Clinical decision support (CDS) includes a variety of tools and interventions computerized as well as non- computerized. High-quality clinical decision support systems (CDSS), computerized CDS, are essential to achieve the full benefits of electronic health records and computerized physician order entry. A CDSS can take into account all data available in the EHR making it possible to notice changes outside the scope of the professional and notice changes specific for a certain patient, within normal limits. However, to use of CDSS in practice, it is important to understand the basic requirements of these systems.This chapter shows in what way CDSS can support the use of clinical data science in daily clinical practice. Moreover, it explains what types of CDSS are available and how such systems can be used. However, to achieve high-quality CDSS which is effective in use requires thoughtful design, implementation and critical evaluation. Therefore, challenges surrounding implementation of a CDSS are discussed, as well as a strategies to develop and validate CDSS.

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Background Administering medication through an enteral feeding tube (FT) is a frequent cause of errors resulting in increased morbidity and cost. Studies on interventions to prevent these errors in hospitalized patients, however, are limited. Objective The objective was to study the effect of a clinical decision support system (CDSS)–assisted pharmacy intervention on the incidence of FT‐related medication errors (FTRMEs) in hospitalized patients. Methods A pre‐post intervention study was conducted between October 2014 and May 2015 in Catharina Hospital, the Netherlands. Patients who were admitted to the wards of bowel and liver disease, oncology, or neurology; using oral medication; and had an enteral FT were included. Preintervention patients were given care as usual. The intervention consisted of implementing a CDSS‐assisted pharmacy check while also implementing standard operating procedures and educating personnel. An FTRME was defined as the administration of inappropriate medication through an enteral FT. The incidence was expressed as the number of FTRMEs per medication administration. Multivariate Poisson regression was used to calculate the incidence ratio (IR) comparing both phases. Results Eighty‐one patients were included, 38 during preintervention and 43 during the intervention phase. Incidence of FTRMEs in the preintervention phase was 0.15 (95% CI, 0.07–0.23) vs 0.02 (95% CI, 0.00–0.04) in the intervention phase, resulting in an adjusted IR of 0.13 (95% CI, 0.10–0.18). Discussion Incidence of FTRMEs, as well as the IR, is comparable to previous studies. Conclusion The intervention resulted in a substantial reduction in the incidence of FTRMEs.

Identifying adverse drug reactions from free-text electronic hospital health record notes

Drug-drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.