Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors.
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ABSTRACT:
SUBMITTER: Hirsch JS
PROVIDER: S-EPMC8242134 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Poorly designed labels and packaging are key contributors to medication errors. To identify attributes of labels and dosing tools that could be improved, we examined the extent to which dosing error rates are affected by tool characteristics (ie, type, marking complexity) and discordance between units of measurement on labels and dosing tools; along with differences by health literacy and language.Randomized controlled experiment in 3 urban pediatric clinics. English- or Spanish-speaking parents (n = 2110) of children ?8 years old were randomly assigned to 1 of 5 study arms and given labels and dosing tools that varied in unit pairings. Each parent measured 9 doses of medication (3 amounts [2.5, 5, and 7.5 mL] and 3 tools [1 cup, 2 syringes (0.2- and 0.5-mL increments)]), in random order. Outcome assessed was dosing error (>20% deviation; large error defined as > 2 times the dose).A total of 84.4% of parents made ?1 dosing error (21.0% ?1 large error). More errors were seen with cups than syringes (adjusted odds ratio = 4.6; 95% confidence interval, 4.2-5.1) across health literacy and language groups (P < .001 for interactions), especially for smaller doses. No differences in error rates were seen between the 2 syringe types. Use of a teaspoon-only label (with a milliliter and teaspoon tool) was associated with more errors than when milliliter-only labels and tools were used (adjusted odds ratio = 1.2; 95% confidence interval, 1.01-1.4).Recommending oral syringes over cups, particularly for smaller doses, should be part of a comprehensive pediatric labeling and dosing strategy to reduce medication errors.
| S-EPMC5051204 | biostudies-literature
Project description:Poorly designed labels and dosing tools contribute to dosing errors. We examined the degree to which errors could be reduced with pictographic diagrams, milliliter-only units, and provision of tools more closely matched to prescribed volumes.This study involved a randomized controlled experiment in 3 pediatric clinics. English- and Spanish-speaking parents (n = 491) of children ?8 years old were randomly assigned to 1 of 4 groups and given labels and dosing tools that varied in label instruction format (text and pictogram, or text only) and units (milliliter-only ["mL"] or milliliter/teaspoon ["mL/tsp"]). Each parent measured 9 doses of liquid medication (3 amounts [2, 7.5, and 10 mL] and 3 tools [1 cup, 2 syringes (5- and 10-mL capacities)]) in random order. The primary outcome was dosing error (>20% deviation), and large error (>2× dose).We found that 83.5% of parents made ?1 dosing error (overdosing was present in 12.1% of errors) and 29.3% of parents made ?1 large error (>2× dose). The greatest impact on errors resulted from the provision of tools more closely matched to prescribed dose volumes. For the 2-mL dose, the fewest errors were seen with the 5-mL syringe (5- vs 10-mL syringe: adjusted odds ratio [aOR] = 0.3 [95% confidence interval: 0.2-0.4]; cup versus 10-mL syringe: aOR = 7.5 [5.7-10.0]). For the 7.5-mL dose, the fewest errors were with the 10-mL syringe, which did not necessitate measurement of multiple instrument-fulls (5- vs 10-mL syringe: aOR = 4.0 [3.0-5.4]; cup versus 10-mL syringe: aOR = 2.1 [1.5-2.9]). Milliliter/teaspoon was associated with more errors than milliliter-only (aOR = 1.3 [1.05-1.6]). Parents who received text only (versus text and pictogram) instructions or milliliter/teaspoon (versus milliliter-only) labels and tools made more large errors (aOR = 1.9 [1.1-3.3], aOR = 2.5 [1.4-4.6], respectively).Provision of dosing tools more closely matched to prescribed dose volumes is an especially promising strategy for reducing pediatric dosing errors.
| S-EPMC5495522 | biostudies-literature
Project description:BackgroundAdministering medication through an enteral feeding tube (FT) is a frequent cause of errors resulting in increased morbidity and cost. Studies on interventions to prevent these errors in hospitalized patients, however, are limited.ObjectiveThe objective was to study the effect of a clinical decision support system (CDSS)-assisted pharmacy intervention on the incidence of FT-related medication errors (FTRMEs) in hospitalized patients.MethodsA pre-post intervention study was conducted between October 2014 and May 2015 in Catharina Hospital, the Netherlands. Patients who were admitted to the wards of bowel and liver disease, oncology, or neurology; using oral medication; and had an enteral FT were included. Preintervention patients were given care as usual. The intervention consisted of implementing a CDSS-assisted pharmacy check while also implementing standard operating procedures and educating personnel. An FTRME was defined as the administration of inappropriate medication through an enteral FT. The incidence was expressed as the number of FTRMEs per medication administration. Multivariate Poisson regression was used to calculate the incidence ratio (IR) comparing both phases.ResultsEighty-one patients were included, 38 during preintervention and 43 during the intervention phase. Incidence of FTRMEs in the preintervention phase was 0.15 (95% CI, 0.07-0.23) vs 0.02 (95% CI, 0.00-0.04) in the intervention phase, resulting in an adjusted IR of 0.13 (95% CI, 0.10-0.18).DiscussionIncidence of FTRMEs, as well as the IR, is comparable to previous studies.ConclusionThe intervention resulted in a substantial reduction in the incidence of FTRMEs.
| S-EPMC8048796 | biostudies-literature
Project description:ObjectiveTo evaluate the impact of computerized physician order entry (CPOE) with decision support on the frequency of antithrombotic medication errors in patients with chronic kidney disease (CKD) admitted with acute coronary syndrome (ACS) and to measure what effect it would have on in-hospital bleeding.Patients and methodsWe evaluated 80 patients with CKD who were admitted with ACS between January 1, 2009, and December 31, 2010, using either a standardized order set or CPOE with decision support to assess the frequency of medication errors and in-hospital bleeding.ResultsOf the 80 patients, 47 were admitted with standard orders vs 33 with CPOE. In-hospital bleeding occurred in 13 patients: 10 in the standard orders group vs 3 in the CPOE group (P=.12). In-hospital bleeding occurred in 5 (63%) of 8 patients receiving a contraindicated antithrombotic medication compared with 8 (11%) of 72 patients receiving appropriate medications (P=.002); the corresponding length of stay was 12.0 days compared with 6.8 days (P=.10). Contraindicated medications were given to no patients in the CPOE group vs 8 patients (17%) in the standard orders group (P=.01).ConclusionMedication errors occur frequently in patients with CKD admitted with ACS and result in a high risk of in-hospital bleeding. Use of CPOE with decision support is feasible in the ACS setting and may lead to improved patient safety.
| S-EPMC3228615 | biostudies-literature
Project description:ObjectiveHispanic parents in the United States are disproportionately affected by low health literacy and limited English proficiency (LEP). We examined associations between health literacy, LEP, and liquid medication dosing errors in Hispanic parents.MethodsCross-sectional analysis of data from a multisite randomized controlled experiment to identify best practices for the labeling/dosing of pediatric liquid medications (SAFE Rx for Kids study); 3 urban pediatric clinics. Analyses were limited to Hispanic parents of children aged ≤8 years with health literacy and LEP data (n = 1126). Parents were randomized to 1 of 5 groups that varied by pairing of units of measurement on the label/dosing tool. Each parent measured 9 doses (3 amounts [2.5, 5, 7.5 mL] using 3 tools [2 syringes in 0.2 or 0.5 mL increments, and 1 cup]) in random order. Dependent variable was a dosing error of >20% dose deviation. Predictor variables included health literacy (Newest Vital Sign) (limited = 0-3; adequate = 4-6) and LEP (speaks English less than "very well").ResultsA total of 83.1% made dosing errors (mean [SD] errors per parent = 2.2 [1.9]). Parents with limited health literacy and LEP had the greatest odds of making a dosing error compared to parents with adequate health literacy who were English proficient (trials with errors per parent = 28.8 vs 12.9%; adjusted odds ratio = 2.2 [95% confidence interval 1.7-2.8]). Parents with limited health literacy who were English proficient were also more likely to make errors (trials with errors per parent = 18.8%; adjusted odds ratio = 1.4 [95% confidence interval 1.1-1.9]).ConclusionsDosing errors are common among Hispanic parents; those with both LEP and limited health literacy are at particular risk. Further study is needed to examine how the redesign of medication labels and dosing tools could reduce literacy- and language-associated disparities in dosing errors.
| S-EPMC5424611 | biostudies-literature
Project description:BACKGROUND:The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS:Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ("unaided"), versus after use of the DDSS ("aided"). RESULTS:The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p?<?0.0001). Improvement was greatest for emergency medicine physicians (p?=?0.013) and clinicians in practice for less than 10 years (p?=?0.012). This error reduction occurred despite the fact that testers employed an "open book" approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS:These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists' ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION:ClinicalTrials.gov ID: NCT02205086.
| S-EPMC5155385 | biostudies-literature
Project description:Cooking with polluting and inefficient fuels and technologies is responsible for a large set of global harms, ranging from health and time losses among the billions of people who are energy poor, to environmental degradation at a regional and global scale. This paper presents a new decision-support model-the BAR-HAP Tool-that is aimed at guiding planning of policy interventions to accelerate transitions towards cleaner cooking fuels and technologies. The conceptual model behind BAR-HAP lies in a framework of costs and benefits that is holistic and comprehensive, allows consideration of multiple policy interventions (subsidies, financing, bans, and behavior change communication), and realistically accounts for partial adoption and use of improved cooking technology. It incorporates evidence from recent efforts to characterize the relevant set of parameters that determine those costs and benefits, including those related to intervention effectiveness. Practical aspects of the tool were modified based on feedback from a pilot testing workshop with multisectoral users in Nepal. To demonstrate the functionality of the BAR-HAP tool, we present illustrative calculations related to several cooking transitions in the context of Nepal. In accounting for the multifaceted nature of the issue of household air pollution, the BAR-HAP model is expected to facilitate cross-sector dialogue and problem-solving to address this major health, environment and development challenge.
| S-EPMC7822293 | biostudies-literature
Project description:Appropriate thromboprophylaxis for patients with atrial fibrillation or atrial flutter (AF) remains a national challenge. The recent availability of direct oral anticoagulants (DOACs) with comparable efficacy and improved safety compared with warfarin alters the balance between risk factors for stroke and benefit of anticoagulation. Our objective was to examine the impact of DOACs as an alternative to warfarin on the net benefit of oral anticoagulant therapy (OAT) in a real-world population of AF patients.This is a retrospective cohort study of patients with paroxysmal or persistent nonvalvular AF. We updated an Atrial Fibrillation Decision Support Tool (AFDST) to include DOACs as treatment options. The tool generates patient-specific recommendations based upon individual patient risk factor profiles for stroke and major bleeding using quality-adjusted life-years (QALYs) calculated for each treatment strategy by a decision analytic model. The setting included inpatient and ambulatory sites in an academic health center in the midwestern United States. The study involved 5,121 adults with nonvalvular AF seen for any ambulatory visit or inpatient hospitalization over the 1-year period (January through December 2016). Outcome measure was net clinical benefit in QALYs.When DOACs are a therapeutic option, the AFDST recommends OAT for 4,134 (81%) patients and no antithrombotic therapy or aspirin for 489 (9%). A strong recommendation for OAT could not be made in 498 (10%) patients. When warfarin is the only option, OAT is recommended for 3,228 (63%) patients and no antithrombotic therapy or aspirin for 973 (19%). A strong recommendation for OAT could not be made in 920 (18%) patients. In total, 1,508 QALYs could be gained if treatment were changed to that recommended by the AFDST.Availability of DOACs increases the proportion of patients for whom oral anticoagulation therapy is recommended in a real-world cohort of AF patients and increased projected QALYs by more than 1,500 when all patients are receiving thromboprophylaxis as recommended by the AFDST compared with current treatment.
| S-EPMC5726779 | biostudies-literature
Project description:The introduction and spread of emerging infectious diseases is increasing in both prevalence and scale. Whether naturally, accidentally or maliciously introduced, the substantial uncertainty surrounding the emergence of novel viruses, specifically where they may come from and how they will spread, demands robust and quantifiably validated outbreak control policies that can be implemented in real time. This work presents a novel mathematical modeling framework that integrates both outbreak dynamics and outbreak control into a decision support tool for mitigating infectious disease pandemics that spread through passenger air travel. An ensemble of border control strategies that exploit properties of the air traffic network structure and expected outbreak behavior are proposed. A stochastic metapopulation epidemic model is developed to evaluate and rank the control strategies based on their effectiveness in reducing the spread of outbreaks. Sensitivity analyses are conducted to illustrate the robustness of the proposed control strategies across a range of outbreak scenarios, and a case study is presented for the 2009 H1N1 influenza pandemic. This study highlights the importance of strategically allocating outbreak control resources, and the results can be used to identify the most robust border control policy that can be implemented in the early stages of an outbreak.
| S-EPMC6379393 | biostudies-literature
Project description:BackgroundComputerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far.ObjectiveOur objective was to develop machine learning prediction models to predict physicians' responses in order to reduce alert fatigue from disease medication-related CDSSs.MethodsWe collected data from a disease medication-related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets.ResultsA total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively.ConclusionsIn this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication-related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.
| S-EPMC7714650 | biostudies-literature