Critical Alerts Research Articles

LBTMA: An integrated P4-enabled framework for optimized traffic management in SD-IoT networks

This research introduces LBTMA, a novel framework for effective traffic management in Internet of Things (IoT) networks employing software-defined networking (SDN). LBTMA comprises three modules: P4-enabled Stateful Traffic Monitoring (P4-STM), P4-enabled Distributed Load Balancing (P4-DLBS), and P4-enabled Distributed Packet Aggregation and Disaggregation (P4-DPADS). Operating entirely within the data plane, the three modules collaboratively address the challenges of managing high communication traffic from IoT devices. P4-STM utilizes state tables for flow monitoring and anonymization, while introducing a novel multi-controller communication scheme (MCCS) that separates routine data from critical alerts through two dedicated channels. MCCS demonstrated a 25% improvement in throughput and a 51% decrease in latency compared to single controller architecture. P4-DLBS features Enhanced Weighted Round Robin (P4-EWRR) load balancing algorithm, which leverages P4′s distributed decision-making capabilities and inter-switch coordination for enhanced scalability and reduced controller burden. P4-EWRR continuously adjusts server weights based on real-time factors (e.g., queue length, server resource pool, CPU utilization) to ensure efficient resource allocation. In testing, P4-EWRR achieved an average response time of 15 ms and an average packet drop rate of 2%. P4-DPADS employs a hierarchical data plane to efficiently handle high volumes of small IoT packets. It demonstrated an average disaggregation accuracy of 98%, communication overhead reduction rate of 70%, and an impressive average aggregation ratio of 95%. Additionally, P4-DPADS contributes to a 25% reduction in latency and a 40% increase in throughput. The LBTMA framework's modularity and P4 programmability provide flexible, scalable, and efficient traffic management in IoT networks.

Anogenital Lesions in Children: A Critical Alert for Pediatricians.

A 7-y-old girl presented to the pediatric department with a two-week history of a skin lesion in the anogenital region. She appeared well-nourished, quiet, afebrile; vitals and her systemic examination was normal. Examination revealed a greyish white wart-like perianal lesion, unusual for her age. Further history revealed the child had become withdrawn, avoiding play recently, and had oral and vaginal ulcers a month earlier without treatment. These findings raised suspicion of sexual abuse. Investigations confirmed positive VDRL (1:64 titres), positive TPHA, and negative results for her parents' TPHA and HIV. A medicolegal case was registered, involving child welfare services and police. A psychiatrist diagnosed post-traumatic stress disorder and recommended counselling. Treatment with a single dose of intramuscular Benzathine penicillin led to improvement, with VDRL titres decreasing to 1:8 and complete resolution of the lesion after 3 months. Pediatricians are pivotal in identifying and managing child sexual abuse cases promptly.

Internet of Things (IoT) Based Patient Information and Heart Beat Monitoring System for Health Information System

The integration of Internet of Things (IoT) technology into healthcare systems has revolutionized patient monitoring and Information management, leading to enhanced efficiency and improved patient outcomes. This research developed and implemented an IoT-enabled Patient Information and Heartbeat Monitoring System designed to improve Health Information System (HIS). The system employs a network of interconnected sensors and devices to continuously monitor vital signs, especially focusing on heartbeat data. These sensors transmit real-time data to a centralized cloud platform, where it is processed and analyzed. Healthcare professionals can access this data through a user-friendly interface, enabling timely and informed decision-making. The system not only ensures accurate and continuous monitoring but also provides critical alerts in case of abnormal heart rate variations, thereby facilitating prompt medical intervention. The deployment of this IoT-based solution demonstrates significant potential in improving patient care, reducing hospital readmissions, and optimizing resources utilization within healthcare facilities. Results from pilot implementations indicate a promising future for IoT-enabled patient monitoring in transforming modern healthcare practices.

Alert Prioritisation in Security Operations Centres: A Systematic Survey on Criteria and Methods

Security Operations Centres (SOCs) are specialised facilities where security analysts leverage advanced technologies to monitor, detect, and respond to cyber incidents. However, the increasing volume of security incidents has overwhelmed security analysts, leading to alert fatigue. Effective alert prioritisation (AP) becomes crucial to address this problem through the utilisation of proper criteria and methods. Human-AI teaming (HAT) has the potential to significantly enhance AP by combining the complementary strengths of humans and AI. AI excels in processing large volumes of alert data, identifying anomalies, uncovering hidden patterns, and prioritising alerts at scale, all at machine speed. Human analysts can leverage their expertise to investigate prioritised alerts, re-prioritise them based on additional context, and provide valuable feedback to the AI system, reducing false positives and ensuring critical alerts are prioritised. This work provides a comprehensive review of the criteria and methods for AP in SOC. We analyse the advantages and disadvantages of the different categories of AP criteria and methods based on HAT, specifically considering automation, augmentation, and collaboration. We also identify several areas for future research. We anticipate that our findings will contribute to the advancement of AP techniques, fostering more effective security incident response in SOCs.

Valproate and Pediatric Stevens-Johnson Syndrome: A Critical Alert

Stevens-Johnson Syndrome is a severe condition that affects the skin and mucous membranes, often caused by medications or infections. This case study details the experience of a three-year-old boy who developed SJS after beginning valproate treatment for seizure management following a traumatic brain injury. Initially, he showed symptoms of fever, facial redness, and a runny nose, which progressed to a severe rash and blisters. A dermatologist confirmed the diagnosis of SJS linked to valproate. Swift diagnosis, discontinuation of the offending drug, and supportive care are essential in managing SJS and preventing serious complications. Early intervention and comprehensive care from a variety of medical professionals are crucial for improving patient outcomes.

Humans and Automation: Augmenting Security Operation Centers

The continuous integration of automated tools into security operation centers (SOCs) increases the volume of alerts for security analysts. This amplifies the risk of automation bias and complacency to the point that security analysts have reported missing, ignoring, and not acting upon critical alerts. Enhancing the SOC environment has predominantly been researched from a technical standpoint, failing to consider the socio-technical elements adequately. However, our research fills this gap and provides practical insights for optimizing processes in SOCs. The synergy between security analysts and automation can potentially augment threat detection and response capabilities, ensuring a more robust defense if effective human-automation collaboration is established. A scoping review of 599 articles from four databases led to a final selection of 49 articles. Thematic analysis resulted in 609 coding references generated across four main themes: SOC automation challenges, automation application areas, implications on analysts, and human factor sentiment. Our findings emphasize the extent to which automation can be implemented across the incident response lifecycle. The SOC Automation Matrix represents our primary contribution to achieving a mutually beneficial relationship between analyst and machine. This matrix describes the properties of four distinct human-automation combinations. This is of practical value to SOCs striving to optimize their processes, as our matrix mentions socio-technical system characteristics for automated tools.

Smart Power Monitoring System Using IoT

The "Smart Power Monitoring System using IoT" constitutes a pioneering solution poised to revolutionize the landscape of power management in both residential and industrial settings. Anchored by the ESP8266 NODEMCU microcontroller, this system seamlessly integrates an array of state-of-the-art sensors and modules, all working in concert to deliver real-time monitoring, controlling capabilities. The predominant objective of this innovative system is multifaceted: to elevate energy efficiency, bolster security measures and enable remote monitoring through the dynamic fabric of the IoT. The ESP8266 NODEMCU, functioning as the system's central hub, orchestrates a symphony of components, including a current sensor for precise power consumption insights and a PIR sensor to intelligently detect human motion for enhanced automation. The inclusion of a GSM module ensures timely communication of critical alerts to users via SMS, underscoring the system's responsiveness. Additionally, a relay module facilitates the remote control of appliances, promoting user-friendly power management. A half-horsepower induction motor acts as a tangible load indicator, providing users with a visual representation of power fluctuations. The system, powered by a stable 9V 1 Amp DC supply, culminates its capabilities by leveraging the “ThingSpeak” platform to graphically display power consumption trends, empowering users to make informed decisions for sustainable and efficient energy practices. In essence, the "Smart Power Monitoring System using IoT" emerges as a comprehensive and forward-thinking solution, poised to redefine the paradigm of intelligent power management. Keywords: IoT technology, real-time monitoring, energy consumption analysis.

Assessment of Pre-analytical Errors and Fostering Strategies to Enhance Accurate Results and Efficient Turnaround Times in the Cytology Laboratory of a Tertiary Care Hospital.

Introduction Pre-analytical errors in cytology laboratories can significantly impact the accuracy of diagnostic results and turnaround times, ultimately affecting patient care. This article presents an evaluation of pre-analytical errors and proposes fostering strategies to enhance accuracy and efficiency in the cytology laboratory of a tertiary care hospital. The background discusses the importance of pre-analytical processes in ensuring reliable cytological diagnoses and the common errors encountered in specimen collection, handling, and transportation. Strategies for error reduction and improvement in turnaround times include staff education, standardization of procedures, utilization of appropriate collection and transport devices, implementation of quality control measures, and utilization of automation technologies. By addressing pre-analytical errors and implementing fostering strategies, cytology laboratories can optimize diagnostic accuracy, improve patient care outcomes, and enhance overall laboratory efficiency. Aims and objectives This study aims to assess the prevalence and nature of pre-analytical errors in the cytology laboratory of a tertiary care hospital to understand the extent of the issue, identify the specific factors contributing to pre-analytical errors like specimen collection, handling, and transportation processes, and pinpoint areas for improvement. It seeks to evaluate the impact of pre-analytical errors on the accuracy of cytological results and the efficiency of turnaround times, highlighting the consequences for patient care. Furthermore, the study aims to develop targeted strategies to minimize pre-analytical errors and enhance the accuracy of cytological results. Materials and methods This study was conducted at the Cytology Laboratory of our hospital from January 2023 to December 2023 after getting proper approval from the Institutional Review Board (IRB approval number 101/02/2024/PG/SRB/SMCH). It is a retrospective analytical study, and a total of 5412 samples from patients of the outpatient (OP) department, inpatient (IP) department, and community health outreach program facilities received in the cytology laboratory were analyzed during the study period. The inclusion criteria were the test samples sent specifically for cytological analysis. The samples sent for biochemical or microbiological examination were excluded. The frequency of sample distribution and rejected samples were calculated and the results were correlated. Results A total of 5,412 samples received in the cytology laboratory were analyzed during the study period. The majority of the samples were Papanicolaou smears (2,352, 43.5%), followed by fluid cytology (1,008, 18.6%) and ultrasound-guided fine-needle aspiration cytology (FNAC, 984, 18.2%). Of the total number of samples, 225 (4.16%) were repeated and 27 (0.5%) were rejected. Conclusions Pre-analytical, analytical, and post-analytical processes are the three key factors that determine the dependability and precision of cytological test results. Detecting critical alerts such as the positivity of malignancy underscores the paramount importance of result accuracy. Implementing good laboratory practices and conducting both external and internal audits can reduce the frequency of preventable errors in a cytology laboratory, thereby ensuring enhanced precision and expedited outputs.

Assessment of Population-Based Approach to Direct Oral Anticoagulant Management.

Background: As preferences for oral anticoagulation shift from warfarin to direct oral anticoagulants (DOACs), a new care management model is needed. A population approach leveraging a DOAC Dashboard was implemented to track all patients on a DOAC followed by a physician at an academic medical center. The DOAC Dashboard is a real-time report within the electronic health record (EHR) that identifies patients who require evaluation for DOAC dose/therapy adjustment due to changing renal function, age, weight, indication, and/or significant drug-drug interaction (DDI). Objective: This study aims to describe the initial phase of DOAC Dashboard implementation, to evaluate the effectiveness of interventions, and to assess a multidisciplinary approach to management. Method: Retrospective descriptive study of the DOAC Dashboard from August 22, 2019, to January 20, 2022. Primary outcomes include total number of alerts addressed and interventions needed. Secondary outcome is the proportion of interventions implemented by the prescribing clinician. Result: A total of 10 912 patients were identified by the DOAC Dashboard at baseline. A total of 5038 alerts were identified, with 668 critical alerts, 3337 possible critical alerts, and 1033 other alerts. Pharmacists addressed 1796 alerts during the study period (762 critical alerts and 1034 possible critical). Critical alerts included 62 significant DDI, 379 inappropriate dosing, and 321 others. Of the critical alerts, intervention was needed in 291 cases (38%), with 255 (88%) of proposed interventions implemented. Critical alerts and possible critical alerts not requiring intervention were resolved by data entry. Conclusion: The DOAC Dashboard provides an efficient method of identifying patients on DOACs that require dose adjustments or therapeutic modifications.

Continual Improvement in Clinical Bacteriology Laboratory with Quality Indicators: A Retrospective Observational Study

Introduction: Healthcare management is undergoing significant changes with the evolution of new and re-emerging infections. A clinical microbiologist plays an important role in giving an accurate and timely report to the clinicians. Quality Indicators (QIs) act as a measure of the quality of services offered by the laboratory and are tools to monitor and evaluate the laboratory’s performance throughout the Total Testing Process (TTP). Aim: To measure the performance of the clinical bacteriology laboratory using QIs. Materials and Methods: A retrospective study was conducted in the Department of Microbiology at Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, Maharashtra, India. The study evaluated QIs from the records of 94,624 samples received in the bacteriology section of the clinical Microbiology laboratory between January 2018 and March 2021. Data analysis was conducted over a six-month period from December 2021 to May 2022. In 2018, one QI was identified for each phase, with an additional QI added in each phase to the pre-existing QI in 2019. In 2020, a QI was added in the preanalytical phase only. In 2021, the acceptable limit for one preanalytical QI was reduced from 2% to 1%. Data analysis was performed using an Excel sheet. Results: Data from records of 94,624 clinical bacteriology samples collected over 39 months were analyzed retrospectively. The preanalytical indicators included the number of samples rejected (135, 0.14%) and the number of requisition forms with three patient identifiers (59,645, 93.95%). Analytical phase QIs consisted of the average External Quality Assurance Scheme (EQAS) performance score (97.44% from January 2018 to March 2021) and outliers in the Internal Quality Control (IQC) (25 from January 2019 till March 2021). Failures in the IQC were not assessed in 2018. Postanalytical phase QIs included Turnaround Time (TAT) (average of 2.55 days for aerobic growth) and reporting time for critical alerts, which was within 24 hours of alert finding (100% for smear and culture-positive results). Conclusion: Regular monitoring of QIs helps to identify potential errors. This laboratory chose to analyse and monitor its processes using practically feasible QIs. It was found that the laboratory consistently maintained its performance throughout the study period.

Evaluation of the effectiveness of critical result notification protocol of biochemistry laboratory at a tertiary care hospital

Objectives: Critical value reporting is an essential aspect of laboratory medicine, ensuring prompt communication of life-threatening results to clinicians for immediate action. Existing guidelines emphasize the importance of timely notification, but protocols may vary. This study evaluated the effectiveness of the protocol for reporting critical results in the biochemistry laboratory of a tertiary care hospital and proposed alternative protocols for improvement. Materials and Methods: Data were collected over a 6-month period, including the total number of investigations, critical values detected, parameters analyzed, and clinical departments involved. Quality indicators such as turnaround time (TAT), unsuccessful reporting rates, and clinical actions taken were assessed. Statistical analysis was performed using Microsoft Excel. Statistical Analysis: All statistical analyses were performed using Microsoft Excel spreadsheets (Microsoft, Redmond, WA, United States). Data were presented as numbers and percentages. Results: Out of 390,000 investigations, 0.5% had critical values. Serum potassium (55.9%) had the highest frequency of critical values, followed by sodium, calcium, and glucose. The emergency department received the highest number of critical alerts. The success rate of critical result reporting within 1 hour was 86.8%, while unsuccessful reporting accounted for 10.7%. The causes of unsuccessful reporting included outpatient requests and delayed intimation due to patient transfers. Corrective action was taken in 91% of cases, with documentation primarily in nurse charts. Conclusion: Evaluation of the protocol for reporting critical results identified areas for improvement. Recommendations included revising the critical value list, reducing TAT through process optimization and automation, enhancing staff training and awareness, fostering interdisciplinary collaboration, and maintaining incident records. Implementing these changes can enhance compliance, reduce errors, and improve patient care.

Pharmacists' Perceptions on Safety Alerts of the Drug Utilization Review (DUR) in Electronic Health Records in a Tertiary Healthcare Hospital.

Electronic Drug Alarms and Drug Utilization Reviews (DURs) are crucial in improving patient safety by reducing the dispensing of contraindicated medications and minimizing adverse drug events. The DUR system often generates low-level alerts, making it challenging for pharmacists and doctors to discern more critical alerts. This can result in alert fatigue, causing burnout and jeopardizing patient safety. A cross-sectional study was conducted in a tertiary hospital to explore pharmacists' perspectives and experience with the DUR system. This study aimed to identify their responses to alerts indicating a need to change the original prescription and the difficulties encountered. Out of all the participants, 85% had prior experience with DUR alerts. However, 40% of them expressed dissatisfaction with the alerts. Moreover, 88% of the participants received highly frequent DUR alerts, but only 40% believed that DUR alerts could identify rare adverse drug reactions. Additionally, only 27% of the participants altered their prescriptions based on alerts for the MAOI/serotonin modulator. The survey showed that 66% of participants believe improvements are necessary for the DUR system. Specifically, 77% of participants felt that more information is needed on overlapping prescriptions, 82% on patients with chronic diseases, and 82% on potential reactions caused by co-administration. At the same time, 75% raised concern about the need for backup for any server breakdown. Positive perceptions about DUR lead to changing the prescription in response to an alert. Therefore, improving the DUR system is crucial to prevent pharmacists from missing important alerts and to increase their awareness of clinically significant alarm signals. By doing so, we can optimize patient safety and contribute to providing high-quality healthcare services.

117-LB: Impact of Bigfoot Unity Integrated CGM on HbA1c

Introduction: Continuous glucose monitoring (CGM) is a preferred method of glucose monitoring. Bigfoot Unity Diabetes Management is an integrated CGM (iCGM) system that incorporates remote patient monitoring with a mobile app, smart pen caps, and CGM via Libre Freestyle 2 sensors. Smart pen caps attach to rapid-acting and long-acting insulin pens and sync with patients’ CGM sensors to provide insulin dose recommendations based on the health care provider's (HCP) instructions. The smart pen caps also allow patients to view their last insulin dose and receive alerts for possible missed doses. The mobile app allows patients to review their HCP's instructions and receive critical alerts. Likewise, HCP can more closely track data to guide clinical assessments and plans. Objectives: Examine the efficacy of the Bigfoot Unity system in improving glycemic control. Methods: Baseline HbA1c values were obtained from 91 patients enrolled in the iCGM system and compared with Glucose Management Indicators (GMI), or estimated future HbA1c, after at least 30 days of iCGM use. Results: Patients were in the target blood glucose range 59.9% of the time with low incidences (0.01%) of very low blood glucose. Patients’ mean baseline HbA1c was 8.87% (SD 1.91). After at least 30 days of iCGM, patients’ mean GMI was 7.55%, (SD 1.02) indicating a statistically significant mean difference of 1.32%, (95% CI 0.86 to 1.79%, p <0. 0001). Conclusions: Use of Bigfoot Unity improves HbA1c. Disclosure P. Haque: None. S. Hall: None. P. Walker: None. K. Latif: None.

Increasing Utilization and Improving Documentation in a Radiology Critical Alert System

Effective communication of critical imaging findings is an important patient safety issue. Despite an increase in exam volumes, our institution saw a decrease in the number of alerts sent through our critical alert system, indicating that critical findings were not being communicated. The purpose of our interventions was to increase the number of critical alerts, while also improving documentation and improving our provider database. We used a program of education for our radiologists and systematic reinforcement to increase the usage of our critical alert system. We also implemented a new time-stamp macro in our dictation system to improve documentation of emergency alerts, and engaged with other departments to improve the contact information in our provider database. Our interventions led to an increase in the monthly number of critical alerts, most notably for findings that require clinical or imaging follow-up (17 alerts per month). There was also a steady improvement in documentation (96.9% compliance), along with an increase in the number of alerts to providers with current contact information (0.5% per month). Our efforts show that educational and collaborative efforts can result in improved communication of radiologic critical results.

Detection of Drug-Related Problems through a Clinical Decision Support System Used by a Clinical Pharmacy Team.

Clinical decision support systems (CDSSs) are intended to detect drug-related problems in real time and might be of value in healthcare institutions with a clinical pharmacy team. The objective was to report the detection of drug-related problems through a CDSS used by an existing clinical pharmacy team over 22 months. It was a retrospective single-center study. A CDSS was integrated in the clinical pharmacy team in July 2019. The investigating clinical pharmacists evaluated the pharmaceutical relevance and physician acceptance rates for critical alerts (i.e., alerts for drug-related problems arising during on-call periods) and noncritical alerts (i.e., prevention alerts arising during the pharmacist's normal work day) from the CDSS. Of the 3612 alerts triggered, 1554 (43.0%) were critical, and 594 of these 1554 (38.2%) prompted a pharmacist intervention. Of the 2058 (57.0%) noncritical alerts, 475 of these 2058 (23.1%) prompted a pharmacist intervention. About two-thirds of the total pharmacist interventions (PI) were accepted by physicians; the proportion was 71.2% for critical alerts (i.e., 19 critical alerts per month vs. 12.5 noncritical alerts per month). Some alerts were pharmaceutically irrelevant-mainly due to poor performance by the CDSS. Our results suggest that a CDSS is a useful decision-support tool for a hospital pharmacist's clinical practice. It can help to prioritize drug-related problems by distinguishing critical and noncritical alerts. However, building an appropriate organizational structure around the CDSS is important for correct operation.

PHOTOCATALYTIC DEGRADATION OF LAMBDACYHALOTHRIN (PYRETHROID) IN WASTEWATER USING ZINC OXIDE NANOPARTICLE

Accumulating synthetic pesticides in the living system has brought about several critical alerts from researchers as it interferes with the essential processes in the body. Metal oxide nanoparticles possess suitable properties including band gap and have played an important role in the photocatalytic degradation of toxic chemicals in wastewater and the environment under natural solar radiation. This research aims at the photocatalytic degradation of Type II Pyrethroid Pesticide (Lambda-cyhalothrin) in wastewater and agricultural runoff using Zinc Oxide Nanoparticles. The synthesized ZnO Nanoparticle (NP) was ascertained by characterization using XRD for material crystallinity and crystallite size, SEM, and particle size analysis for surface morphology and size. The EDAX spectra confirmed the presence of Zinc elements and showed that the synthesized zinc oxide nanoparticles were pure. The degradation experiment was conducted under natural solar irradiation. The initial and final/equilibrium concentration of Lambda-cyhalothrin residue were determined using High-performance liquid chromatography (HPLC) followed by computation of degradation capacity. Photocatalytic degradation of LCY was successful following the application of Zinc oxide NPs as nanocatalysts with an efficiency of about 87% as compared to the control. The synthesized Nanoparticle was found to degrade a large percentage of Lambda-cyhalothrin residue in wastewater. Exploiting its application in the remediation of pesticides in agricultural runoff may be crucial at this point.

In-silico STUDIES OF UNIQUE SCHIFF BASE OF BENZIMIDAZOLE DERIVATIVES AS ANTICANCER AGENTS

Accumulating synthetic pesticides in the living system has brought about several critical alerts from researchers as it interferes with the essential processes in the body. Metal oxide nanoparticles possess suitable properties including band gap and have played an important role in the photocatalytic degradation of toxic chemicals in wastewater and the environment under natural solar radiation. This research aims at the photocatalytic degradation of Type II Pyrethroid Pesticide (Lambda-cyhalothrin) in wastewater and agricultural runoff using Zinc Oxide Nanoparticles. The synthesized ZnO Nanoparticle (NP) was ascertained by characterization using XRD for material crystallinity and crystallite size, SEM, and particle size analysis for surface morphology and size. The EDAX spectra confirmed the presence of Zinc elements and showed that the synthesized zinc oxide nanoparticles were pure. The degradation experiment was conducted under natural solar irradiation. The initial and final/equilibrium concentration of Lambda-cyhalothrin residue were determined using High-performance liquid chromatography (HPLC) followed by computation of degradation capacity. Photocatalytic degradation of LCY was successful following the application of Zinc oxide NPs as nanocatalysts with an efficiency of about 87% as compared to the control. The synthesized Nanoparticle was found to degrade a large percentage of Lambda-cyhalothrin residue in wastewater. Exploiting its application in the remediation of pesticides in agricultural runoff may be crucial at this point.

Quality audit of haematology laboratory: Challenges and opportunities

Introduction: Laboratory results play an essential role in both medical decision-making and inpatient management. In many clinical scenarios, laboratory test results are essential to make the right diagnosis or choosing the suitable treatment regimen. The aims and objectives were to study 13 National Accreditation Board for Testing and Calibration Laboratories (NABL)-specific common quality indicators (QIs) in the haematology laboratory of a tertiary care teaching hospital. Materials and Methods: NABL accredited a laboratory-based, cross-sectional study done in the haematology laboratory of NABH-recognised tertiary care teaching hospital over 5 years from January 2017 to April 2022 with a sample size of 1586,096. Quality parameters studied include sample rejection rates, sample redo rates, routine turnaround time (TAT), critical reports and their TAT, corrected reports, staining quality etc. The QI rates were calculated on a monthly basis, and trends were analysed. P < 0.05 was considered statistically significant. Results: The most common error was clotted sample 634 (0.04%), followed by rejection due to the misidentification of sample 128 (0.008%). Several monthly critical alerts showed an upward trend (P = 0.045) which could be attributed to increased awareness amongst laboratory staff. Redo rates showed no significant change in trend over the study period. Conclusion: Updating the knowledge on laboratory services, adequate training of the staff and sensitisation about the importance of the QIs in all three phases will help minimise errors.

Investigation the role of contrast on habituation and sensitisation effects in peripheral areas of graphical user interfaces

Graphical user interfaces are designed so that the most important elements are usually located in the central part of the screen, where they catch the user’s attention. However, there are situations where it is necessary to attract the user’s attention to make him/her notice, e.g., a critical alert, which is customarily displayed in the peripheral area so as not to interact with the main content. Therefore, our focus is to deliver an increased visibility of content in the peripheral area of the display in a non-intrusive way. Thus, the main purpose of this work is to analyze the visibility of the stimulus (in the form of colored discs), displayed in the peripheral area of a screen, which distracts users from the central part of the interface. The habituation and sensitization effects were considered to study which parameters catch and hold the user’s attention, despite the length of their interaction with the system. The experiments performed indicated how the parameters should be set to reduce the habituation effect without the need to use content with the highest visual intensity. The results showed that a high visual intensity is not necessarily needed for the best impact. A medium contrast level, a horizontal or vertical display localization, and a flashing frequency of 2 Hz are sufficient to obtain the best visibility in the peripheral area. In the case of critical alerts and the need for short-term intensive stimuli, it is worth highlighting these with high contrast. This configuration should be the most effective if it is not a continuous operation. However, they can cause unnecessary irritation or even cognitive load for more extended usage.

AIOPS FRAMEWORK FOR ALERTING PERFORMANCE ISSUES IN MICROSERVICES USING TIME SERIES FORECASTING

For large scale applications based on Microservices architecture and hosted on scalable platforms like Pivotal Cloud Foundry or OpenShift, it is imperative to monitor the performance of microservices on a continuous basis and trigger performance critical alerts upfront when abnormal patterns are observed in the immediate past. One of the critical application performance indicators is Major Garbage Collection (Major GC), an increase of which causes application performance bottlenecks and might indicate insufficient memory allocation to the JVM or a potential memory leak. In this paper, we discuss a solution built using machine learning Algorithms that forecasts Major GC cycles (Number of Major Garbage Collection) of business critical microservices for the near future time periods. This paper discusses various algorithms within python’s stats models. tsa package that were explored and details the concepts and results of forecast models trained using Seasonal Autoregressive Integrated Moving Average, or SARIMA algorithm that was finally considered for forecasting based on model performance. The paper also elucidates the Forecast accuracy of the model, showing results of the forecast vs actual occurrences of Major Garbage collection. The objective of this solution based on AIOps framework is to enable development and production support teams get proactive notification when forecast Major GC patterns breach the set threshold limits, so that corrective action can be taken to improve the application performance by reducing frequency of Major GC cycles.