Medication optimisation: methodological aspects and new strategies

Older people often suffer from different diseases. To treat them, several medicinal drugs are prescribed. In order to assure a safe and beneficial use of these drugs, medication reviews are performed. The standard way to perform medication reviews is far from optimal as they are a mere snapshot of the situation. By using a computerized system to support medication reviews, the right information is filtered in such way that an alert is generated whenever something has to be addressed. In this way medication reviews can be performed continuously, at a faster rhythm and in an objective and standardised way

Medication management in the elderly patient:The development of SCREEN

Aging, multimorbidity and related polypharmacy account for a high incidence of medication-related problems. Recently, the research project SCREEN has started, first to assess the current situation regarding medication reviews in the Netherlands and second, to develop an electronic medication surveying system that takes into account the patient's clinical information and enables a continuous medication evaluation. It is expected that this novel approach of medication surveillance will decrease medication related problems, improve the quality of life and mitigate care-related costs.</p

Fall incidents in nursing home residents: development of a predictive clinical rule (FINDER)

Objectives To develop (part I) and validate (part II) an electronic fall risk clinical rule (CR) to identify nursing home residents (NH-residents) at risk for a fall incident.Design Observational, retrospective case–control study.Setting Nursing homes.Participants A total of 1668 (824 in part I, 844 in part II) NH-residents from the Netherlands were included. Data of participants from part I were excluded in part II.Primary and secondary outcome measures Development and validation of a fall risk CR in NH-residents. Logistic regression analysis was conducted to identify the fall risk-variables in part I. With these, three CRs were developed (ie, at the day of the fall incident and 3 days and 5 days prior to the fall incident). The overall prediction quality of the CRs were assessed using the area under the receiver operating characteristics (AUROC), and a cut-off value was determined for the predicted risk ensuring a sensitivity ≥0.85. Finally, one CR was chosen and validated in part II using a new retrospective data set.Results Eleven fall risk-variables were identified in part I. The AUROCs of the three CRs form part I were similar: the AUROC for models I, II and III were 0.714 (95% CI: 0.679 to 0.748), 0.715 (95% CI: 0.680 to 0.750) and 0.709 (95% CI: 0.674 to 0.744), respectively. Model III (ie, 5 days prior to the fall incident) was chosen for validation in part II. The validated AUROC of the CR, obtained in part II, was 0.603 (95% CI: 0.565 to 0.641) with a sensitivity of 83.41% (95% CI: 79.44% to 86.76%) and a specificity of 27.25% (95% CI 23.11% to 31.81%).Conclusion Medication data and resident characteristics alone are not sufficient enough to develop a successful CR with a high sensitivity and specificity to predict fall risk in NH-residents.Trial registration number Not available

Assessing the strengths and weaknesses of a computer assisted medication review in hospitalized patients

Introduction Medication reviews are an essential part of daily routine at a hospital ward but are prone to mistakes. With this study we want to assess the strengths and weaknesses of a Clinical Decision Support System (CDSS) and evaluate the additional value on the reduction of medication errors compared with manual medication reviews. Materials and Methods We gathered all remarks related to (potential) errors in the current medication regime (notifications) regarding medication errors for 332 patients from 12 grand rounds of the internal medicine ward and orthopedic ward at the Maastricht University Medical Centre during four months. Simultaneously, we electronically extracted data regarding the patient’s medication list, laboratory data and patient characteristics and entered these data into our CDSS. Results and Discussion One hundred thirty-eight notifications were made during grand rounds. One-hundred and seventy-nine relevant alerts were reported by the CDSS. Only three of the relevant notifications were reported by both. Overall, errors regarding indication without medication and medication without indication were most frequently noticed during grand rounds and contraindications or side effects were most frequently noticed by the CDSS. The CDSS may be a relevant addition to the manual performed medication reviews in the hospital. The strength of the present CDSS lies in the detection of errors regarding contraindications and side effects. Future developments include optimizing the cut off values at which the CDSS should provide an alert is an important next step in improving the CDSS. Additionally, in order to increase notifications about indication without medication and medication without indication, the medical history should be incorporated into the CDSS. Finally, relevance on patient outcome should be determined

Development of a computer system to support medication reviews in nursing homes

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 revie