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Background A delirium is common in hospital settings resulting in increased mortality and costs. Prevention of a delirium is clearly preferred over treatment. A delirium risk prediction model can be helpful to identify patients at risk of a delirium, allowing the start of preventive treatment. Current risk prediction models rely on manual calculation of the individual patient risk. Objective The aim of this study was to develop an automated ward independent delirium riskprediction model. To show that such a model can be constructed exclusively from electronically available risk factors and thereby implemented into a clinical decision support system (CDSS) to optimally support the physician to initiate preventive treatment. Setting A Dutch teaching hospital. Methods A retrospective cohort study in which patients, 60 years or older, were selected when admitted to the hospital, with no delirium diagnosis when presenting, or during the first day of admission. We used logistic regression analysis to develop a delirium predictive model out of the electronically available predictive variables. Main outcome measure A delirium risk prediction model. Results A delirium risk prediction model was developed using predictive variables that were significant in the univariable regression analyses. The area under the receiver operating characteristics curve of the "medication model" model was 0.76 after internal validation. Conclusions CDSSs can be used to automatically predict the risk of a delirium in individual hospitalised patients' by exclusively using electronically available predictive variables. To increase the use and improve the quality of predictive models, clinical risk factors should be documented ready for automated use.

The frail elderly populations of nursing homes frequently use drugs and suffer from considerable comorbidities. Medication reviews are intended to support evidence based prescribing and optimise therapy. However, literature is still ambiguous regarding the optimal method and the effects of medication reviews. Innovative computerised systems may support the medication reviews in the future. We are developing a clinical decision support system (CDSS) that, independently of the prescribing software, continuously monitors all prescribed drugs while taking into account co-medication, laboratory-data and co-morbidities. The CDSS will be developed in five phases: (1) development of the computerised system, (2) development of the clinical rules, (3) validation of the CDSS, (4) randomised controlled trial, and (5) feasibility for implementation in different nursing homes. The clinical decision support system aims at supporting the traditional medication review.

ObjectivesFirst, to estimate the added value of a clinical decision support system (CDSS) in the performance of medication reviews in hospitalised elderly. Second, to identify the limitations of the current CDSS by analysing generated drug-related problems (DRPs).MethodsMedication reviews were performed in patients admitted to the geriatric ward of the Zuyderland medical centre. Additionally, electronically available patient information was introduced into a CDSS. The DRP notifications generated by the CDSS were compared with those found in the medication review. The DRP notifications were analysed to learn how to improve the CDSS.ResultsA total of 223 DRP strategies were identified during the medication reviews. The CDSS generated 70 clinically relevant DRP notifications. Of these DRP notifications, 63 % (44) were also found during the medication reviews. The CDSS generated 10 % (26) new DRP notifications and conveyed 28 % (70) of all 249 clinically relevant DRPs that were found. Classification of the CDSS generated DRP notifications related to ‘medication error type’ revealed that ‘contraindications/interactions/side effects’ and ‘indication without medication’ were the main categories not identified during the manual medication review. The error types ‘medication without indication’, ‘double medication’, and ‘wrong medication’ were mostly not identified by the CDSS.ConclusionsThe CDSS used in this study is not yet sufficiently advanced to replace the manual medication review, though it does add value to the manual medication review. The strengths and weaknesses of the current CDSS can be determined according to the medication error types.

BackgroundThe aim of this study was to evaluate to what extent laboratory data, actual medication, medical history, and/or drug indication influence the quality of medication reviews for nursing home patients.MethodsForty-six health care professionals from different fields were requested to perform medication reviews for three different cases. Per case, the amount of information provided varied in three subsequent stages: stage 1, medication list only; stage 2, adding laboratory data and reason for hospital admission; and stage 3, adding medical history/drug indication. Following a slightly modified Delphi method, a multidisciplinary team performed the medication review for each case and stage. The results of these medication reviews were used as reference reviews (gold standard). The remarks from the participants were scored, according to their potential clinical impact, from relevant to harmful on a scale of 3 to −1. A total score per case and stage was calculated and expressed as a percentage of the total score from the expert panel for the same case and stage.ResultsThe overall mean percentage over all cases, stages, and groups was 37.0% when compared with the reference reviews. For one of the cases, the average score decreased significantly from 40.0% in stage 1, to 30.9% in stage 2, and 27.9% in stage 3; no significant differences between stages was found for the other cases.ConclusionThe low performance, against the gold standard, of medication reviews found in the present study highlights that information is incorrectly used or wrongly interpreted, irrespective of the available information. Performing medication reviews without using the available information in an optimal way can have potential implications for patient safety.