ABSTRACT
OBJECTIVE: The objective of this study is to estimate the amount of severe drug-drug interaction warnings per medical specialist group triggered by prescribed drugs of a patient before and after the introduction of a nationwide eMedication system in Austria planned for 2015. METHODS: The estimations of interaction warnings are based on patients' prescriptions of a single health care professional per patient, as well as all patients' prescriptions from all visited health care professionals. We used a research database of the Main Association of Austrian Social Security Organizations that contains health claims data of the years 2006 and 2007. RESULTS: The study cohort consists of about 1 million patients, with 26.4 million prescribed drugs from about 3,400 different health care professionals. The estimation of interaction warnings show a heterogeneous pattern of severe drug-drug-interaction warnings across medical specialist groups. CONCLUSION: During an eMedication implementation it must be taken into consideration that different medical specialist groups require customized support.
Subject(s)
Adverse Drug Reaction Reporting Systems/statistics & numerical data , Clinical Pharmacy Information Systems/statistics & numerical data , Drug Interactions , Drug-Related Side Effects and Adverse Reactions/epidemiology , Electronic Prescribing/statistics & numerical data , Medical Order Entry Systems/statistics & numerical data , Medicine/statistics & numerical data , Austria/epidemiology , Humans , PrevalenceABSTRACT
OBJECTIVE: The objective of our project was to create a tool for physicians to explore health claims data with regard to adverse drug reactions. The Java Adverse Drug Event (JADE) tool should enable the analysis of prescribed drugs in connection with diagnoses from hospital stays. METHODS: We calculated the number of days drugs were taken by using the defined daily doses and estimated possible interactions between dispensed drugs using the Austria Codex, a database including drug-drug interactions. The JADE tool was implemented using Java, R and a PostgreSQL database. RESULTS: Beside an overview of the study cohort which includes selection of gender and age groups, selected statistical methods like association rule learning, logistic regression model and the number needed to harm have been implemented. CONCLUSION: The JADE tool can support physicians during their planning of clinical trials by showing the occurrences of adverse drug events with population based information.
