Rate Of Alerts Research Articles

A sane approach to the high volume of alert transmissions in remote monitoring- turning of clinically non-relevant alerts decreases substantially workload

Abstract Background Remote monitoring (RM) offers a safe and effective method of monitoring patients with cardiac implantable electronic devices (CIEDs). The downside of RM is the data deluge of various transmissions, the majority of which are non-actionable. It has been suggested that optimizing alert transmissions could partly overcome this problem. Purpose The purpose of this study is to evaluate how the workload in RM can be safely decreased by thoroughly assessing the clinical relevance of each alert and deactivating non-relevant alerts. Methods Data on the number of alerts, scheduled and patient-initiated transmissions, as well as the causes and actions initiated by the transmissions, were systematically collected from the year 2023, during which the approach of actively deactivating clinically non-relevant alerts was conducted (A sane approach, Figure 1). The trend of alert transmissions and the proportion of actionable scheduled transmissions are presented. Results Overall, during the study period, 8182 transmissions were generated from 3732 followed CIEDs. Of these, 2306 were alert transmissions, and the rate of alerts decreased by 56% during the follow-up, from 0.07 to 0.04 per device per month. Additionally, our data shows that new alerts that have not occurred in the previous six months occurred on average at a rate of 0.03 per CIED per month, and this trend remained unchanged during the follow-up. Of the 3335 scheduled transmissions, 11% (364) were actionable, and both the number and proportion of actionable scheduled transmissions remained unchanged during the follow-up period. Interestingly, no alerts were triggered from 1124 (70%) of all patients but 88 (2%) of all CIEDs generated 30% of all alerts. Conclusions In this study, we demonstrate that RM workload can be decreased by actively evaluating the clinical relevance of each alert transmission. Our approach, where non-relevant alerts are turned off, likely does not impair patient safety and thus this practise enhance the RM.

Collaborative Intrusion Detection System with Snort Machine Learning Plugin

The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established attack mitigation system. However, the integration of NIDS and HIDS introduces formidable challenges, notably the elevated rates of False Positive and False Negative alerts. To surmount these challenges, the researcher employs machine learning techniques in the form of Snort plugins and comparison methods to heighten the precision of attack detection. The obtained results unequivocally illustrate the effectiveness of this approach. Using a Support Vector Machine for static analysis of the NSL-KDD dataset attains an outstanding 99% detection rate for Denial of Service (DoS) attacks and an impressive 98% detection rate for Probe attacks. Furthermore, in dynamic real-time attack simulations, the machine learning plugins exhibit remarkable proficiency in detecting various types of DoS attacks, concurrently offering more comprehensive identification of SYN Flooding DoS attacks compared to the Snort community rules set. These findings signify a significant advancement in intrusion detection, paving the way for more robust and accurate network security systems in an era of escalating cyber threats.

Risk predictive score and cord morphology classification for intraoperative neuromonitoring alerts in kyphosis surgery

BACKGROUNDIntraoperative neuromonitoring (IONM) alert is one of the worrying events of kyphosis corrective surgery, which can result in a postoperative neurological deficit. To our knowledge, there is no risk prediction score to predict such events in patients undergoing kyphosis surgery. PURPOSETo develop a new preoperative MRI-based cord morphology classification (CMC) and risk prediction score for predicting IONM alerts in patients with kyphotic deformity. STUDY DESIGNRetrospective analysis of prospectively collected data. PATIENT SAMPLEAbout 114 patients undergoing surgical correction for kyphotic deformity. OUTCOME MEASURESIntraoperative neuromonitoring alerts and postoperative neurological status using AIS grading. METHODSKyphotic deformity patients undergoing posterior spinal fusion were retrospectively reviewed. Based on the morphology of the spinal cord and surrounding CSF in MRI, there are 5 types of cord. Type 1 (normal cord): circular cord with surrounding visible CSF between the cord and the apex, Type 2 (flattened cord): cord with <50% distortion at the apex with obliteration of the anterior CSF; Type 3 (deformed cord): cord with >50% distortion at the apex with complete obliteration of the surrounding CSF; Type 4 (stretched cord): the cord is stretched and atrophied over the apex of the curve. Type 5 (translated cord): horizontal translation of the cord at the apex with buckling collapse of the vertebral column. Preoperative radiographs were used to measure the preoperative sagittal cobbs angle, sagittal deformity angular ratio (S-DAR), sagittal vertical axis (SVA), apex of the curve, and type of kyphosis. Clinical data like the duration of symptoms, clinical signs of myelopathy, neurological status (AIS grade), grade of myelopathy using the mJOA score, and type of osteotomy were documented. Multivariate logistic regression was used to determine the risk factors for IONM alerts and the risk prediction score was developed which was validated with new cohort of 30 patients. RESULTSA total of 114 patients met the inclusion criteria. IONM alerts were documented in 33 patients (28.9%), with full recovery of the signal in 25 patients and a postoperative deficit in 8 patients. Rate of IONM alerts was significantly higher in Type 5 (66%), followed by Type 4 (50%), Type 3 (21.1%), Type 2 (11.1%), and Type 1 (11.1%) (p-value<.001). Based on multiple logistic regression, 7 factors, namely preoperative neurological status, mJOA score≤6, presence of signs of myelopathy, apex of the curve above T5, preoperative sagittal cobbs, S-DAR, and MRI-based CMC, were identified as risk predictors. The value for the risk factors varies from 0 to 4, and the maximum total risk score was 13. The cut-off value of 6 had good sensitivity (84.9%) and specificity (77.8%) indicating a high risk for IONM alerts. The AUC of the predictive model was 0.92, indicating excellent discriminative ability. CONCLUSIONWe developed and validated a risk predictive score that identifies patients at risk of IONM alerts during kyphosis surgery. Identification of such high-risk patients (risk score≥6) helps in proper evaluation and preoperative counselling and helps in providing a proper evidence-based reference for treatment strategies.

Barriers and facilitators to effective electronic health record-based sepsis screening in the pediatric intensive care unit.

The Pediatric Surviving Sepsis Campaign supports the implementation of automated tools for early sepsis recognition. In 2019 the C.S. Mott Children's Hospital Pediatric Intensive Care Unit deployed an electronic medical record (EMR)-based screening for early recognition and treatment of sepsis. We analyzed all automated primary sepsis alerts, secondary screens, and bedside huddles from November 2019 to January 2020 (Cohort 1) and from November 2020 to January 2021 (Cohort 2) to identify barriers and facilitators for the use of this tool. We distributed surveys to frontline providers to gather feedback on end-user experience. In Cohort 1, 895 primary alerts were triggered, yielding 503 completed secondary screens and 40 bedside huddles. In Cohort 2, 925 primary alerts were triggered, yielding 532 completed secondary screens and 12 bedside huddles. Surveys assessing end-user experience identified the following facilitators: (1) 73% of nurses endorsed the bedside huddle as value added; (2) 74% of medical providers agreed the bedside huddle increased the likelihood of interventions. The greatest barriers to successful implementation included the (1) overall large number of primary alerts from the automated tool and (2) rate of false alerts, many due to routine respiratory therapy interventions. Our data suggests that the successful implementation of EMR-based sepsis screening tools requires countermeasures focusing on 3 key drivers for change: education, technology, and patient safety. While both medical providers and bedside nurses found merit in our EMR-based sepsis early recognition system, continued refinement is necessary to avoid sepsis alert fatigue.

Identifying the deficiencies of currently available CGM to improve uptake and benefit.

The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them. Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems. CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics. Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.

Home telemonitoring in smart rurality: results from the HIS2R interreg feasibility pilot study

AimsThe Health in Smart Rurality Interreg project aims to assess the feasibility of telemonitoring in rural areas across the Franco-Belgian border among patients affected by heart failure or chronic obstructive pulmonary disease. The objectives were to better understand strengths or barriers to implementing telemonitoring for early detection of potential adverse events, for improving quality of life, communication, and care coordination.MethodsUsing a prospective 6-month observational design, interconnected pads were provided to community-dwelling adults aged over 60 years. The device monitored daily body weight, temperature, cardiac rate, blood pressure, and oxygen saturation. Using predefined warning thresholds, data were analyzed by a nurse case-manager who also provided therapeutic education during their contacts.ResultsOut of 87 eligible and screened patients, 21 (24%) were included in the study. At the end of the follow-up, 19 patients (90%) were re-assessed. The rate of hospitalization and mortality was high (32% and 10%, respectively). A total of 644 alerts were recorded (median of 29 alerts/patients) with a high rate of technically-related alerts (TRA) (26%). Out of the 475 non-TRA, 79% and 1% have led to an intervention by the case-manager or the physician, respectively. Therapeutic adjustment was proposed for 12 patients during that period.ConclusionTelemonitoring appears to be a promising solution for the follow-up of patients living far from medical resources. The contribution of a case-manager is of added-value in managing alerts, therapeutic education, and coaching. Many questions remain open such as the improvement of technical aspects and long-term compliance in a real-world setting.

Effects of COVID-19 on a mature citizen first responder system in the German district of Gütersloh: an observational study.

The aim of this study was to examine the impact of COVID-19 on the response rate of community-first-responders (CFR) and other out-of-hospital-cardiac-arrest (OHCA) outcomes using the smartphone-first-responder-system (SFRS) "Mobile Retter." All adult non-traumatic OHCA in the district of Gütersloh between 01.01.2018-31.12.2021 were included. Periods of interest were 1) prior to the first COVID-19-lockdown; to 2) both lockdowns; and 3) the time in between, as well as after the COVID-19-lockdowns (pre-COVID-19, COVID-19-lockdown and COVID-19-pandemic respectively). The primary outcome was the CFR response rate defined as proportion of CFR alerts that were accepted by a CFR and in which at least one CFR arrived on scene of the emergency out of all CFR alerts. Secondary outcomes included the rate of CFR alerts, defined as proportion of OHCA to which CFR were summoned by the emergency medical dispatcher, as well as the rate of return-of-spontaneous-circulation (ROSC) and rate of survival until hospital discharge. We also examined the incidence COVID-19-infection of CFR in context of the SFRS. A total of 1064 OHCA-patients (mean age: 71.4±14.5 years; female: 33.8%) were included in the study (Pre-COVID-19: 539; COVID-19-lockdown: 109; COVID-19-pandemic: 416). The response rate was 64.0% (pre-COVID-19: 58.7%; COVID-19-lockdown: 63.5%; COVID-19-pandemic: 71.8%, P=0.002 vs. pre-COVID-19). The alert rate was 52.7% (pre-COVID-19: 56.2%; COVID-19-lockdown: 47.7%, P=0.04 vs. Pre-COVID-19; COVID-19-Pandemic: 49.5%, P=0.02 vs. pre-COVID-19). The ROSC-rate was 40.4% (pre-COVID-19: 41.0%; COVID-19-lockdown: 33.9%; COVID-19-pandemic: 41.4%) and hospital discharge rate 31.2% (Pre-COVID-19: 33.0%; COVID-19-lockdown: 36.8%; COVID-19-pandemic: 28.7%). The use of CFR was associated with favorable effects in terms of hospital admission (odds ratio [OR]: 0.654 (CI95: 0.444-0.963), P=0.03), hospital discharge (OR: 2.343 (CI95: 1.002-5.475), P=0.04). None of the CFR became infected with COVID-19. "Mobile-Retter" was associated with high response rates, improved outcome in OHCA patients and no COVID-19-infections of CFR during the COVID-19-pandemic and -lockdowns.

Override rate of drug-drug interaction alerts in clinical decision support systems: A brief systematic review and meta-analysis.

Primary studies have demonstrated that despite being useful, most of the drug-drug interaction (DDI) alerts generated by clinical decision support systems are overridden by prescribers. To provide more information about this issue, we conducted a systematic review and meta-analysis on the prevalence of DDI alerts generated by CDSS and alert overrides by physicians. The search strategy was implemented by applying the terms and MeSH headings and conducted in the MEDLINE/PubMed, EMBASE, Web of Science, Scopus, LILACS, and Google Scholar databases. Blinded reviewers screened 1873 records and 86 full studies, and 16 articles were included for analysis. The overall prevalence of alert generated by CDSS was 13% (CI95% 5-24%, p-value <0.0001, I^2 = 100%), and the overall prevalence of alert override by physicians was 90% (CI95% 85-95%, p-value <0.0001, I^2 = 100%). This systematic review and meta-analysis presents a high rate of alert overrides, even after CDSS adjustments that significantly reduced the number of alerts. After analyzing the articles included in this review, it was clear that the CDSS alerts physicians about potential DDI should be developed with a focus on the user experience, thus increasing their confidence and satisfaction, which may increase patient clinical safety.

The operational and financial impact of adding anaerobic screening of platelets.

We evaluated the operational and safety impact of implementing anaerobic culture screening of apheresis and pooled platelets at the American Red Cross on the already established use of the aerobic culture screening of each donation performed no sooner than 24 h following collection. Platelets were screened for bacterial contamination with the BACT/ALERT 3D® (bioMérieux, Durham, NC) microbial detection testing system. The addition of anaerobic culture to the already existing aerobic culture resulted in sampling an additional 8-10 mL from each donation. Implementation of anaerobic testing resulted in an approximate 3.5-fold increased rate of False Positive BACT/ALERT alarms. There was a modest increase in the rate of True Positive alarms of 1.4-fold with increased detection of Klebsiella and Propionibacterium species, including Cutibacterium acnes. In addition, there was an approximate 3.5-fold increase rate of False Positives and a 13.5-fold increase rate of Indeterminates, the majority (~57%) were due to Cutibacterium acnes. The combined costs and lost revenue associated with adding anaerobic screening increased by ~$1,000,000/year due to testing cost and product discards. The addition of anaerobic culture to aerobic culture to the original donation (without the introduction of sampling delay) resulted in a significant increase in the rate of alerts. The 40% increased rate of True Positive alarms may have modestly improved platelet safety. However, there was a disproportionate increase in the rate of False Positive and Indeterminate bacterial culture alarms, which added substantial cost and overall loss of platelet products.

Effect of knowledgebase transition of a clinical decision support system on medication order and alert patterns in an emergency department

A knowledgebase (KB) transition of a clinical decision support (CDS) system occurred at the study site. The transition was made from one commercial database to another, provided by a different vendor. The change was applied to all medications in the institute. The aim of this study was to analyze the effect of KB transition on medication-related orders and alert patterns in an emergency department (ED). Data of patients, medication-related orders and alerts, and physicians in the ED from January 2018 to December 2020 were analyzed in this study. A set of definitions was set to define orders, alerts, and alert overrides. Changes in order and alert patterns before and after the conversion, which took place in May 2019, were assessed. Overall, 101,450 patients visited the ED, and 1325 physicians made 829,474 prescription orders to patients during visit and at discharge. Alert rates (alert count divided by order count) for periods A and B were 12.6% and 14.1%, and override rates (alert override count divided by alert count) were 60.8% and 67.4%, respectively. Of the 296 drugs that were used more than 100 times during each period, 64.5% of the drugs had an increase in alert rate after the transition. Changes in alert rates were tested using chi-squared test and Fisher’s exact test. We found that the CDS system knowledgebase transition was associated with a significant change in alert patterns at the medication level in the ED. Careful consideration is advised when such a transition is performed.

Digital Tool-Assisted Hospitalization Detection in the Tailored Antiplatelet Initiation to Lessen Outcomes due to Decreased Clopidogrel Response After Percutaneous Coronary Intervention Study Compared to Traditional Site-Coordinator Ascertainment: Intervention Study.

Accurate, timely ascertainment of clinical end points, particularly hospitalizations, is crucial for clinical trials. The Tailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response after Percutaneous Coronary Intervention (TAILOR-PCI) Digital Study extended the main TAILOR-PCI trial's follow-up to 2 years, using a smartphone-based research app featuring geofencing-triggered surveys and routine monthly mobile phone surveys to detect cardiovascular (CV) hospitalizations. This pilot study compared these digital tools to conventional site-coordinator ascertainment of CV hospitalizations. The objectives were to evaluate geofencing-triggered notifications and routine monthly mobile phone surveys' performance in detecting CV hospitalizations compared to telephone visits and health record reviews by study coordinators at each site. US and Canadian participants from the TAILOR-PCI Digital Follow-Up Study were invited to download the Eureka Research Platform mobile app, opting in for location tracking using geofencing, triggering a smartphone-based survey if near a hospital for ≥4 hours. Participants were sent monthly notifications for CV hospitalization surveys. From 85 participants who consented to the Digital Study, downloaded the mobile app, and had not previously completed their final follow-up visit, 73 (85.8%) initially opted in and consented to geofencing. There were 9 CV hospitalizations ascertained by study coordinators among 5 patients, whereas 8 out of 9 (88.9%) were detected by routine monthly hospitalization surveys. One CV hospitalization went undetected by the survey as it occurred within two weeks of the previous event, and the survey only allowed reporting of a single hospitalization. Among these, 3 were also detected by the geofencing algorithm, but 6 out of 9 (66.7%) were missed by geofencing: 1 occurred in a participant who never consented to geofencing, while 5 hospitalizations occurred among participants who had subsequently turned off geofencing prior to their hospitalization. Geofencing-detected hospitalizations were ascertained within a median of 2 (IQR 1-3) days, monthly surveys within 11 (IQR 6.5-25) days, and site coordinator methods within 38 (IQR 9-105) days. The geofencing algorithm triggered 245 notifications among 39 participants, with 128 (52.2%) from true hospital presence and 117 (47.8%) from nonhospital health care facility visits. Additional geofencing iterative improvements to reduce hospital misidentification were made to the algorithm at months 7 and 12, elevating the rate of true alerts from 35.4% (55 true alerts/155 total alerts before month 7) to 78.7% (59 true alerts/75 total alerts in months 7-12) and ultimately to 93.3% (14 true alerts/5 total alerts in months 13-21), respectively. The monthly digital survey detected most CV hospitalizations, while the geofencing survey enabled earlier detection but did not offer incremental value beyond traditional tools. Digital tools could potentially reduce the burden on study coordinators in ascertaining CV hospitalizations. The advantages of timely reporting via geofencing should be weighed against the issue of false notifications, which can be mitigated through algorithmic refinements.

Performance of a multisensor implantable defibrillator algorithm for HF monitoring in the presence of atrial fibrillation

Abstract Background Some implantable cardioverter defibrillators (ICD) are equipped with diagnostic algorithms that combine data from multiple sensors. The HeartLogic Index was shown to accurately stratify patients at risk of heart failure (HF) events. However, there are no data on its performance in the presence of atrial fibrillation (AF). We evaluated and compared the performance of the algorithm during sinus rhythm and during long-lasting AF episodes. Methods The feature was activated in 568 ICD patients from 26 centers. We analyzed stored data to identify periods of ≥30 consecutive days with atrial high-rate episode (AHRE) burden &amp;lt;1hour/day, and periods with AHRE burden ≥20hours/day. Then, we identified patients who met both conditions during their follow-up (AHRE group, N=53), to allow pairwise comparison of periods. For control purposes, we identified patients with AHRE burden &amp;lt;1h during the entire follow-up and we implemented a 2:1 propensity score matching versus the AHRE group (Matched non-AHRE group, N=106). Results In the AHRE group, the rate of HeartLogic alerts was 1.2 (95%CI:1.0-1.5) /patient-year during periods with AHRE burden &amp;lt;1hour/day and 2.0 (95%CI:1.5-2.6) /patient-year during AHRE burden ≥20hours/day periods (p=0.004). Significantly higher values of the combined index were recorded during periods with AHRE burden ≥20 hours/day (in Figure the average combined index – Day 0: alert day). The rate of HF hospitalizations was 0.34 (95%CI:0.15-0.69) /patient-year during IN-alert periods and 0.06 (95%CI:0.02-0.14) /patient-year during OUT-of-alert periods (p&amp;lt;0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95%CI:1.67-55.31) during periods with AHRE burden &amp;lt;1hour/day and 2.70 (95%CI:1.01-28.33) during AHRE burden ≥20hours/day periods. In the Matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95%CI:0.12-0.60) /patient-year during IN-alert periods, and 0.04 (95%CI:0.02-0.08) /patient-year during OUT-of-alert periods (p&amp;lt;0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44). Conclusions Patients seemed more exposed to alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio when compared to non-AF periods and non-AF patients.

A Data-Driven Approach to Evaluate Barcode-Assisted Medication Preparation Alerts at a Large Academic Medical Center.

The purpose of this study was to develop a data-driven process to analyze barcode-assisted medication preparation alert data with a goal of minimizing inaccurate alerts. Medication preparation data for the prior three-month period was obtained from an electronic health record system. A dashboard was developed to identify recurrent, high-volume alerts and associated medication records. A randomization tool was used to obtain a prespecified proportion of the alerts to review for appropriateness. Alert root causes were identified by chart review. Depending on the alert's cause(s), targeted informatics build changes, workflow and purchasing changes, and/or staff education were implemented. The rate of alerts was measured postintervention for select drugs. The institution averaged 31,000 medication preparation alerts per month. The "barcode not recognized" alert (13,000) was the highest volume over the study period. Eighty-five medication records were identified as contributing to a high volume of alerts (5,200/31,000), representing 49 unique drugs. Of the 85 medication records triggering alerts, 36 required staff education, 22 required informatics build changes, and 8 required workflow changes. Targeted interventions for 2 medications, resulted in reducing the rate of the "barcode not recognized" alert from 26.6% to 1.3% for polyethylene glycol and from 48.7% to 0% for cyproheptadine. This quality improvement project highlighted opportunities to improve medication purchasing, storage, and preparation through development of a standard process to evaluate barcode-assisted medication preparation alert data. A data-driven approach can help identify and minimize inaccurate alerts ("noise") and promote medication safety.

Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication

Background and objectiveThe overall benefits of using clinical decision support systems (CDSSs) can be restrained if physicians inadvertently ignore clinically useful alerts due to “alert fatigue” caused by an excessive number of clinically irrelevant warnings. Moreover, inappropriate drug errors, look-alike/sound-alike (LASA) drug errors, and problem list documentation are common, costly, and potentially harmful. This study sought to evaluate the overall performance of a machine learning-based CDSS (MedGuard) for triggering clinically relevant alerts, acceptance rate, and to intercept inappropriate drug errors as well as LASA drug errors. MethodsWe conducted a retrospective study that evaluated MedGuard alerts, the alert acceptance rate, and the rate of LASA alerts between July 1, 2019, and June 31, 2021, from outpatient settings at an academic hospital. An expert pharmacist checked the suitability of the alerts, rate of acceptance, wrong-drug errors, and confusing drug pairs. ResultsOver the two-year study period, 1,206,895 prescriptions were ordered and a total of 28,536 alerts were triggered (alert rate: 2.36 %). Of the 28,536 alerts presented to physicians, 13,947 (48.88 %) were accepted. A total of 8,014 prescriptions were changed/modified (28.08 %, 8,014/28,534) with the most common reasons being adding and/or deleting diseases (52.04 %, 4,171/8,014), adding and/or deleting drugs (21.89 %, 1,755/8,014) and others (35.48 %, 2,844/ 8,014). However, the rate of drug error interception was 1.64 % (470 intercepted errors out of 28,536 alerts), which equates to 16.4 intercepted errors per 1000 alerted orders. ConclusionThis study shows that machine learning based CDSS, MedGuard, has an ability to improve patients’ safety by triggering clinically valid alerts. This system can also help improve problem list documentation and intercept inappropriate drug errors and LASA drug errors, which can improve medication safety. Moreover, high acceptance of alert rates can help reduce clinician burnout and adverse events.

Dynamic Analysis of an Epidemic Model Considering Personal Alert on a Complex Network.

This paper proposes a SIQRS epidemic model with birth and death on a complex network, considering individual alertness. In particular, we investigate the influence of the individual behavior in the transmission of epidemics and derive the basic reproduction number depending on birth rate, death rate, alertness rate, quarantine rate. In addition, the stabilities of the disease-free equilibrium point and endemic equilibrium point are analyzed via stability theory. It is found that the emergence of individual behavior can influence the process of transmission of epidemics. Our results show that individual alertness rate is negatively correlated with basic reproduction number, while the impact of individual alertness on infectious factor is positively correlated with basic reproduction number. When the basic reproduction number is less than one, the system is stable and the disease is eventually eradicated. Nevertheless, there is an endemic equilibrium point under the condition that the basic reproduction number is more than one. Finally, numerical simulations are carried out to illustrate theoretical results.

Intraoperative Neuromonitoring for Spines at Risk During Nonspine Surgery: A 9-Year Review.

Patients with certain spinal anomalies are at risk for rare but devastating spinal cord injuries under anesthesia. We created a Spine at Risk (SAR) program to evaluate and recommend precautions for such patients, including intraoperative neuromonitoring (IONM) use for the highest-risk patients. We aimed to review all monitored nonspine procedures to determine rate of potential spinal cord injuries avoided in those who would otherwise have been unmonitored. We performed a retrospective review of our institutional SAR program from 2011 to 2019 to analyze the number of nonspine anesthetized procedures that were done under IONM, the characteristics of those that had an IONM alert; and the clinical outcomes. Of the 3,453 patients flagged for SAR review, 1121 (33%) received a precaution recommendation, and 359 (10% of all flagged) were given IONM recommendations. Of those, 57 patients (16% of recommendations, 2% of all flagged) had a total of 102 nonspine anesthetized procedures done under IONM. Seven patients had a total of 10 cases with IONM alerts. Two cases were aborted when improved signals could not be obtained after working through a checklist; one of these patients woke with transient neurological deficits. Signals improved to baseline in 7 cases by working through a signal loss checklist. One case was aborted preoperatively when monitorable baseline signals could not be obtained. In the highest-risk spinal anomaly patients, we monitored an average of 11.7 nonspine cases per year, with a 10% rate of IONM alerts, and no permanent neurological deficits. Although the majority of patients remain safe during procedures, in the most critical patients IONM allowed the team to identify and react to alerts that may have otherwise led to permanent neurological injury. This is the largest series of spinal cord-monitored nonspine pediatric cases. It is important for pediatric orthopedic surgeons to evaluate at-risk patients and recommend IONM where appropriate, to protect both patients and our procedural colleagues. Case series, level IV.

Sepsis Prediction in Hospitalized Children: Clinical Decision Support Design and Deployment.

Following development and validation of a sepsis prediction model described in a companion article, we aimed to use quality improvement and safety methodology to guide the design and deployment of clinical decision support (CDS) tools and clinician workflows to improve pediatric sepsis recognition in the inpatient setting. CDS tools and sepsis huddle workflows were created to implement an electronic health record-based sepsis prediction model. These were proactively analyzed and refined using simulation and safety science principles before implementation and were introduced across inpatient units during 2020-2021. Huddle compliance, alerts per non-ICU patient days, and days between sepsis-attributable emergent transfers were monitored. Rapid Plan-Do-Study-Act (PDSA) cycles based on user feedback and weekly metric data informed improvement throughout implementation. There were 264 sepsis alerts on 173 patients with an 89% bedside huddle completion rate and 10 alerts per 1000 non-ICU patient days per month. There was no special cause variation in the metric days between sepsis-attributable emergent transfers. An automated electronic health record-based sepsis prediction model, CDS tools, and sepsis huddle workflows were implemented on inpatient units with a relatively low rate of interruptive alerts and high compliance with bedside huddles. Use of CDS best practices, simulation, safety tools, and quality improvement principles led to high utilization of the sepsis screening process.

Hypoxia impairs reaction time but not response accuracy in a visual choice reaction task

We investigated the effect of hypoxia on the reaction time (RT) and response accuracy of pilots performing a visual choice reaction task that corresponded to the scanning of helmet mounted display (HMD) symbology. Eighteen male military pilots performed the task in a hypobaric chamber at two simulated altitudes (92 m and 4572 m) in a single-blinded repeated measures and counter-balanced design. The visual stimuli were displayed in low and high contrast and at a 30- and 50-degree field of view (FoV). We measured the pilots' RT and response accuracy. Using an eye tracker, we measured the pilot's glance time at each stimulus location. Finally, we collected subjective ratings of alertness. The results show that hypoxia increased the RT and glance time. Lowering the stimulus contrast and increasing the FoV further increased the RT, independent of hypoxia. These findings provide no evidence for hypoxia-induced changes in visual contrast sensitivity or visual field. Instead, hypoxia seemed to affect RT and glance time by reducing alertness. Despite the increased RT, the pilots maintained their accuracy on the visual task, suggesting that visual scanning of HMD symbology may be resistant to the effects of acute hypoxia.

Design, development, implementation, and evaluation of a severe drug–drug interaction alert system in the ICU: An analysis of acceptance and override rates

Background and objectiveThe override rate of Drug-Drug Interaction Alerts (DDIA) in Intensive Care Units (ICUs) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in a system of ICUs and measure the override rate of this system. MethodsThis is a cross-sectional study that details the design, development, implementation, and evaluation of a DDIAS for severe interactions into a Computerized Provider Order Entry (CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAS, acceptance and overridden of DDIAS, and usability of DDIAS have been collected. The study was approved by the local Institutional Review Board (IRB) and; the ethics committee of Shiraz University of Medical Science on date: 2019-11-23 (Approval ID: IR.SUMS.REC.1398.1046). ResultsThe knowledge base of the DDIAS contains 9,809 severe potential drug-drug interactions (pDDIs). A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for the DDIAS were 81 % and 97.5 %, respectively. The override rate was 2.5 %. The mean System Usability Scale (SUS) score of the DDIAS was 75. ConclusionThis study demonstrates that implementing high-risk DDIAS at the point of prescribing in ICUs improves adherence to alerts. In addition, the usability of the DDIAS was reasonable. Further studies are needed to investigate the establishment of severe DDIAS and measure the prescribers’ response to DDIAS on a larger scale.

Improved adaptive neuro-fuzzy inference system based on modified salp swarm algorithm and golden eagle optimizer algorithm for intrusion detection in networks

With the increase in the growth of computer networks throughout the past years, network security has become an essential issue. Among the numerous network security measures, intrusion detection systems play a dynamic function with integrity, confidentiality, and accessibility of resources. An Intrusion Detection System (IDS) is a software program or hardware device which monitors computer system and/or network activities for malicious activities and produces alerts to security experts. In IDS there are three major problems namely generating many alerts, a huge rate of false positive alerts, and unknown attack types per generated alerts. Alert management methods are used to manage these problems. One of the methods of alert management is alert reduction and alert classification. The proposed approach focuses on enhancing the efficiency of the adaptive neuro-fuzzy inference system (ANFIS) using a modified salp swarm algorithm (SSA) and Golden Eagle optimizer (GEOSSA). The present study uses the Golden Eagle optimization algorithm to improve SSA behaviors. The proposed model (GEO-SSA-ANFIS) intends to determine the appropriate parameters using the GEO-SSA algorithm because these parameters are considered the main component affecting the ANFIS forecasting process. The results of the intrusion detection based on the NSL-KDD dataset were better and more efficient compared with those models because the detection rate was 96.68% and the FAR result was 0.438%.