Near-miss Events Research Articles

Post Take-Over Performance Varies in Drivers of Automated and Connected Vehicle Technology in Near-Miss Scenarios.

This study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios. Past research indicates partial ADS reduces the driver's situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events. Forty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants' mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured. Participants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant's trust in the automated driving system did not vary between the beginning and end of the experiment. Drivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers' ability to anticipate and safely navigate near-miss scenarios. Designers of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events.

Transforming Team Performance Through Reimplementation of the Surgical Safety Checklist

Patient safety interventions, like the World Health Organization Surgical Safety Checklist, require effective implementation strategies to achieve meaningful results. Institutions with underperforming checklists require evidence-based guidance for reimplementing these practices to maximize their impact on patient safety. To assess the ability of a comprehensive system of safety checklist reimplementation to change behavior, enhance safety culture, and improve outcomes for surgical patients. This prospective type 2 hybrid implementation-effectiveness study took place at 2 large academic referral centers in Singapore. All operations performed at either hospital were eligible for observation. Surveys were distributed to all operating room staff. The study team developed a comprehensive surgical safety checklist reimplementation package based on the Exploration, Preparation, Implementation, Sustainment framework. Best practices from implementation science and human factors engineering were combined to redesign the checklist. The revised instrument was reimplemented in November 2021. Implementation outcomes included penetration and fidelity. The primary effectiveness outcome was team performance, assessed by trained observers using the Oxford Non-Technical Skills (NOTECH) system before and after reimplementation. The Agency for Healthcare Research and Quality Hospital Survey on Patient Safety Culture was used to assess safety culture and observers tracked device-related interruptions (DRIs). Patient safety events, near-miss events, 30-day mortality, and serious complications were tracked for exploratory analyses. Observers captured 252 cases (161 baseline and 91 end point). Penetration of the checklist was excellent at both time points, but there were significant improvements in all measures of fidelity after reimplementation. Mean NOTECHS scores increased from 37.1 to 42.4 points (4.3 point adjusted increase; 95% CI, 2.9-5.7; P < .001). DRIs decreased by 86.5% (95% CI, -22.1% to -97.8%; P = .03). Significant improvements were noted in 9 of 12 composite areas on culture of safety surveys. Exploratory analyses suggested reductions in patient safety events, mortality, and serious complications. Comprehensive reimplementation of an established checklist intervention can meaningfully improve team behavior, safety culture, patient safety, and patient outcomes. Future efforts will expand the reach of this system by testing a structured guidebook coupled with light-touch implementation guidance in a variety of settings.

Near-miss Women Causes and Prevalence in Alobied Maternity Hospital

Background: Maternal near-miss (MNM) events occur more frequently than maternal deaths; therefore, more detailed and comprehensive studies on maternal morbidity have been conducted and are of value to clinical audits and practices. Purpose: This study aimed to determine the frequency of maternal near misses and the nature of near-missevents. Methodology: This descriptive, retrospective, cross-sectional study over 12 months duration was conducted at the Alobied Teaching Hospital in 2018. Data were collected from patient notes, partographs, and other relevant documents. Demographic and clinical data concerning personal history, obstetric history, and near-miss events. Results: A total of 15202 women were admitted, 339 cases of maternal near misses, maternal near-missrate (MNMR) of 22.3|1000 live births, 200(59%) had an infection, 80(23.6%) hemorrhage, 20(5.9%) severe pre-eclampsia,12(3.5%) eclampsia, 20(5.9%) anemia, convulsions 5 (1.5%) 17(5%) of the cases were admitted to intensive care unit (ICU), 9(2.7%) had liver dysfunction, 9(2.7%) coagulation dysfunction, 8(2.4%) renal dysfunction, 5(1.5%) cerebral problems, 4(1.2%) cardiac dysfunction, and 2(0.6%) had developed respiratory dysfunction. Conclusion: The maternal near-miss rate was 22.3|1000 live births. Most near-miss cases occurred before the women arrived at the hospital. The major causes of maternal near misses were infection, hemorrhage anemia, pre-eclampsia, and eclampsia.

The effect of health insurance coverage on antenatal care utilizations in Ethiopia: evidence from national survey.

About three-fourths of maternal near-miss events and two-fifths of the risk of neonatal mortality can be reduced by having at least one antenatal visit. Several studies have identified potential factors related to maternal health seeking behavior. However, the association between health insurance membership and antenatal care utilization was not well investigated in Ethiopia. Therefore, this study was aimed at assessing the effect of health insurance coverage on antenatal care use in Ethiopia. The study utilized data from the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS). The analysis included a weighted sample of 3,919 women who gave birth in the last five years. A logistic regression model was employed to assess the association between antenatal care use and health insurance coverage and other covariates. The results were presented as adjusted odds ratios (AOR) at a 95% confidence interval (CI). Statistical significance was declared at a p-value <0.05 in all analyses. Antenatal care was used by 43% (95% CI: 41.46 to 44.56%) of Ethiopian women. Those with health insurance coverage had higher odds of antenatal care use than those without health insurance coverage. Women were 33% more likely to use antenatal care (ANC) if they were covered by health insurance. Age, Media access, marital status, education status, wealth index, and economic regions were also factors associated with antenatal care utilizations. According to our findings, less than half of Ethiopian women had four or more antenatal care visits. Health insurance membership, respondent age, media access, marital status, education status, wealth index, and economic region were factors associated with antenatal care utilization. Improving health insurance, women's economic empowerment, and education coverage are critical determinants of antenatal care utilization.

Toward AI-supported evaluation for safety control measures against near-miss events in pharmaceutical products

The Japan Council for Quality Health Care (JCQHC) promotes medical safety by providing health professionals in pharmacies and the public with information regarding near-miss events from pharmacies. The Pharmaceuticals and Medical Devices Agency (PMDA) evaluates the pharmaceutical near-miss events published by the JCQHC to determine whether safety measures need to be taken in terms of drug names and packaging. We propose an artificial intelligence (AI) evaluation model that performs efficient and reproducible evaluations to support PMDA reviewers. We prepared a dataset consisting of pairs of pharmaceutical near-miss events and their human-annotated evaluation in consultation with the PMDA Safe Use Measures Review Committee reports. Pharmaceutical near-miss events consist of semi-structured texts such as drug names and free descriptive comments. To extract text features such as the similarity of drug names from the semi-structured text and predict the evaluation from these text features, a light gradient boosting machine was used. The AI model was evaluated by ablation experiments on these features. Model construction using the metrics of precision, recall, f1-score, and macro-f1 and verification were performed using cross-validation. The developed AI model classified events with a high degree of accuracy close to that of PMDA's human evaluation; however, achieving human classification accuracy proved difficult. Further study is needed to improve the accuracy of the classification evaluation model. The developed model should serve as an operational rule for primary screening in PMDA operations.

308 Safety Incidents and Near-Miss Events in Pediatric Radiotherapy

308 Safety Incidents and Near-Miss Events in Pediatric Radiotherapy

An Improved Object Detection and Trajectory Prediction Method for Traffic Conflicts Analysis

Although computer vision-based methods have seen broad utilisation in evaluating traffic situations, there is a lack of research on the assessment and prediction of near misses in traffic. In addition, most object detection algorithms are not very good at detecting small targets. This study proposes a combination of object detection and tracking algorithms, Inverse Perspective Mapping (IPM), and trajectory prediction mechanisms to assess near-miss events. First, an instance segmentation head was proposed to improve the accuracy of the object frame box detection phase. Secondly, IPM was applied to all detection results. The relationship between them is then explored based on their distance to determine whether there is a near-miss event. In this process, the moving speed of the target was considered as a parameter. Finally, the Kalman filter is used to predict the object's trajectory to determine whether there will be a near-miss in the next few seconds. Experiments on Closed-Circuit Television (CCTV) datasets showed results of 0.94 mAP compared to other state-of-the-art methods. In addition to improved detection accuracy, the advantages of instance segmentation fused object detection for small target detection are validated. Therefore, the results will be used to analyse near misses more accurately.

Design and requirements of a hydrogen component reliability database (HyCReD)

Hydrogen technologies are expected to play a key role in the decarbonization of several sectors including energy storage and transportation. Rigorous investigation and quantification of the risk and reliability issues associated with hydrogen technologies will be critical to ensuring both their wider adoption and safe, economical operations. Quantitative risk assessment (QRA) is an important tool that has been used to enable the safe deployment of many engineering systems, including hydrogen fueling stations and hydrogen storage systems. However, QRA studies require reliability data which is currently lacking for expanding applications of hydrogen systems. To address this gap, we present a new structure for a hydrogen component reliability database (HyCReD) that can be used to generate reliability data to be used in QRA, reliability, safety studies, maintenance planning, and more. Building on our previous work examining four major hydrogen safety data collection tools (West et al., 2022) [1], our approach in this work was to consult scientific literature on reliability data collection as well as a number of existing reliability engineering databases in the oil & gas, chemical processing, and nuclear power plant sectors. The evaluation of these databases led to identifying best practices to be implemented in a data collection framework for a hydrogen component reliability database. Based on these best practices, a set of 24 requirements for the proposed database are presented, covering its characteristics and the types of data to be collected. We define the structure of the HyCReD database and 25 data elements to be collected, spanning system description, failure, shutdown, or near-miss events, and maintenance events. The data elements are then defined according to international standards used in the safety and reliability practice and potential choice lists are provided for each field. Since this database is being piloted for hydrogen fueling stations, a generic station component hierarchy developed by West (2021) [2] is used to standardize system data. Finally, we demonstrate populating the database with information extracted from five narrative reports on hydrogen fueling station incidents.

Reasons for delay in reaching healthcare with severe abortion-related morbidities: Qualitative results from women in the fragile context of Jigawa state, Nigeria (AMoCo)

Maternal near-miss events are a key measure of maternal health; abortion-related complications are one source of near-miss events. To understand the pathway to care of women with severe abortion-related events in a fragile context, we conducted in-depth semi-structured interviews with women who obtained treatment at a referral hospital in Jigawa State, Nigeria, in 2020–2021 (n = 61). We used the Three Delays Model (Thaddeus & Maine, 1994) to examine impediments in reaching care.The first delay (from the onset of symptoms of the pregnancy loss to the decision to seek care) was characterized by the duration of time it took to recognize the pregnancy and pregnancy loss in addition to religious beliefs that it is the will of a higher power that she lost the pregnancy. The second delay (from the decision to seek care to arriving at a place that could provide adequate care for her complication, i.e. the study site); was due to lack of money, lack of passable roads and transport, use of traditional healers, challenges being seen by providers at lower-level facilities, referrals not being facilitated and misdirection by healthcare providers. The third delay was not present in our results. No respondent said she knowingly interfered with the pregnancy; understanding why these pregnancy losses resulted in near-miss complications is critical to reducing maternal morbidity and mortality in Northern Nigeria. Addressing health literacy as well as social and financial barriers holds the potential to get women to care sooner and avert these near-miss or potentially life-threatening events.

Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model

The World Health Organization has revealed that Thailand ranks first in Asia with regard to the region’s road traffic death rate. Due to the growth in the domestic economy and demands in logistics, traffic congestion regularly occurs and brings higher risks to transportation, resulting in a constant increase in the accident rate involving heavy-duty vehicles (HDVs), with a tendency to escalate in the future. To prevent its occurrence and solve the problem, this research aims to present a “Safety HDV Model” based on four dimensions, namely, driver behaviors, unsafe roadway environment, types of vehicles and near-miss events, which are all considered as causes of accidents. In this study, the researchers use the Delphi method to obtain a consensus from experts in logistics and safety from both public and private organizations, and then they define indicators and assess the complex dimensions. Based on the consensus, the researchers find 4 dimensions, 15 factors and 55 indicators with a high level of consensus at the Kendall’s coefficient of concordance (W) of 0.402 and P less than 0.001 to be relevant to safety in logistics. To estimate the influences among dimensions, the researchers apply a structural equation model and find that both absolute fit indices and incremental fit indices demonstrate good fit, with a CMIN/DF of 1.90, RMSEA of 0.048, GFI of 0.95, AGFI of 0.92 and RMR of 0.032 for the absolute fit indices and NFI of 0.97, CFI of 0.98, TLI of 0.98 and IFI of 0.98 for the incremental fit indices. As the model is consistent with data and variables, it is considered to be valid to be adopted by responsible authorities to improve unsafe roadway environments and behaviors of HDV drivers. As the data in the model can be altered by location, the model can be utilized as a tool in strategic planning and management to prevent accidents in each area of the country in the future.

Defining, collecting, and sharing perishable disaster data.

Researchers across disciplines have long sought to collect 'perishable data' in the context of disasters. Yet, this data type is neither consistently defined nor discussed in specific detail in the literature. To address this gap, this paper defines perishable data and provides guidance on ways to improve both how it is collected and shared. Here, perishable data is conceptualised as highly transient data that may degrade in quality, be irrevocably altered, or be permanently lost if not gathered soon after it is generated. Perishable data may include ephemeral information that must be collected to characterise pre-existing hazardous conditions, near-miss events, actual disasters, and longer-term recovery processes. This data may need to be gathered at multiple points in time across varying geographic scales to accurately characterise exposure, susceptibility to harm, or coping capacity. The paper considers ethical and logistical challenges and discusses opportunities to advance equitable perishable data collection and dissemination.

Maternal Near-Miss and Maternal Mortality in a Tertiary Care Center of Western Uttar Pradesh: A Retrospective Study.

Maternal mortality is an important indicator to assess the quality of services provided by the health care system. However, maternal near-misses as well as maternal mortality are also indicators of how well the health care system serves pregnant women. To improve our healthcare system in terms of investigative capacity, infrastructure, and personnel, a near-miss registry can provide important information on gaps in pregnancy facilities. This will help us to identify the requirements for referral facility improvements and the need for various health awareness programs. We, therefore, designed this study to analyze the various near-miss events in mothers and compare them with maternal mortality. Present study was conducted in the Department of Obstetrics and Gynecology, Lala Lajpat Rai Memorial (L.L.R.M.) Medical College associated with Sardar Vallabh Bhai Patel (S.V.B.P.) Hospital Meerut, Uttar Pradesh (UP), India for a period of one year and data were collected retrospectively from January 2022 to January 2023. All patients with life-threatening conditions such as excessive bleeding during pregnancy, hypertensive disorders of pregnancy (HDP), and septicemia that occurred during pregnancy or childbirth or within 42 days of termination of pregnancy and required ICU admissions, were included in the study. The total number of deliveries during the study period was 4,360 with 4,333 live births (LB). The total number of eligible cases was 79, out of which 52 were identified as maternal near misses and 27 were maternal mortality. Various maternal mortality and near-miss indices were analysed and statistical analysis was done using the SPSS version 21 (IBM Corp., Armonk, NY, USA). Our hospital's maternal mortality ratio (MMR)was 623/1lakh (0.623%), which is higher than the probability due to the deficiency of appropriate medical services in the nearby areas of western UP. The number of maternal near misses per 1000 LB (maternal near-miss ratio [MNMR]) was 12/1000 LB and the severe maternal outcome rate (SMOR) was 18/1000 LB (1.82%). In our study, hemorrhage and hypertensive disorder in pregnancy were the leading cause of morbidity and mortality followed by sepsis and severe anemia. Among organ dysfunction cardiac illness followed by respiratory dysfunction was the leading cause of morbidity and mortality. It is clear that there is a high burden of maternal near-miss in developing countries. There should be the establishment of well-equipped referral units at the periphery with trained manpower. The establishment of obstetrical high-dependence units (HDUs), rapid availability of blood and blood products, training of staff, and availability of multidisciplinary teams can minimize maternal mortality and morbidity.

Impact of near-miss event during pregnancy and childbirth on maternal health at 12 months.

To assess the impact of maternal near-miss on late maternal death and the prevalence of hypertension or chronic kidney disease (CKD) and mental health problems at 12 months of follow up. This prospective cohort study was conducted in a tertiary hospital in the southeastern region of India from May 2018 to August 2019, enrolling those with maternal near-miss and with follow up for 12 months. The primary outcomes were incidence of late maternal deaths and prevalence of hypertension and CKD during follow up. Incidence of maternal near miss was 6.7 per 1000 live births. Among those who had a near miss, late maternal deaths occurred in 7.2% (95% confidence interval [CI] 3.1%-11.3%); prevalence of CKD was 23.0% (95% CI 16.2%-29.8%), and of hypertension was 56.2% (95% CI 50.5%-66.5%) and only two women had depression on follow up. After adjusting for age, parity, socioeconomic status, gestational age at delivery, hemoglobin levels, and perinatal loss, only serum creatinine was independently associated with late maternal death and CKD on follow up. Women who survive a life-threatening complication during pregnancy and childbirth are at increased risk of mortality and one or more long-term sequelae contributing to the non-communicable disease burden. A policy shift to increase postpartum follow-up duration, following a high-risk targeted approach after a near-miss event, is needed.

Application of Near-Miss Management Systems: An Exploratory Field Analysis in the Italian Industrial Sector

Near-miss events are usually identified as adverse events that could have turned into incidents/injuries but, due to an intervention of a safety system or by chance, developed into harmless situations instead. Past and present studies have also outlined the importance of collecting and analyzing near-miss events, as they have same causes of more serious events, thereby allowing for more effective preventative measures at the workplace. Although their importance has been outlined for several years, standard models for designing near-miss management systems (NMMSs) are still lacking and cannot yet support companies in their full-scale application. Despite this condition, NMMSs are applied in several industrial sectors, such as in the construction, mining, chemical, and nuclear industries. The aim of this study is to analyze how companies are developing their own NMMSs. An exploratory analysis was developed through survey analysis; it was provided to a sample of Italian companies in order to evaluate the adoption level of NMMSs as well as current practices applied by companies. The sample included companies of different sizes in the industrial sector. The results extracted from the field analysis outline interesting issues that point out current procedures adopted for the identification, collection, and analysis of near-miss events as well as real benefits and criticalities related to the application of NMMSs.

An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos

Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur before the incident happens for a wide range of driving scenarios and conditions. One challenge to building advanced driver assistance systems is to create an early warning system for the driver to react safely and accurately while perceiving the diversity of traffic-risk predictions in real-world applications. In this paper, we aim to bridge the gap by investigating two key research questions regarding the driver’s current status of driving through online videos and the types of other moving objects that lead to dangerous situations. To address these problems, we proposed an end-to-end two-stage architecture: in the first stage, unsupervised learning is applied to collect all suspicious events on actual driving; in the second stage, supervised learning is used to classify all suspicious event results from the first stage to a common event type. To enrich the classification type, the metadata from the result of the first stage is sent to the second stage to handle the data limitation while training our classification model. Through the online situation, our method runs 9.60 fps on average with 1.44 fps on standard deviation. Our quantitative evaluation shows that our method reaches 81.87% and 73.43% for the average F1-score on labeled data of CST-S3D and real driving datasets, respectively. Furthermore, the proposed method has the potential to assist distribution companies in evaluating the driving performance of their driver by automatically monitoring near-miss events and analyzing driving patterns for training programs to reduce future accidents.

Assessing bicycle-vehicle conflicts at urban intersections utilizing a VR integrated simulation approach

Animosity between drivers and cyclists has existed on urban road networks for many years. Conflicts between these two groups of road users are exceptionally high in the shared right-of-way environments. Benchmarking methods of conflict assessments are mostly based on statistical analysis with limited data sources. The actual crash data would be valuable to understand the features of bike-car collisions, however the available data are spatially and temporally sparse. To this end, this paper proposes a simulation-based bicycle-vehicle conflict data generation and assessment approach. The proposed approach uses a three-dimensional visualization and virtual reality platform, integrating traffic microsimulation to reproduce a naturalistic driving/cycling-enabled experimental environment. The simulation platform is validated to reflect the human-resembled driving/cycling behaviors under different infrastructure designs. Comparative experiments are carried out on bicycle-vehicle interactions under different conditions, with data collected from a total of 960 scenarios. Based on the results of the surrogate safety assessment model (SSAM), the obtained key insights include: (1) scenarios of a high conflict probability do not lead to actual crashes, which suggests that the classic SSM-based measurements such as TTC or PET values may not sufficiently reflect real cyclist-driver interactions; (2) the major cause of conflicts is variation in vehicle acceleration, which suggests that drivers are considered to be the main party responsible for bicycle-vehicle conflict/crash occurrence; (3) the proposed approach is able to generate near-miss events and reproduce interaction patterns between cyclists and drivers, facilitating experiments and data collections which would be typically unavailable for this type of study.

Interface of Clinical Nurse Specialist Practice and Healthcare Technology.

This article demonstrates the leadership role of the clinical nurse specialist in developing and implementing healthcare technology across the continuum of care. Three virtual nursing practices-facilitated self-care, remote patient monitoring, and virtual acute care nursing-illustrate how the clinical nurse specialist is well suited to transform traditional practice models to ones that use healthcare technology effectively. These 3 practices use interactive healthcare technology to gather patient data and allow communication and coordination with the healthcare team to meet patient-specific needs. Use of healthcare technology in virtual nursing practices led to early care team intervention, optimized care team processes, proactive patient outreach, timely access to care, and reduction in healthcare-associated errors and near-miss events. Clinical nurse specialists are well positioned to develop innovative, effective, accessible, and high-quality virtual nursing practices. Integrating healthcare technology with nursing practice augments care for various patients, ranging from those with low illness severity in the outpatient setting to acutely ill patients in the inpatient hospital environment.

Near-Miss events in Blood Centre at a Tertiary Care Hospital, North India : "Near Hit or Narrow Escape"

Near-Miss events in Blood Centre at a Tertiary Care Hospital, North India : "Near Hit or Narrow Escape"

Coproduction and Usability of a Smartphone App for Falls Reporting in Parkinson Disease.

The purpose of this study was to coproduce a smart-phone application for digital falls reporting in people with Parkinson disease (PD) and to determine usability using an explanatory mixed-methods approach. This study was undertaken in 3 phases. Phase 1 was the development phase, in which people with PD were recruited as co-researchers to the project. The researchers, alongside a project advisory group, coproduced the app over 6months. Phase 2 was the implementation phase, in which 15 people with PD were invited to test the usability of the app. Phase 3 was the evaluation phase, in which usability was assessed using the systems usability scale by 2 focus groups with 10 people with PD from phase 2. A prototype was successfully developed by researchers and the project advisory group. The usability of the app was determined as good (75.8%) by people with PD when rating using the systems usability scale. Two focus groups (n = 5 per group) identified themes of 1) usability, 2) enhancing and understanding management of falls, and 3) recommendations and future developments. A successful prototype of the iFall app was developed and deemed easy to use by people with PD. The iFall app has potential use as a self-management tool for people with PD alongside integration into clinical care and research studies. This is the first digital outcome tool to offer reporting of falls and near-miss fall events. The app may benefit people with PD by supporting self-management, aiding clinical decisions in practice, and providing an accurate and reliable outcome measure for future research. A smartphone application designed in collaboration with people who have PD to record their falls was acceptable and easy to use by people with PD.

Analysis of Near-Miss Events and Errors in Handling Thyroid Specimens; A Gross Room Experience from a Pathology Lab

&lt;p&gt;Background and Objective: Thyroid specimens constitute a major bulk of the head and neck surgical specimens received at the Pathology Departments. Careful handling of the specimens in the grossing room will result in an efficient reporting of useful histological parameters required for patient management and prognosis. The objective of this study was to analyze different errors and near-miss events in the grossing of thyroid specimens in the surgical pathology gross room of a Tertiary Care Hospital in Lahore, Pakistan.&lt;/p&gt;&#x0D; &lt;p&gt;Methods: A cross-sectional analysis was done on different thyroid specimens received at the Pathology reception of Lahore General Hospital, Lahore, Pakistan, from January 2022 to April 2023. Different types of errors involved in standard gross examinations were studied. Data were entered using SPSS version 23.0.&lt;/p&gt;&#x0D; &lt;p&gt;Results: Errors observed in the preanalytical phase included the use of inappropriate fixative (15.2%), insufficient clinical information (76.5), mislabeled jar/request form (7.6%), and loss of specimen (8%). Grossing errors included insufficient grossing notes (12.2%), cutting thick slices (10.7%), missing lesions on gross examination (9.9%), inappropriate inking (6.9%), overfilling cassette with large tissue sections (6.9%), mislabeled cassettes (93.8%), and incomplete submission of capsule (2.3%).&lt;/p&gt;&#x0D; &lt;p&gt;Conclusion: This study concludes that errors and near-miss events in handling thyroid specimens can be avoided by effective training of the handling staff and grossing residents. Collaboration between clinical wards and histopathology laboratories can also be helpful in this regard.&lt;/p&gt;