Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation

OBJECTIVE: To apply human factors engineering principles to improve alert interface design. We hypothesized that incorporating human factors principles into alerts would improve usability, reduce workload for prescribers, and reduce prescribing errors. MATERIALS AND METHODS: We performed a scenario-based simulation study using a counterbalanced, crossover design with 20 Veterans Affairs prescribers to compare original versus redesigned alerts. We redesigned drug-allergy, drug-drug interaction, and drug-disease alerts based upon human factors principles. We assessed usability (learnability of redesign, efficiency, satisfaction, and usability errors), perceived workload, and prescribing errors. RESULTS: Although prescribers received no training on the design changes, prescribers were able to resolve redesigned alerts more efficiently (median (IQR): 56 (47) s) compared to the original alerts (85 (71) s; p=0.015). In addition, prescribers rated redesigned alerts significantly higher than original alerts across several dimensions of satisfaction. Redesigned alerts led to a modest but significant reduction in workload (p=0.042) and significantly reduced the number of prescribing errors per prescriber (median (range): 2 (1-5) compared to original alerts: 4 (1-7); p=0.024). DISCUSSION: Aspects of the redesigned alerts that likely contributed to better prescribing include design modifications that reduced usability-related errors, providing clinical data closer to the point of decision, and displaying alert text in a tabular format. Displaying alert text in a tabular format may help prescribers extract information quickly and thereby increase responsiveness to alerts. CONCLUSIONS: This simulation study provides evidence that applying human factors design principles to medication alerts can improve usability and prescribing outcomes

How the presentation of patient information and decision-support advisories influences opioid prescribing behavior: A simulation study

ObjectiveThe United States faces an opioid crisis. Integrating prescription drug monitoring programs into electronic health records offers promise to improve opioid prescribing practices. This study aimed to evaluate 2 different user interface designs for prescription drug monitoring program and electronic health record integration.Materials and MethodsTwenty-four resident physicians participated in a randomized controlled experiment using 4 simulated patient cases. In the conventional condition, prescription opioid histories were presented in tabular format, and computerized clinical decision support (CDS) was provided via interruptive modal dialogs (ie, pop-ups). The alternative condition featured a graphical opioid history, a cue to visit that history, and noninterruptive CDS. Two attending pain specialists judged prescription appropriateness.ResultsParticipants in the alternative condition wrote more appropriate prescriptions. When asked after the experiment, most participants stated that they preferred the alternative design to the conventional design.ConclusionsHow patient information and CDS are presented appears to have a significant influence on opioid prescribing behavior

Owning Attention: Applying Human Factors Principles to Support Clinical Decision Support

In the best examples, clinical decision support (CDS) systems guide clinician decision-making and actions, prevent errors, improve quality, reduce costs, save time, and promote the use of evidence-based recommendations. However, the potential solution that CDS represents are limited by problems associated with improper design, implementation, and local customization. Despite an emphasis on electronic health record usability, little progress has been made to protect end-users from inadequately designed workflows and unnecessary interruptions. Intelligent and personalized design creates an opportunity to tailor CDS not just at the patient level but specific to the disease condition, provider experience, and available resources at the healthcare system level. This chapter leverages the Five Rights of CDS framework to demonstrate the application of human factors engineering principles and emerging trends to optimize data analytics, usability, workflow, and design

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

A Systematic Review Of The Types And Causes Of Prescribing Errors Generated From Using Computerized Provider Order Entry Systems in Primary and Secondary Care

Objective To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems. Materials and Methods We conducted a systematic review of the literature published between January 2004 and June 2015 using three large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Studies that reported qualitative data about the types and causes of these errors were included. A narrative synthesis of all eligible studies was undertaken. Results A total of 1185 publications were identified, of which 34 were included in the review. We identified 8 key themes associated with CPOE-related prescribing errors: computer screen display, drop-down menus and auto-population, wording, default settings, nonintuitive or inflexible ordering, repeat prescriptions and automated processes, users’ work processes, and clinical decision support systems. Displaying an incomplete list of a patient’s medications on the computer screen often contributed to prescribing errors. Lack of system flexibility resulted in users employing error-prone workarounds, such as the addition of contradictory free-text comments. Users’ misinterpretations of how text was presented in CPOE systems were also linked with the occurrence of prescribing errors. Discussion and Conclusions Human factors design is important to reduce error rates. Drop-down menus should be designed with safeguards to decrease the likelihood of selection errors. Development of more sophisticated clinical decision support, which can perform checks on free-text, may also prevent errors. Further research is needed to ensure that systems minimize error likelihood and meet users’ workflow expectations

Recommendations for selecting drug-drug interactions for clinical decision support

To recommend principles for including drug-drug interactions (DDIs) in clinical decision support

Physician Satisfaction and Usability of Clinical Decision Support Tools in an Academic Medical Center’s Electronic Patient Record

Despite the available research on the benefits, capabilities, and implementation barriers and challenges of electronic Clinical Decision Support (CDS) tools physicians are still reluctant to utilize them. There are multiple studies that demonstrate limited buy-in and overall disinclination to use them however few studies evaluate physician satisfaction with CDS tools and the usability factors that may be associated with increasing satisfaction. The Questionnaire for User Interaction Satisfaction (QUIS) was disseminated to all P4 Residents and P4 Physician Hospitalists who routinely use the academic medical center’s electronic medical record (EMR). Overall user satisfaction was most correlated with the Layout/Screen Design and System Learning usability factors. It was unexpectedly not associated with Capabilities. The development of these tools should consider and encourage practices that invite analysts and physicians to collaborate on the principles and standards to guide design. Studies that focus on human-computer interactions can assist with the development of meaningful design strategies that will increase physician satisfaction resulting in increased physician usage of available CDS tools. Since CDS tools are often implemented to assist physicians with effective decision making to improve patient outcomes, ongoing efforts are needed to foster any long term successes of CDS tools

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

Background: Clinical 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. Methods: A 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. Results: We 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. Conclusions: We 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

Pervasive Technologies and Support for Independent Living

A broad range of pervasive technologies are used in many domains, including healthcare: however, there appears to be little work examining the role of such technologies in the home, or the different wants and needs of elderly users. Additionally, there exist ethical issues surrounding the use of highly personal healthcare-related data, and interface issues centred on the novelty of the technologies and the disabilities experienced by the users. This report examines these areas, before considering the ways in which they might come together to help support independent-living users with disabilities which may be age-related

Development of a context model to prioritize drug safety alerts in CPOE systems

Background: Computerized physician order entry systems (CPOE) can reduce the number of medication errors and adverse drug events (ADEs) in healthcare institutions. Unfortunately, they tend to produce a large number of partly irrelevant alerts, in turn leading to alert overload and causing alert fatigue. The objective of this work is to identify factors that can be used to prioritize and present alerts depending on the 'context' of a clinical situation. Methods: We used a combination of literature searches and expert interviews to identify and validate the possible context factors. The internal validation of the context factors was performed by calculating the inter-rater agreement of two researcher's classification of 33 relevant articles. Results: We developed a context model containing 20 factors. We grouped these context factors into three categories: characteristics of the patient or case (e. g. clinical status of the patient); characteristics of the organizational unit or user (e. g. professional experience of the user); and alert characteristics (e. g. severity of the effect). The internal validation resulted in nearly perfect agreement (Cohen's Kappa value of 0.97). Conclusion: To our knowledge, this is the first structured attempt to develop a comprehensive context model for prioritizing drug safety alerts in CPOE systems. The outcome of this work can be used to develop future tailored drug safety alerting in CPOE systems