the minimum viable product (MVP), and d) developing prototypes for usability validation. Results. There was evidence of great heterogeneity in the current records and a lack of subsequent data analysis. A MVP was developed, which includes a form for recording falls, a visualization of preventive measures, differentiated user profiles and educational tools. The system was internally validated by caregivers, managers and health care professionals in LTCF. Discussion. Using ML would make it possible to automate data analysis and customize preventive measures. The participatory design and preventive approach were key to its acceptability. Conclusions. The developed prototype has the potential to optimize how falls are recorded in LTCF, improve prevention and strengthen care for institutionalized older adults.
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