A Smart Service Platform for Cost Efficient Cardiac Health Monitoring.

Faust, O; Lei, N; Chew, E; Ciaccio, EJ; Acharya, UR

A Smart Service Platform for Cost Efficient Cardiac Health Monitoring.

Faust, O Lei, N Chew, E Ciaccio, EJ Acharya, UR

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: International journal of environmental research and public health, 2020, 17, (17), pp. 6313-6313
Issue Date:: 2020-08-30

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Field	Value	Language
dc.contributor.author	Faust, O
dc.contributor.author	Lei, N
dc.contributor.author	Chew, E
dc.contributor.author	Ciaccio, EJ
dc.contributor.author	Acharya, UR
dc.date.accessioned	2021-05-24T22:28:44Z
dc.date.available	2020-08-24
dc.date.available	2021-05-24T22:28:44Z
dc.date.issued	2020-08-30
dc.identifier.citation	International journal of environmental research and public health, 2020, 17, (17), pp. 6313-6313
dc.identifier.issn	1661-7827
dc.identifier.issn	1660-4601
dc.identifier.uri	http://hdl.handle.net/10453/149177
dc.description.abstract	<h4>Aim</h4>In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective.<h4>Subject and methods</h4>There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients.<h4>Results</h4>Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service.<h4>Conclusion</h4>Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI AG
dc.relation.ispartof	International journal of environmental research and public health
dc.relation.isbasedon	10.3390/ijerph17176313
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Toxicology
dc.subject.mesh	Humans
dc.subject.mesh	Atrial Fibrillation
dc.subject.mesh	Monitoring, Physiologic
dc.subject.mesh	Monitoring, Ambulatory
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Heart Rate
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Computer Communication Networks
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Information Systems
dc.subject.mesh	Efficiency, Organizational
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Atrial Fibrillation
dc.subject.mesh	Computer Communication Networks
dc.subject.mesh	Efficiency, Organizational
dc.subject.mesh	Heart Rate
dc.subject.mesh	Humans
dc.subject.mesh	Information Systems
dc.subject.mesh	Monitoring, Ambulatory
dc.subject.mesh	Monitoring, Physiologic
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.title	A Smart Service Platform for Cost Efficient Cardiac Health Monitoring.
dc.type	Journal Article
utslib.citation.volume	17
utslib.location.activity	Switzerland
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-24T22:28:41Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	17

Abstract:

Aim

In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective.

Subject and methods

There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients.

Results

Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service.

Conclusion

Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149177