Abstract

MARTINEZ BERNABE, E. et al. Clinical decision-making support systems in renal failure. Farm Hosp. [online]. 2014, vol.38, n.3, pp.216-222. ISSN 2171-8695.  https://dx.doi.org/10.7399/FH.2014.38.3.753.

Introduction: Support systems in clinical decision-making use individual characteristics of the patient to generate recommendations to the clinician. Objective: To assess the impact of a tool for adjusting drug dosing in renal failure as a support system in clinical decision-making regarding the level of acceptance of the interventions as well as the time invested by the pharmacist. Method: Non-randomized, prospective and hospital interventional study comparing pre- and post-implementation phases of an automated renal function alert system, carried out at two county hospitals. Forty drugs were monitored before the intervention (2007). The blood work of the patients receiving any of these drugs was reviewed. In case of impaired renal function, an adjustment recommendation was inserted in the medical prescription. If the physician accepted it, it was rated as success. The average time was 1 minute per blood work reviewed and 3 minutes per recommendation. An automated adjustment recommendation system according to renal function with alert pop-ups was implemented in 2008 for 100 drugs. Later (2009), the number of interventions and the success rate for this tool were assessed and compared. Results: Pre-implementation phase: 28,234 electronic medical prescriptions corresponding to a mean number of 205 hospitalized patients/day were validated and 4,035 blood works were reviewed. One hundred and twenty-one pharmaceutical interventions (0.43% of the medical prescriptions) were inserted. A success rate of 33.06% of the interventions was obtained. The time invested by the pharmacist for consulting the blood works and making the recommendations was 73.3 hours (67.25 hours corresponding to patients without renal function impairment and in whom no intervention was made). Post-implementation phase: 26,584 electronic medical orders corresponding to 193 hospitalized patients/day were validated and 1,737 automated interventions were performed (6.53% of total medical orders), of which 65.69% were accepted (success). Conclusions: The implementation of clinical decision-making support systems allows extending the number of patients and drugs monitored, optimizing the time invested by the pharmacist. Simultaneous occurrence of an alert during prescription may have contributed to the greater success rate observed.

Keywords : Clinical decision-making support systemas; Automated alert; Renal failure.

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