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Survey of rehabilitation clinicians in the united states: Barriers and critical use-cases for mrehab adoption

Publication ,  Conference
Morris, J; Thompson, N; Wallace, T; Jones, M; DeRuyter, F
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2020

This paper presents data and analysis from survey research conducted by the Rehabilitation Engineering Research Center on Information and Communications Technology Access for Information and Communications Technology (ICT) Access for Community Living, Health and Function (LiveWell RERC) on the perceptions and attitudes of clinical professionals in rehabilitation medicine regarding mobile health (mHealth) and mobile rehabilitation (mRehab) practices, techniques and technology in the United States. The analytical focus of this paper is on two key survey questions related to specific barriers and opportunities (most critical use-cases) for adopting mHealth/mRehab interventions. We present response data to these two questions segmented by clinical specialty – physical, occupational, speech and recreation therapy – to identify possible variation between and among these rehabilitation professions. This analysis provides a detailed map of the terrain of clinician expectations and experiences for the adoption and implementation of mHealth/mRehab interventions in the United States, and possibly other countries. Results show substantial support for mRehab interventions and technologies across all four clinical specialties. The most frequently identified barriers to effective use of mobile and internet technologies to support patients remotely focused on patients (ability to learn and use the technology, and internet access), not clinicians. The was more variability among clinical specializations regarding best use-cases. Tracking patient adherence to prescribed activities and supporting patients in the home and community were the most frequently cited best use cases across the whole sample.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030588045

Publication Date

January 1, 2020

Volume

12377 LNCS

Start / End Page

250 / 258

Related Subject Headings

  • Artificial Intelligence & Image Processing
 

Citation

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ICMJE
MLA
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Morris, J., Thompson, N., Wallace, T., Jones, M., & DeRuyter, F. (2020). Survey of rehabilitation clinicians in the united states: Barriers and critical use-cases for mrehab adoption. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12377 LNCS, pp. 250–258). https://doi.org/10.1007/978-3-030-58805-2_30
Morris, J., N. Thompson, T. Wallace, M. Jones, and F. DeRuyter. “Survey of rehabilitation clinicians in the united states: Barriers and critical use-cases for mrehab adoption.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12377 LNCS:250–58, 2020. https://doi.org/10.1007/978-3-030-58805-2_30.
Morris J, Thompson N, Wallace T, Jones M, DeRuyter F. Survey of rehabilitation clinicians in the united states: Barriers and critical use-cases for mrehab adoption. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020. p. 250–8.
Morris, J., et al. “Survey of rehabilitation clinicians in the united states: Barriers and critical use-cases for mrehab adoption.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12377 LNCS, 2020, pp. 250–58. Scopus, doi:10.1007/978-3-030-58805-2_30.
Morris J, Thompson N, Wallace T, Jones M, DeRuyter F. Survey of rehabilitation clinicians in the united states: Barriers and critical use-cases for mrehab adoption. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020. p. 250–258.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030588045

Publication Date

January 1, 2020

Volume

12377 LNCS

Start / End Page

250 / 258

Related Subject Headings

  • Artificial Intelligence & Image Processing