A Mathematical Model to Plan the Adoption of EHR Systems

A Mathematical Model to Plan the Adoption of EHR Systems

Oscar Tamburis, Fabrizio L. Ricci, Fabrizio Pecoraro
Copyright: © 2014 |Pages: 16
DOI: 10.4018/978-1-4666-5202-6.ch002
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Background

National healthcare systems are called to face considerable challenges, mainly due to fundamental demographic changes, decreasing financial budgets for healthcare and innovative technological developments (Schlessinger & Eddy, 2002; Arning & Ziefle, 2009). Particularly, a systemic diffusion and deployment of eHealth systems and services are then occurring, since those are expected to cover the interaction between patients and health-service providers, institution-to-institution transmission of data as well as peer-to-peer communication between patients or health professionals. Furthermore, the development of high–quality information systems is having a major impact in the delivery of patient services improving the procedures implemented for storing, organizing and sharing clinical data, knowledge and information among healthcare operators. Moreover, information systems foster a stronger connection between hospital and territory, through the development of new skills as well as the diffusion of new technologies (Djellal & Gallouj, 2005).

The successful application and the consequent systematic adoption of Health Information Technologies is broadly considered a promising strategy to improve the economic sustainability of healthcare, while ensuring and enhancing the quality of services (Serbanati, Ricci, Mercurio & Vasilateanu, 2011).

Key Terms in this Chapter

Diabetes Mellitus: A group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin, or because cells do not respond to the insulin that is produced.

Electronic Health Record (EHR): Healthcare record that provides clinician (and increasingly consumer) access to clinical details captured from one or more encounters.

Additive Model: A generalized nonparametric linear regression model.

e-Healthcare: Healthcare practice supported by Information and Communication Technologies.

Mathematical Modeling: Description of a system using mathematical concepts and language.

Healthcare Information System: An integrated information system designed to manage the medical, administrative, financial and legal aspects of a hospital and its service processing.

LUMIR: Project that aims to foster collaborative, cross organizational and patient-centric healthcare processes, supporting different stakeholders in the communication of patient clinical information.

Cost–Benefit Analysis: A systematic process for calculating and comparing benefits and costs of a project, decision or government policy.

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