A Mathematical Model to Plan the Adoption of EHR Systems

A Mathematical Model to Plan the Adoption of EHR Systems

Oscar Tamburis (Institute for Biomedical Technologies, National Research Council, Italy), Fabrizio L. Ricci (Institute for Biomedical Technologies, National Research Council, Italy) and Fabrizio Pecoraro (Institute for Biomedical Technologies, National Research Council, Italy)
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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