Assessment System Research Articles

Point-of-care ultrasonography spotlight: Could venous excess ultrasound serve as a shared language for internists and intensivists?

Point-of-care ultrasonography (POCUS), particularly venous excess ultrasound (VExUS) is emerging as a valuable bedside tool to gain real-time hemodynamic insights. This modality, derived from hepatic vein, portal vein, and intrarenal vessel Doppler patterns, offers a scoring system for dynamic venous congestion assessment. Such an assessment can be crucial in effective management of patients with heart failure exacerbation. It facilitates diagnosis, quantification of congestion, prognostication, and monitoring the efficacy of decongestive therapy. As such, it can effectively help to manage cardiorenal syndromes in various clinical settings. Extended or eVExUS explores additional veins, potentially broadening its applications. While VExUS demonstrates promising outcomes, challenges persist, particularly in cases involving renal and liver parenchymal disease, arrhythmias, and situations of pressure and volume overload overlap. Proficiency in utilizing spectral Doppler is pivotal for clinicians to effectively employ this tool. Hence, the integration of POCUS, especially advanced applications like VExUS, into routine clinical practice necessitates enhanced training across medical specialties.

Ship collision risk assessment: A multi-criteria decision-making framework based on Dempster–Shafer evidence theory

Ship collision risk modelling is dominated by the utilization of the experts’ experiential insights and knowledge. To construct an effective assessment model for evaluating potential ship collision risks grounded in expert judgments, this paper introduces a novel multi-criteria decision-making framework based on Dempster–Shafer evidence theory (DSET). Firstly, a novel index-based assessment system is provided to generate the potential collision risk value of ships based on automatic identification system (AIS) data, which encompasses ship condition parameters, deviation coefficient, and frequency coefficient. Further, to generate the weights of these indicators, a framework to extract expert opinions is provided, which includes two parts: a) converting experts’ opinions into D-S framework through hesitant fuzzy linguistic terms and b) generating experts’ opinions using improved DSET. Regarding part a, a methodology is proposed to evaluate the trustworthiness of experts by considering both the level of uncertainty in their opinions and the degree of similarity between their viewpoints. Concerning part b, strategies are integrated to ascertain the most suitable weight for the basic probability assignment, thereby mitigating conflicts within the evidence. Finally, based on the collected AIS data, the proposed framework has been applied to six of the world's busiest waterways: (1) the Strait of Malacca, (2) the English Channel, (3) the Panama Canal, (4) the Suez Canal, (5) the Danish Straits, and (6) the Strait of Hormuz. Subsequently, comprehensive analytical results and recommendations are furnished, such as wide and smooth channels showing elevated risk.

System for assessment and prediction of the technical condition of power oil-filled transformer equipment of distribution networks using machine learning

RELEVANCE the research is to develop a new system for assessing the technical condition of power oil-filled transformer equipment of distribution networks.OBJECT. To increase the accuracy of assessing the technical condition of power oil-filled transformer equipment (POTE) of distribution networks through the use of machine learning methods. Currently, an increase in the volume of analyzed information about the state of the management system of distribution networks leads to significant changes in the choice of data processing methods. The use of machine learning methods is associated both with the need to apply operational experience (in the form of expert assessments) and to obtain objective assessments of the condition of transformer equipment of distribution networks from instrumentation and sensors.METHODS. This work uses research methods such as mathematical modeling and the method of paired comparisons. As an example, we consider the oil-filled power transformer TMN-6300, its diagnostic parameters, external and operating parameters. The technical condition of the TMN-6300 transformer is assessed and a predictive model is created based on the existing monitoring system and machine learning methods, which make it possible to formalize expert knowledge and automate the process of data processing and analysis.RESULTS. A database has been created to assess and predict the technical condition of POTE of distribution network management systems. The algorithm for predicting the technical condition of POTE of the technical equipment in the form of an artificial neural network model was tested in the developed assessment system.CONCLUSION. The results of assessing and predicting the technical condition of POTE of the metering system of distribution networks obtained in this work prove the unconditional relationship between the parameters of the metering system and external, operating parameters. The data obtained as a result of modeling helps to increase the accuracy of forecasting the technical condition and determine the longterm prospects for the functioning of POTE the equipment management system, timely maintenance and repairs over the course of years and months.

Investigating the Impact of Machine Learning in Personalized Education Systems

Personalized education systems leverage robust capabilities of machine learning (ML). Tailoring learning experiences to individual needs preferences and abilities. This survey explores integration of ML in personalized education. It examines methodologies. It highlights applications impacts Future directions are discussed. Key ML techniques include supervised learning for performance prediction and feedback personalization. Unsupervised learning for identifying learning styles. Reinforcement learning.Personalized education systems leverage robust capabilities of machine learning (ML). Tailoring learning experiences to individual needs preferences and abilities. This survey explores integration of ML in personalized education. It examines methodologies. Highlighting applications. Impacts and future directions are discussed. Key ML techniques include supervised learning for performance prediction, feedback personalization unsupervised learning for identifying learning styles, reinforcement learning. Optimizing content sequences. Deep learning for tasks like automated grading. Intelligent tutoring included. Applications such as intelligent tutoring systems. Adaptive learning platforms. Learning analytics. Personalized learning paths and automated assessment systems discussed. The impact of ML-driven personalized education systems is profound. They enhance engagement. Motivation. Academic performance and reduce dropout rates. Challenges such as data privacy algorithmic bias, scalability and resistance to change are also addressed. Future research directions include interdisciplinary collaboration. Advancements in natural language processing are essential. Enhanced personalization algorithms are needed. Long-term studies. Considered crucial. Areas for ongoing work. Key Words: Machine learning, Personalized education, Adaptive learning, Intelligent tutoring systems, Predictive analytics, Recommendation systems, Ethical considerations, Student engagement, Educational technology

An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development.

Integrated electronic system for FET biosensor assessment based on current-voltage curve tracing

Field-effect transistor (FET) biosensors are pivotal in diverse applications, from environmental monitoring to healthcare diagnostics. Current-voltage (I-V) curve tracing is a powerful method for evaluating FET biosensor behavior, enabling comprehensive analysis of their FET biosensor characteristics. Traditional I-V curve tracing methods often require complex and expensive equipment, limiting their accessibility and practicality for routine sensor assessment. This study aims to develop and demonstrate an integrated electronic system for assessing FET biosensors using I-V curve tracing. The integrated electronic system uses readily available components, including microcontrollers, analog circuitry, and user-friendly software. We developed a compact, low-cost device that generates I-V curves for the FET biosensor. The integrated electronic system successfully generated I-V curves for various FET biosensors. The system demonstrated consistent, reliable performance, portability, and ease of use, making it a practical solution for routine sensor assessment. The average error in measurements using bipolar junction transistors (BJT) and metal-oxide-semiconductor field-effect transistors (MOSFETs) results in 2.62%, and measurements at different pH levels have a sensitivity of 21.6 mV/pH and a linearity of 0.9892. This innovation contributes to the advancement of FET biosensor technology. In the future, the developments should focus on ensuring their accuracy and reliability in various sensor fields.

Dental Pulp Vascular Response to Early Stages of Caries

ObjectivesDuring caries progression, dental pulp is increasingly pathologically affected. Since the accurate assessment of pulp is of vital importance in clinical decision-making, this study aimed to evaluate pulpal condition in the early stages of caries via laser Doppler (LD) flowmetry and histologic analysis and determine their agreement. MethodsFourteen patients with severe dental crowding were included. Prior to extractions and orthodontic treatment, dental pulp condition of 52 premolars was evaluated via LD flowmetry. Teeth were assessed for the presence of caries and lesions were graded according to the International Caries Detection and Assessment System (ICDAS). After extractions, teeth were split and histologically stained for endothelial cells with anti–von Willebrand factor and Movat pentachrome for collagen. Volume densities of vessels (Vvasc) and collagen were calculated. ResultsThere was a significant negative correlation between LD flow and Vvasc of the dental pulp with ICDAS grade. Pulpal LD flow and Vvasc in teeth with the initial lesion were increased, decreasing with progressing stages of caries. A significant positive correlation between the the pulpal LD flow and Vvasc, and a negative correlation of LD flow with collagen fibre density were noted. ConclusionsCaries affects the physiology of the dental pulp, initially with increasing vascularity, and decreasing vascularity at later stges of caries progression. Collagen contents increase with grades of ICDAS. LD flow shows good agreement with the histologic constitution of the dental pulp. Use of clinical measurements of pulpal LD flow could provide a good noninvasive indication of pulpal vascular state and its health.

A sustainability-oriented approach for performance assessment of existing buildings and a case study

The practice of sustainable buildings has become a general trend in urban development. Yet, with the rapid development of urban renewal, the performance of existing buildings often fails to meet the actual needs of sustainable urban development. To improve the performance of existing buildings, it is urgently needed to transform the existing structures and upgrade their functions and facilities. However, the pertinent guidelines have often been unclear for assessing the performance of existing structures, and a sounder and quantitative sustainable performance assessment approach has been lacking to guide decisions on the restoration, protection, or demolition of existing buildings. Based on a multi-expert consultation methodology, we proposed a revised flexible comprehensive assessment system and a comprehensive sustainability assessment approach. The system includes 4 categories, 7 quantitative and 47 qualitative indicators. Specifically, the assessment system involves a series of steps: 1) building a hierarchical model, 2) defining assessment indicators/items and constructing a judgment matrix, 3) determining the weight coefficients of the indicators, 4) collecting/calculating quantitative and qualitative data, 5) analyzing the data and results, and 6) drawing the assessment conclusions. A case study was presented to demonstrate the application of the approach. Compared to current practices, this comprehensive assessment system may serve as a scientifically sounder and quantitative approach to guide sustainable development of existing urban buildings.

Comprehensive Method for Estimating the Time and Expenditures Required for Mine Liquidation Processes of Business Processes

Abstract The European Green Deal (EU Green Deal) has set the direction for the EU’s energy transition towards climate neutrality by 2050. In Poland, this means moving away from the extraction and use of coal. The Social Contract for the Mining Industry signed in 2021 states the necessity of last mine closure by 2049. Mine closure is a complex, lengthy and costly process. A complex scientific solution may concern the use of rational operations and minimization of mine closure costs. This article presents a system for the elementary assessment of the potential time and cost of coal mine liquidation. Estimating coal mine closure costs in the early design phase is an key aspect of supporting the company dealing with mine closure. The aim of the research was to improve the tool for assessing mine liquidation price. The extended assessment solution proposed in the article is formed on base of statistics of past mining institution liquidation processes. This method can, with minor modifications, be used for each restructuring and revitalizing task for mining industries in the process of liquidation. At the core of the developed method is a preliminary data analysis, which should be confirmed by a complex and multi-criteria estimation of the costs of the planned mine liquidation.

Sustavi ocjenjivanja održivosti infrastrukturnih građevina

In light of global changes and in line with the principles of sustainable development, there is a need to find criteria for assessing the sustainability of infrastructure in the construction sector. This paper provides an overview of the theory of sustainability in general and in the construction sector, as well as the policies and strategies at the global and national levels that aim to achieve sustainable development. Various rating systems for the sustainability assessment of infrastructure are listed as a methodology for evaluating the implementation of sustainable approaches in the realisation of new infrastructure projects. The three rating systems for sustainability assessment Envision, BREEAM Infrastructure and Infrastructure Sustainability (IS), which can be used to assess the sustainability of all types of infrastructure and to obtain a certificate for the application of sustainability principles, are presented and discussed in more detail.

Consideration of socially significant personality traits of a convicted person in the organization of the process of penitentiary prevention

The problem of the recidivism of punishment in the form of imprisonment, traditionally refers to the most pressing problems of social science. This is due to the fact that being isolated from society, the convict is forced to integrate into the social education that has been formed in places of deprivation of liberty. The specific relationships that exist among convicts in the penitentiary society also leave a certain imprint on the nature of those relationships that are considered acceptable in this environment. The administration of correctional institutions is forced to take into account the system of informal relations that has developed among convicts in order to implement penitentiary practices necessary for the correction of convicts. For many decades, a system of qualitative assessment of the personality of convicts serving sentences of imprisonment has been formed. The article notes that there is a rather unhealthy practice of focusing on the criminal characteristics of the convict's personality, which began to influence the shift in emphasis in the application of penitentiary practices from correction to punishment. This is most clearly seen in the institute of preventive accounting used in correctional institutions. The Administration is making efforts to identify negative personality traits in convicts in order to use them in determining priorities in the application of penitentiary practices. The authors propose to consider such an approach as hindering the effective use of penitentiary prevention. As a result, the article proposes to shift the focus in the activities of the correctional institution administration to socially significant personality traits of the convicted person in order to correctly determine priorities in the process of penitentiary prevention.

Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions

Histopathological assessment of esophageal biopsies is a key part in the management of patients with Barrett esophagus (BE) but prone to observer variability and reliable diagnostic methods are needed. Artificial intelligence (AI) is emerging as a powerful tool for aided diagnosis but often relies on abstract test and validation sets while real-world behavior is unknown. In this study, we developed a 2-stage AI system for histopathological assessment of BE-related dysplasia using deep learning to enhance the efficiency and accuracy of the pathology workflow. The AI system was developed and trained on 290 whole-slide images (WSIs) that were annotated at glandular and tissue levels. The system was designed to identify individual glands, grade dysplasia, and assign a WSI-level diagnosis. The proposed method was evaluated by comparing the performance of our AI system with that of a large international and heterogeneous group of 55 gastrointestinal pathologists assessing 55 digitized biopsies spanning the complete spectrum of BE-related dysplasia. The AI system correctly graded 76.4% of the WSIs, surpassing the performance of 53 out of the 55 participating pathologists. Furthermore, the receiver-operating characteristic analysis showed that the system’s ability to predict the absence (nondysplastic BE) versus the presence of any dysplasia was with an area under the curve of 0.94 and a sensitivity of 0.92 at a specificity of 0.94. These findings demonstrate that this AI system has the potential to assist pathologists in assessment of BE-related dysplasia. The system’s outputs could provide a reliable and consistent secondary diagnosis in challenging cases or be used for triaging low-risk nondysplastic biopsies, thereby reducing the workload of pathologists and increasing throughput.

Systematic analysis of levels of evidence supporting Chinese clinical practice guidelines for gastrointestinal disease.

Clinical practice guidelines (CPGs) inform healthcare decisions and improve patient care. However, an evaluation of guidelines on gastrointestinal diseases (GIDs) is lacking. This study aimed to systematically analyze the level of evidence (LOE) supporting Chinese CPGs for GIDs. CPGs for GIDs were identified by systematically searching major databases. Data on LOEs and classes of recommendations (CORs) were extracted. According to the Grades of Recommendation, Assessment, Development, and Evaluation system, LOEs were categorized as high, moderate, low, or very low, whereas CORs were classified as strong or weak. Statistical analyses were conducted to determine the distribution of LOEs and CORs across different subtopics and assess changes in evidence quality over time. Only 27.9% of these recommendations were supported by a high LOE, whereas approximately 70% were strong recommendations. There was a significant disparity among different subtopics in the proportion of strong recommendations supported by a high LOE. The number of guidelines has increased in the past 5 years, but there has been a concomitant decline in the proportion of recommendations supported by a high LOE. There is a general lack of high-quality evidence supporting Chinese CPGs for GIDs, and there are inconsistencies in strong recommendations that have not improved. This study identified areas requiring further research, emphasizing the need to bridge these gaps and promote the conduct of high-quality clinical trials. This study was supported by the National Key R&D Program of China (2022YFC2503604 and 2022YFC2503605) and Special Topics in Military Health Care (22BJZ25).

The Opportunity for Educational Innovations and Requirements in Academic System Reform of Medical Schools

The amendment to the Higher Education Act enforced on February 20, 2024, abolishing the traditional 2-year pre-med and 4-year medical school programs, marks a significant shift in medical education in Korea. The academic system reform is expected to be a driving force for large-scale curriculum revision, presenting an opportunity to introduce new educational innovations not only in curriculum but also in student assessment and support systems. Addressing these challenges requires collaborative efforts among educators, students, and communities to navigate the evolving landscape of medical education effectively. In this regard, I will illustrate the recruitment and development of educators to implement the reform and the collaboration between communities and medical schools to innovate medical education.

Assessment of an AI-based tool for population-wide collection of placental morphological data

ObjectivesAutomated placental assessment could allow accurate and timely morphological/pathological measurements at scale. We undertook a pilot study using an artificial intelligence-based assessment system (AI-PLAX) to ascertain the potential of a state-wide rollout as part of Generation Victoria, assessing the impact of time post-delivery, user, and technology used for image capture, on a range of derived placental data. Study designTen placentas were imaged by three different users and imaging technologies (iPad, iPhone, Samsung) at (0 h), 24 h, and 48 h post-delivery. Using AI-PLAX, disc size (short and long length, perimeter, area), shape (normal, abnormal), cord insertion type (central, eccentric), cord coiling, abruption (retroplacental hematoma), and meconium staining were determined. ResultsWhen analysing the maternal surface of the placenta, time in cold storage post-delivery had modest effects on placental dimensions, with decreases in the short length (24–48 h: −3.7 %), disc area (0–24 h: 4.7 % and 0–48 h: −7.4 %), and perimeter (0–48 h: −3.8 %) observed. There was marginal impact on placental dimensions when the placenta was imaged by different users, including long length (+1.9 %), disc area (+2.9 %), and perimeter (+2.0 %). Measures of placental size were not impacted by the type of technology used to capture the images. When analysing the fetal surface of the placenta, more variance in placental size measures were observed between users. Abruption detection was not affected by any parameter. Time between delivery and imaging impacted apparent meconium staining – likely reflecting changes in fetal surface colour over time. Meconium staining was not affected by technology or user. ConclusionsThis study supports the feasibility of the collection of placenta images for later morphological analysis by AI-PLAX, with measures obtained minimally influenced by time in cold storage, user imaging the placenta, or technology to capture the images.

Longitudinal changes in patient-reported outcomes (PROs) among early phase cancer clinical trial (EP-CT) participants.

e23188 Background: Little research has explored associations of longitudinal changes in PROs and clinical outcomes among EP-CT participants. We sought to describe changes in PROs over the first month of EP-CT participation and examine associations of changes with clinical outcomes. Methods: We prospectively enrolled adults participating in EP-CTs at Massachusetts General Hospital from 04/2021-01/2023. We assessed quality of life (QOL) (Functional Assessment of Cancer Therapy-General, with subdomains: physical wellbeing [PWB], emotional wellbeing [EWB], social wellbeing [SWB], functional wellbeing [FWB]); hope (Herth Hope Index); symptom burden (Edmonton Symptom Assessment System); and financial wellbeing (Comprehensive Score for Financial Toxicity) at time of EP-CT enrollment and one month later (1-month). We used descriptive statistics and regression models to describe 1-month changes in PROs and explore associations of patient-reported QOL, hope, symptom burden, and financial wellbeing with patient factors (age, diagnosis, performance status) and clinical outcomes (time on trial [TOT], treatment response, ED visits, hospitalizations, and survival). Results: Of 205 enrolled patients, 159 (77.9%) had baseline and 1-month data evaluable for analysis (median age: 63.0 [range 33.0 – 88.6], 59.8% female, 93.6% metastatic cancer, ECOG 0 = 46.2% and ECOG 1 = 53.8%). Most common cancer types were gastrointestinal (31.5%) and breast (22.0%). There was no significant change from baseline to 1-month in mean QOL (75.2 to 76.2), hope (27.1 to 26.7), ESAS total symptom burden (20.4 to 20.9), ESAS physical symptoms (13.3 to 13.7), and financial wellbeing (28.5 to 28.0). There was no significant change in overall QOL and in each subdomain with the exception of EWB (16.2 to 17.0, p < .001). Older age was associated with increased hope (B = 1.29, p = .008) and increased QOL (B = 3.02, p = 0.024). Increasing EWB at 1-month was associated with longer TOT (HR = 0.93, p = .011). Increased symptom burden at 1-month was associated with greater risk of ED visits (HR = 1.04, p = .005) and hospitalizations (HR = 1.03, p = .012). Improving financial wellbeing at 1-month was associated with lower risk of hospitalization (HR = 0.96, p = .046). We found no other significant associations of 1-month changes in PROs with treatment response or survival. Conclusions: We described changes in PROs from baseline to 1-month and identified factors associated with these changes. Despite the rigors and intensity associated with EP-CT initiation, most PROs did not worsen, with significantly improved EWB after 1-month of participation. Results demonstrate changes in physical symptoms predicted for greater risk of ED visits and hospitalizations. Future studies should build upon this work and measure PROs throughout trial participation to identify factors associated with changes and guide intervention development.

Associations of symptom burden with patient-reported outcomes (PROs) and clinical outcomes among early-phase cancer clinical trial (EP-CT) participants.

11101 Background: Individuals with cancer often endure a substantial physical and psychological symptom burden. However, little is known about the baseline symptom burden of patients with cancer participating in EP-CTs or the associations of this symptom burden with other PROs and clinical outcomes. Methods: We prospectively enrolled adults with cancer participating in EP-CTs at Massachusetts General Hospital from 4/2021-1/2023. Participants completed baseline surveys prior to EP-CT initiation that assessed symptoms (Edmonton Symptom Assessment System [ESAS]), quality of life (QOL; Functional Assessment of Cancer Therapy-General), hope (Herth Hope Index), depression/anxiety symptoms (Patient Health Questionnaire-4 [PHQ-4]), and financial wellbeing (COST tool, higher scores indicate greater financial wellbeing). We used regression models to explore associations of baseline symptom burden with other PROs (QOL, hope, depression/anxiety, financial wellbeing) and clinical outcomes (time on trial, hospitalizations, overall survival). Results: Among 205 participants (median age=63.3 [range: 31.8-88.6], 57.1% female), the most common cancer types were gastrointestinal (34.6%), breast (20.0%), and head and neck (10.2%). Based on the ESAS, approximately half of participants reported moderate/severe fatigue (50.2%) and poor wellbeing (49.5%). Under half reported moderate/severe drowsiness (35.7%), pain (25.9%), lack of appetite (25.4%), shortness of breath (20.0%), anxiety (18.0%), depression (15.4%), and nausea (9.2%). Higher baseline ESAS total, physical, and psychological scores were associated with worse QOL, lower hope, greater PHQ-4 depression/anxiety symptoms, and worse financial wellbeing (see Table). Higher baseline ESAS scores were associated with decreased time on trial and worse overall survival. Conclusions: In this study of EP-CT participants, we found associations of baseline symptom burden with other important PROs and clinical outcomes. Specifically, higher baseline symptom burden was associated with decreased QOL, lower hope, increased depression/anxiety symptoms, and diminished financial wellbeing as well as greater risk for shorter time on trial and worse survival. Interventions seeking to enhance care delivery and outcomes for EP-CT participants should strive to address the symptom burden of this population. [Table: see text]

EHR documentation of cost discussions and financial interventions for a cohort of patients with advanced cancer: Findings from a retrospective chart review-based study.

e23150 Background: Financial Toxicity (FT), the adverse impact of high medical costs on quality of life, notably affects many oncology patients. In a prior initiative to improve quality of life for advanced cancer patients, a palliative care-trained nurse practitioner (PCNP) was embedded in an academic oncology practice. Patients completed the Edmonton Symptom Assessment System (ESAS) and Canadian Problem Checklist (CPC) to gauge physical symptoms, psychosocial issues, and financial concerns. However, the incidence of self-reported financial issues and electronic health record (EHR) documentation of cost discussions and interventions remain unclear for this patient group. Methods: The study included adult oncology patients with advanced cancer seen by the PCNP from January 1, 2020, to January 1, 2022. We assessed incidence of self-reported financial concerns via CPC checklists in this cohort. Ten charts underwent detailed manual review for evidence of cost discussions or referral to finance-related interventions for up to 1 year from the initial consult with the PCNP or until death. During the review, we identified keywords in documentation of cost discussions and finance-related interventions to create a keyword library. We iteratively compared the library to our reference standard manual chart review to ensure 100% sensitivity. We then employed this keyword library to evaluate the remaining charts. Descriptive statistics were employed to assess correlations between self-reported financial concerns, chart documentation of cost discussions or financial interventions, and patient demographics. Results: Out of 109 eligible patients, 71% (77) completed pre-visit questionnaires, with 30% (23) reporting financial issues. Of this group, 43% (10) died during the study period. Average follow-up was 5.0 months among the decedents. Among those reporting financial concerns, 65% (15) had documented cost discussions or financial assistance referrals in the EHR, involving physicians, social workers, and other clinic staff. Most interventions addressed medication costs and referrals to financial assistance as indicated. Patient demographics (age, sex, insurance type) showed no apparent association with chart documentation of cost discussions or financial assistance. Conclusions: We used retrospective chart review to evaluate the extent of documentation of cost discussions and finance-related intervention in a cohort of patients with advanced cancer. In our patient cohort, 30% self-reported financial concerns, a proportion consistent with previously reported incidence of FT among oncology patients. Notably, 35% of the patients with known financial concerns had no other relevant documentation in the EHR. More work is needed to establish best practices for systematic screening for FT and interventions to support patients.

An Easy-to-Use Assessment System for Spasticity Severity Quantification in Post-Stroke Rehabilitation

An Easy-to-Use Assessment System for Spasticity Severity Quantification in Post-Stroke Rehabilitation

Improving symptom screening rates for patients with head and neck cancer.

e23246 Background: Effective symptom management in cancer care relies on regular and accurate reporting using validated patient-reported outcome measures (PROMs), such as the Edmonton Symptom Assessment System (ESAS). The ESAS measures nine common cancer symptoms, rating them on a scale from 0 to 10 to improve monitoring and communication regarding patient symptoms and prevent adverse outcomes such as emergency department visits. There is a need to enhance completion rates to ensure that head and neck cancer (HNC) symptoms are effectively addressed. High completion rates provide valuable data for cancer centers and clinicians to intervene early and support patients through their cancer journey. This makes it an important target for quality improvement (QI) with wide-ranging implications for patient outcomes and care quality. Our objectives were to: Investigate underlying reasons for poor ESAS completion rates. Lay the groundwork for strategic interventions aimed at bolstering symptom screening among HNC patients. Methods: This was a QI project targeting the HNC patient population at a regional cancer centre (RCC). Our approach included stakeholder engagement meetings alongside the expertise of QI specialists. Comprehensive data collection was conducted to analyze symptom screening rates and identify potential barriers to ESAS completion. We tracked ESAS completion on weekly HNC clinic days from 2022-2023 via manual and automated chart abstraction. Patient, staff, and volunteer interviews, along with direct clinic observations, provided data for Ishikawa diagrams, facilitating a root cause analysis. Results: We identified a marked decline in ESAS completion rates, steadily decreasing from an average of 29.4% during the 2022-2023 fiscal year to an average of 7.6% between April-November 2023, with rates dropping as low as 3.3% in September 2023. Root cause analysis pinpointed several barriers to ESAS completion, including the transition from paper-based assessments used during the pandemic to electronic formats, low patient awareness of the purpose of symptom screening, perceived lack of value in screening by patients and staff, and the absence of direct guidance from staff in helping patients complete their ESAS. Variable communication practices and inconsistencies in registration staff directing patients to kiosks was also observed. Improving infrastructure and support for volunteer services was identified as a potential solution, which may optimize their effectiveness in symptom screening roles. Conclusions: The project has laid the foundation for targeted QI interventions aimed at improving ESAS screening adherence. By addressing the identified barriers, this work endeavors to re-engage patients in active symptom reporting and to integrate this critical aspect of care into the daily routine of the outpatient hospital setting, thereby enhancing the overall management of HNC symptoms.