MobiGuide is a project devoted to the development of a patient-centric decision support system based on computerized clinical guidelines for chronic illnesses including Atrial Fibrillation (AF). In this paper we describe the process of (1) identifying guideline recommendations that will require patients to take actions (e.g., take measurement, take drug), thus impacting patients' daily-life behavior, (2) eliciting from the medical experts the corresponding set of personalized operationalized advices that are not explicitly written in the guideline (patient-tailored workflow patterns) and (3) delivering this advice to patients. The analysis of the AF guideline has resulted in four types of patient-tailored workflow patterns: therapy-related advisors, measurements advisors, suggestions for dealing with interventions that may require modulating patient therapy, and personalized packages for close monitoring of patients. We will show how these patterns can be generated using information stored in a patient health record that embeds clinical data and data about the patient's personal context and preferences.

Patient-tailored Workflow Patterns from Clinical Practice Guidelines Recommendations.

SACCHI, LUCIA;Napolitano C;QUAGLINI, SILVANA;TORMENE, PAOLO
2013-01-01

Abstract

MobiGuide is a project devoted to the development of a patient-centric decision support system based on computerized clinical guidelines for chronic illnesses including Atrial Fibrillation (AF). In this paper we describe the process of (1) identifying guideline recommendations that will require patients to take actions (e.g., take measurement, take drug), thus impacting patients' daily-life behavior, (2) eliciting from the medical experts the corresponding set of personalized operationalized advices that are not explicitly written in the guideline (patient-tailored workflow patterns) and (3) delivering this advice to patients. The analysis of the AF guideline has resulted in four types of patient-tailored workflow patterns: therapy-related advisors, measurements advisors, suggestions for dealing with interventions that may require modulating patient therapy, and personalized packages for close monitoring of patients. We will show how these patterns can be generated using information stored in a patient health record that embeds clinical data and data about the patient's personal context and preferences.
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: https://hdl.handle.net/11571/720022
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 8
social impact