Remote Patient Monitoring: What Impact Can Data Analytics Have on Cost?

Sunghoon Ivan Lee, Hassan Ghasemzadeh, Bobak Mortazavi, Mars Lan, Nabil Alshurafa, Michael Ong, Majid Sarrafzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations


While significant effort has been made on designing Remote Monitoring Systems (RMS), limited research has been conducted on the potential cost savings that these systems offer in terms of reduction in readmission costs, as well as the costs associated with human resources involved in the intervention process. This paper is particularly interested in exploring potential cost savings that an analytics engine can provide in presence of intelligent back-end data processing and machine learning algorithms against conventional RMS that operate based on simple thresholding approaches. Using physiological data collected from 486 heart failure patients through a clinical study in collaboration with the UCLA School of Medicine, we conduct a retrospective data analysis to estimate prediction accuracy as well as associated costs of the two remote monitoring approaches. Our results show that analytics-based RMS can reduce false negative rates by 61.4% while maintaining a false positive performance close to that of conventional RMS. Furthermore, the proposed analytics engine achieves 61.5% reduction in the overall readmission costs.

Original languageEnglish (US)
Title of host publicationProceedings - Wireless Health 2013, WH 2013
PublisherAssociation for Computing Machinery
ISBN (Print)9781450322904
StatePublished - 2013
Externally publishedYes
Event4th Conference on Wireless Health, WH 2013 - Baltimore, MD, United States
Duration: Nov 1 2013Nov 3 2013

Publication series

NameProceedings - Wireless Health 2013, WH 2013


Conference4th Conference on Wireless Health, WH 2013
Country/TerritoryUnited States
CityBaltimore, MD


  • Cost Analysis
  • Heart Failure
  • Intervention
  • Readmission
  • Remote Monitoring Systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Health Informatics


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