Subscribe to RSS
DOI: 10.1055/s-0041-1726485
Enhancing Safety During a Pandemic Using Virtual Care Remote Monitoring Technologies and UML Modeling
Summary
Objectives: This paper describes a methodology for gathering requirements and early design of remote monitoring technology (RMT) for enhancing patient safety during pandemics using virtual care technologies. As pandemics such as COrona VIrus Disease (COVID-19) progress there is an increasing need for effective virtual care and RMT to support patient care while they are at home.
Methods: The authors describe their work in conducting literature reviews by searching PubMed.gov and the grey literature for articles, and government websites with guidelines describing the signs and symptoms of COVID-19, as well as the progression of the disease. The reviews focused on identifying gaps where RMT could be applied in novel ways and formed the basis for the subsequent modelling of use cases for applying RMT described in this paper.
Results: The work was conducted in the context of a new Home of the Future laboratory which has been set up at the University of Victoria. The literature review led to the development of a number of object-oriented models for deploying RMT. This modeling is being used for a number of purposes, including for education of students in health infomatics as well as testing of new use cases for RMT with industrial collaborators and projects within the smart home of the future laboratory.
Conclusions: Object-oriented modeling, based on analysis of gaps in the literature, was found to be a useful approach for describing, communicating and teaching about potential new uses of RMT.
Keywords
Remote monitoring technology - assistive living - COVID-19 - pandemics - user requirements - safety - public health informatics - health informaticsPublication History
Article published online:
21 April 2021
© 2021. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Coronavirus disease (COVID-19) Situation Report – 191. World Health Organization. 2020 Jul 29. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200729-covid-19-sitrep-191.pdf?sfvrsn=2c327e9e_2
- 2 Guan W, Ni Z, Hu Y, Liang WH, Ou CQ, He JX. China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med 2020; 382 (18) 1708-20
- 3 Livingston E, Bucher K. Coronavirus Disease 2019 (COVID-19) in Italy. JAMA 2020; 323 (14) 1335
- 4 Onder G, Rezza G, Brusaferro S. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA 2020; 323 (18) 1775-6
- 5 Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med 2020; 172 (09) 577-82
- 6 Bhargava A, Fukushima EA, Levine M, Zhao W, Tanveer F, Szpunar SM. et al. Predictors for Severe COVID-19 Infection. Clin Infect Dis 2020; 71 (08) 19628
- 7 Raisi-Estabragh Z, McCracken C, Bethell MS, Cooper J, Cooper C, Caulfield MJ. et al. Greater risk of severe COVID-19 in Black, Asian and Minority Ethnic populations is not explained by cardiometabolic, socioeconomic or behavioural factors, or by 25(OH)-vitamin D status: study of 1326 cases from the UK Biobank. J Public Health (Oxf) 2020; 42 (03) 451-60
- 8 Demenech LM, Dumith SC, Vieira MECD, Neiva-Silva L. Income inequality and risk of infection and death by COVID-19 in Brazil. Rev Bras Epidemiol 2020; 23: e200095 . Portuguese, English
- 9 Vaid A, Somani S, Russak AJ, DeFreitas JK, Chaudhry FF, Paranjpe I. et al. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. J Med Internet Res 2020; 22 (11) e24018
- 10 Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic?. Lancet 2020; 395 (10228): 931-4
- 11 Berekaa MM. Insights into the COVID-19 pandemic: Origin, pathogenesis, diagnosis, and therapeutic interventions. Front Biosci (Elite Ed) 2021; 13: 117-39
- 12 Alhazzani W, M⊘ller MH, Arabi YM, Loeb M, Gong MN, Fan E. et al. Surviving sepsis campaign: guidelines on the management of critically ill adults with Coronavirus Disease 2019 (COVID-19). Read Online: Crit Care Med 2020; 48 (06) e440-e469
- 13 Whitten JL, Bentley LD, Dittman KC. Systems Analysis and Design Methods 5e. McGraw-Hill Higher Education; 2000
- 14 Hollander JE, Carr BG. Virtually perfect? Telemedicine for covid-19. N Engl J Med 2020; 382 (18) 1679-81
- 15 Borycki E, Cummings E, Dexheimer JW, Gong Y, Kennebeck S, Kushniruk A. et al. Patient-Centred Coordinated Care in Times of Emerging Diseases and Epidemics. Contribution of the IMIA Working Group on Patient Safety. Yearb Med Inform 2015; 10 (01) 207-15
- 16 Frisch LE, Borycki EM, Capron A, Mawudeku A, St John R, . Public health informatics in Canada. In: Maguson JA, Fu PC. editors. Public health informatics and information systems (2nd ed.). New York: Springer Verlag; 2013: 603-18
- 17 Vimarlund V, Borycki E, Kushniruk A, Avenburg K. Ambient assisted living: Identifying new challenges and needs for digital technologies and service innovation. Yearb Med Inform 2021
- 18 Peng Y, Li C, Rong Y, Chen X, Chen H. Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning. J Glob Health 2020; 10 (02) 020511
- 19 Vaid A, Somani S, Russak AJ, DeFreitas JK, Chaudhry FF, Paranjpe I. et al. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. J Med Internet Res 2020; 22 (11) e24018
-
20 British Columbia. Support App & Self-Assessment Tool. Available from: https://bc.thrive.health/
- 21 Martin A, Nateqi J, Gruarin S, Munsch N, Abdarahmane I, Zobel M. et al. An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. Sci Rep 2020; 10 (01) 19012
- 22 Booch G. The unified modeling language user guide (2nd Ed.). New York: Addison Wesley; 2005
- 23 Zhang Y, Dong C, Zhang X. Description Method of Emergency Plan Base on Multiple Views Framework. In: 2011 International Conference of Information Technology, Computer Engineering and Management Sciences 2011 Sep 24, Vol. 4. IEEE; 2011. p. 160-3
- 24 Hamid AH, Rozan MZ, Ibrahim R, Deris S, Rushdi HN, Yunus MM. Understanding and designing business process modelling for emergency plan. In: 2013 International Conference on Research and Innovation in Information Systems (ICRIIS) 2013 Nov 27. IEEE; 2013. p. 564-69
- 25 Drăgoicea M, Walletzký L, Carrubbo L, Badr NG, Toli AM, Romanovská F. et al. Service Design for Resilience: A Multi-Contextual Modeling Perspective. IEEE Access 2020; 8: 185526-43
- 26 Hadzic M, Dillon D, Dillon T. Use and Modeling of Multi-agent Systems in Medicine. 2009 20th International Workshop on Database and Expert Systems Application, Linz, Austria, 2009. p. 303-7
- 27 Qin H, Shapiro A, Yang L. Emerging Infectious Disease: A Computational Multi-agent Model. 2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom). Washington, DC, USA; 2012. p. 28-33
- 28 Suphakul T, Senivongse T. Development of privacy design patterns based on privacy principles and UML. 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Kanazawa, 2017. p. 369-75
- 29 Basso T, Montecchi L, Moraes R, Jino M, Bondavalli A. Towards a UML Profile for Privacy-Aware Applications. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. Liverpool, UK; 2015. p. 371-8
- 30 Mian A, Khan S. Coronavirus: the spread of misinformation. BMC Med 2020; 18 (01) 89
- 31 Nath A. Long-haul COVID. Neurology 2020; 95 (13) 559-60