PURPOSE: To develop a computerized prescreening procedure for the identification of possible/probably Hospital Admissions potential Related to Medications (HARMs). METHOD: Pairs of drugs and reasons for hospitalization (generated automatically fromthe PHARMO record linkage database by using two data mining techniques) were assessed manually to determine whether they represented pharmacologically plausible adverse drug events (PP-ADEs). Two crude samples of these PP-ADEs (from 2005 and 2008) were examined manually to establish causality and preventability on the basis of hospital discharge letters plus medication dispensing data. The results were used to calculate the positive predictive value (PPV) of the crude causality PP-ADEs, the net percentage of possible/probably HARMs, and their potential preventability. RESULTS: Data mining by Gamma Poisson Shrinkage and trend analysis produced 1330 and 2941 significant drug-event pairs, respectively. After manual assessment, 307 different PP-ADEs remained. The annual prevalence of these PP-ADEs was stable at approximately 8% throughout 2000-2009. Manual assessment of two samples of crude PP-ADEs showed that their causality PPV was 53.7% (95%CI: 52.7%-54.7%) in 2005 and 47.9% (95%CI: 46.9%-49.0%) in 2008. The net contribution of possible/probably HARMs to all acute admissions was 4.6% (95%CI: 4.5%-4.8%) in 2005 and 3.9% (95%CI: 3.8%-4.0%) in 2008. The potential preventability of all possible/probably HARMs in the two samples was 19.3% (95%CI: 18.5-20.1). CONCLUSION: Automated pre-selection of PP-ADEs is an efficient way to monitor crude trends. Further validation and manual assessment of the automatically selected hospitalizations is necessary to get a more detailed and precise picture. Copyright (c)2015 John Wiley & Sons, Ltd.