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      Teleneurology and mobile technologies: the future of neurological care

      , , , ,
      Nature Reviews Neurology
      Springer Nature

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          Abstract

          Neurological disorders are the leading cause of global disability. However, for most people around the world, current neurological care is poor. In low-income countries, most individuals lack access to proper neurological care, and in high-income countries, distance and disability limit access. With the global proliferation of smartphones, teleneurology - the use of technology to provide neurological care and education remotely - has the potential to improve and increase access to care for billions of people. Telestroke has already fulfilled this promise, but teleneurology applications for chronic conditions are still in their infancy. Similarly, few studies have explored the capabilities of mobile technologies such as smartphones and wearable sensors, which can guide care by providing objective, frequent, real-world assessments of patients. In low-income settings, teleneurology can increase the capacity of local care systems through professional development, diagnostic support and consultative services. In high-income settings, teleneurology is likely to promote the expansion and migration of neurological care away from institutions, incorporate systems of asynchronous communication (such as e-mail), integrate clinicians with diverse skill sets and reach new populations. Inertia, outdated policies and social barriers - especially the digital divide - will slow this progress at considerable cost. However, a future increasingly will be possible in which neurological care can be accessed by anyone, anywhere. Here, we examine the emerging evidence regarding the benefits of teleneurology for chronic conditions, its role and risks in low-income countries and the promise of mobile technologies to measure disease status and deliver care. We conclude by discussing the future trends, barriers and timing for the adoption of teleneurology.

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          Most cited references121

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          The Extension for Community Healthcare Outcomes (ECHO) model was developed to improve access to care for underserved populations with complex health problems such as hepatitis C virus (HCV) infection. With the use of video-conferencing technology, the ECHO program trains primary care providers to treat complex diseases. We conducted a prospective cohort study comparing treatment for HCV infection at the University of New Mexico (UNM) HCV clinic with treatment by primary care clinicians at 21 ECHO sites in rural areas and prisons in New Mexico. A total of 407 patients with chronic HCV infection who had received no previous treatment for the infection were enrolled. The primary end point was a sustained virologic response. A total of 57.5% of the patients treated at the UNM HCV clinic (84 of 146 patients) and 58.2% of those treated at ECHO sites (152 of 261 patients) had a sustained viral response (difference in rates between sites, 0.7 percentage points; 95% confidence interval, -9.2 to 10.7; P=0.89). Among patients with HCV genotype 1 infection, the rate of sustained viral response was 45.8% (38 of 83 patients) at the UNM HCV clinic and 49.7% (73 of 147 patients) at ECHO sites (P=0.57). Serious adverse events occurred in 13.7% of the patients at the UNM HCV clinic and in 6.9% of the patients at ECHO sites. The results of this study show that the ECHO model is an effective way to treat HCV infection in underserved communities. Implementation of this model would allow other states and nations to treat a greater number of patients infected with HCV than they are currently able to treat. (Funded by the Agency for Healthcare Research and Quality and others.).
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                Author and article information

                Journal
                Nature Reviews Neurology
                Nat Rev Neurol
                Springer Nature
                1759-4758
                1759-4766
                April 6 2018
                April 6 2018
                :
                :
                Article
                10.1038/nrneurol.2018.31
                29623949
                cd7c2cad-eb61-4ce0-91e0-c98d3e8b95a4
                © 2018
                History

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