Predicting hospital admissions and emergency room visits using remote home monitoring data

Inibhunu, C; Schauer, A; Redwood, O; Clifford, P; Mcgregor, C

Predicting hospital admissions and emergency room visits using remote home monitoring data

Inibhunu, C Schauer, A Redwood, O Clifford, P Mcgregor, C

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE Life Sciences Conference, LSC 2017, 2018, 2018-January pp. 282 - 285
Issue Date:: 2018-01-23

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Field	Value	Language
dc.contributor.author	Inibhunu, C	en_US
dc.contributor.author	Schauer, A	en_US
dc.contributor.author	Redwood, O	en_US
dc.contributor.author	Clifford, P	en_US
dc.contributor.author	Mcgregor, C https://orcid.org/0000-0002-0491-4403	en_US
dc.date.issued	2018-01-23	en_US
dc.identifier.citation	2017 IEEE Life Sciences Conference, LSC 2017, 2018, 2018-January pp. 282 - 285	en_US
dc.identifier.isbn	9781538610305	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127103
dc.description.abstract	© 2017 IEEE. The costs of lengthy hospital admissions (HA) and multiple emergency room visits (ER Visits) from patients with conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD) can place a significant burden on healthcare systems. Understanding the various factors contributing to hospitalization and ER visits could aid cost-effective management in the delivery of services leading to potential improvement on quality of life for patients. This can be facilitated by collecting data using remoting patient monitoring (RPM) services and using analytics to discover important factors about patients. This paper presents our research that utilizes predictive modeling to determine key factors that are significant determinant to hospitalization and multiple ER Visits. The results shows that gender, past medical history and vital status are key factors to hospital admissions and ER Visits. Additionally, when a factor to indicate the period before, during and after an ER Visits was included, the resulting model shows a very high likelihood ratio and improved p values on all vital status. Our results shows that more research is needed to fully understand the temporal patterns among variables during hospitalization or ER visit.	en_US
dc.relation.ispartof	2017 IEEE Life Sciences Conference, LSC 2017	en_US
dc.relation.isbasedon	10.1109/LSC.2017.8268198	en_US
dc.title	Predicting hospital admissions and emergency room visits using remote home monitoring data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	1103 Clinical Sciences	en_US
utslib.for	0804 Data Format	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. The costs of lengthy hospital admissions (HA) and multiple emergency room visits (ER Visits) from patients with conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD) can place a significant burden on healthcare systems. Understanding the various factors contributing to hospitalization and ER visits could aid cost-effective management in the delivery of services leading to potential improvement on quality of life for patients. This can be facilitated by collecting data using remoting patient monitoring (RPM) services and using analytics to discover important factors about patients. This paper presents our research that utilizes predictive modeling to determine key factors that are significant determinant to hospitalization and multiple ER Visits. The results shows that gender, past medical history and vital status are key factors to hospital admissions and ER Visits. Additionally, when a factor to indicate the period before, during and after an ER Visits was included, the resulting model shows a very high likelihood ratio and improved p values on all vital status. Our results shows that more research is needed to fully understand the temporal patterns among variables during hospitalization or ER visit.

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http://hdl.handle.net/10453/127103