Uncovering clinical rehabilitation technology trends: field observations, mixed methods analysis, and data visualization.
Celian, C.; Redd, H.; Smaller, K.; Ryali, P.; Patton, J. L.; Reinkensmeyer, D. J.; Rafferty, M. R.
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ObjectiveTo analyze real-world rehabilitation technology (RT) use, with a view toward enhancing RT development and adoption. DesignA convergent, mixed-methods study using direct field observations, semi-structured templates, and summative content analysis. SettingTen neurorehabilitation units in a single health system. Participants3 research clinicians (1OT, 2PTs) observed [~]60 OTs and 70 PTs in inpatient; [~]18 OTs and 30 PTs in outpatient. InterventionsNot applicable Main Outcome MeasuresCharacteristics of RT, time spent setting up and using RT, and clinician behaviors. Results90 distinct devices across 15 different focus areas were inventoried. 329 RT-uses were documented over 44 hours with 42% of inventoried devices used. RT was used more during interventions (72%) than measurement (28%). Intervention devices used frequently were balance/gait (39%), strength/endurance (30%), and transfer/mobility training (16%). Measurement devices were frequently used to measure vitals (83%), followed by grip strength (7%), and upper extremity function (5%). Device characteristics were predominately AC-powered (56%), actuated (57%), monitor-less (53%), multi-use (68%), and required little familiarization (57%). Set-up times were brief (mean {+/-} SD = 3.8{+/-}4.21 and 0.8{+/-}1.3 for intervention and measurement, respectively); more time was spent with intervention RT (25.6{+/-}15) than measurement RT (7.3{+/-}11.2). RT nearly always involved verbal instructions (72%) with clinicians providing more feedback on performance (59.7%) than on results (30%). Therapists attention was split evenly between direct attention towards the patient during clinician treatment (49.7%) and completing other tasks such as documentation (50%). ConclusionsEven in a tech-friendly hospital, majority of available RT were observed un-used, but identifying these usage patterns is crucial to predict eventual adoption of new designs from earlier stages of RT development. An interactive data visualization page supplement is provided to facilitate this study.
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