How to improve drug dosing for patients with renal impairment in primary care - a cluster-randomized controlled trial

Background: Patients with chronic kidney disease (CKD) are at increased risk for inappropriate or potentially harmful prescribing. The aim of this study was to examine whether a multifaceted intervention including the use of a software programme for the estimation of creatinine clearance and recommendation of individual dosage requirements may improve correct dosage adjustment of relevant medications for patients with CKD in primary care. Methods: A cluster-randomized controlled trial was conducted between January and December 2007 in small primary care practices in Germany. Practices were randomly allocated to intervention or control groups. In each practice, we included patients with known CKD and elderly patients (>=70 years) suffering from hypertension. The practices in the intervention group received interactive training and were provided a software programme to assist with individual dose adjustment. The control group performed usual care. Data were collected at baseline and at 6 months. The outcome measures, analyzed across individual patients, included prescriptions exceeding recommended maximum daily doses, with the primary outcome being prescriptions exceeding recommended standard daily doses by 30% or more. Results: Data from 44 general practitioners and 404 patients are included. The intervention was effective in reducing prescriptions exceeding the maximum daily dose per patients, with a trend in reducing prescriptions exceeding the standard daily dose by more than 30%. Conclusions: A multifaceted intervention including the use of a software program effectively reduced inappropriately high doses of renally excreted medications in patients with CKD in the setting of small primary care practices

Reducing prescribing errors through creatinine clearance alert redesign

Background Literature has shown that computerized creatinine clearance alerts reduce errors during prescribing, and applying human factors principles may further reduce errors. Our objective was to apply human factors principles to creatinine clearance alert design and assess whether the redesigned alerts increase usability and reduce prescribing errors compared with the original alerts. Methods Twenty Veterans Affairs (VA) outpatient providers (14 physicians, 2 nurse practitioners, and 4 clinical pharmacists) completed 2 usability sessions in a counterbalanced study to evaluate original and redesigned alerts. Each session consisted of fictional patient scenarios with 3 medications that warranted prescribing changes because of renal impairment, each associated with creatinine clearance alerts. Quantitative and qualitative data were collected to assess alert usability and the occurrence of prescribing errors. Results There were 43% fewer prescribing errors with the redesigned alerts compared with the original alerts (P = .001). Compared with the original alerts, redesigned alerts significantly reduced prescribing errors for allopurinol and ibuprofen (85% vs 40% and 65% vs 25%, P = .012 and P = .008, respectively), but not for spironolactone (85% vs 65%). Nine providers (45%) voiced confusion about why the alert was appearing when they encountered the original alert design. When laboratory links were presented on the redesigned alert, laboratory information was accessed 3.5 times more frequently. Conclusions Although prescribing errors were high with both alert designs, the redesigned alerts significantly improved prescribing outcomes. This investigation provides some of the first evidence on how alerts may be designed to support safer prescribing for patients with renal impairment

ASSESSMENT OF PHYSICIANS' RESPONSE TO TEXT ALERTS IN PATIENTS WITH REDUCED RENAL FUNCTION BY USING A TEXT MESSAGE ALERTING SYSTEM

Introduction: Adverse drug events are mostly dose dependent and preventable. About 50% of these adverse effects are due to inappropriate dosing especially in patients with renal failure. Objective: We aimed to determine the impact of short message alerting on physicians' drug dosing of patients with decreased renal function. Methods: Eighteen physicians accepted to enroll in the study. Their patients who received at least one of the six selected drugs were selected for evaluation. The patients with an estimated glomerular filtration rate of 50 ml/minute or lower were randomly divided into two groups of case and control. An alert was sent to the physician in charge of the intervention (case) group. Physicians' reactions was recorded as "dose adjustment", "discontinuation of medication" or "none" and were compared in both groups. The reaction time of physicians before and after receiving alerts was recorded as well. Results: One hundred and thirty seven patients entered the study. The study results showed a significant difference in overall changes between the two groups (*** P <0.001). The rate of dose adjustment increased significantly after sending alerts to physicians (*** P <0.001). However, there was not a significant difference regarding discontinuation of medication between groups (P= 0.76). On the other hand, prompt reaction of physicians (0-6 hours after sending short message) significantly increased after intervention (* P < 0.05). Nevertheless, physicians' reaction time in 6-24 hours and 24-48 hours was not changed significantly after intervention. Conclusion: The results of this study show that informing physicians about the renal function of the patients leads to appropriate dosing

Using a computerized provider order entry system to meet the unique prescribing needs of children: description of an advanced dosing model

<p>Abstract</p> <p>Background</p> <p>It is well known that the information requirements necessary to safely treat children with therapeutic medications cannot be met with the same approaches used in adults. Over a 1-year period, Duke University Hospital engaged in the challenging task of enhancing an established computerized provider order entry (CPOE) system to address the unique medication dosing needs of pediatric patients.</p> <p>Methods</p> <p>An advanced dosing model (ADM) was designed to interact with our existing CPOE application to provide decision support enabling complex pediatric dose calculations based on chronological age, gestational age, weight, care area in the hospital, indication, and level of renal impairment. Given that weight is a critical component of medication dosing that may change over time, alerting logic was added to guard against erroneous entry or outdated weight information.</p> <p>Results</p> <p>Pediatric CPOE was deployed in a staggered fashion across 6 care areas over a 14-month period. Safeguards to prevent miskeyed values became important in allowing providers the flexibility to override the ADM logic if desired. Methods to guard against over- and under-dosing were added. The modular nature of our model allows us to easily add new dosing scenarios for specialized populations as the pediatric population and formulary change over time.</p> <p>Conclusions</p> <p>The medical needs of pediatric patients vary greatly from those of adults, and the information systems that support those needs require tailored approaches to design and implementation. When a single CPOE system is used for both adults and pediatrics, safeguards such as redirection and suppression must be used to protect children from inappropriate adult medication dosing content. Unlike other pediatric dosing systems, our model provides active dosing assistance and dosing process management, not just static dosing advice.</p

Chronology of prescribing error during the hospital stay and prediction of pharmacist's alerts overriding: a prospective analysis

<p>Abstract</p> <p>Background</p> <p>Drug prescribing errors are frequent in the hospital setting and pharmacists play an important role in detection of these errors. The objectives of this study are (1) to describe the drug prescribing errors rate during the patient's stay, (2) to find which characteristics for a prescribing error are the most predictive of their reproduction the next day despite pharmacist's alert (<it>i.e</it>. override the alert).</p> <p>Methods</p> <p>We prospectively collected all medication order lines and prescribing errors during 18 days in 7 medical wards' using computerized physician order entry. We described and modelled the errors rate according to the chronology of hospital stay. We performed a classification and regression tree analysis to find which characteristics of alerts were predictive of their overriding (<it>i.e</it>. prescribing error repeated).</p> <p>Results</p> <p>12 533 order lines were reviewed, 117 errors (errors rate 0.9%) were observed and 51% of these errors occurred on the first day of the hospital stay. The risk of a prescribing error decreased over time. 52% of the alerts were overridden (<it>i.e </it>error uncorrected by prescribers on the following day. Drug omissions were the most frequently taken into account by prescribers. The classification and regression tree analysis showed that overriding pharmacist's alerts is first related to the ward of the prescriber and then to either Anatomical Therapeutic Chemical class of the drug or the type of error.</p> <p>Conclusions</p> <p>Since 51% of prescribing errors occurred on the first day of stay, pharmacist should concentrate his analysis of drug prescriptions on this day. The difference of overriding behavior between wards and according drug Anatomical Therapeutic Chemical class or type of error could also guide the validation tasks and programming of electronic alerts.</p

Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients

Purpose: Clinical decision support systems (CDSS) are promoted as powerful screening tools to improve pharmacotherapy. The aim of our study was to evaluate the potential contribution of CDSS to patient management in clinical practice. Methods: We prospectively analyzed the pharmacotherapy of 100 medical inpatients through the parallel use of three CDSS, namely, Pharmavista, DrugReax, and TheraOpt. After expert discussion that also considered all patient-specific clinical information, we selected apparently relevant alerts, issued suitable recommendations to physicians, and recorded subsequent prescription changes. Results: For 100 patients with a median of eight concomitant drugs, Pharmavista, DrugReax, and TheraOpt generated a total of 53, 362, and 328 interaction alerts, respectively. Among those we identified and forwarded 33 clinically relevant alerts to the attending physician, resulting in 19 prescription changes. Four adverse drug events were associated with interactions. The proportion of clinically relevant alerts among all alerts (positive predictive value) was 5.7, 8.0, and 7.6%, and the sensitivity to detect all 33 relevant alerts was 9.1, 87.9, and 75.8% for Pharmavista, DrugReax and TheraOpt, respectively. TheraOpt recommended 31 dose adjustments, of which we considered 11 to be relevant; three of these were followed by dose reductions. Conclusions: CDSS are valuable screening tools for medication errors, but only a small fraction of their alerts appear relevant in individual patients. In order to avoid overalerting CDSS should use patient-specific information and management-oriented classifications. Comprehensive information should be displayed on-demand, whereas a limited number of computer-triggered alerts that have management implications in the majority of affected patients should be based on locally customized and supported algorithm