Methods Inf Med 2014; 53(06): 482-492
DOI: 10.3414/ME14-01-0027
Original Articles
Schattauer GmbH

From Adverse Drug Event Detection to Prevention

A Novel Clinical Decision Support Framework for Medication Safety
V. G. Koutkias
1  Lab of Medical Informatics, Dept. of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
,
P. McNair
2  Capital Region of Denmark, Copenhagen, Denmark
,
V. Kilintzis
1  Lab of Medical Informatics, Dept. of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
,
K. Skovhus Andersen
2  Capital Region of Denmark, Copenhagen, Denmark
,
J. Niès
3  MEDASYS, Gif-Sur-Yvette, France
4  UDSL EA 2694, Univ Lille Nord de France, Lille, France
,
J.-C. Sarfati
5  ORACLE, Colombes, France
,
E. Ammenwerth
6  Institute for Health Informatics, UMIT – University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria UDSL EA 2694, Univ Lille Nord de France, Lille, France
,
E. Chazard
4  UDSL EA 2694, Univ Lille Nord de France, Lille, France
,
S. Jensen
2  Capital Region of Denmark, Copenhagen, Denmark
,
R. Beuscart
4  UDSL EA 2694, Univ Lille Nord de France, Lille, France
,
N. Maglaveras
1  Lab of Medical Informatics, Dept. of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
› Author Affiliations
Further Information

Publication History

received: 19 February 2014

accepted: 28 July 2014

Publication Date:
20 January 2018 (online)

Summary

Background: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care.

Objectives: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention.

Methods: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frame-work’s services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards.

Results: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing health-care IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use.

Conclusions: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. None-theless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.