ADDRESSING PATIENTS' NEEDS: USING DIGITAL TOOLS TO ENHANCE PHYSIOTHERAPY
A.G. Silva1, R. Park2, F. Reis3
All authors, affiliations and abstracts have been published as submitted.
1University of Aveiro, CINTESIS.UA, School of Health Sciences, Aveiro, Portugal, 2University of Cape Town, Pain Unit, Dept of Anaesthesia and Perioperative Medicine, Cape Town, South Africa, 3Federal Institute of Rio de janeiro, Rio de Janeiro, Brazil
Learning objective 1: To understand the role of physiotherapists in the development of mobile/web digital solutions focusing on patient records and the delivery of exercise and physical activity-based interventions. It will include the definition of functional requirements, the specification of the digital intervention and details of delivery, and the assessment of the final solution.
Learning objective 2: To describe how physiotherapists can integrate digital solutions into their treatments to enhance rehabilitation and increase access to physiotherapy in resource poor settings. Data will be presented on process analysis of implementing group-based telehealth rehabilitation for chronic pain and qualitative evaluation of acceptability of the treatment by patients.
Learning objective 3: To describe the fundamentals of artificial intelligence and Big Data in the context of Physiotherapy. Illustrative examples of potential benefits will be presented, covering epidemiology, precision treatment, patient screening, and vision augmentation. Questions on ethical issues and the implications for the future roles of specialists will be discussed.
Description: The number of persons in need of physiotherapy and the diversity of conditions and settings call for approaches that enable physiotherapists to monitor and deliver timely and quality care to an increasingly growing number of individuals. New technologies, including digital tools related to telehealth and intelligent health (iHealth), have the potential to overcome current limitations, such as difficulties accessing timely and continuous physiotherapy, mobility limitations, or long waiting (1). However, evidence suggests that existing digital tools rarely adhere to evidence-based guidelines, and are often developed without the collaboration of health professionals and end-users (2), which results in poor quality solutions (3).
Having multidisciplinary teams involved in the process of development of technologies contributes to improving their final quality. Physiotherapists have a key role in identifying the functional requirements of these tools (i.e., what they should do), matching these requirements with both the needs of patients and the expectations of physiotherapists to deliver high-quality care, and assessing the quality of the final product. To optimize the potential of physiotherapists’ contributions, there is the need to know the process of development of telehealth tools, understand the terminology of the field and the particularities of transposing a face-to-face intervention into an online intervention, and of assessing the final solution. The understanding of these processes also facilitates the adoption of technology.
A key component to facilitate the integration of digital solutions into physiotherapy practice is partnership with the end user – the patient. Developing digital tools in collaboration with patient partners is now accepted as best practice. However, ongoing engagement with patients about their experiences in the process of development and the acceptability of the end product is still lacking (4). Understanding of methods to enhance patient partnerships in the development and implementation of digital solutions for physiotherapy facilitates the iterative processes which optimize treatments. This is particularly relevant in resource poor settings where a partnership with the patient and the wider community is key to overcoming specific challenges and potential barriers, including patients’ and professionals’ readiness to use digital tools, level of education, infrastructure, or computer literacy (5).
Digital solutions have been used in the Physiotherapy field for diagnosis and risk assessment, prevention, therapeutic interventions, and follow-up. The concept of intelligent health (iHealth), which integrates Big Data analytics and artificial intelligence (AI), is recent. Big Data is associated with the massive computational resources needed to cope with the increasing volume and complexity of data from many sources. Big Data includes information that is structured, semi-structured, or unstructured, and there may be complex interrelationships that are syntactic, semantic, social, cultural, economic, and organizational in nature (6). AI can use sophisticated algorithms to ‘learn’ features from a large volume of healthcare data, and then use the obtained insights to assist clinical practice. It can also be equipped with learning and self-correcting abilities to improve its accuracy based on feedback. Thus, iHealth can provide more contextual data from the patient's personal environment by combining patient self-monitoring and self-reports, monitoring via sensors (either wearables or stationary), and data mining technology (7). iHealth is seen as an opportunity for enhanced real-time self-monitoring, integration of assessment into the patient's environment, and data mining to support decision-making and the personalization of treatment. By focusing on the complex connections between individual symptoms and behavior in specific situations and the environment of the patient, iHealth could be used for a better risk assessment, stratification, tailoring of treatment to the individual needs of each patient, preventing the onset of episodes or symptoms, and for empowering the patient's self-management (8). However, iHealth is in its infancy in the Physiotherapy field. Therefore, clinicians and researchers should consider that discussing ethical issues, implementation, and the implications for the future roles in Physiotherapy is timely and needed.
Having multidisciplinary teams involved in the process of development of technologies contributes to improving their final quality. Physiotherapists have a key role in identifying the functional requirements of these tools (i.e., what they should do), matching these requirements with both the needs of patients and the expectations of physiotherapists to deliver high-quality care, and assessing the quality of the final product. To optimize the potential of physiotherapists’ contributions, there is the need to know the process of development of telehealth tools, understand the terminology of the field and the particularities of transposing a face-to-face intervention into an online intervention, and of assessing the final solution. The understanding of these processes also facilitates the adoption of technology.
A key component to facilitate the integration of digital solutions into physiotherapy practice is partnership with the end user – the patient. Developing digital tools in collaboration with patient partners is now accepted as best practice. However, ongoing engagement with patients about their experiences in the process of development and the acceptability of the end product is still lacking (4). Understanding of methods to enhance patient partnerships in the development and implementation of digital solutions for physiotherapy facilitates the iterative processes which optimize treatments. This is particularly relevant in resource poor settings where a partnership with the patient and the wider community is key to overcoming specific challenges and potential barriers, including patients’ and professionals’ readiness to use digital tools, level of education, infrastructure, or computer literacy (5).
Digital solutions have been used in the Physiotherapy field for diagnosis and risk assessment, prevention, therapeutic interventions, and follow-up. The concept of intelligent health (iHealth), which integrates Big Data analytics and artificial intelligence (AI), is recent. Big Data is associated with the massive computational resources needed to cope with the increasing volume and complexity of data from many sources. Big Data includes information that is structured, semi-structured, or unstructured, and there may be complex interrelationships that are syntactic, semantic, social, cultural, economic, and organizational in nature (6). AI can use sophisticated algorithms to ‘learn’ features from a large volume of healthcare data, and then use the obtained insights to assist clinical practice. It can also be equipped with learning and self-correcting abilities to improve its accuracy based on feedback. Thus, iHealth can provide more contextual data from the patient's personal environment by combining patient self-monitoring and self-reports, monitoring via sensors (either wearables or stationary), and data mining technology (7). iHealth is seen as an opportunity for enhanced real-time self-monitoring, integration of assessment into the patient's environment, and data mining to support decision-making and the personalization of treatment. By focusing on the complex connections between individual symptoms and behavior in specific situations and the environment of the patient, iHealth could be used for a better risk assessment, stratification, tailoring of treatment to the individual needs of each patient, preventing the onset of episodes or symptoms, and for empowering the patient's self-management (8). However, iHealth is in its infancy in the Physiotherapy field. Therefore, clinicians and researchers should consider that discussing ethical issues, implementation, and the implications for the future roles in Physiotherapy is timely and needed.
References:
- Talboom-Kamp EP, Verdijk NA, Harmans LM, et al. An eHealth Platform to Manage Chronic Disease in Primary Care: An Innovative Approach. Interactive Journal of Medical Research, 2016;5:e5.
- Simões P, Silva AG, Amaral J, Queirós A, Rocha NP, Rodrigues M. Features, behavioral change techniques, and quality of the most popular mobile apps to measure physical activity: Systematic search in app stores. JMIR mHealth and uHealth, 2018;6(10):e11281.
- Marques J, Borges L, Andias R, Silva AG. Characterization and assessment of the most popular mobile apps designed for neck pain self-management: A systematic search in app stores. Musculoskeletal Care, 2022;20(1):192-199.
- Bombard Y, Baker GR, Orlando E, et al. Engaging patients to improve quality of care: a systematic review. Implementation Science. 2018;13(1):98.
- Reis FJJ, Fernandes LG, Saragiotto BT. Telehealth in low- and middle-income countries: Bridging the gap or exposing health disparities? Health Policy Technology. 2021;10(4):100577.
- Benke K, Benke G. Artificial intelligence and big data in public health. International Journal of Environmental Research and Public Health. 2018; 15(12):2796.
- Tack C. Artificial intelligence and machine learning: applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice. 2019;39:164-169.
- Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2017;2(4):230-243.
All authors, affiliations and abstracts have been published as submitted.
