Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation

Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), there are also challenges including lack of informati...

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Main Authors: Mike Jones, George Collier, David J. Reinkensmeyer, Frank DeRuyter, John Dzivak, Daniel Zondervan, John Morris
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/17/3/748
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spelling doaj-244e7f11166144409917143ad91040b02020-11-25T02:05:45ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012020-01-0117374810.3390/ijerph17030748ijerph17030748Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical RehabilitationMike Jones0George Collier1David J. Reinkensmeyer2Frank DeRuyter3John Dzivak4Daniel Zondervan5John Morris6Virginia C. Crawford Research Institute, Shepherd Center, Atlanta, GA 30309, USAVirginia C. Crawford Research Institute, Shepherd Center, Atlanta, GA 30309, USACenter for the Neurobiology of Learning and Memory, University of California, Irvine, CA 92606, USADepartment of Surgery, Duke University, Durham, NC 27708, USAPt Pal, Altadena, CA 91001, USAFlint Rehabilitation Devices, LLC, Irvine, CA 92614, USAVirginia C. Crawford Research Institute, Shepherd Center, Atlanta, GA 30309, USANumerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), there are also challenges including lack of information about how patient progress observed in the outpatient clinic translates into improved functional performance at home. At present, outpatient providers must rely on patient-reported information about functional progress (or lack thereof) at home and in the community. Information and communication technologies (ICT) offer another option—data collected about the patient’s adherence, performance and progress made on home exercises could be used to help guide course corrections between clinic visits, enhancing effectiveness and efficiency of outpatient care. In this article, we describe our efforts to explore use of sensor-enhanced home exercise and big data analytics in medical rehabilitation. The goal of this work is to demonstrate how sensor-enhanced exercise can improve rehabilitation outcomes for patients with significant neurological impairment (e.g., from stroke, traumatic brain injury, and spinal cord injury). We provide an overview of big data analysis and explain how it may be used to optimize outpatient rehabilitation, creating a more efficient model of care. We describe our planned development efforts to build advanced analytic tools to guide home-based rehabilitation and our proposed randomized trial to evaluate effectiveness and implementation of this approach.https://www.mdpi.com/1660-4601/17/3/748mobile rehabilitationdisabilityrehabilitationinformation and communication technology
collection DOAJ
language English
format Article
sources DOAJ
author Mike Jones
George Collier
David J. Reinkensmeyer
Frank DeRuyter
John Dzivak
Daniel Zondervan
John Morris
spellingShingle Mike Jones
George Collier
David J. Reinkensmeyer
Frank DeRuyter
John Dzivak
Daniel Zondervan
John Morris
Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation
International Journal of Environmental Research and Public Health
mobile rehabilitation
disability
rehabilitation
information and communication technology
author_facet Mike Jones
George Collier
David J. Reinkensmeyer
Frank DeRuyter
John Dzivak
Daniel Zondervan
John Morris
author_sort Mike Jones
title Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation
title_short Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation
title_full Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation
title_fullStr Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation
title_full_unstemmed Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation
title_sort big data analytics and sensor-enhanced activity management to improve effectiveness and efficiency of outpatient medical rehabilitation
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1660-4601
publishDate 2020-01-01
description Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), there are also challenges including lack of information about how patient progress observed in the outpatient clinic translates into improved functional performance at home. At present, outpatient providers must rely on patient-reported information about functional progress (or lack thereof) at home and in the community. Information and communication technologies (ICT) offer another option—data collected about the patient’s adherence, performance and progress made on home exercises could be used to help guide course corrections between clinic visits, enhancing effectiveness and efficiency of outpatient care. In this article, we describe our efforts to explore use of sensor-enhanced home exercise and big data analytics in medical rehabilitation. The goal of this work is to demonstrate how sensor-enhanced exercise can improve rehabilitation outcomes for patients with significant neurological impairment (e.g., from stroke, traumatic brain injury, and spinal cord injury). We provide an overview of big data analysis and explain how it may be used to optimize outpatient rehabilitation, creating a more efficient model of care. We describe our planned development efforts to build advanced analytic tools to guide home-based rehabilitation and our proposed randomized trial to evaluate effectiveness and implementation of this approach.
topic mobile rehabilitation
disability
rehabilitation
information and communication technology
url https://www.mdpi.com/1660-4601/17/3/748
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