Abstract
Wearable gadgets, portable mHealth applications, and geo-location advancements have the capacity to track, screen, and report information related to COVID-19 pandemic. These developments make an “associated wellbeing,” where people gather information outside of the medical care experience and acknowledge caretakers for it. Assortment of this PGHD or Patient Generated Health Data can possibly sway conveyance of medical care through distant observing, and by permitting patients and medical care groups to give focused on and productive consideration that lines up with the wellbeing status of individual patients. We examine the idea of a participatory computerized contact notice way to deal with help following of contacts who are presented to affirmed instances of Covid infection (COVID-19); It includes two types of approaches; one is based on apps and other on data collection. The proposed tool fills in as a supplemental agreement following way to deal with check the scarcity of medical services staff. To comprehend the worth and boundaries related to clinical reconciliation of PGHD, this study took data of stakeholders, looking at their viewpoints and encounters of PGHD use. Moreover, this research looked to exhibit the utilization of a cell phone and tablet application that upholds PHR (patient health records) based wellbeing perception by coordinating checking capacities explicit to Coronavirus. Interpreting progresses in innovation and data following into effective clinical usage requires seeing how stakeholders conceptualize and utilize PGHD, the potential worth that PGHD can add to mind, and the difficulties that may restrict PGHD's guarantee. Results represent the worth and difficulties related to health-framework execution of PGHD.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wood WA, Bennett AV, Basch E (2014) Emerging uses of patient generated health data in clinical research. https://doi.org/10.1016/j.molonc.2014.08.006. 1574–7891/ª 2014 Federation of European Biochemical Societies. Published by Elsevier B.V
Johnston NW, Lambert K, Hussack P et al (2013) Detection ofCOPD exacerbations and compliance with patient-reported daily symptom diaries using a smart phone-based information system. Chest 144(2):507e514
World Health Organization (2020) WHO Director-General’s opening remarks at the media briefing on COVID-19 11 March 2020. https://www.who.int/dg/speeches/detail/who-director-general-sopening-remarks-at-the-media-briefing-on-covid-19—11-march-2020. Accessed on 8 Mar 2020
Peeples MM, Iyer AK, Cohen JL (2013) Integration of a mobile-integrated therapy with electronic health records: lessons learned. J Diabetes Sci Technol 7(3):602–611 [FREE Full text] [Medline: 23759392]
Hoffmann CL, Marie JD, Uncover beliefs about PGHD. webMed
Cohen DJ, Keller SR, Hayes GR, Dorr DA, Ash JS, Sittig DF (2016) Integrating patient-generated health data into clinical care settings or clinical decision-making: lessons learned from project HealthDesign. http://humanfactors.jmir.org/2016/2/e26/
Act on the prevention of infectious diseases and medical care for patients with infectious diseases. Act No. 114. Ministry of Health, Labor and Welfare. URL:http://www.japaneselawtranslation.go.jp/law/detail/?vm=04&re=01&id=2830. Accessed on 20 June 2020
National Institute of Infectious Diseases (2020) URL: https://www.niid.go.jp/niid/en/2019-ncov-e/2484-idsc/9472-2019-ncov-02-en.html. Accessed on 20 June 2020
Mobi Health News. NHS healthcare organization Salford Royal taps Validic to integrate patient-generated health data into EHR. Available from: http://www.mobihealthnews.com/content/nhshealthcare- organization-salford-royal-taps-validic-integratepatient- generated-health. Accessed on 12 Apr 2018
NHS Digital (2018) NHS Digital Launches New API Lab. Available from: https://digital.nhs.uk/article/7927/NHS-Digital-launchesnew-API-Lab. Accessed on 12 Apr 2018
SMART: Tech Stack for Health Apps. Available from http://docs.smarthealthit.org/. Accessed on 12 Apr 2018
GOV.UK (2018) Medical Devices: Software Applications (apps). Available from:https://www.gov.uk/government/publications/medical-devices-software-applications-apps. Accessed on 12 Apr 2018
Cheng KG, Hayes GR, Hirano SH, Nagel MS, Baker D (2015) Challenges of integrating patient-centered data into clinical workflow for care of high-risk infants. Pers UbiquitComput 19(1):45–57. https://doi.org/10.1007/s00779-014-0807-y
Grudin J (1994) Groupware and social dynamics: eight challenges for developers. In: Commun ACM. United States: association for computing machinery, Jan 1994, pp 92–105. https://doi.org/10.1145/175222.175230
Using Patient-Generated Health Data to Transform Healthcare: Milestone B000061603 Evaluation Report. July 2017. Available online: https://www.pcori.org/research-results/2017/using-patient-generated-health-data-transform-healthcare
Dedoose Version 7.0.23, web application for managing, analyzing, and presenting qualitative and mixed method research data 2016. Los Angeles, CA: SocioCultural Research Consultants, LLC. Available online: www. dedoose.com
Scott K, Lewis CC (2015) Using measurement-based care to enhance any treatment. Cogn Behav Pract 22:49–59
Chung CF, Dew K, Cole A et al (2016) Boundary negotiating artifacts in personal informatics: patient-provider collaboration with patient-generated data. CSCW Conf Comput Support Coop Work 2016:770–786
Petersen C, DeMuro P (2015) Legal and regulatory considerations associated with use of patient-generated health data from social media and mobile health (mHealth) devices. Appl Clin Inform 6:16–26
Saxena A, Sharma S, Analysis of Hadoop and MapReduce tectonics through hive big data. Int J Control Theory Appl 9/14:3811–3911
Saxena A, Kaushik N, Kaushik N, Dwivedi A (2016) Implementation of cloud computing and big data with Java based web application. In: 2016 3rd international conference on computing for sustainable global development (INDIACom), New Delhi, pp 1289–1293
Saxena A, Chaurasia A, Kaushik N, Dwivedi A, Kaushik N (2018) Handling big data using map-reduce over hybrid cloud. In: International conference on innovative computing and communications. Springer, pp 135–144
Saxena M, Saxena A (2018) Advancements in systems medicine using big data analytics. In: 4th international conference on computers and management (ICCM) 2018 ELSEVIER-SSRN, pp 1–7. ISSN: 1556-5068
Saxena M, Saxena A (2020) Evolution of mHealth eco-system: a step towards personalized medicine. In: International conference on innovative computing and communications. Advances in intelligent systems and computing, vol 1087. Springer, Singapore, pp 351–370
Nagpal D, Sood S, Mohagaonkar S, Sharma H, Saxena A (2019) Analyzing viral genomic data using Hadoop framework in big data. In: 2019 6th international conference on computing for sustainable global development (INDIACom), New Delhi, India, pp 680–685
Saluja MK, Agarwal I, Rani U, Saxena A (2020) Analysis of diabetes and heart disease in big data using MapReduce framework. In: Gupta D, Khanna A, Bhattacharyya S, Hassanien AE, Anand S, Jaiswal A (eds) International conference on innovative computing and communications. Advances in intelligent systems and computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_3
Saxena M, Deo A, Saxena A (2020) mHealth for mental health. In: Gupta D, Khanna A, Bhattacharyya S, Hassanien AE, Anand S, Jaiswal A (eds) International conference on innovative computing and communications. Advances in intelligent systems and computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_84
Mohanty S, Sharma R, Saxena M, Saxena A (2021) Heuristic approach towards COVID-19: big data analytics and classification with natural language processing. In: Khanna A, Gupta D, Pólkowski Z, Bhattacharyya S, Castillo O (eds) Data analytics and management. Lecture notes on data engineering and communications technologies, vol 54. Springer, Singapore. https://doi.org/10.1007/978-981-15-8335-3_59
Srivastava S, Prithivi PPR, Srija1 K, Vaishnavi P, Savitha HSS, Grover A, Saxena M, Chandra S, Saxena A (2021)Analysis and visualization of the pandemics using artificial intelligence. In: IOP conference series: materials science and engineering, 1st international conference on computational research and data analytics (ICCRDA 2020), vol 1022, 24th Oct 2020, Rajpura, India
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Taneja, V., Mishra, S., Saxena, A. (2022). A Systematic Approach to mHealth in COVID-19: Patient Generated Health Data on Opportunities and Barriers for Transforming Healthcare. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_17
Download citation
DOI: https://doi.org/10.1007/978-981-16-6289-8_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6288-1
Online ISBN: 978-981-16-6289-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)