The Impact of Customized Screening Intervals on the Burden of Drug-Drug Interaction Alerts: An Interrupted Time Series Analysis.

Fixed and broad screening intervals for drug-drug interaction (DDI) alerts lead to false positive alerts, thereby contributing to alert fatigue among healthcare professionals. Hence, we aimed to investigate the impact of customized screening intervals on the daily incidence of DDI alerts. An interrupted time series analysis was performed at the University Hospitals Leuven to evaluate the impact of a pragmatic intervention on the daily incidence of DDI alerts per 100 prescriptions. The study period encompassed 100 randomly selected days between April 2021 and December 2022. Preceding the intervention, a fixed and broad screening interval of 7 days before and after prescribing an interacting drug was applied. The intervention involved implementing customized screening intervals for a subset of highly prevalent or clinically relevant DDIs into the hospital information system. Additionally, the sensitivity of the tailored approach was evaluated. During the study period, a mean of 5731 (± 2909) new prescriptions per day was generated. The daily incidence of DDI alerts significantly decreased from 9.8% (95% confidence interval (CI) 8.4;11.1) before the intervention, to 6.3% (95% CI 5.4;7.2) afterwards, p < 0.0001. This corresponded to avoiding 201 (0.035*5731) false positive DDI alerts per day. Sensitivity was not compromised by our intervention. Defining and implementing customized screening intervals was feasible and effective in reducing the DDI alert burden without compromising sensitivity.

Preventing Overrides of Severe Drug Allergy Alerts Initiative: an Implemented System Upgrade.

Administering medications to patients with documented drug hypersensitivity reactions (DHR) poses a significant risk for adverse events, ranging from mild reactions to life-threatening incidents. Electronic healthcare systems have revolutionized the modern clinical decision-making process, with built in warnings. However, as these alerts become a routine part of healthcare provider's workflow, alert fatigue becomes a challenge. This study was conducted within the Ministry of National Guard Health Affairs (MNGHA), a government healthcare system in Saudi Arabia. A taskforce of experts was formed to develop an electronic path that would prevent unintentional overrides of severe drug allergy alerts. The system underwent rigorous testing, and monitoring parameters were established. We outline the implementation of a system upgrade designed to trigger an alternative interruption in the computerized physician order entry (CPOE) process, distinct from the regular allergy pop-up alerts. The alternate path is activated upon a CPOE with a drug-to-drug match and a documented severe drug allergy symptom, necessitating co-signature form another prescriber before proceeding. The adopted upgrade is a proactive approach to enhance medication safety in electronic healthcare systems, ensuring that serious allergy-related warnings are not overridden, ultimately enhancing patient safety. Further monitoring will confirm the safety and effectiveness of this measure. This study provides a model for institutions seeking to prevent allergy-related harm within their patient population.

Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration.

BACKGROUND: Hospitalized adults whose condition deteriorates while they are in wards (outside the intensive care unit [ICU]) have considerable morbidity and mortality. Early identification of patients at risk for clinical deterioration has relied on manually calculated scores. Outcomes after an automated detection of impending clinical deterioration have not been widely reported. METHODS: On the basis of a validated model that uses information from electronic medical records to identify hospitalized patients at high risk for clinical deterioration (which permits automated, real-time risk-score calculation), we developed an intervention program involving remote monitoring by nurses who reviewed records of patients who had been identified as being at high risk; results of this monitoring were then communicated to rapid-response teams at hospitals. We compared outcomes (including the primary outcome, mortality within 30 days after an alert) among hospitalized patients (excluding those in the ICU) whose condition reached the alert threshold at hospitals where the system was operational (intervention sites, where alerts led to a clinical response) with outcomes among patients at hospitals where the system had not yet been deployed (comparison sites, where a patient's condition would have triggered a clinical response after an alert had the system been operational). Multivariate analyses adjusted for demographic characteristics, severity of illness, and burden of coexisting conditions. RESULTS: The program was deployed in a staggered fashion at 19 hospitals between August 1, 2016, and February 28, 2019. We identified 548,838 non-ICU hospitalizations involving 326,816 patients. A total of 43,949 hospitalizations (involving 35,669 patients) involved a patient whose condition reached the alert threshold; 15,487 hospitalizations were included in the intervention cohort, and 28,462 hospitalizations in the comparison cohort. Mortality within 30 days after an alert was lower in the intervention cohort than in the comparison cohort (adjusted relative risk, 0.84, 95% confidence interval, 0.78 to 0.90; P<0.001). CONCLUSIONS: The use of an automated predictive model to identify high-risk patients for whom interventions by rapid-response teams could be implemented was associated with decreased mortality. (Funded by the Gordon and Betty Moore Foundation and others.).

Reducing Alert Fatigue by Sharing Low-Level Alerts With Patients and Enhancing Collaborative Decision Making Using Blockchain Technology: Scoping Review and Proposed Framework (MedAlert).

BACKGROUND: Clinical decision support (CDS) is a tool that helps clinicians in decision making by generating clinical alerts to supplement their previous knowledge and experience. However, CDS generates a high volume of irrelevant alerts, resulting in alert fatigue among clinicians. Alert fatigue is the mental state of alerts consuming too much time and mental energy, which often results in relevant alerts being overridden unjustifiably, along with clinically irrelevant ones. Consequently, clinicians become less responsive to important alerts, which opens the door to medication errors. OBJECTIVE: This study aims to explore how a blockchain-based solution can reduce alert fatigue through collaborative alert sharing in the health sector, thus improving overall health care quality for both patients and clinicians. METHODS: We have designed a 4-step approach to answer this research question. First, we identified five potential challenges based on the published literature through a scoping review. Second, a framework is designed to reduce alert fatigue by addressing the identified challenges with different digital components. Third, an evaluation is made by comparing MedAlert with other proposed solutions. Finally, the limitations and future work are also discussed. RESULTS: Of the 341 academic papers collected, 8 were selected and analyzed. MedAlert securely distributes low-level (nonlife-threatening) clinical alerts to patients, enabling a collaborative clinical decision. Among the solutions in our framework, Hyperledger (private permissioned blockchain) and BankID (federated digital identity management) have been selected to overcome challenges such as data integrity, user identity, and privacy issues. CONCLUSIONS: MedAlert can reduce alert fatigue by attracting the attention of patients and clinicians, instead of solely reducing the total number of alerts. MedAlert offers other advantages, such as ensuring a higher degree of patient privacy and faster transaction times compared with other frameworks. This framework may not be suitable for elderly patients who are not technology savvy or in-patients. Future work in validating this framework based on real health care scenarios is needed to provide the performance evaluations of MedAlert and thus gain support for the better development of this idea.

Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection.

BACKGROUND: Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos. METHODS: We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices. RESULTS: A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical. CONCLUSIONS: The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.

[Research of Methods to Reduce Alarm Fatigue of Monitoring System].

OBJECTIVE: In order to solve alarm fatigue, the algorithm optimization strategies were researched to reduce false and worthless alarms. METHODS: A four-lead arrhythmia analysis algorithm, a multiparameter fusion analysis algorithm, an intelligent threshold reminder, a refractory period delay technique were proposed and tested with collected 28 679 alarms in multi-center study. RESULTS: The sampling survey indicate that the 80.8% of arrhythmia false alarms were reduced by the four-lead analysis, the 55.9% of arrhythmia and pulse false alarms were reduced by the multi-parameter fusion analysis, the 28.0% and 29.8% of clinical worthless alarms were reduced by the intelligent threshold and refractory period delay techniques respectively. Finally, the total quantity of alarms decreased to 12 724. CONCLUSIONS: To increase the dimensionality of parametric analysis and control the alarm limits and delay time are conducive to reduce alarm fatigue in intensive care units.

Diagnostic heuristics in dermatology, part 2: metacognition and other fixes.

Diagnostic errors are the most common, costly and dangerous of medical mistakes. In part 1 of this series, we described how general and dermatology-specific cognitive and perceptual biases underlie most of our correct diagnoses, as well as being a source of diagnostic medical errors. In this second part of the series, we describe some tactics to combat diagnostic error. Metacognition, or thinking about how we think, is the central approach advocated to avoid errors of 'uncritical' diagnostic thinking. Current individual and medical cultural attitudes need to be modified in order to incorporate improvements in diagnosis. Algorithms, artificial intelligence and system changes are being developed to address error and improve diagnostic accuracy.

JOURNAL CLUB: Predictors of Provider Response to Clinical Decision Support: Lessons Learned From the Medicare Imaging Demonstration.

OBJECTIVE: The efficacy of imaging clinical decision support (CDS) varies. Our objective was to identify CDS factors contributing to imaging order cancellation or modification. SUBJECTS AND METHODS: This pre-post study was performed across four institutions participating in the Medicare Imaging Demonstration. The intervention was CDS at order entry for selected outpatient imaging procedures. On the basis of the information entered, computerized alerts indicated to providers whether orders were not covered by guidelines, appropriate, of uncertain appropriateness, or inappropriate according to professional society guidelines. Ordering providers could override or accept CDS. We considered actionable alerts to be those that could generate an immediate order behavior change in the ordering physician (i.e., cancellation of inappropriate orders or modification of orders of uncertain appropriateness that had a recommended alternative). Chi-square and logistic regression identified predictors of order cancellation or modification after an alert. RESULTS: A total of 98,894 radiology orders were entered (83,114 after the intervention). Providers ignored 98.9%, modified 1.1%, and cancelled 0.03% of orders in response to alerts. Actionable alerts had a 10 fold higher rate of modification (8.1% vs 0.7%; p < 0.0001) or cancellation (0.2% vs 0.02%; p < 0.0001) orders compared with nonactionable alerts. Orders from institutions with preexisting imaging CDS had a sevenfold lower rate of cancellation or modification than was seen at sites with newly implemented CDS (1.4% vs 0.2%; p < 0.0001). In multivariate analysis, actionable alerts were 12 times more likely to result in order cancellation or modification. Orders at sites with preexisting CDS were 7.7 times less likely to be cancelled or modified (p < 0.0001). CONCLUSION: Using results from the Medicare Imaging Demonstration project, we identified potential factors that were associated with CDS effect on provider imaging ordering; these findings may have implications for future design of such computerized systems.

User Experience Design for Intensive Care Alarm Management.

Alarm fatigue is a pressing issue in intensive care units. Based on user experience design, including clinical shadowings and feedback loops, we developed a prototype for a redesigned patient monitor: The prototype moves away from today's threshold-based alarm systems. It combines a sleek design with machine learning driven clinical insights to mitigate alarm fatigue.

Navigating Alert Fatigue: A Case Study in Electronic Health Record Alert Design Optimization.

Clinical decision support (CDS) systems play a crucial role in enhancing patient outcomes, but inadequate design contributes to alert fatigue, inundating clinicians with disruptive alerts that lack clinical relevance. This case study delves into a quality improvement (QI) project addressing nursing electronic health record (EHR) alert fatigue by strategically redesigning four high-firing/low action alerts. Employing a mixed-methods approach, including quantitative analysis, empathy mapping sessions, and user feedback, the project sought to understand and alleviate the challenges posed by these alerts. Virtual empathy mapping sessions with clinical nurses provided valuable insights into user experiences. Qualitative findings, CDS design principles, and organizational practice expectations informed the redesign process, resulting in the removal of all four identified disruptive alerts and redesign of passive alerts. This initiative released 877 unactionable disruptive nursing hours, emphasizing the significance of proper alert design and the necessity for organizational structures ensuring sustained governance in healthcare system optimization.

Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals.

OBJECTIVE: To identify and summarize the current internal governance processes adopted by hospitals, as reported in the literature, for selecting, optimizing, and evaluating clinical decision support (CDS) alerts in order to identify effective approaches. MATERIALS AND METHODS: Databases (Medline, Embase, CINAHL, Scopus, Web of Science, IEEE Xplore Digital Library, CADTH, and WorldCat) were searched to identify relevant papers published from January 2010 to April 2020. All paper types published in English that reported governance processes for selecting and/or optimizing CDS alerts in hospitals were included. RESULTS: Eight papers were included in the review. Seven papers focused specifically on medication-related CDS alerts. All papers described the use of a multidisciplinary committee to optimize alerts. Other strategies included the use of clinician feedback, alert data, literature and drug references, and a visual dashboard. Six of the 8 papers reported evaluations of their CDS alert modifications following the adoption of optimization strategies, and of these, 5 reported a reduction in alert rate. CONCLUSIONS: A multidisciplinary committee, often in combination with other approaches, was the most frequent strategy reported by hospitals to optimize their CDS alerts. Due to the limited number of published processes, variation in system changes, and evaluation results, we were unable to compare the effectiveness of different strategies, although employing multiple strategies appears to be an effective approach for reducing CDS alert numbers. We recommend hospitals report on descriptions and evaluations of governance processes to enable identification of effective strategies for optimization of CDS alerts in hospitals.

Skin Preparation and Electrode Replacement to Reduce Alarm Fatigue in a Community Hospital Intensive Care Unit.

BACKGROUND: Nurses in intensive care units are exposed to hundreds of alarms during a shift, and research shows that most alarms are not clinically relevant. Alarm fatigue can occur when a nurse becomes desensitized to alarms. Alarm fatigue can jeopardize patient safety, and adverse alarm events can lead to patients dying. OBJECTIVE: To evaluate how a process intervention affects the number of alarms during an 8-hour shift in an intensive care unit. METHODS: A total of 62 patients from an intensive care unit were included in the study; 32 of these patients received the intervention, which included washing the patient's chest with soap and water and applying new electrocardiography electrodes at the start of a shift. The number of alarms, clinical diagnoses, and demographic variables were collected for each patient. A Poisson regression model was used to evaluate the impact of the intervention on the overall number of clinical alarms during the shift, with no adjustments to the alarm settings or other interventions. RESULTS: After relevant covariates are controlled for, the results suggest that patients in the intervention group presented significantly fewer alarms than did patients in the control group. CONCLUSIONS: Managing clinical alarms is a main issue in terms of both patient safety and staff workload management. The results of this study demonstrate that a relatively simple process-oriented strategy can decrease the number of alarms.

Daily Electrocardiogram Electrode Change and the Effect on Frequency of Nuisance Alarms.

BACKGROUND: In 2018, The Joint Commission identified false telemetry alarms as a significant technology hazard placing patients at risk of injury. Reasons include poor skin preparation when applying electrodes and improper placement of electrodes. OBJECTIVES: The purpose of this quality improvement project was to determine if changing electrocardiogram electrodes daily would decrease the frequency of nuisance alarms. METHODS: Study design was quantitative/comparative on all patients receiving telemetry monitoring on a 36-bed adult inpatient cardiac telemetry unit. Data collection occurred for 14 days before the intervention and 14 days during the intervention of daily electrode change. Comparison analysis determined if frequency of alarms decreased after the intervention with daily electrode change. RESULTS: Postintervention data showed a 74.15% reduction in telemetry alarms following implementation of a daily electrode change. DISCUSSION: Daily electrocardiogram electrode changes may be an effective strategy for reducing nuisance alarms on telemetry units. Outcomes can be used in conjunction with existing evidence to drive current practice.

Effect of modifying dose alerts in an electronic health record on frequency of alerts.

PURPOSE: Results of a study to reduce the number of medication order-entry alerts and perceived alert fatigue by optimizing alert logic are reported. METHODS: Data on dosage alerts registered throughout a health system over 2 days per study phase (preintervention and postintervention) were collected from the electronic health record. The 5 medications most frequently associated with dosage alerts during computerized prescriber order entry (CPOE) were evaluated for appropriateness in relation to patient-specific characteristics. Additionally, the 10 alerts most frequently marked by prescribers as "inaccurate warning" during alert override were evaluated for appropriateness. Recommendations were made for all alerts deemed inappropriate or unnecessary. The percent change in the number of alerts from the preintervention to the postintervention period was determined. To evaluate clinician perceptions of the alert updates, a pre-post survey was distributed to hospitalists and pharmacists at 1 facility within the health system. RESULTS: Changes were recommended for 8 alerts; 2 alerts within the dosage category overlapped with alerts in the inaccurate-warning group, resulting in a total of 6 recommended changes. Two recommended alert changes were made within the clinical drug information system, and 4 alerts were changed at the health-system level. As a result, a 3.6% dosage alert decrease occurred. CONCLUSION: The proportion of dose alerts, among all CPOE-generated alerts, decreased after some of the alerts were modified in accordance with institution-specific medication and population evaluations.

Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review.

OBJECTIVE: Alert fatigue limits the effectiveness of medication safety alerts, a type of computerized clinical decision support (CDS). Researchers have suggested alternative interactive designs, as well as tailoring alerts to clinical roles. As examples, alerts may be tiered to convey risk, and certain alerts may be sent to pharmacists. We aimed to evaluate which variants elicit less alert fatigue. MATERIALS AND METHODS: We searched for articles published between 2007 and 2017 using the PubMed, Embase, CINAHL, and Cochrane databases. We included articles documenting peer-reviewed empirical research that described the interactive design of a CDS system, to which clinical role it was presented, and how often prescribers accepted the resultant advice. Next, we compared the acceptance rates of conventional CDS-presenting prescribers with interruptive modal dialogs (ie, "pop-ups")-with alternative designs, such as role-tailored alerts. RESULTS: Of 1011 articles returned by the search, we included 39. We found different methods for measuring acceptance rates; these produced incomparable results. The most common type of CDS-in which modals interrupted prescribers-was accepted the least often. Tiering by risk, providing shortcuts for common corrections, requiring a reason to override, and tailoring CDS to match the roles of pharmacists and prescribers were the most common alternatives. Only 1 alternative appeared to increase prescriber acceptance: role tailoring. Possible reasons include the importance of etiquette in delivering advice, the cognitive benefits of delegation, and the difficulties of computing "relevance." CONCLUSIONS: Alert fatigue may be mitigated by redesigning the interactive behavior of CDS and tailoring CDS to clinical roles. Further research is needed to develop alternative designs, and to standardize measurement methods to enable meta-analyses.

Successful deployment of drug-disease interaction clinical decision support across multiple Kaiser Permanente regions.

OBJECTIVE: The study sought to develop a criteria-based scoring tool for assessing drug-disease knowledge base content and creation of a subset and to implement the subset across multiple Kaiser Permanente (KP) regions. MATERIALS AND METHODS: In Phase I, the scoring tool was developed, used to create a drug-disease alert subset, and validated by surveying physicians and pharmacists from KP Northern California. In Phase II, KP enabled the alert subset in July 2015 in silent mode to collect alert firing rates and confirmed that alert burden was adequately reduced. The alert subset was subsequently rolled out to users in KP Northern California. Alert data was collected September 2015 to August 2016 to monitor relevancy and override rates. RESULTS: Drug-disease alert scoring identified 1211 of 4111 contraindicated drug-disease pairs for inclusion in the subset. The survey results showed clinician agreement with subset examples 92.3%-98.5% of the time. Postsurvey adjustments to the subset resulted in KP implementation of 1189 drug-disease alerts. The subset resulted in a decrease in monthly alerts from 32 045 to 1168. Postimplementation monthly physician alert acceptance rates ranged from 20.2% to 29.8%. DISCUSSION: Our study shows that drug-disease alert scoring resulted in an alert subset that generated acceptable interruptive alerts while decreasing overall potential alert burden. Following the initial testing and implementation in its Northern California region, KP successfully implemented the disease interaction subset in 4 regions with additional regions planned. CONCLUSIONS: Our approach could prevent undue alert burden when new alert categories are implemented, circumventing the need for trial live activations of full alert category knowledge bases.

Implementing Practice Standards for Inpatient Electrocardiographic Monitoring.

BACKGROUND: Although electrocardiographic monitoring is common in hospitalized patients, many patients receive unnecessary monitoring, contributing to patients' inconvenience, clinicians' alarm fatigue, and delayed admissions. OBJECTIVE: To evaluate the impact of implementation of an electronic order set based on the American Heart Association practice standards for electrocardiographic monitoring on the occurrence of appropriate monitoring. METHODS: The sample for this preintervention-to-postintervention quasi-experimental study consisted of 297 adult patients on medical, surgical, neurological, oncological, and orthopedic patient care units that used remote electrocardiographic monitoring in a 627-bed hospital in Minneapolis, Minnesota. The intervention was the introduction into the electronic health record of order sets prompting physicians to order electrocardiographic monitoring per the American Heart Association practice standards. Indications for monitoring according to the practice standards and adverse outcomes (unexpected transfer to intensive care unit, death, code blue events, and call for the rapid response team) were compared before and after implementation of the order set. RESULTS: Implementation of the order set was associated with an increase in appropriate monitoring (48.0% to 61.2%; P = .03); the largest increase was in ordering by medical residents (30.8% to 76.5%; P = .001). No significant increase in adverse patient outcomes was noted. CONCLUSIONS: Implementation of the practice standards via an electronic order set was associated with a statistically significant increase in appropriate monitoring, with no increase in adverse events. Use of electronic order sets is an effective and safe way to enhance appropriate electrocardiographic monitoring.