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UNLABELLED: OBJECTIVE To determine whether a computerized clinical decision support system providing patient-specific recommendations in real-time improves the quality of prescribing for long-term care residents with renal insufficiency. DESIGN Randomized trial within the long-stay units of a large long-term care facility. Randomization was within blocks by unit type. Alerts related to medication prescribing for residents with renal insufficiency were displayed to prescribers in the intervention units and hidden but tracked in control units. Measurement The proportions of final drug orders that were appropriate were compared between intervention and control units within alert categories: (1) recommended medication doses; (2) recommended administration frequencies; (3) recommendations to avoid the drug; (4) warnings of missing information. RESULTS The rates of alerts were nearly equal in the intervention and control units: 2.5 per 1,000 resident days in the intervention units and 2.4 in the control units. The proportions of dose alerts for which the final drug orders were appropriate were similar between the intervention and control units (relative risk 0.95, 95% confidence interval 0.83, 1.1) for the remaining alert categories significantly higher proportions of final drug orders were appropriate in the intervention units: relative risk 2.4 for maximum frequency (1.4, 4.4); 2.6 for drugs that should be avoided (1.4, 5.0); and 1.8 for alerts to acquire missing information (1.1, 3.4). Overall, final drug orders were appropriate significantly more often in the intervention units-relative risk 1.2 (1.0, 1.4). CONCLUSIONS Clinical decision support for physicians prescribing medications for long-term care residents with renal insufficiency can improve the quality of prescribing decisions. TRIAL REGISTRATION: http://clinicaltrials.gov Identifier: NCT00599209.

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OBJECTIVES: To evaluate the efficacy of computerized provider order entry with clinical decision support for preventing adverse drug events in long-term care. DESIGN: Cluster-randomized controlled trial. SETTING: Two large long-term care facilities. PATIENTS: One thousand one hundred eighteen long-term care residents of 29 resident care units. INTERVENTION: The 29 resident care units, each with computerized provider order entry, were randomized to having a clinical decision support system (intervention units) or not (control units). MEASUREMENTS: The number of adverse drug events, severity of events, and whether the events were preventable. RESULTS: Within intervention units, 411 adverse drug events occurred over 3,803 resident-months of observation time; 152 (37.0%) were deemed preventable. Within control units, there were 340 adverse drug events over 3,257 resident-months of observation time; 126 (37.1%) were characterized as preventable. There were 10.8 adverse drug events per 100 resident-months and 4.0 preventable events per 100 resident-months on intervention units. There were 10.4 adverse drug events per 100 resident-months and 3.9 preventable events per 100 resident-months on control units. Comparing intervention and control units, the adjusted rate ratios were 1.06 (95% confidence interval (CI)=0.92-1.23) for all adverse drug events and 1.02 (95% CI=0.81-1.30) for preventable adverse drug events. CONCLUSION: Computerized provider order entry with decision support did not reduce the adverse drug event rate or preventable adverse drug event rate in the long-term care setting. Alert burden, limited scope of the alerts, and a need to more fully integrate clinical and laboratory information may have affected efficacy.

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A team of physicians, pharmacists, and informatics professionals developed a CDSS added to a commercial electronic medical record system to provide prescribers with patient-specific maximum dosing recommendations based on renal function. We tracked the time spent by team members and used US national averages of relevant hourly wages to estimate costs. The team required 924.5 hours and $48,668.57 in estimated costs to develop 94 alerts for 62 drugs. The most time intensive phase of the project was preparing the contents of the CDSS (482.25 hours, $27,455.61). Physicians were the team members with the highest time commitment (414.25 hours, $25,902.04). Estimates under alternative scenarios found lower total cost estimates with the existence of a valid renal dosing database ($34,200.71) or an existing decision support add-on for renal dosing ($23,694.51). Development of a CDSS for a commercial computerized prescriber order entry system requires extensive commitment of personnel, particularly among clinical staff.

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Although computerized physician order entry (CPOE) has been successfully implemented in many acute care hospitals, few descriptions of its use in the long-term care (LTC) setting are available. This report describes the experiences of one LTC facility in developing and implementing a CPOE system with clinical decision support (CDS). Even when a facility has the necessary resources and "institutional will," many challenges are associated with the implementation of this application. The system was designed to meet the needs of healthcare providers in the LTC setting, in particular by informing prescribing decisions, reducing the frequency of prescribing and monitoring errors, and reducing adverse drug event rates. Based on experience adopting this technology early, 10 insights are offered that it is hoped will assist others who are considering the implementation of CPOE systems with CDS in the LTC setting.

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PURPOSE: To assess the incidence of and risk factors for adverse drug events in the long-term care setting. METHODS: We performed a cohort study of all long-stay residents of two academic long-term care facilities over a period of up to 9 months during 2000 to 2001. We assessed the number of adverse drug events, the severity of events (classified as less serious, serious, life threatening, or fatal), and whether the events were preventable. A case-control study was nested within the prospective study to identify resident-level risk factors for the occurrence of adverse drug events. RESULTS: There were 815 adverse drug events, of which 42% were judged preventable. The overall rate of adverse drug events was 9.8 per 100 resident-months, with a rate of 4.1 preventable adverse drug events per 100 resident-months. Errors associated with preventable events occurred most often at the stages of ordering and monitoring. Residents taking medications in several drug categories were at increased risk of a preventable adverse event. In multivariate analyses, the adjusted odds ratio was 3.4 (95% confidence interval [CI]: 2.0 to 5.9) for those taking antipsychotic agents, 2.8 (95% CI: 1.6 to 4.7) for those taking anticoagulants, 2.2 (95% CI: 1.2 to 4.0) for those taking diuretics, and 2.0 (95% CI: 1.1 to 3.7) for those taking antiepileptics. CONCLUSION: Our findings reinforce the need for a special focus on the ordering and monitoring stages of pharmaceutical care for preventing adverse drug events in the long-term care setting. Patients taking antipsychotic agents, anticoagulants, diuretics, and antiepileptics are at increased risk.

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