From adverse drug event detection to prevention. A novel clinical decision support framework for medication safety.
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.


OBJECTIVE
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 framework'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 healthcare 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. Nonetheless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.
V. G. Koutkias, Lab of Medical Informatics, Dept. of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece, E-mail: bikout@med.auth.gr.
V G
VG
Koutkias
P
P
McNair
V
V
Kilintzis
K
K
Skovhus Andersen
J
J
Niès
J-C
JC
Sarfati
E
E
Ammenwerth
E
E
Chazard
S
S
Jensen
R
R
Beuscart
N
N
Maglaveras
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D054316:Biomarkers, Pharmacological / Q000032:analysis*; D003199:Computer Systems; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis* / Q000453:epidemiology / Q000517:prevention & control*; D057286:Electronic Health Records; D006801:Humans; D050316:Medical Order Entry Systems; D008508:Medication Errors / Q000517:prevention & control* / Q000706:statistics & numerical data*; D012985:Software Design
D054316:Biomarkers, Pharmacological
Adverse drug events; clinical decision support systems (CDSS); connectivity; contextualization; interoperability; medication safety; service-oriented architecture (SOA)