RESUMEN

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.

Asunto(s)

RESUMEN

Integration of smartphone technology with the patient call-bell system provides the opportunity to enhance patient safety by supporting nurses' ability to communicate and prioritize care delivery directly. However, challenges are associated with achieving a balance between alarm support and alarm fatigue, including distracting nurses from patient care or desensitizing the nurse to other alarms and calls [1]. Our hospitals have quantitative and anecdotal reports of seriously high volumes of wireless alerts on the nurses' smartphones. Nurses have complained that the phones are generating too much noise to consume or timely prioritize. Preliminary alarm inventory revealed the Bed Exit wireless alert as a leading contributor of signal volume across many units and hospitals. The lack of standard policies and workflow improvement processes has increased nuisance alarms, making these Health Information Technologies less useful and safe. Using system data, workflow observations, and nursing interviews, Singh and Sittig's HIT Safety framework [2] was applied to identify and prioritize sociotechnical factors and interventions that impact the end-to-end Bed Exit alarm workflow. This study reviews the application of sociotechnical models and frameworks to reduce wireless calls without introducing risk and impacting patient care.

Asunto(s)

RESUMEN

BACKGROUND: Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. OBJECTIVE: This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. METHODS: We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. RESULTS: The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57). CONCLUSIONS: The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.

RESUMEN

This study was designed to investigate the state quo of the appropriateness of alerts overrides of the medication-related clinical decision support system (MRCDSS) in China. The medication-related alerts in one hospital from Jan 2022 to Dec 2022 were acquired and sampled. Rates of alert overrides, appropriateness of alert generation and physicians' responses were observed. Total 14,612 medication-related alerts (≤ level 3) were recorded, of those, 12,659 (86.6%) alerts were overridden. The top 3 alert types were: drug and diagnosis contraindications (23.8%), drug and test value contraindications (23.3%), and compatibility issues (17.7%). Of all sampled 1,501 alerts, 80.2% of them were appropriately overridden by the physicians. The appropriate rate of alert generation was 57.9% and the inappropriate rate was 42.1%. The inappropriate rate of physicians' responses was 17.8%, and 2.0% physicians' responses were undetermined. A few medications accounted for over 10% of overrides, 88.3% of "overridden reasons" inputted by the physicians were meaningless characters or values, indicating an obvious "alert fatigue" in these physicians. Our results indicated that the overridden rate of MRCDSS in China was still high, and appropriateness of generation of alert was quite low. These data indicated that the MRCDSS currently using in China still needs constantly optimization and timely maintenance. Proper sensitivity to reduce triggering of useless alerts and generation of alert fatigue might play a vital role. We believed that these findings are helpful for better understanding the state quo of MRCDSS in China and providing useful insights for future developing and improving MRCDSS.

Asunto(s)

RESUMEN

OBJECTIVES: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts. MATERIALS AND METHODS: We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness. RESULTS: Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001). CONCLUSION: End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline.

Asunto(s)

RESUMEN

INTRODUCTION: Duplicate prescribing clinical decision support alerts can prevent important prescribing errors but are frequently the cause of much alert fatigue. Stat dose prescriptions are a known reason for overriding these alerts. This study aimed to evaluate the effect of excluding stat dose prescriptions from duplicate prescribing alerts for antithrombotic medicines on alert burden, prescriber adherence, and prescribing. MATERIALS AND METHODS: A before (January 1st, 2017 to August 31st, 2022) and after (October 5th, 2022 to September 30th, 2023) study was undertaken of antithrombotic duplicate prescribing alerts and prescribing following a change in alert settings. Alert and prescribing data for antithrombotic medicines were joined, processed, and analysed to compare alert rates, adherence, and prescribing. Alert burden was assessed as alerts per 100 prescriptions. Adherence was measured at the point of the alert as whether the prescriber accepted the alert and following the alert as whether a relevant prescription was ceased within an hour. Co-prescribing of antithrombotic stat dose prescriptions was assessed pre- and post-alert reconfiguration. RESULTS: Reconfiguration of the alerts reduced the alert rate by 29 % (p < 0.001). The proportion of alerts associated with cessation of antithrombotic duplication significantly increased (32.8 % to 44.5 %, p < 0.001). Adherence at the point of the alert increased 1.2 % (4.8 % to 6.0 %, p = 0.012) and 11.5 % (29.4 % to 40.9 %, p < 0.001) within one hour of the alert. When ceased after the alert over 80 % of duplicate prescriptions were ceased within 2 min of overriding. Antithrombotic stat dose co-prescribing was unchanged for 4 out of 5 antithrombotic duplication alert rules. CONCLUSION: By reconfiguring our antithrombotic duplicate prescribing alerts, we reduced alert burden and increased alert adherence. Many prescribers ceased duplicate prescribing within 2 min of alert override highlighting the importance of incorporating post-alert measures in accurately determining prescriber alert adherence.

Asunto(s)

RESUMEN

Alert fatigue, a decrease in sensitivity to alerts, is a problem in the medical field. In this study, a survey was conducted on medical accidents in order to develop an alert that could be expected to reduce alert fatigue. As a result, medical accidents related to drugs are common worldwide, and the need for an alert system that can detect the implementation of medical treatment was found.

Asunto(s)

RESUMEN

OBJECTIVES: Continuous vital sign monitoring at the general hospital ward has major potential advantages over intermittent monitoring but generates many alerts with risk of alert fatigue. We hypothesized that the number of alerts would decrease using different filters. METHODS: This study was an exploratory analysis of the alert reducing effect from adding two different filters to continuously collected vital sign data (peripheral oxygen saturation, blood pressure, heart rate, and respiratory rate) in patients admitted after major surgery or severe medical disease. Filtered data were compared to data without artifact removal. Filter one consists of artifact removal, filter two consists of artifact removal plus duration criteria adjusted for severity of vital sign deviation. Alert thresholds were based on the National Early Warning Score (NEWS) threshold. RESULTS: A population of 716 patients admitted for severe medical disease or major surgery with continuous wireless vital sign monitoring at the general ward with a mean monitoring time of 75.8 h, were included for the analysis. Without artifact removal, we found a median of 137 [IQR: 87-188] alerts per patient/day, artifact removal resulted in a median of 101 [IQR: 56-160] alerts per patient/day and with artifact removal combined with a duration-severity criterion, we found a median of 19 [IQR: 9-34] alerts per patient/day. Reduction of alerts was 86.4% (p < 0.001) for values without artifact removal (137 alerts) vs. the duration criteria and a reduction (19 alerts) of 81.5% (p < 0.001) for the criteria with artifact removal (101 alerts) vs. the duration criteria (19 alerts). CONCLUSION: We conclude that a combination of artifact removal and duration-severity criteria approach substantially reduces alerts generated by continuous vital sign monitoring.

Asunto(s)

RESUMEN

In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a "Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care" model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system's validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams' confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.

Asunto(s)

RESUMEN

OBJECTIVES: A scoping review identified interventions for optimizing hospital medication alerts post-implementation, and characterized the methods used, the populations studied, and any effects of optimization. MATERIALS AND METHODS: A structured search was undertaken in the MEDLINE and Embase databases, from inception to August 2023. Articles providing sufficient information to determine whether an intervention was conducted to optimize alerts were included in the analysis. Snowball analysis was conducted to identify additional studies. RESULTS: Sixteen studies were identified. Most were based in the United States and used a wide range of clinical software. Many studies used inpatient cohorts and conducted more than one intervention during the trial period. Alert types studied included drug-drug interactions, drug dosage alerts, and drug allergy alerts. Six types of interventions were identified: alert inactivation, alert severity reclassification, information provision, use of contextual information, threshold adjustment, and encounter suppression. The majority of interventions decreased alert quantity and enhanced alert acceptance. Alert quantity decreased with alert inactivation by 1%-25.3%, and with alert severity reclassification by 1%-16.5% in 6 of 7 studies. Alert severity reclassification increased alert acceptance by 4.2%-50.2% and was associated with a 100% acceptance rate for high-severity alerts when implemented. Clinical errors reported in 4 studies were seen to remain stable or decrease. DISCUSSION: Post-implementation medication optimization interventions have positive effects for clinicians when applied in a variety of settings. Less well reported are the impacts of these interventions on the clinical care of patients, and how endpoints such as alert quantity contribute to changes in clinician and pharmacist perceptions of alert fatigue. CONCLUSION: Well conducted alert optimization can reduce alert fatigue by reducing overall alert quantity, improving clinical acceptance, and enhancing clinical utility.

Asunto(s)

RESUMEN

BACKGROUND: There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset. Additionally, AKI prediction models are known to have a high rate of false positive (FP) events regardless of baseline methods. This warrants further exploration of FP events to provide insight into potential underlying reasons. OBJECTIVE: The first aim of this study was to assess the variance in performance of ML models using three methods of baseline sCr on a retrospective dataset. The second aim was to conduct an error analysis to gain insight into the underlying factors contributing to FP events. MATERIALS AND METHODS: The Intensive Care Unit (ICU) patients of the Medical Information Mart for Intensive Care (MIMIC)-IV dataset was used with the KDIGO (Kidney Disease Improving Global Outcome) definition to identify AKI episodes. Three different methods of estimating baseline sCr were defined as (1) the minimum sCr, (2) the Modification of Diet in Renal Disease (MDRD) equation and the minimum sCr and (3) the MDRD equation and the mean of preadmission sCr. For the first aim of this study, a suite of ML models was developed for each baseline and the performance of the models was assessed. An analysis of variance was performed to assess the significant difference between eXtreme Gradient Boosting (XGB) models across all baselines. To address the second aim, Explainable AI (XAI) methods were used to analyse the XGB errors with Baseline 3. RESULTS: Regarding the first aim, we observed variances in discriminative metrics and calibration errors of ML models when different baseline methods were adopted. Using Baseline 1 resulted in a 14% reduction in the f1 score for both Baseline 2 and Baseline 3. There was no significant difference observed in the results between Baseline 2 and Baseline 3. For the second aim, the FP cohort was analysed using the XAI methods which led to relabelling data with the mean of sCr in 180 to 0 days pre-ICU as the preferred sCr baseline method. The XGB model using this relabelled data achieved an AUC of 0.85, recall of 0.63, precision of 0.54 and f1 score of 0.58. The cohort size was 31,586 admissions, of which 5,473 (17.32%) had AKI. CONCLUSION: In the absence of a widely accepted method of baseline sCr, AKI prediction studies need to consider the impact of different baseline methods on the effectiveness of ML models and their potential implications in real-world implementations. The utilisation of XAI methods can be effective in providing insight into the occurrence of prediction errors. This can potentially augment the success rate of ML implementation in routine care.

Asunto(s)

RESUMEN

Digital cognitive aids have the potential to serve as clinical decision support platforms, triggering alerts about process delays and recommending interventions. In this mixed-methods study, we examined how a digital checklist for pediatric trauma resuscitation could trigger decision support alerts and recommendations. We identified two criteria that cognitive aids must satisfy to support these alerts: (1) context information must be entered in a timely, accurate, and standardized manner, and (2) task status must be accurately documented. Using co-design sessions and near-live simulations, we created two checklist features to satisfy these criteria: a form for entering the pre-hospital information and a progress slider for documenting the progression of a multi-step task. We evaluated these two features in the wild, contributing guidelines for designing these features on cognitive aids to support alerts and recommendations in time- and safety-critical scenarios.

RESUMEN

OBJECTIVE: Despite the extensive literature exploring alert fatigue, most studies have focused on describing the phenomenon, but not on fixing it. The authors aimed to identify data useful to avert clinically irrelevant alerts to inform future research on clinical decision support (CDS) design. METHODS: We conducted a retrospective observational study of opioid drug allergy alert (DAA) overrides for the calendar year of 2019 at a large academic medical center, to identify data elements useful to find irrelevant alerts to be averted. RESULTS: Overall, 227,815 DAAs were fired in 2019, with an override rate of 91 % (n = 208196). Opioids represented nearly two-thirds of these overrides (n = 129063; 62 %) and were the drug class with the highest override rate (96 %). On average, 29 opioid DAAs were overridden per patient. While most opioid alerts (97.1 %) are fired for a possible match (the drug class of the allergen matches the drug class of the prescribed drug), they are overridden significantly less frequently for definite match (exact match between allergen and prescribed drug) (88 % vs. 95.9 %, p < 0.001). When comparing the triggering drug with previously administered drugs, override rates were equally high for both definite match (95.9 %), no match (95.5 %), and possible match (95.1 %). Likewise, when comparing to home medications, overrides were excessively high for possible match (96.3 %), no match (96 %), and definite match (94.4 %). CONCLUSION: We estimate that 74.5% of opioid DAAs (46.4% of all DAAs) at our institution could be relatively safely averted, since they either have a definite match for previous inpatient administrations suggesting drug tolerance or are fired as possible match with low risk of cross-sensitivity. Future research should focus on identifying other relevant data elements ideally with automated methods and use of emerging standards to empower CDS systems to suppress false-positive alerts while avoiding safety hazards.

Asunto(s)

RESUMEN

OBJECTIVES: Clinical decision support systems (CDSSs) are used in various aspects of healthcare to improve clinical decision-making, including in the ICU. However, there is growing evidence that CDSS are not used to their full potential, often resulting in alert fatigue which has been associated with patient harm. Clinicians in the ICU may be more vulnerable to desensitization of alerts than clinicians in less urgent parts of the hospital. We evaluated facilitators and barriers to appropriate CDSS interaction and provide methods to improve currently available CDSS in the ICU. DESIGN: Sequential explanatory mixed-methods study design, using the BEhavior and Acceptance fRamework. SETTING: International survey study. PATIENT/SUBJECTS: Clinicians (pharmacists, physicians) identified via survey, with recent experience with clinical decision support. INTERVENTIONS: An initial survey was developed to evaluate clinician perspectives on their interactions with CDSS. A subsequent in-depth interview was developed to further evaluate clinician (pharmacist, physician) beliefs and behaviors about CDSS. These interviews were then qualitatively analyzed to determine themes of facilitators and barriers with CDSS interactions. MEASUREMENTS AND MAIN RESULTS: A total of 48 respondents completed the initial survey (estimated response rate 15.5%). The majority believed that responding to CDSS alerts was part of their job (75%) but felt they experienced alert fatigue (56.5%). In the qualitative analysis, a total of five facilitators (patient safety, ease of response, specificity, prioritization, and feedback) and four barriers (excess quantity, work environment, difficulty in response, and irrelevance) were identified from the in-depth interviews. CONCLUSIONS: In this mixed-methods survey, we identified areas that institutions should focus on to improve appropriate clinician interactions with CDSS, specific to the ICU. Tailoring of CDSS to the ICU may lead to improvement in CDSS and subsequent improved patient safety outcomes.

RESUMEN

BACKGROUND: Alerts in computerized physician order entry (CPOE) systems can improve patient safety. However, alerts in rule-based systems cannot be customized based on individual patient or user characteristics. This limitation can lead to the presentation of irrelevant alerts and subsequent alert fatigue. OBJECTIVE: We used machine learning approaches with alert dwell time to filter out irrelevant alerts for physicians based on contextual factors. METHODS: We utilized five machine learning algorithms and a total of 1,120 features grouped into six categories: alert, demographic, environment, diagnosis, prescription, and laboratory results. The output of the models was the alert dwell time within a specified time window to determine the optimal range by the sensitivity analysis. RESULTS: We used 813,026 records (19 categories) from the hospital's outpatient clinic data from 2020 to 2021. The sensitivity analysis showed that a time window with a range of 0.3-4.0 s had the best performance, with an area under the receiver operating characteristic (AUROC) curve of 0.73 and an area under the precision-recall curve (AUPRC) of 0.97. The model built with alert and demographic feature groups showed the best performance, with an AUROC of 0.73. The most significant individual feature groups were alert and demographic, with AUROCs of 0.66 and 0.62, respectively. CONCLUSION: Our study found that alerts and user and patient demographic features are more crucial than clinical features when constructing universal context-aware alerts. Using alert dwell time in combination with a time window is an effective way to determine the trigger status of an alert. The findings of this study can provide useful insights for researchers working on specific and universal context-aware alerts.

Asunto(s)

RESUMEN

A large volume of security events, generally collected by distributed monitoring sensors, overwhelms human analysts at security operations centers and raises an alert fatigue problem. Machine learning is expected to mitigate this problem by automatically distinguishing between true alerts, or attacks, and falsely reported ones. Machine learning models should first be trained on datasets having correct labels, but the labeling process itself requires considerable human resources. In this paper, we present a new selective sampling scheme for efficient data labeling via unsupervised clustering. The new scheme transforms the byte sequence of an event into a fixed-size vector through content-defined chunking and feature hashing. Then, a clustering algorithm is applied to the vectors, and only a few samples from each cluster are selected for manual labeling. The experimental results demonstrate that the new scheme can select only 2% of the data for labeling without degrading the F1-score of the machine learning model. Two datasets, a private dataset from a real security operations center and a public dataset from the Internet for experimental reproducibility, are used.

Asunto(s)

RESUMEN

OBJECTIVE: To compare the effectiveness of 2 clinical decision support (CDS) tools to avoid prescription of nonsteroidal anti-inflammatory drugs (NSAIDs) in patients with heart failure (HF): a "commercial" and a locally "customized" alert. METHODS: We conducted a retrospective cohort study of 2 CDS tools implemented within a large integrated health system. The commercial CDS tool was designed according to third-party drug content and EHR vendor specifications. The customized CDS tool underwent a user-centered design process informed by implementation science principles, with input from a cross disciplinary team. The customized CDS tool replaced the commercial CDS tool. Data were collected from the electronic health record via analytic reports and manual chart review. The primary outcome was effectiveness, defined as whether the clinician changed their behavior and did not prescribe an NSAID. RESULTS: A random sample of 366 alerts (183 per CDS tool) was evaluated that represented 355 unique patients. The commercial CDS tool was effective for 7 of 172 (4%) patients, while the customized CDS tool was effective for 81 of 183 (44%) patients. After adjusting for age, chronic kidney disease, ejection fraction, NYHA class, concurrent prescription of an opioid or acetaminophen, visit type (inpatient or outpatient), and clinician specialty, the customized alerts were at 24.3 times greater odds of effectiveness compared to the commercial alerts (OR: 24.3 CI: 10.20-58.06). CONCLUSION: Investing additional resources to customize a CDS tool resulted in a CDS tool that was more effective at reducing the total number of NSAID orders placed for patients with HF compared to a commercially available CDS tool.

Asunto(s)

RESUMEN

OBJECTIVE: To assess pharmacy student responses to medication problems with and without clinical decision support (CDS) alerts during simulated order verification. METHODS: Three classes of students completed an order verification simulation. The simulation randomized students to a different series of 10 orders with varying CDS alert frequency. Two of the orders contained medication-related problems. The appropriateness of the students' interventions and responses to the CDS alerts were evaluated. In the following semester for 2 classes, 2 similar simulations were completed. All 3 simulations contained 1 problem with and 1 without an alert. RESULTS: During the first simulation, 384 students reviewed an order with a problem and an alert. Students exposed to prior inappropriate alerts within the simulation had less appropriate responses (66% vs 75%). Of 321 students who viewed a second order with a problem, those reviewing an order lacking an alert recommended an appropriate change less often (45% vs 87%). Among 351 students completing the second simulation, those who participated in the first simulation appropriately responded to the alert for a problem more often than those who only received a didactic debrief (95% vs 87%). Among those completing all 3 simulations, appropriate responses increased between simulations for problems with (n = 238, 72-95-93%) and without alerts (n = 49, 53-71-90%). CONCLUSIONS: Some pharmacy students displayed baseline alert fatigue and overreliance on CDS alerts for medication problem detection during order verification simulations. Exposure to the simulations improved CDS alert response appropriateness and detection of problems.

Asunto(s)

RESUMEN

INTRODUCTION: With the development of medical technology, clinical alarms from various medical devices, which are rapidly increasing, are becoming a new problem in intensive care units. The aim of this study was to evaluate alarm fatigue in Polish nurses employed in Intensive Care Units and identify the factors associated with alarm fatigue. METHODS: A cross-sectional study. The study used the nurses' alarm fatigue questionnaire by Torabizadeh. The study covered 400 Intensive Care Unit nurses. The data were collected from February to June 2021. RESULTS: The overall mean score of alarm fatigue was 25.8 ± 5.8. Participation in training programs related to the use of monitoring devices available in the ward, both regularly (ß = -0.21) and once (ß = -0.17), negatively correlated with nurses' alarm fatigue. On the other hand, alarm fatigue was positively associated with 12 h shifts [vs. 8 h shifts and 24 h shifts] (ß = 0.11) and employment in Intensive Cardiac Surveillance Units-including Cardiac Surgery [vs. other Intensive Care Units] (ß = 0.10). CONCLUSION: Monitoring device alarms constitute a significant burden on Polish Intensive Care Unit nurses, in particular those who do not take part in training on the operation of monitoring devices available in their ward. It is necessary to improve Intensive Care Unit personnel's awareness of the consequences of overburdening and alarm fatigue, as well as to identify fatigue-related factors.