Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol

Introduction

Frailty is defined as a state of increased vulnerability to adverse health outcomes arising from multiple impairments in the physical, cognitive, psychological, nutritional and/or social domains.1 2 Frailty can be conceptualised as a continuum with a series of intermediate stages that may be reversible.3 With appropriate screening, identification and/or assessment of frailty, an upstream and proactive approach can be taken to prevent or delay decline.4

Though routine population screening for frailty has not been recommended,5 6 there is evidence that case-finding strategies with appropriate tools could be beneficial.7 8 The lack of agreement on an operational definition for frailty,9 however, has led to the development of many different measurement tools and identification criteria, hindering the evaluation of evidence-based interventions.10–13 Given the time-intensive nature and substantial variability of the current methods for frailty identification,14 15 healthcare providers often fail to identify frailty at the early stages, leading to missed opportunities for timely intervention and potential prevention of adverse health outcomes.16 17

The integration and analysis of electronic medical records and other digital data sources present the potential for early detection of frailty.18–21 Leveraging the increasing availability of these data sources could provide benefits for a much wider group of people, by moving beyond reliance on the one-on-one interactions between the clinician and patient. Health service planning and community programming targeted towards those identified at the early stages could lead to improved functional status, thereby delaying progression of frailty and onset of adverse outcomes.22 The use of artificial intelligence (AI) techniques may support early recognition of the signs and symptoms of frailty, which can help initiate care plans to address the needs of adults living with frailty in a timely manner to promote overall health and improve the quality of life for patients.23–25

The utilisation of AI is currently being explored by healthcare payers, providers and numerous health-related industries in a variety of ways,26 27 most commonly to support diagnosis, monitoring, care, patient engagement and adherence, and medical administrative activities. The recent advances in deep learning, inspired by biological computing and brain-like capabilities of neural networks, have opened the door to new opportunities in addressing complex clinical challenges.28–31 AI methods are being developed, tested and implemented to individual patient data generated either through the monitoring of their activities or through digital health information, with the objective of improving efficiency in the early identification or management of frailty.32 33 By providing healthcare professionals with supplemental information about their patients’ needs, these AI applications can enable better informed decision-making, thereby promoting more proactive, patient-centred care.

A growing number of studies are focusing on the classification and prediction of frailty through the application of AI to various types of patient data.34 A cursory PubMed search combining the terms AI and frailty reveals an exponential increase in the number of publications on this topic over the last 5 years (figure 1). However, due to the inconsistencies in the conceptualisation of frailty,35 there exist significant disparities in the methods and results across studies. Similarly, the range of AI approaches may also be extremely broad, with important potential differences in terms of their objectives and methods. Moreover, it is unclear whether and how knowledge users including clinicians, patients and caregivers and policy makers, have been engaged in this emerging area to ensure that it meets their needs. To address these gaps, this protocol describes a scoping review that aims to provide a comprehensive overview of current evidence regarding the development and use of AI technologies for the identification and management of frailty. Specifically, we will focus on clinical tools and frameworks of frailty used, the outcomes assessed and the involvement of knowledge users.

Figure 1

Number of publications related to artificial intelligence and frailty indexed in Medline between 2010 and 2022.

To our knowledge, no systematic literature review on the topic of AI and frailty has been conducted to date. In a recent narrative review examining machine learning methodologies for frailty screening, Oliosi and colleagues summarised the results of six publications on the topic and identified notable heterogeneity in the frailty definitions and AI methods used.36 Building on this recent work, we are adopting a scoping review methodology which will cover a much broader range of topics from the literature on AI and frailty. Our review will include a systematic search of published and grey literature, thereby including many more references and a more comprehensive overview of the literature in this area than what was presented in Oliosi et al’s narrative review.

We anticipate that the proposed synthesis will inform researchers, healthcare providers, policy makers as well as patients and their caregivers in understanding the current scope of work in the intersecting fields of AI and frailty. By offering insights into the current state of research and applications, this proposed review will facilitate informed decision-making and guide future work in this field. In this protocol, we provide detailed methods for the scoping review on the use of AI for the identification and management of frailty. A separate publication will present the review outcomes once the review has been completed.

Discussion

The use of AI in medicine is revolutionising healthcare.42 44 However, applications of this emerging technology in frailty are still in the early stages and have not been comprehensively described. Given the lack of agreement on a definition of frailty, applications of AI for frailty may be particularly diverse compared with those for other health conditions. Summarising the rapidly emerging research in this area will provide important insights into the approaches that are being used, the gaps and limitations of this work and avenues for future research.