Telematics In Healthcare

Orphanoudakis, Stelios C.; Chronaki, Catherine E.; Tsiknakis, Manolis; Kostomanolakis, S.

doi:10.1007/978-1-4615-5553-7_10

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Currently, there is a growing list of applications of telematics technology in diverse fields. Examples include radio receivers installed in drones for journalism reporting or private investigation; smart home systems that remotely control temperature, lighting, appliances, or even alarms for security; electronic systems used for better communication between healthcare professionals and patients in health telematics (Orphanoudakis et al (1998)); and, predominantly, devices installed in cars or mobile apps to remotely monitor driving habits to determine insurance rating.…”

Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Multi-Class Adaboost for Understanding Driving Behavior Based on Telematics

So¹,

Boucher²,

Valdez³

2021

ASTIN Bull.

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Using telematics technology, insurers are able to capture a wide range of data to better decode driver behavior, such as distance traveled and how drivers brake, accelerate, or make turns. Such additional information also helps insurers improve risk assessments for usage-based insurance, a recent industry innovation. In this article, we explore the integration of telematics information into a classification model to determine driver heterogeneity. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero accidents, a lower proportion with exactly one accident, and a far lower proportion with two or more accidents. We here introduce a cost-sensitive multi-class adaptive boosting (AdaBoost) algorithm we call SAMME.C2 to handle such class imbalances. We calibrate the algorithm using empirical data collected from a telematics program in Canada and demonstrate an improved assessment of driving behavior using telematics compared with traditional risk variables. Using suitable performance metrics, we show that our algorithm outperforms other learning models designed to handle class imbalances.

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Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Multi-Class Adaboost for Understanding Driving Behavior Based on Telematics

So¹,

Boucher²,

Valdez³

2021

ASTIN Bull.

View full text Add to dashboard Cite

show abstract

“…It is derived from the French word, télématique, which combines the words "telecommunications" and "computing science." We are seeing a growing list of applications of telematics technology in many diverse fields: a radio receiver installed in a drone for journalism reporting or for private investigation, a smart home system that remotely controls temperature, lighting, appliance, and even alarms for security, an electronic system for better communication between healthcare professionals and their patients in health telematics (Orphanoudakis et al (1998)), and most predominantly, a device installed in a car or a mobile app to remotely monitor driving habits for insurance rating.…”

Section: Introductionmentioning

confidence: 99%

Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics

Boucher

Valdez

2020

Preprint

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Powered with telematics technology, insurers can now capture a wide range of data, such as distance traveled, how drivers brake, accelerate or make turns, and travel frequency each day of the week, to better decode driver's behavior. Such additional information helps insurers improve risk assessments for usage-based insurance (UBI), an increasingly popular industry innovation. In this article, we explore how to integrate telematics information to better predict claims frequency. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero claims, a less proportion with exactly one claim, and far lesser with two or more claims. We introduce the use of a cost-sensitive multi-class adaptive boosting (AdaBoost) algorithm, which we call SAMME.C2, to handle such imbalances. To calibrate SAMME.C2 algorithm, we use empirical data collected from a telematics program in Canada and we find improved assessment of driving behavior with telematics relative to traditional risk variables. We demonstrate our algorithm can outperform other models that can handle class imbalances: SAMME, SAMME with SMOTE, RUSBoost, and SMOTEBoost. The sampled data on telematics were observations during 2013-2016 for which 50,301 are used for training and another 21,574 for testing. Broadly speaking, the additional information derived from vehicle telematics helps refine risk classification of drivers of UBI.

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An HL7/CDA framework for the design and deployment of telemedicine services

Chronaki¹,

Lelis²,

Demou³

et al.

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The advance of regional healthcare networks has created increasing demand for telemedicine services tailored to particular medical specialties and healthcare settings. At the same time, emerging telemedicine protocols and guidelines combined with various medical standards provide specifications not only for the exchanged clinical content but also for the physical setting and technology in use. This paper presents a framework for the design and deployment of telemedicine services based on open standards: HL7/ CDA for clinical documents, DICOM 3 for imaging, and SCP-ECG for ECGs. The originality of the approach lays on: (a) the creation, maintenance, and reuse of clinical document templates adhering to the HL7 Clinical Document Architecture (CDA) -a recent ISO standard and (b) interoperability with middleware services of the health information infrastructure (HII). This work adds value to the WebOnCOLL collaboration infrastructure, which has been deployed successfully for provision of remote consultation services in cardiology. Keywords -Telemedicine, medical standards, Clinical Document Architecture, middleware, regional healthcare networks I. INTRODUCTION -BACKGROUNDTelemedicine as the use of telecommunication technologies to facilitate healthcare delivery dates back to the 1920s when radio was used to link physicians at shore stations with ships in case of medical emergencies [1]. With traditional telemedicine consultation, relevant health information is communicated verbally among the participants. In some cases, video-conferencing equipment, post or fax services are also used to enhance communication. It is only recently with the convergence of information and telecommunication technologies that clinical documents including objective medical data and diagnostic reports, can be exchanged electronically in the context of a telemedicine session.However, as telemedicine is gradually accepted as a typical healthcare procedure, there is increasing demand for accessible, accountable, secure, efficient, and effective services. Accessibility refers to making available specialized medical expertise at the point of need in a timely manner. That entails availability of the networking infrastructure and provision of up-to-date resource information -a real challenge for the health information infrastructure (HII) of the regional network [2,3].Accountable telemedicine services require detailed records for the telemedicine sessions. Telemedicine records should include besides the management data necessary for assessment and reimbursement, clinical data including the consultation request, diagnostic examinations (x-rays, ECGs, etc.), and diagnostic reports. Since telemedicine records outlive the session in which they were created, clinical documents and other objective medical data need to be in a standard format to facilitate portability and accessibility. They should also be digitally signed to meet integrity and nonrepudiation requirements. Digital signing and verification requires access to the services of the pu...

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Telematics In Healthcare

Cited by 4 publications

References 23 publications

Cost-Sensitive Multi-Class Adaboost for Understanding Driving Behavior Based on Telematics

Cost-Sensitive Multi-Class Adaboost for Understanding Driving Behavior Based on Telematics

Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics

An HL7/CDA framework for the design and deployment of telemedicine services

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