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Record W4386856231 · doi:10.1097/npt.0000000000000457

Telehealth Models of Service Delivery—A Brave New World

2023· editorial· en· W4386856231 on OpenAlexaboutno aff
Coralie English, N. E. Fritz, Joyce Gomes‐Osman

Bibliographic record

VenueJournal of Neurologic Physical Therapy · 2023
Typeeditorial
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelerehabilitationTelehealthTelemedicineHealth careIntervention (counseling)TerminologyDigital healthMedicineService delivery frameworkService (business)NursingBusiness

Abstract

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Providing physiotherapy or other health care services in alternative ways to face to face in-person delivery has been around for many years. One of the earliest examples is a trial published in 1977 comparing telephone consultations to in-person education to parents of children with disabilities.1 As technology has developed, research into alternative models of remote health care delivery has exploded. A large range of options now exist, covering the spectrum from assessment to intervention and including real-time video consultations, remote monitoring, health care mobile applications, and Web-based platforms. As the different ways in which remote health care can be offered have increased, so too has the variation in terminology. This can be problematic; for example, a search of the Cochrane Database of Systematic Reviews using the key words “telehealth or telerehabilitation or telemedicine or e-health or m-health” resulted in only 41 hits. Adding the term “digital health” and “virtual care” increased the yield to 137 reviews, but this included unrelated topics, for example, related to digital nerve blocks, or virtual reality training (that can occur either in-person or remotely). In this Digital Health Special Issue of JNPT, we include 5 papers that span the range from assessment to intervention, including activity monitoring for people with Parkinson disease, telerehabilitation models of pelvic floor retraining for people with multiple sclerosis, tele-exercise programs for people with spinal cord injury, development and testing of a modified version of the Fugl-Meyer assessment for use in telerehabilitation, and consensus-based best practice strategies for implementing telehealth in neurological clinical practice. The papers included in this issue all use different terminology, including telerehabilitation, telehealth, tele-exercise, and digital health interventions. Other common terms include “remote or virtual health care.” In the United States, the Health Resource Services Administration defines telehealth as “the use of electronic information and telecommunication technologies to support long-distance clinical health care, patient and professional health-related education, public health, and health administration.”2 The American Telemedicine Association Rehabilitation Group3 describes the difference between the term “telehealth” (“broad term used to describe the use of electronic or digital information and communications technologies to support clinical healthcare,...”) and “telerehabilitation” (“the delivery of rehabilitation and habilitation services via information and communication technologies...”) Variations in terminology can lead to lack of clarity about the specifics of interventions delivered in trials, as well as difficulties in identifying and synthesizing relevant evidence. A recent review of the evidence for telehealth for stroke rehabilitation services recommends a taxonomy of terminology.4 The taxonomy recommends that the term “telehealth” be used as an overarching term to describe health care provided remotely, and that interventions are described according to their purpose (assessment, intervention), timing of delivery (synchronous, asynchronous, continuous), and type of technology used (telephone, videoconferencing, Web sites, or digital apps). For example, in the case of the paper in this issue by Kahraman et al, the intervention would be described as “Pelvic floor muscle training using synchronous videoconferencing.” We urge authors to implement this taxonomy to create greater clarity in the field. We argue that the time for trials comparing telehealth versus in-person delivery has past, and instead research efforts should focus on the development and implementation of interventions with known effectiveness via telehealth. Development of telehealth modes of delivering interventions should involve careful codesign in partnership with lived experience experts. This is important to ensure what is created is fit for purpose, accessible, and acceptable to the end user. An example of this is the development of the i-REBOUND after Stroke Web site.5 This Web site was developed in close partnership with people with stroke, and the design features (eg, large icons, ease of navigation, page layouts, and strengths-based approach) reflect this. Qualitative studies, such as that conducted by Fowler King et al in this edition, provide valuable information about barriers and facilitators to effective implementation. Being able to accurately assess the effect of interventions using valid and reliable assessments that can be delivered remotely is essential to drive the field forward. The study by Carmona et al in this issue provides valuable information about the adaptation of the Fugl-Meyer assessment tool for telehealth. Telehealth modes of delivery can increase the accessibility of services. Many people with mobility difficulties find difficulties with transport a barrier to attending in-person appointments. Across urban and rural environments, telehealth may also overcome financial burdens and distance from major medical centers that offer specialty care for persons with neurological disorders. In countries with sparse populations over wide geographical areas, such as in Australia or rural areas of the United States and Canada, telehealth models of care can mean more people have access to the services they need. Telehealth also holds promise for providing more health care services to people living in low- to middle-income countries. In India, a nation-wide study has been tracking Internet use since 1998. Their latest report estimates 67% of the urban population and 31% of the rural population are regular Internet users, with increases in rural areas driving growth, particularly in mobile phone usage.6 However, another report suggests India has the lowest smartphone ownership, well below the median of 45% across emerging economies and 76% in advanced economies.7 Accessibility issues for telehealth are not limited to low-income countries. Studies in the United States have reported uptake of telehealth services is lower amongst older age groups, Black Americans, unemployed individuals, those without high school education, and those living outside of metropolitan centres.8–10 Development and implementation of telehealth models of care must include strategies to ensure equity of access. We hope you enjoy this issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.382
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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