Increasing clinical information helps physicians and pharmacists to improve their medication reviews, however, additional information was still related with a high margin of error. Detection of certain errors becomes easier with additional information, whereas other errors remain undetected. To achieve a high standard of medication review, we have to change the way medication reviews should be performed.

Increasing clinical information helps physicians and pharmacists to improve their medication reviews, however, additional information was still related with a high margin of error. Detection of certain errors becomes easier with additional information, whereas other errors remain undetected. To achieve a high standard of medication review, we have to change the way medication reviews should be performed.

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ObjectivesTo 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.DesignObservational, retrospective case–control study.SettingNursing homes.ParticipantsA 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 measuresDevelopment 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.ResultsEleven 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%).ConclusionMedication 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 numberNot available.

drugs prescribed per patient were 7 days and 10±3.5 drugs, respectively. Fifty-nine patients (72%) had at least one DACE prescribed (an average of two DACE per patient). Most common DACE grouped by ATC were: anxiolytics (N05B, n=30), antidepressants (N06A, n=28), antipsychotics (N05A, n=22), opioids (N02A, n=16) and antiepileptic (N03A, n=14). Thirty-two (39%) patients had a moderate anticholinergic risk (median DBI 0.6) and 27 (33%) patients had a HAR (median DBI 1.5). Four out of 27 (15%) interventions were accepted and consisted of two dose reductions and two DACE deprescriptions. The interventions were not accepted mainly because the drugs were part of the patient's chronic psychiatric or neurological treatment, the presence of refractory pain or insomnia disorders. Conclusion and relevance Our pharmacological intervention was poorly accepted by physicians. During the hospitalisation process it is difficult to re-evaluate the need for adjusting chronic medication, especially related to psychiatric or neurological pathologies. For future studies we believe that this type of study would have more impact at the primary care level.

Objective: To assess the methods and frequency by which medication reviews are performed by general practitioners and nursing home physicians by means of a survey. Methods: 134 nursing home physicians and general practitioners working in the southern part of the Netherlands, the province of Limburg were asked to fill in a digital questionnaire. Non response was followed by second emailing and a questionnaire on paper by regular post. The questionnaire was developed by an expert panel, consisting of two hospital pharmacists, an internist, a nursing home physician and a neuropsychiatrist. Results: There was substantial inconsistency in the frequency of performing medication reviews, ranging from monthly (in 40%) by the nursing home physicians to four times a year (in 50%) by the general practitioners. Time spent on one review also varied significantly between groups, namely 10 minutes for a nursing home physician and 20 minutes for general practitioners. Meetings between the physician and pharmacist took place regularly (91%), but these were not organised for medication reviewing of individual patients. When medication was changed by another doctor, 47% of nursing home physicians and 44% of the general practitioners were informed often, whereas 40% and 50% respectively were only informed sometimes, and 13% of nursing home physicians and 6% of general practitioners never received any notice. 59% of the nursing home physicians and 89% of general practitioners considered workload to be a limiting factor in performing reviews. Conclusions: This survey shows great inconsistency in the way medication reviews are done. To achieve a high standard, we may have to reconsider the way medication reviews are done.