A Comprehensive E-prescribing Model to Allow Representing, Comparing, and Analyzing Available Systems

S. Marceglia; L. Mazzola; S. Bonacina; P. Tarquini; P. Donzelli; F. Pinciroli

doi:10.3414/ME12-01-0069

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2013; 52(03): 199-219
DOI: 10.3414/ME12-01-0069

Original Articles

Schattauer GmbH

A Comprehensive E-prescribing Model to Allow Representing, Comparing, and Analyzing Available Systems

Authors

S. Marceglia^*

¹e-Health Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
L. Mazzola^*

¹e-Health Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
S. Bonacina

¹e-Health Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
P. Tarquini

²Ufficio progetti strategici per l’innovazione digitale, Dipartimento per la digitalizzazione della pubblica amministrazione e l’innovazione tecnologica, Presidenza del Consiglio dei Ministri, Rome, Italy
P. Donzelli

²Ufficio progetti strategici per l’innovazione digitale, Dipartimento per la digitalizzazione della pubblica amministrazione e l’innovazione tecnologica, Presidenza del Consiglio dei Ministri, Rome, Italy
F. Pinciroli

¹e-Health Lab, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

³Honorary Visiting Professor, City University, London, UK

Abstract

Summary

Background: Even though ePrescribing systems are now available in many healthcare systems and have been a crucial milestone of the roadmaps towards eHealth in the last years, there is still a large heterogeneity among functionalities and performances of different systems.

Objective: In this paper, we propose an updated comprehensive model for the ePre-scribing process able to represent, analyze, and compare current systems and to support the design of new, more general, systems suitable also to sustain the ePrescription process in National Healthcare Systems.

Methods: After a preliminary literature review, we identified six main phases of the ePrescribing process, namely Assign, Transmit, Dispense, Administer, Monitor, and Analysis & Decision. Each phase produces a digital object characterized by formal properties that ensure the collection of appropriate data and information and works as input for the next one. The impact, in terms of benefits, of ePrescribing on governance, drug sur -veillance, and quality of care at the individual, territorial, and governmental levels are related to the formal properties of the digital objects created at the end of each phase.

Results and Conclusions: The model-based implementation of each phase has an impact on the quality of care, the access to care, and the effectiveness of care delivery. The model does not cover cost evaluation, but the benefits identified can be used as basis for cost-benefit or cost-effectiveness analysis of heterogeneous systems.

Keywords

ePrescribing process - modelling - national healthcare system - healthcare information system - quality of care

Full Text

References

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