Evaluating new telemedicine interventions for chronic disease is not easy and it remains unclear how to use the existing evidence to inform the design of new telemedicine programs. In particular, there are not structured methods to define relevant aspects of a new home monitoring intervention as frequency of monitoring (daily vs weekly), complexity of monitoring (symptoms vs biopotentials) and the severity of target population (age, pathology severity). This paper describes a second order polynomial model that has been used to define the target population for the clinical protocol of the UE-funded research project titled “Smart health and artificial intelligence for Risk Estimation” (SHARE).

Evaluating new telemedicine interventions for chronic disease is not easy and it remains unclear how to use the existing evidence to inform the design of new telemedicine programs. In particular, there are not structured methods to define relevant aspects of a new home monitoring intervention as frequency of monitoring (daily vs weekly), complexity of monitoring (symptoms vs biopotentials) and the severity of target population (age, pathology severity). This paper describes a second order polynomial model that has been used to define the target population for the clinical protocol of the UE-funded research project titled “Smart health and artificial intelligence for Risk Estimation” (SHARE).

A preliminary model to choose the most appropriate target population for home monitoring telemedicine interventions basing on the best available evidence

MELILLO, Paolo;
2014

Abstract

Evaluating new telemedicine interventions for chronic disease is not easy and it remains unclear how to use the existing evidence to inform the design of new telemedicine programs. In particular, there are not structured methods to define relevant aspects of a new home monitoring intervention as frequency of monitoring (daily vs weekly), complexity of monitoring (symptoms vs biopotentials) and the severity of target population (age, pathology severity). This paper describes a second order polynomial model that has been used to define the target population for the clinical protocol of the UE-funded research project titled “Smart health and artificial intelligence for Risk Estimation” (SHARE).
9783319131047
Evaluating new telemedicine interventions for chronic disease is not easy and it remains unclear how to use the existing evidence to inform the design of new telemedicine programs. In particular, there are not structured methods to define relevant aspects of a new home monitoring intervention as frequency of monitoring (daily vs weekly), complexity of monitoring (symptoms vs biopotentials) and the severity of target population (age, pathology severity). This paper describes a second order polynomial model that has been used to define the target population for the clinical protocol of the UE-funded research project titled “Smart health and artificial intelligence for Risk Estimation” (SHARE).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/374774
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact