Root cause analysis of cases involving diagnosis.

Diagnostic errors comprise the leading threat to patient safety in healthcare today. Learning how to extract the lessons from cases where diagnosis succeeds or fails is a promising approach to improve diagnostic safety going forward. We present up-to-date and authoritative guidance on how the existing approaches to conducting root cause analyses (RCA's) can be modified to study cases involving diagnosis. There are several diffierences: In cases involving diagnosis, the investigation should begin immediately after the incident, and clinicians involved in the case should be members of the RCA team. The review must include consideration of how the clinical reasoning process went astray (or succeeded), and use a human-factors perspective to consider the system-related contextual factors in the diagnostic process. We present detailed instructions for conducting RCA's of cases involving diagnosis, with advice on how to identify root causes and contributing factors and select appropriate interventions.

Bridging the Gaps: A Prospective Analysis of Root Causes for Rejection and Incompleteness in Blood Requisition Forms.

INTRODUCTION: Blood request form (BRF) stands as a pivotal document in ensuring safe and effective blood transfusions within healthcare settings. Incomplete or erroneous data on BRF can heighten risk of adverse reactions and compromise patient safety. Aim of study was to assess level of completion of BRFs by clinicians and to evaluate root cause analysis (RCA) of incompleteness of BRFs and factors leading to their rejection. MATERIALS AND METHODS: This prospective study was carried out from February 2024 to April 2024 on BRFs received in the blood centre. They were audited and RCA for factors leading to their incompleteness and rejection were analysed. RESULTS: Total number of BRFs received in blood centre was 14,468. 13,358 (92.3%) BRFs were accepted and 1,110 (7.7%) BRFs were rejected. 12,804 (95.85%) of accepted BRFs were incomplete. Weight was the most common missing parameter (89% {n=11403}) while name of the requesting clinician was least common (2.5% {n-318}). 3.52% (n=510) BRFs were rejected due to mismatch in name and patient registration number on BRF and samples. 0.14% (n=21) BRFs were rejected due to hemolysed samples. RCA for incompleteness of BRFs showed that main reason was manpower (61%-83%) while environment was least common (17%-67%). RCA for rejection of BRFs showed that environment was most common cause (13.3%-80.15%) while manpower was least common (9%-19.85%). CONCLUSION: Regular audits and personnel training, and quality assurance measures can help identify and address deficiencies in BRF completion to enhance patient safety and reduce incidence of transfusion-related errors and complications.

A Comparative Study of Causality Detection Methods in Root Cause Diagnosis: From Industrial Processes to Brain Networks.

Abstracting causal knowledge from process measurements has become an appealing topic for decades, especially for fault root cause analysis (RCA) based on signals recorded by multiple sensors in a complex system. Although many causality detection methods have been developed and applied in different fields, some research communities may have an idiosyncratic implementation of their preferred methods, with limited accessibility to the wider community. Targeting interested experimental researchers and engineers, this paper provides a comprehensive comparison of data-based causality detection methods in root cause diagnosis across two distinct domains. We provide a possible taxonomy of those methods followed by descriptions of the main motivations of those concepts. Of the two cases we investigated, one is a root cause diagnosis of plant-wide oscillations in an industrial process, while the other is the localization of the epileptogenic focus in a human brain network where the connectivity pattern is transient and even more complex. Considering the differences in various causality detection methods, we designed several sets of experiments so that for each case, a total of 11 methods could be appropriately compared under a unified and reasonable evaluation framework. In each case, these methods were implemented separately and in a standard way to infer causal interactions among multiple variables to thus establish the causal network for RCA. From the cross-domain investigation, several findings are presented along with insights into them, including an interpretative pitfall that warrants caution.

A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process.

The monoclonal antibody (mAb) manufacturing process comes with high profits and high costs, and thus mAb productivity is of vital importance. However, many factors can impact the cell culture process, and lead to mAb productivity reduction. Nowadays, the biopharma industry is actively employing manufacturing information systems, which enable the integration of both online data and offline data. Although the volume of data is large, related data mining studies for mAb productivity improvement are rare. Therefore, a data-driven approach is proposed in this study to leverage both the inline and offline data of the cell culture process to discover the causes of mAb productivity reduction. The approach consists of four steps, namely data preprocessing, phase division, feature extraction and fusion, and cluster comparing. First, data quality issues are solved during the data preprocessing step. Next, the inline data are divided into several phases based on the moving window k-nearest neighbor method. Then, the inline data features are extracted via functional data analysis and combined with the offline data features. Finally, the causes of mAb productivity reduction are identified using the contrasting clusters via the principal component analysis method. A commercial-scale cell culture process case study is provided in this research to verify the effectiveness of the approach. Data from 35 batches were collected, and each batch contained nine inline variables and seven offline variables. The causes of mAb productivity reduction were identified to be the lack of nutrients, and recommended actions were taken according to the result, which was subsequently proven by six validation batches.

NLP-based ergonomics MSD risk root cause analysis and risk controls recommendation.

An ergonomics assessment of the physical risk factors in the workplace is instrumental in predicting and preventing musculoskeletal disorders (MSDs). Using Artificial Intelligence (AI) has become increasingly popular for ergonomics assessments because of the time savings and improved accuracy. However, most of the effort in this area starts and ends with producing risk scores, without providing guidance to reduce the risk. This paper proposes a holistic job improvement process that performs automatic root cause analysis and control recommendations for reducing MSD risk. We apply deep learning-based Natural Language Processing (NLP) techniques such as Part of Speech (PoS) tagging and dependency parsing on textual descriptions of the physical actions performed in the job (e.g. pushing) along with the object (e.g. cart) being acted upon. The action-object inferences provide the entry point to an expert-based Machine Learning (ML) system that automatically identifies the targeted work-related causes (e.g. cart movement forces are too high, due to caster size too small) of the identified MSD risk (e.g. excessive shoulder forces). The proposed framework utilises the root causes identified to recommend control strategies (e.g. provide larger diameter casters, minimum diameter 8" or 203 mm) most likely to mitigate risk, resulting in a more efficient and effective job improvement process.

We propose an ergonomics framework that identifies the root causes of MSD risk and recommends control actions. A key insight exploited using artificial intelligence is that when the estimated risk is high for a body joint, the actions of the worker in question and the associated objects constitute valuable information.

Application of root cause analysis and TEAMSTEPPS post intravesical gas explosion during transurethral resection of the prostate: a rare case report.

BACKGROUND: An intravesical gas explosion is a rare complication of transurethral resection of the prostate (TURP). It was first reported in English literature in 1926, and up to 2022 were only forty-one cases. Injury from an intravesical gas explosion, in the most severe cases appearing as extraperitoneal or intraperitoneal bladder rupture needed emergent repair surgery. CASE PRESENTATION: We present a case of a 75-year-old man who suffered an intravesical gas explosion during TURP. The patient underwent an emergent exploratory laparotomy for bladder repair and was transferred to the intensive care unit for further observation and treatment. Under the medical team's care for up to sixty days, the patient recovered smoothly without clinical sequelae. CONCLUSIONS: This case report presents an example of a rare complication of intravesical gas explosion during TURP, utilizing root cause analysis (RCA) to comprehend causal relationships and team strategies and tools to improve performance and patient safety (TeamSTEPPS) method delivers four teamwork skills that can be utilized during surgery and five recommendations to avoid gas explosions during TURP to prevent the recurrence of medical errors. In modern healthcare systems, promoting patient safety is crucial. Once complications appear, RCA and TeamSTEPPS are helpful means to support the healthcare team reflect and improve as a team.

Root cause analysis and strategies for reducing falls among inpatients in healthcare facilities: A narrative review.

Background and Aims: Root Cause Analysis (RCA) is a systematic process which can be applied to analyze fall incidences in reactive manner to identify contributing factors and propose actions for preventing future falls. To better understand cause of falls and effective interventions for their reduction we conducted a narrative review of RCA and Strategies for Reducing Falls among Inpatients in Healthcare Facilities. Methods: In this narrative review, databases including Scopus, ISI Web of Science, Cochrane, and PubMed were searched to obtain the related literature published. Databases were searched from January 2005 until the end of March 2023. The Joanna Briggs Institute (JBI) tool was used for quality assessment of articles. To analyze the data, a five-stage framework analysis method was utilized. Results: Seven articles that fulfilled the inclusion criteria were identified for this study. All of the selected studies were interventional in nature and employed the RCA method to ascertain the underlying causes of inpatient falls. The root causes discovered for falls involved patient-related factors (37.5%), environmental factors (25%), organizational and process factors (19.6%), staff and communication factors (17.9%). Strategies to reduce falls involved environmental measures and physical protection (29.4%), identifying, and displaying the causes of risk (23.5%), education and culturalization (21.6%), standard fall risk assessment tool (13.7%), and supervision and monitoring (11.8%). Conclusion: the findings identify the root causes of falls in inpatient units and provide guidance for successful action plan execution. Additionally, it emphasizes the importance of considering the unique characteristics of healthcare organizations and adapting interventions accordingly for effectiveness in different settings.

Identification and Root Cause Analysis of the Visible Particles Commonly Encountered in the Biopharmaceutical Industry.

Visible particle is an important issue in the biopharmaceutical industry, and it may occur across all the stages in the life cycle of biologics. Upon the occurrence of visible particles, it is often necessary to conduct chemical identification and root cause analysis to safeguard the safety and efficacy of the biotherapeutic products. In this article, we present a number of typical particles and relevant root cause analysis in the categories of extrinsic, intrinsic and inherent particles that are commonly encountered in the biopharma industry. In particular, the optical images of particles obtained both in situ and after isolation are provided, along with the spectral and elemental information. The particle identification was carried out with multiple microscopic and microspectroscopic techniques, including stereo optical microscopy, Fourier transform infrared microscopy, confocal Raman microscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy. Both commercial and in-house spectral databases were used for comparison and identification. In addition to particle identification, our significant efforts are placed on the root cause analysis of the addressed particles with the intention to provide a relatively whole picture of the particle related issues and practical references to particle mitigation for our peers in the biopharmaceutical industry.

Postcolonoscopy colorectal cancer: Prevalence, categorization and root-cause analysis based on the World Endoscopic Organization systemCáncer colorrectal poscolonoscopia / Prevalencia, categorización y análisis de las causas subyacentes basado en las recomendaciones de la Organización Mundial de Endoscopia

Aims: The World Endoscopy Organization (WEO) recommends that endoscopy units implement a process to identify postcolonoscopy colorectal cancer (PCCRC). The aims of this study were to assess the 3-year PCCRC rate and to perform root-cause analyses and categorization in accordance with the WEO recommendations.Patients and methods: Cases of colorectal cancers (CRCs) in a tertiary care center were retrospectively included from January 2018 to December 2019. The 3-year and 4-year PCCRC rates were calculated. A root-cause analysis and categorization of PCCRCs (interval and type A, B, C noninterval PCCRCs) were performed. The level of agreement between two expert endoscopists was assessed. Results: A total of 530 cases of CRC were included. A total of 33 were deemed PCCRCs (age 75.8±9.5 years; 51.5% women). The 3-year and 4-year PCCRC rates were 3.4% and 4.7%, respectively. The level of agreement between the two endoscopists was acceptable either for the root-cause analysis (k=0.958) or for the categorization (k=0.76). The most plausible explanations of the PCCRCs were 8 “likely new PCCRCs”, 1 (4%) “detected, not resected”, 3 (12%) “detected, incomplete resection”, 8 (32%) “missed lesion, inadequate examination”, and 13 (52%) “missed lesion, adequate examination”. Most PCCRCs were deemed noninterval Type C PCCRCs (N=17, 51.5%). Conclusion: WEO recommendations for root-cause analysis and categorization are useful to detect areas for improvement. Most PCCRCs were avoidable and were likely due to missed lesions during an otherwise adequate examination.(AU)

Objetivo: La Organización Mundial de Endoscopia recomienda que las unidades de endoscopia implementen procedimientos para identificar el cáncer colorrectal poscolonoscopia (CCRPC). Los objetivos de este estudio fueron evaluar la tasa de CCRPCP a los 3 y 4 años, realizar un análisis de causalidad potencial y categorización siguiendo las recomendaciones de la Organización Mundial de Endoscopia.Pacientes y métodos: Se incluyeron retrospectivamente los cánceres colorrectales diagnosticados de enero de 2018 a diciembre de 2019 en un hospital de tercer nivel. Se calculó la tasa de CCRPC a 3 años. Se realizó un análisis de causalidad potencial y categorización de los CCRPC (intervalo y CCRPC de no intervalo tipo A, B, C). Se evaluó la concordancia entre dos endoscopistas expertos. Resultados: Se incluyeron 530 cánceres colorrectales. Un total de 33 se consideraron CCRPC (edad 75,8±9,5 años; 51,5% mujeres). La tasa de CCRPC a 3 y 4 años fue del 3,4% y 4,7% respectivamente. La concordancia entre los dos endoscopistas fue aceptable para el análisis de causalidad (k=0,958) y para la categorización (k=0,76). La explicación probable de los CCRPC fue: 8 «probable CCRPC de novo», 1 (4%) «detectado, no resecado», 3 (12%) «detectado, resección incompleta», 8 (32%) «no detectado, examen inadecuado» y 13 (52%) «no detectado, examen adecuado». La mayoría de los CCRPC se consideraron de no intervalo tipo C (N=17, 51,5%). Conclusión: Las recomendaciones de la Organización Mundial de Endoscopia para el análisis de causalidad y la categorización son útiles para detectar áreas de mejora. La mayoría de los CCRPC eran evitables debido a lesiones no detectadas a pesar de realizar un examen adecuado.(AU)

Primer Part 1 - Preparing a laboratory quality improvement project.

Quality in laboratory medicine encompasses multiple components related to total quality management, including quality control (QC), quality assurance (QA), quality indicators, and quality improvement (QI). Together, they contribute to minimizing errors (pre-analytical, analytical, or post-analytical) in clinical service delivery and improving process appropriateness and efficiency. In contrast to static quality benchmarks (QC, QA, quality indicators), the QI paradigm is a continuous approach to systemic process improvement for optimizing patient safety, timeliness, effectiveness, and efficiency. Healthcare institutions have placed emphasis on applying the QI framework to identify and improve healthcare delivery. Despite QI's increasing importance, there is a lack of guidance on preparing, executing, and sustaining QI initiatives in the field of laboratory medicine. This has presented a significant barrier for clinical laboratorians to participate in and lead QI initiatives. This three-part primer series will bridge this knowledge gap by providing a guide for clinical laboratories to implement a QI project that issuccessful and sustainable. In the first article, we introduce the steps needed to prepare a QI project with focus on relevant methodology and tools related to problem identification, stakeholder engagement, root cause analysis (e.g., fishbone diagrams, Pareto charts and process mapping), and SMART aim establishment. Throughout, we describe a clinical vignette of a real QI project completed at our institution focused on serum protein electrophoresis (SPEP) utilization. This primer series is the first of its kind in laboratory medicine and will serve as a useful resource for future engagement of clinical laboratory leaders in QI initiatives.

The effect of family-centered care on unplanned emergency room visits, hospital readmissions and intensive care admissions after surgery: a root cause analysis from a prospective multicenter study in the Netherlands.

BACKGROUND: Optimizing transitional care by practicing family-centered care might reduce unplanned events for patients who undergo major abdominal cancer surgery. However, it remains unknown whether involving family caregivers in patients' healthcare also has negative consequences for patient safety. This study assessed the safety of family involvement in patients' healthcare by examining the cause of unplanned events in patients who participated in a family involvement program (FIP) after major abdominal cancer surgery. METHODS: This is a secondary analysis focusing on the intervention group of a prospective cohort study conducted in the Netherlands. Data were collected from April 2019 to May 2022. Participants in the intervention group were patients who engaged in a FIP. Unplanned events were analyzed, and root causes were identified using the medical version of a prevention- and recovery-information system for monitoring and analysis (PRISMA) that analyses unintended events in healthcare. Unplanned events were compared between patients who received care from family caregivers and patients who received professional at-home care after discharge. A Mann-Whitney U test was used to analyze data. RESULTS: Of the 152 FIP participants, 68 experienced an unplanned event and were included. 112 unplanned events occurred with 145 root causes since some unplanned events had several root causes. Most root causes of unplanned events were patient-related factors (n = 109, 75%), such as patient characteristics and disease-related factors. No root causes due to inadequate healthcare from the family caregiver were identified. Unplanned events did not differ statistically (interquartile range 1-2) (p = 0.35) between patients who received care from trained family caregivers and those who received professional at-home care after discharge. CONCLUSION: Based on the insights from the root-cause analysis in this prospective multicenter study, it appears that unplanned emergency room visits and hospital readmissions are not related to the active involvement of family caregivers in surgical follow-up care. Moreover, surgical follow-up care by trained family caregivers during hospitalization was not associated with increased rates of unplanned adverse events. Hence, the concept of active family involvement by proficiently trained family caregivers in postoperative care appears safe and feasible for patients undergoing major abdominal surgery.

Rapid Root Cause Analysis: Improving OBGYN Resident Exposure to Quality Improvement and Patient Safety Curricula.

Introduction Each year, millions of patients in the United States experience harm as a result of the healthcare they receive. One mechanism used by health systems to learn how and why errors occur is root cause analysis (RCA). RCA teams develop action plans to create and implement systemic changes in healthcare delivery in order to prevent future harm. The American Council on Graduate Medical Education (ACGME) recognizes the importance of analyzing adverse events, and it requires that all residents participate in real or simulated patient safety activities, such as RCAs. Often, institutional RCAs necessitate the assimilation of participants on short notice and demand considerable time investment, limiting the feasible participation of graduate medical education (GME) trainees. This presents a gap between ACGME expectations and the reality of resident involvement in patient safety activities. We present the first iteration of a quality improvement project encompassing a three-hour resident physician training course with simulated RCA-experiential learning. The purpose of this project was to produce a condensed, educational RCA experience that adequately trains all GME learners to serve as informed healthcare safety advocates while also satisfying ACGME requirements. Methods The course ("rapid RCA") was conducted during protected weekly academic training. All residents of the San Antonio Uniformed Services Health Education Consortium (SAUSHEC) Obstetrics and Gynecology (OBGYN) residency program who had not previously participated in a real or simulated RCA were required to take the "rapid RCA." Pre- and post-course surveys were completed anonymously to assess baseline knowledge, new knowledge gained from the course, and attitudes toward the course and its importance to resident training. Results Fourteen OBGYN residents attended the "rapid RCA," indicating that 64% (14 out of 22) of the program had no previous experience or opportunity to participate in a real or simulated RCA. Participation in the course demonstrated a significant gain of new knowledge with an increase from 0/14 to 10/14 (71%) residents correctly answering all pre- and post-course questions, respectively (p < 0.001). Additionally, on a Likert scale from 1 to 5, with 5 indicating "expert level," residents indicated they felt more comfortable on patient safety topics after taking the course (mean pre-course score 1.85 to post-course score 3.64, p < 0.001). All participants indicated they would prefer to take the "rapid RCA" as opposed to the only available local alternative option for a simulated RCA, currently offered as a full-day intensive course. Conclusion A meaningful increase in patient safety knowledge and attitudes toward topics covered in an RCA was demonstrated through the implementation of a "rapid RCA" in OBGYN residents. We plan to incorporate this into our annual curriculum to satisfy ACMGE requirements. This format could be adapted for other specialties as applicable.

Root Cause Analysis of Gaps in Non-communicable Disease Monitoring in a Sub-district Hospital, Tamil Nadu: A Quality Improvement Initiative.

Introduction Non-communicable diseases (NCDs) present a significant public health challenge globally, and India is deeply affected. With the largest population in the world, India struggles with a high burden of NCDs, encompassing cardiovascular diseases, diabetes, cancer, and chronic respiratory conditions. These ailments contribute substantially to morbidity and mortality, placing a strain on healthcare systems. Despite efforts through public health initiatives, NCD monitoring and management remain deficient, especially at grassroots levels. Methods At a sub-district hospital in Tamil Nadu, India, a quality improvement initiative targeted diabetes and hypertension, prevalent NCDs. Utilizing Fishbone analysis and process flow diagrams, we identified gaps in NCD monitoring. Employing the Plan-Do-Study-Act model and reorienting the patient flow, we enhanced NCD monitoring by optimizing patient health record maintenance within the hospital. Results Root cause analysis identified a lack of patient record protocols and patient loss of records as key hindrances in NCD monitoring. We revamped patient flow and implemented a robust record-keeping system, boosting access to patient health records. This initiative was embraced by healthcare providers, enhancing NCD management. Leveraging these records, we assessed control rates of diabetes and hypertension patients effectively. Conclusion The research underscores the importance of maintaining comprehensive patient health records in healthcare centers for enhancing NCD monitoring. These records serve as valuable tools for healthcare providers, aiding in the monitoring and treatment of patients with diabetes and hypertension. By leveraging these records, healthcare providers can achieve better disease control outcomes, thereby improving the overall management of NCDs.

Safety incident reporting and barriers (SIRaB) study: Strategies and approaches for investigating patient safety events in a hospital set-up.

BACKGROUND: Unsafe patient events not only entail a clinical impact but also lead to economic burden in terms of prolonged hospitalization or unintended harm and delay in care delivery. Monitoring and time-bound investigation of patient safety events (PSEs) is of paramount importance in a healthcare set-up. OBJECTIVES: To explore the safety incident reporting behaviour and the barriers in a hospital set-up. METHODS: The study had two sections: (a) Retrospective assessment of all safety incidents in the past 1 year, and (b) Understanding the barriers of safety reporting by interviewing the major stakeholders in patient safety reporting framework. Further root cause analysis and failure mode effect analysis were performed for the situation observed. Results were statistically analyzed. RESULTS: Of the total of 106 PSEs reported voluntarily to the system, the highest reporting functional group was that of nurses (40.57%), followed by physicians (18.87%) and pharmacists (17.92%). Among the various factors identified as barriers in safety incident reporting, fear of litigation was the most observed component. The most commonly observed event was those pertaining to medication management, followed by diagnostic delay. Glitches in healthcare delivery accounted for 8.73% of the total reported PSEs, followed by 5.72% of events occurring due to inter-stakeholder communication errors. 4.22% of the PSEs were attributed to organizational managerial dysfunctionalities. Majority of medication-related PSE has moderate risk prioritization gradation. CONCLUSION: Effective training and sensitization regarding the need to report the patient unsafe incidents or near misses to the healthcare system can help avert many untoward experiences. The notion of 'No Blame No Shame' should be well inculcated within the minds of each hospital unit such that even if an error occurs, its prompt reporting does not get harmed.

Factors impacting clinical data and documentation quality in Australian aged care and disability services: a user-centred perspective.

BACKGROUND: Research has highlighted a need to improve the quality of clinical documentation and data within aged care and disability services in Australia to support improved regulatory reporting and ensure quality and safety of services. However, the specific causes of data quality issues within aged care and disability services and solutions for optimisation are not well understood. OBJECTIVES: This study explored aged care and disability workforce (referred to as 'data-users') experiences and perceived root causes of clinical data quality issues at a large aged care and disability services provider in Western Australia, to inform optimisation solutions. METHODS: A purposive sample of n = 135 aged care and disability staff (including community-based and residential-based) in clinical, care, administrative and/or management roles participated in semi-structured interviews and web-based surveys. Data were analysed using an inductive thematic analysis method, where themes and subthemes were derived. RESULTS: Eight overarching causes of data and documentation quality issues were identified: (1) staff-related challenges, (2) education and training, (3) external barriers, (4) operational guidelines and procedures, (5) organisational practices and culture, (6) technological infrastructure, (7) systems design limitations, and (8) systems configuration-related challenges. CONCLUSION: The quality of clinical data and documentation within aged care and disability services is influenced by a complex interplay of internal and external factors. Coordinated and collaborative effort is required between service providers and the wider sector to identify behavioural and technical optimisation solutions to support safe and high-quality care and improved regulatory reporting.

[Investigation on the current situation and influencing factors of sitting time and health literacy among high school students in China].

OBJECTIVE: To investigate the current situation of sitting time and health literacy among high school students in China, in order to provide a basis for improving their physical and mental health levels. METHODS: A stratified random cluster sampling method was used to investigate the length of sitting time and health literacy of first and second grade high school students from 31 provinces, cities, and autonomous regions in China(data did not include that of Hong Kong and Macao Special Administrative Region, and Taiwan Province of China). The Kruskal-Wallis H method, independent sample Mann-Whitney U test, and regression model were used to analyze the influencing factors of sitting time and total health literacy score. RESULTS: (1) The total score of health literacy was statistically significant (P < 0.01) in different regions, urban and rural distribution, annual family income, parents' educational background, age, and gender. (2) The length of sitting was statistically significant (P < 0.01) among multiple groups in different regions, family annual income, parental education, and gender. However, there was no statistically significant difference between groups of different ages and urban-rural distribution (P>0.05). (3) The analysis of multiple linear regression model showed that the total score of health literacy was positively correlated with the family' s annual income and the mother' s education, and negatively correlated with the father' s education and the length of sitting. Standardized regression coefficient ß comparison: Father' s education (-0.32) > family annual income (0.15) > mother' s education (0.09) > average daily sitting time (-0.02), with father' s education having the greatest impact, followed by family annual income. The length of sitting was positively related to the family' s annual income and the mother' s educational background, and negatively related to the total score of health literacy. Standardized regression coefficient ß comparison: Annual family income (0.14) > education background of mother (0.13)> total score of health literacy (-0.02), with the impact of annual family income the largest, followed by education background of mother. CONCLUSION: China' s first and second grade high school students generally spend a long time sitting every day, and the level of health literacy is generally low. The level of health literacy and sitting time are negatively correlated with each other, and are most influenced by the educational background of high school students' parents and their family economic levels.

A 5-step root cause analysis model for test overutilization: A study on its application to plasma transferrin testing.

OBJECTIVES: This study aimed to develop a root cause analysis (RCA) model for test overutilization, applying it to transferrin overordering at our institution. METHODS: A comprehensive review was undertaken to establish a systematic RCA model. Upon implementation, the questionnaire identifying the root causes of transferrin overordering with infographic intervention was distributed to clinicians and nurses. RESULTS: The RCA model comprises 5 steps: (1) problem identification, (2) causal factor determination, (3) data collection, (4) significant factor identification, and (5) corrective action development and outcome measurement. The major causes of transferrin overutilization were confusion between transferrin and transferrin saturation, as well as unfamiliarity with the laboratory handbook. An infographic reduced postintervention transferrin ordering among clinicians (84.9%, P < .001) and nurses (46.8%, P < .001). CONCLUSIONS: This study presents a 5-step RCA model that offers a customized method to identify the causes of test overutilization. Applying this model to transferrin at our institution revealed 22 leading root causes. Laboratories are encouraged to adopt this RCA model as it can contribute to optimized patient care and more efficient resource allocation.

Root Cause Analysis: Unraveling Common Laboratory Challenges.

Diverse errors occur in a pathology laboratory and manual mistakes are the most common. There are various advancements to replace manual procedures with digitized automation techniques. Guidelines and protocols are available to run a standard pathology laboratory. But, even with such attempts to reinforce and strengthen the protocols, the complete elimination of errors is yet not possible. Root cause analysis (RCA) is the best way forward to develop an error-free laboratory, In this review, the importance of RCA, common errors taking place in laboratories, methods to carry out RCA, and its effectiveness are discussed in detail. The review also highlights the potential of RCA to provide long-term quality improvement and efficient laboratory management.

Building causal models for finding actual causes of unmanned aerial vehicle failures.

Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause-effect phrases are extracted from sentences based on predefined dependency rules between tokens. Finally, the extracted cause-effect pairs are merged to form a causal graph, which we then use for ACA. To demonstrate the applicability of our framework, we scrape online text resources of Ardupilot, an open-source UAV controller software. Our evaluations using real flight logs show that the generated graphs can successfully be used to find the actual causes of unwanted events. Moreover, our hybrid cause-effect extraction module performs better than a purely deep-learning based tool (i.e., CiRA) by 32% in precision and 25% in recall in our Ardupilot use case.