Hanna M. Seidling

Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support.

BACKGROUND Clinical decision support systems can prevent knowledge-based prescription errors and improve patient outcomes. The clinical effectiveness of these systems, however, is substantially limited by poor user acceptance of presented warnings. To enhance alert acceptance it may be useful to quantify the impact of potential modulators of acceptance. METHODS We built a logistic regression model to predict alert acceptance of drug-drug interaction (DDI) alerts in three different settings. Ten variables from the clinical and human factors literature were evaluated as potential modulators of provider alert acceptance. ORs were calculated for the impact of knowledge quality, alert display, textual information, prioritization, setting, patient age, dose-dependent toxicity, alert frequency, alert level, and required acknowledgment on acceptance of the DDI alert. RESULTS 50,788 DDI alerts were analyzed. Providers accepted only 1.4% of non-interruptive alerts. For interruptive alerts, user acceptance positively correlated with frequency of the alert (OR 1.30, 95% CI 1.23 to 1.38), quality of display (4.75, 3.87 to 5.84), and alert level (1.74, 1.63 to 1.86). Alert acceptance was higher in inpatients (2.63, 2.32 to 2.97) and for drugs with dose-dependent toxicity (1.13, 1.07 to 1.21). The textual information influenced the mode of reaction and providers were more likely to modify the prescription if the message contained detailed advice on how to manage the DDI. CONCLUSION We evaluated potential modulators of alert acceptance by assessing content and human factors issues, and quantified the impact of a number of specific factors which influence alert acceptance. This information may help improve clinical decision support systems design.

What, if all alerts were specific – Estimating the potential impact on drug interaction alert burden

PURPOSE Clinical decision support systems (CDSS) may potentially improve prescribing quality, but are subject to poor user acceptance. Reasons for alert overriding have been identified and counterstrategies have been suggested; however, poor alert specificity, a prominent reason of alert overriding, has not been well addressed. This paper aims at structuring modulators that determine alert specificity and estimating their quantitative impact on alert burden. METHODS We developed and summarized optimizing strategies to guarantee the specificity of alerts and applied them to a set of 100 critical and frequent drug interaction (DDI) alerts. Hence, DDI alerts were classified as dynamic, i.e. potentially sensitive to prescription-, co-medication-, or patient-related factors that would change alert severity or render the alert inappropriate compared to static, i.e. always applicable alerts not modulated by cofactors. RESULTS Within the subset of 100 critical DDI alerts, only 10 alerts were considered as static and for 7 alerts, relevant factors are not generally available in todays patient charts or their consideration would not impact alert severity. The vast majority, i.e. 83 alerts, might require a decrease in alert severity due to factors related to the prescription (N=13), the co-medication (N=11), individual patient data (N=36), or combinations of them (N=23). Patient-related factors consisted mainly of three lab values, i.e. renal function, potassium, and therapeutic drug monitoring results. CONCLUSION This paper outlines how promising the refinement of knowledge bases is in order to increase specificity and decrease alert burden and suggests how to structure knowledge bases to refine DDI alerting.

On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop

BackgroundClinical decision support (CDS) for electronic prescribing systems (computerized physician order entry) should help prescribers in the safe and rational use of medicines. However, the best ways to alert users to unsafe or irrational prescribing are uncertain. Specifically, CDS systems may generate too many alerts, producing unwelcome distractions for prescribers, or too few alerts running the risk of overlooking possible harms. Obtaining the right balance of alerting to adequately improve patient safety should be a priority.MethodsA workshop funded through the European Regional Development Fund was convened by the University Hospitals Birmingham NHS Foundation Trust to assess current knowledge on alerts in CDS and to reach a consensus on a future research agenda on this topic. Leading European researchers in CDS and alerts in electronic prescribing systems were invited to the workshop.ResultsWe identified important knowledge gaps and suggest research priorities including (1) the need to determine the optimal sensitivity and specificity of alerts; (2) whether adaptation to the environment or characteristics of the user may improve alerts; and (3) whether modifying the timing and number of alerts will lead to improvements. We have also discussed the challenges and benefits of using naturalistic or experimental studies in the evaluation of alerts and suggested appropriate outcome measures.ConclusionsWe have identified critical problems in CDS, which should help to guide priorities in research to evaluate alerts. It is hoped that this will spark the next generation of novel research from which practical steps can be taken to implement changes to CDS systems that will ultimately reduce alert fatigue and improve the design of future systems.

Successful strategy to improve the specificity of electronic statin?drug interaction alerts

PurposeA considerable weakness of current clinical decision support systems managing drug–drug interactions (DDI) is the high incidence of inappropriate alerts. Because DDI-induced, dose-dependent adverse events can be prevented by dosage adjustment, corresponding DDI alerts should only be issued if dosages exceed safe limits. We have designed a logical framework for a DDI alert-system that considers prescribed dosage and retrospectively evaluates the impact on the frequency of statin–drug interaction alerts.MethodsUpper statin dose limits were extracted from the drug label (SPC) (20 statin-drug combinations) or clinical trials specifying the extent of the pharmacokinetic interaction (43 statin–drug combinations). We retrospectively assessed electronic DDI alerts and compared the number of standard alerts to alerts that took dosage into account.ResultsFrom among 2457 electronic prescriptions, we identified 73 high-risk statin–drug pairs. Of these, SPC dosage information classified 19 warnings as inappropriate. Data from pharmacokinetic trials took quantitative dosage information more often into consideration and classified 40 warnings as inappropriate. This is a significant reduction in the number of alerts by 55% compared to SPC-based information (26%; p < 0.001).ConclusionThis retrospective study of pharmacokinetic statin interactions demonstrates that more than half of the DDI alerts that presented in a clinical decision support system were inappropriate if DDI-specific upper dose limits are not considered.

Evaluation of medication alerts in electronic health records for compliance with human factors principles

INTRODUCTION Increasing the adoption of electronic health records (EHRs) with integrated clinical decision support (CDS) is a key initiative of the current US healthcare administration. High over-ride rates of CDS alerts strongly limit these potential benefits. As a result, EHR designers aspire to improve alert design to achieve better acceptance rates. In this study, we evaluated drug-drug interaction (DDI) alerts generated in EHRs and compared them for compliance with human factors principles. METHODS We utilized a previously validated questionnaire, the I-MeDeSA, to assess compliance with nine human factors principles of DDI alerts generated in 14 EHRs. Two reviewers independently assigned scores evaluating the human factors characteristics of each EHR. Rankings were assigned based on these scores and recommendations for appropriate alert design were derived. RESULTS The 14 EHRs evaluated in this study received scores ranging from 8 to 18.33, with a maximum possible score of 26. Cohens κ (κ=0.86) reflected excellent agreement among reviewers. The six vendor products tied for second and third place rankings, while the top system and bottom five systems were home-grown products. The most common weaknesses included the absence of characteristics such as alert prioritization, clear and concise alert messages indicating interacting drugs, actions for clinical management, and a statement indicating the consequences of over-riding the alert. CONCLUSIONS We provided detailed analyses of the human factors principles which were assessed and described our recommendations for effective alert design. Future studies should assess whether adherence to these recommendations can improve alert acceptance.

Different methods, different results—how do available methods link a patient’s anticholinergic load with adverse outcomes?

PurposeAnticholinergic drugs are known to cause physical and cognitive impairment, particularly in older patients. The total of all anticholinergic influences to which a patient is exposed is referred to as anticholinergic load. Because the anticholinergic load is defined in various ways, this review aimed to describe differences in the development and evaluation of available methods calculating the anticholinergic load.MethodsFrom September 2014 to August 2015, two reviewers performed a literature search in PubMed considering relevant items of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. We aimed to identify articles which calculated the anticholinergic load with a scale or equation and investigated its association with patient-related outcomes. From the included studies, we descriptively analyzed the identification and scoring criteria of the scales and equations with a main emphasis on their association to the reported outcomes.ResultsOut of 465 articles, 55 were included referring to 12 scales and one equation. Main discrepancies were located in eight different identification criteria for anticholinergic drugs, two different scoring principles, and 118 tests used for assessing outcomes. The methods most frequently detecting a significant association between the anticholinergic load and outcomes took into account the drugs’ dosages and anticholinergic potencies. Interestingly, none of the methods included the patient’s susceptibility for anticholinergic effects and they only rarely considered modulators of drug exposure.ConclusionsDue to hugely varying tests for assessing outcomes, the methods are scarcely comparable. For a more valuable comparison, the anticholinergic load should be calculated with all scales and the equation and correlated with patient-related outcomes.

Do we prescribe what patients prefer? Pilot study to assess patient preferences for medication regimen characteristics

Background: The aim of this pilot study was to evaluate patients’ self-reported attitudes towards medication-related factors known to impair adherence and to assess their prevalence in ambulatory care as an essential prerequisite to improve patient adherence. Methods: We conducted a face-to-face interview with 110 primary care patients maintained on at least one drug. For each drug, the patient was asked to specify medication-related factors of interest, ie, dosage form, dosage interval, required relationship with food intake, and the planned time of day for intake, and to rate the individual relevance of each prevalent parameter on a three-point Likert scale (discriminating between prefer, neutral, and dislike). Results: Tablets with a once-daily dosage frequency were the most preferred dosage form, with a high prevalence in the ambulatory setting. Drug intake in the morning and evening were most preferred, and drug intake at noon was least preferred, but also had a low prevalence in contrast with drug intake independent of meals that was most preferred. Interestingly, only one quarter (26.4%) of all the patients were able to indicate clear preferences or dislikes. Conclusion: When patients are asked to specify their preferences for relevant medication regimen characteristics, they clearly indicated regimens that have been associated with better adherence in earlier studies. Therefore, our results suggest that adaptation of drug regimens to individual preferences might be a promising strategy to improve adherence. Because the German health care system may differ from other systems in relevant aspects, our findings should be confirmed by evaluation of patient preferences in other health care systems. Once generalizability of the study results is shown, these findings could be a promising basis upon which to promote patient adherence right from the beginning of drug therapy.

Detection and prevention of prescriptions with excessive doses in electronic prescribing systems

IntroductionDose dependent adverse drug reactions are often caused by prescribing errors ignoring upper dose limits. Thus, computerised physician order entry incorporating maximum recommended therapeutic doses (MRTDs) might reduce prescriptions of excessive doses. We evaluated the suitability of MRTD information as published in the Summary of Product Characteristics (SPC) (MRTDSPC) or by the US Food and Drug Administration (MRTDFDA) and the value of Defined Daily Doses (DDD, World Health Organisation) as knowledge bases for an alerting system.MethodsIn a large set of critical-dose drugs (N = 140) we compared MRTDFDA and DDD values with the corresponding German MRTDSPC. We then retrospectively assessed a set of 633 electronically prescribed drugs (EPDs) issued at a university hospital and calculated prescription rates of excessive doses.ResultsMRTDFDA was similar to MRTDSPC in 37% (N = 140), higher in 32%, and lower in 31% of drugs. On average, available DDD values (N = 129) were 1.6 times lower than MRTDSPC, with 64% being lower, 33% similar, and 3% larger than MRTDSPC. Prescription rates of excessive doses according to MRTDFDA were 2.5-fold higher (6.1%) than according to MRTDSPC (2.5%) (p < 0.01). However, only one in four EPDs categorised as overdosed according to MRTDFDA exceeded MRTDSPC, and MRTDFDA values were available only for 67% of all assessed EPDs.ConclusionOur study revealed a remarkable number of prescriptions with doses exceeding approved limits. Their prevention appears feasible but the choice of an appropriate database for MRTDs is essential, and differences between available information sources are large.

Memorandum on the Use of Information Technology to Improve Medication Safety

BACKGROUND Information technology in health care has a clear potential to improve the quality and efficiency of health care, especially in the area of medication processes. On the other hand, existing studies show possible adverse effects on patient safety when IT for medication-related processes is developed, introduced or used inappropriately. OBJECTIVES To summarize definitions and observations on IT usage in pharmacotherapy and to derive recommendations and future research priorities for decision makers and domain experts. METHODS This memorandum was developed in a consensus-based iterative process that included workshops and e-mail discussions among 21 experts coordinated by the Drug Information Systems Working Group of the German Society for Medical Informatics, Biometry and Epidemiology (GMDS). RESULTS The recommendations address, among other things, a stepwise and comprehensive strategy for IT usage in medication processes, the integration of contextual information for alert generation, the involvement of patients, the semantic integration of information resources, usability and adaptability of IT solutions, and the need for their continuous evaluation. CONCLUSION Information technology can help to improve medication safety. However, challenges remain regarding access to information, quality of information, and measurable benefits.

Design and Evaluation of an Ontology-based Drug Application Database

OBJECTIVES Several recently published cases of preventable adverse drug reactions were associated with flaws in drug application. However, current clinical decision support (CDS) systems do not properly consider drug application issues and thus do not support effective prevention of such medication errors. With the aim to improve CDS in this respect, we developed a comprehensive model precisely describing all aspects of drug application. METHODS The model consists of 1) a schema comprising all relevant attributes of drug application and 2) an ontology providing a hierarchically structured vocabulary of terms that describe the possible values of the schemas attributes. Finally, medical products were annotated by a semi-automatic term assignment process. For evaluation, we developed an algorithm that uses our model to compute a meaningful similarity between medicinal products with respect to their drug application characteristics. RESULTS Our schema consists of 22 attributes. The ontology contains 248 terms, textual descriptions, and synonym lists. More than 58,700 medicinal products were automatically annotated with >386,600 terms. 2,450 drugs were manually reviewed by experts, adding >4500 terms. The annotation and similarity measure allow for (similarity) searches, clustering, and proper discrimination of drugs with different drug application characteristics. We demonstrated the value of our approach by means of a set of case studies. CONCLUSION Our model enables a detailed description of drug application, allowing for semantically meaningful comparisons of drugs. This is an important prerequisite for improving the ability of CDS systems to prevent prescription errors.