Presentation from SocMedHE21 on social media analysis tools
The COVID 19 pandemic adversely challenged the healthcare system in an unprecedented way. Access to neuro-rehabilitation programmes for patients with stroke and other neurological disability was severely restricted including shutting down of most community based and outpatient facilities. There is hardly any organised virtual programme of exploring any potential of stretching and exercising of muscles needed in a rehabilitation programme. There is an impetus to innovate service developments whilst the risks and fear of contracting the Coronvirus remain prevalent. We propose a framework for developing a novel tele-neurorehabilitation system that will guide the patients to perform therapeutic exercises, as proposed by the clinicians, remotely. The system will allow patients to directly interact with doctors through a secure audio-video online portal. Wearable motion tracking sensors will be integrated within a hardware-based home setting for gathering performance data live from patients while they are performing exercises. The paper describes the design components of the framework justifying the tools, hardware, and protocols required to implement a secure online portal for tele-neurorehabilitation. Specifications of the core architectural layers have been reported. Some preliminary work demonstrates how the framework specifies capturing and analysing of physiological data using wearable sensors,
as well as displaying of gait parameters on a software dashboard.
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Several unexpected behaviors may occur during actual treatment of clinical pathways, which will have negative impact on the implementation and the future work. To increase the performance of current deviation detection algorithms, a method is presented according to business alignment, which can effectively detect the anomaly in the implementation of the clinical pathways, provide judgment basis for the intervention in the process of the clinical pathway implementation, and play a crucial role in improving the clinical pathways. Firstly, the noise in diagnosis and treatment logs of clinical pathways will be removed. Then, the synchronous composition model is constructed to embody the deviations between the actual process and the theoretical model. Finally, algorithm is selected to search for optimal alignment. A clinical pathway for ST-Elevation Myocardial Infarction (STEMI) under COVID-19 is used as a case study, and the superiority and effectiveness of this method in deviation detection are illustrated in the result of experiments.
Research Article | Open Access
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