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

Author

Listed:
  • Mike Jones

    (Virginia C. Crawford Research Institute, Shepherd Center, Atlanta, GA 30309, USA)

  • George Collier

    (Virginia C. Crawford Research Institute, Shepherd Center, Atlanta, GA 30309, USA)

  • David J. Reinkensmeyer

    (Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA 92606, USA)

  • Frank DeRuyter

    (Department of Surgery, Duke University, Durham, NC 27708, USA)

  • John Dzivak

    (Pt Pal, Altadena, CA 91001, USA)

  • Daniel Zondervan

    (Flint Rehabilitation Devices, LLC, Irvine, CA 92614, USA)

  • John Morris

    (Virginia C. Crawford Research Institute, Shepherd Center, Atlanta, GA 30309, USA)

Abstract

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.

Suggested Citation

  • Mike Jones & George Collier & David J. Reinkensmeyer & Frank DeRuyter & John Dzivak & Daniel Zondervan & John Morris, 2020. "Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation," IJERPH, MDPI, vol. 17(3), pages 1-13, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:748-:d:312705
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    References listed on IDEAS

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    1. Monica A Konerman & Lauren A Beste & Tony Van & Boang Liu & Xuefei Zhang & Ji Zhu & Sameer D Saini & Grace L Su & Brahmajee K Nallamothu & George N Ioannou & Akbar K Waljee, 2019. "Machine learning models to predict disease progression among veterans with hepatitis C virus," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    2. Fatemeh Rahimian & Gholamreza Salimi-Khorshidi & Amir H Payberah & Jenny Tran & Roberto Ayala Solares & Francesca Raimondi & Milad Nazarzadeh & Dexter Canoy & Kazem Rahimi, 2018. "Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-18, November.
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    Cited by:

    1. Veronica A. Swanson & Vicky Chan & Betsaida Cruz-Coble & Celeste M. Alcantara & Douglas Scott & Mike Jones & Daniel K. Zondervan & Naveen Khan & Jan Ichimura & David J. Reinkensmeyer, 2021. "A Pilot Study of a Sensor Enhanced Activity Management System for Promoting Home Rehabilitation Exercise Performed during the COVID-19 Pandemic: Therapist Experience, Reimbursement, and Recommendation," IJERPH, MDPI, vol. 18(19), pages 1-18, September.

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