Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation
| Autor: | George Collier, Frank DeRuyter, Daniel K. Zondervan, John T. Morris, Michael L. Jones, John Dzivak, David J. Reinkensmeyer |
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| Rok vydání: | 2020 |
| Předmět: |
Big Data
Health Toxicology and Mutagenesis medicine.medical_treatment Big data Medical rehabilitation lcsh:Medicine Toxicology mobile rehabilitation law.invention 0302 clinical medicine Randomized controlled trial Injury - Trauma - (Head and Spine) law Outpatients Medicine Outpatient clinic 0303 health sciences Rehabilitation Concept Paper Health Services Networking and Information Technology R&D Neurological Medical emergency 03 medical and health sciences Ambulatory care Artificial Intelligence Clinical Research Behavioral and Social Science Humans Exercise information and communication technology 030304 developmental biology Aged business.industry lcsh:R Data Science Public Health Environmental and Occupational Health Neurosciences Activity management Reproducibility of Results Bayes Theorem medicine.disease Brain Disorders Physical Rehabilitation disability Information and Communications Technology Injury (total) Accidents/Adverse Effects business 030217 neurology & neurosurgery |
| Zdroj: | International journal of environmental research and public health, vol 17, iss 3 International Journal of Environmental Research and Public Health International Journal of Environmental Research and Public Health, Vol 17, Iss 3, p 748 (2020) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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