Patient Care Decisions Research Articles

Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus.

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD), and studies are able to correlate their relationships with available biological and clinical evidence. The aim of the current study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features relevant to these diseases. ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care. To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery. This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi, involving a total of 300 consented subjects categorized into three groups: CAD with diabetes, CAD without diabetes, and healthy controls, with 100 subjects in each group. The participants were enrolled from the Cardiology IPD & OPD for the sample collection. The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value. The clinical dataset comprised 35 attributes from enrolled subjects. The analysis produced rules with a maximum branching factor of 4 and a rule length of 5, necessitating a 1% probability increase for enhancement. Prominent patterns emerged, highlighting strong links between health indicators and diabetes likelihood, particularly elevated HbA1C and random blood sugar levels. The ARM technique identified individuals with a random blood sugar level > 175 and HbA1C > 6.6 are likely in the "CAD-with-diabetes" group, offering valuable insights into health indicators and influencing factors on disease outcomes. The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes. Implying artificial intelligence techniques with medical data, we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

Artificial Intelligence the Future of Cardiology

Artificial Intelligence (AI) essentially refers to various types of machine learning, often involving deep neural networks. It autocompletes our ideas as we write, enables us to communicate with our phones, and supports language translation.1 According to the 2019 Global Burden of Disease Study, the estimated age-standardized incidence of cardiovascular disease (CVD) in Pakistan was 918.8 per 100,000 people (global: 684.33 per 100,000), and the age-standardized death rate was 357.88 per 100,000 (global: 239.85 per 100,000).2 With AI, new analytical and data-driven approaches could lead to significant advances in understanding multimorbid groups of cardiology patients and potentially improve therapeutic strategies.3 AI has been used to interpret echocardiograms and heart rhythms from ECGs, and to detect indicators of heart disease, such as left ventricular dysfunction, from surface ECGs and nuclear cardiology.4-6 It is a misconception that AI will replace cardiologists. Instead, skilled practitioners will be able to expand their clinical capabilities, make more accurate and prompt diagnoses, and improve management decisions in patient care. As with any statistical application, it is important to understand AI's strengths and limitations. To understand the basics of AI, it starts with developing an algorithm based on human expertise. Programmers create relationships between input and output, known as expert systems. In machine learning, a general algorithm, such as a neural network, approximates a mathematical relationship between input data and expected outputs. In unsupervised learning, such as clustering, only the inputs are fed into the algorithm, which then finds insights in the data using its inner structure and statistics. An AI model can discover new relationships in data that have previously eluded human discovery.1 For research purposes, cardiologists using AI may follow these steps: Type and collection of data. Preprocessing of data. Choosing the right machine learning approach. Validating and evaluating methods and results.3 The application of AI techniques in the healthcare system is still in its infancy and requires more understanding through workshops and integrated learning.7 In conclusion, AI represents a new development in the field of medicine, especially cardiology. However, it is susceptible to significant errors in interpretation and raises safety and ethical concerns. References Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020;95(5):1015-39. Samad Z, Hanif B. Cardiovascular Diseases in Pakistan: Imagining a Postpandemic, Postconflict Future. Circulation. 2023;147(17):1261-3. Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, et al. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J. 2023;44(9):713-25. Cheng LT, Zheng J, Savova GK, Erickson BJ. Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging. 2010; 23(2):119-32. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287-95. Garcia EV, Klein JL, Taylor AT. Clinical decision support systems in myocardial perfusion imaging. J Nucl Cardiol. 2014;21(3):427-39. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res 2022;24:e32215.

Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making.

Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.

Development and Implementation of an Experiential Longitudinal Health Systems Science Thread Into an Existing Medical School Curriculum.

Medical school graduates enter a complex health care delivery system involving interprofessional teamwork and multifaceted value-based patient care decisions. However, current curricula on health systems science (HSS) are piecemeal, lecture based, and confined to preclinical training. The VISTA program is a longitudinal, immersive learning curriculum integrated into the University of Chicago Pritzker School of Medicine curriculum between 2016 and 2018. Key components include a unit-based nursing interprofessional team experience, a discharge objective structured clinical examination (OSCE), a patient safety simulation, and the implementation of a Choosing Wisely SmartPhrase. Graduates before 2016-2017 and after 2018-2020 VISTA implementation completed a Likert-style survey assessing attitudes, knowledge, and behaviors on HSS topics. A free response question solicited improvement areas. The Association of American Medical Colleges (AAMC) Graduation Questionnaire (GQ) was also examined. The overall VISTA survey response rate was 59%, with 126 fourth-year medical student respondents before VISTA and 120 after VISTA. Compared with pre-VISTA graduates, post-VISTA graduates reported a significantly higher rate of competence on the HSS questions, with the greatest increases seen in effective communication at discharge (n = 73/126 [57.9%] to 116/120 [96.7%], P < .001), knowledge on safety event reporting (n = 53/126 [42.1%] to 96/120 [79.8%], P < .001), and considering costs in making health care decisions (n = 76/126 [60.3%] to 117/120 [97.5%], P < .001). All were directly addressed through experiential learning interventions, and 2 were intended practice behaviors. VISTA graduate responses to free-text questions demonstrated a more nuanced understanding of HSS compared with pre-VISTA responses. The AAMC GQ data showed increased agreement with an item that mapped to HSS understanding. The VISTA program provides a model for institutions to enhance HSS education between curricular overhauls. Next steps include implementing value-added roles and additional immersive learning exercises.

Surpassing the natural limits of serological diagnostic tests

Serological tests have an important and irreplaceable role in the diagnosis of infectious diseases, both for individual patients and society. They indirectly identify the causative pathogen through the capture of antibodies, which contrasts with the direct detection by antigen and molecular biology tests that are also limited to active infections. Within point-of-care platforms, serodiagnostic assays can support immediate decisions in patient care and contain the spread of disease as they do not require highly trained personnel or dedicated infrastructure. By employing serodiagnosis, health officials can proactively respond and eliminate emerging health risks. Larger sample numbers can be screened in other formats for surveillance as well as securing donated blood supplies. Furthermore, an assay can be designed to detect any immunoglobulin from IgM to IgG and its subclasses, to IgA and IgE. However, most serological assays employ natural proteins as the defining antibody-capturing reagent, which compromises their performance by limiting the two critical parameters of a serodiagnostic test: specificity and sensitivity. To surpass this natural limitation, we have repurposed the β-barrel of fluorescent proteins to receive epitope sequences that dependably produce high-performing designer immunological reagents. Consequently, serodiagnosis can be conducted more accurately at a lower cost.

Advance care planning and predictive factors among Brazilian patients with cancer from an outpatient palliative care program.

e24062 Background: Advance care planning (ACP) is crucial for informed decision-making in patient care. However, barriers such as time constraints and discomfort with discussions hinder widespread ACP adoption. A previous study (Cohen et al. JNCCN 2023) indicated a low ACP engagement rate (17%), but participation in a palliative care program has shown potential to increase this proportion. Therefore, we sought to determine the prevalence of ACP and identify associated predictor factors among patients with cancer in Brazil enrolled in an outpatient palliative care (OPC) program. Methods: A retrospective analysis was performed from November 2022 to December 2023. Eligible patients were those who passed away during the study period and had received care from an OPC program at one of the largest private joint cancer centers in Latin America. We assessed the prevalence of ACP and explored its potential associations with clinical characteristics (age, gender and type of cancer), OCP support (follow-up period, appointments and family support), and end-of-life care (treatment in the past month, intensive care unit (ICU) admission in the past month, home care service referrals, and place of death).Comparative analyses involved two groups: those who had undergone ACP and those who had not (no-ACP). Logistic models were employed. Results: A total of 849 patients were included in the final analyses. Patients’ characteristics were well balanced between groups. Most patients were women (56%), with an average of 68.2 years (SD =15.3). Patients were mostly diagnosed with gastrointestinal (36%), breast (13%), and lung (13%) cancers. Notably, 43% of patients had ACP, significantly higher than literature controls (95% confidence interval [CI]= 22-31%, P = 0.001). Among the factors associated with ACP, clinical characteristics were not significant. However, OCP support and end-of-life care were significantly predicted by ACP, including the follow-up period (MACP= 179 vs. Mno-ACP= 101; P = 0.001), family support (MACP= 0.6 vs. Mno-ACP= 0.3; P &lt; 0.001) , ICU admission at the last 30 days of life (ACP = 28% vs. no-ACP = 72%; P &lt; 0.001), and home care service referral (ACP = 72% vs. no-ACP = 28%; P &lt; 0.001). Conclusions: These findings underscore that prolonged engagement with the OPC program is associated with increased adoption of ACP among Brazilian patients with cancer. Given the scarcity of data from developing countries, our findings suggest that the OPC program plays a pivotal role in optimizing end-of life care support through ACP.

Injury and Treatment Characteristics of Middle School-Aged Patients Under the Care of Athletic Trainers From 2010 to 2022: A Report From the Athletic Training Practice-Based Research Network.

Exploring sports-related musculoskeletal injuries and treatment characteristics in middle school sports may help inform patient care decisions such as appropriate medical coverage. To describe injury and treatment characteristics of middle school-aged athletes receiving care from athletic trainers within the Athletic Training Practice-Based Research Network (AT-PBRN). Descriptive study. Middle school. Middle school-aged athletes (n = 1011; male = 503, female = 506, 2 declined to answer) with sports-related musculoskeletal injuries. Electronic patient records were analyzed from the AT-PBRN from 59 athletic trainers across 14 states between 2010 and 2022. Summary statistics (frequency, percentages, median, and interquartile ranges [IQR]) were used to describe injury (age at injury, sex, sport, body part, and diagnosis) and treatment characteristics (type of treatment, number of visits, and number of procedures per visit). Football (17.7%, n = 179), basketball (17.6%, n = 178), and soccer (14.9%, n = 151) reported the highest number of injuries. The ankle (17.2%, n = 174), knee (16.5%, n = 167), and head (14.1%, n = 143) were the most common injury locations. Concussion (13.0%, n = 131), ankle strain/sprain (12.4%, n = 125), and thigh/hip/groin sprain/strain (11.1%, n = 112) were the most reported diagnoses. Therapeutic exercise or activities (27.6%, n = 1068), athletic trainer evaluation or re-evaluation (24.7%, n = 957), and hot/cold packs (19.8%, n = 766) were the most common services, with a median of 2 visits (IQR = 1 to 4) and 2 procedures (IQR = 1 to 2) per visit. Football, basketball, and soccer reported the most musculoskeletal injuries for middle school-aged athletes. Concussions were the most frequent diagnosis, followed by ankle sprains/strains. Our findings are similar to prior investigations at the high school and collegiate levels. Treatments at the middle school level were also similar to those that have been previously reported at the high school level with therapeutic exercise/activity, athletic trainer evaluation or re-evaluation, and hot/cold packs being the most common treatments. This information may be useful for informing patient care decisions at the middle-school level.

Temporomandibular Disorders Management-What's New? A Scoping Review.

Temporomandibular disorders include various conditions that impact the masticatory system, affecting its structure, function, or physiology. Clinicians face a complex array of therapeutic options when treating this group of diseases, emphasizing the importance of evaluating the current evidence to guide decisions in patient care. The main objective of this article is to conduct a scoping review on the available treatment approaches to manage temporomandibular disorders (TMDs). An extensive search of the literature was performed on Scopus, Pubmed, Embase, and Web of Science. Systematic reviews published in the last 5 years were considered. Out of the 2183 publications identified, 109 studies were included in this review. Among them, 39 articles focused on the non-invasive approach, while 120 delved into the minimally invasive approach, and 15 explored the surgical approach. Non-invasive or conservative approaches like cognitive-behavioral therapy, physical therapy, and acupuncture offer effective pain management and functional improvements in TMDs. Emerging treatments offer promising alternatives for treating these disorders. Surgery should be reserved for severe cases, with conservative therapies used in conjunction with invasive procedures for optimal patient outcomes.

GLP-1 RA for cardiometabolic risk reduction in obesity – How do we best describe benefit and value?

How do we assess the overall benefit and value of GLP1-RAs? Current clinical trials often focus narrowly on individual atherosclerotic cardiovascular endpoints like MACE, potentially missing broader GLP-1 RA benefits across multiple comorbidities. Herein, we set out a framework for expanding outcome analyses in large trials that we believe will provide a more holistic understanding of GLP-1 RA benefits across the cardio-kidney-metabolic (CKM) spectrum, guiding patient care, guidelines, and insurance coverage decisions.

Assessing Evidence-Based Practice in Operating Room Nursing Students: A Cross-Sectional Study in the Southwest of Iran.

Evidence-based practice (EBP) is a clinical decision-making process that is grounded in the utilization of the most reliable and up-to-date evidence. It involves utilizing resources and evidence to enhance patient management. The application of evidence-based decisions in patient care and education is of utmost importance in the performance of health sciences students. However, the emphasis on this concept among operating room nursing students has been lacking. Hence, the objective of this research is to assess the implementation of evidence-based practice among operating room nursing students studying in the operating room department at the Shiraz University of Medical Sciences in Iran. The study follows a descriptive, cross-sectional design, with a sample of 148 operating room nursing students selected through census sampling based on the student list. After obtaining informed consent, participants completed a demographic information form and the Rubin-Parrish evidence-based practice questionnaire. The data that were gathered underwent analysis through the utilization of SPSS version 22 software, employing descriptive statistics, T-tests, and Pearson correlation coefficient tests. The results revealed that the overall average score of evidence-based practice among the students exceeded the standard scores (172.66 ± 14.74). There was a significant association between the evidence-based practice score and prior familiarity with evidence-based practice, interest in the field of study, research experience, intention to participate in the Master's exam, and the participants' grade point average (GPA) (p < 0.05). These findings indicate that operating room nursing students displayed an average level of evidence-based practice, emphasizing the need for effective plans and strategies to improve their performance. Addressing the identified factors from this study becomes crucial in this regard.

IoT Enabled Real Time Low Cost Cardiac Monitor Using LabVIEW

The rapid advancement of the Internet of Things (IoT) and medical technology has paved the way for innovative healthcare solutions. The system incorporates IoT connectivity to enable remote monitoring, data collection, and analysis, enhancing patient care and medical decision making. Cardiac monitoring system used in hospitals to measure and record the cardiac parameters. Vital parameters are transferred within the hospitals either using Wi-Fi or Bluetooth. Range of transmission for such wireless communication is only effective within the hospitals and further transfer to a long distance is limited. The proposed cardiac monitoring system is developed using LabVIEW environment - a versatile Graphical User Interface (GUI). Data acquired through the electrodes are connected to my DAQ Unit (Data Acquisition Unit) which helps to integrate electrodes with computer or PC. Data acquired from the Software is connected to the firebase console and it will help the practitioners to share the data in live and updated. It offers benefits such as early detection of cardiac irregularities such as arrythmia, reduced hospitalization periods, and improved patient outcomes. Additionally, healthcare practitioners and caretakers can remotely access patient data, enabling timely interventions and personalized treatment plans. Since the cardiac monitors are often used by bedside patients, it may introduce bed sores in them. Hence, in addition to these parameters, body moisture level and temperature of the patient are measured using DHT11(temperature and humidity sensor) to ensure the prevention of bedsores. These additional parameters measured are also enabled to view through IoT.

Abstract PO5-03-03: Nomogram for predicting brain metastasis in early-stage breast cancer patients

Abstract Nomogram for predicting brain metastasis in early-stage breast cancer patients Background: Brain metastasis is a significant concern for patients with early-stage breast cancer, as it can have a detrimental impact on survival and quality of life. Clinical and pathological characteristics of the primary tumor may be modeled into a nomogram that can predict the risk of brain metastasis. The goal is to create a nomogram and clinical calculator to assist in selection of patients with high risk of brain metastasis to enrich patient population for accrual on clinical trials for the purpose of preventing or delaying emergence of brain metastasis, at an early stage of their disease. Methods: We reviewed all early-stage breast cancer patients (stages I to III) N=40290 from the University of Texas MD Anderson Cancer Center, Houston, TX, from 1/1/1997 to 5/8/2020. We utilized multiple predictive statistical models to identify the most relevant variables and optimal predictive criteria. Variables for the model included patient age, tumor characteristics (such as grade,hormone receptor and HER2 status) and medical treatment (such as chemotherapy and endocrine therapy). The Receiver Operating Characteristic (ROC), and its AUC (area under the curve) was used to demonstrate the performance of the multivariate model to predict the occurrence of brain metastasis at different time points (1 year, 2 years, and 5 years), distinguishing between patients who develop brain metastasis in a time-dependent manner and those who do not. Results: The results showed the following clinical variables to be statistically significant in predicting brain metastasis: younger age, high estrogen and progesterone receptor percentage, higher tumor size; use of aromatase inhibitor therapy, and use of selective estrogen receptor modulator therapy were associated with a decreased risk of brain metastasis. Higher grade, Ki-67 levels, HER2-positive status, lymphovascular invasion and the use of chemotherapy was associated with an increased risk. The AUC values for the prediction of brain metastasis at different time points are as follows: 0.85 at 1 year, 0.83 at 2 years, and 0.82 at 5 years. These values indicate that the multivariate model has good discriminative ability, with higher values indicating a better predictive performance. An AUC of 0.85 suggests that the model has a high probability of correctly distinguishing between patients who will develop brain metastasis and those who will not at 1 year. Conclusions: Our predictive tool holds promise in assisting clinicians when making informed, personalized care decisions for patients at risk of developing brain metastasis. The model will be validated using independent patient cohorts from Institut Jules Bordet, Brussel, Belgium, to assess their accuracy and reproducibility. Continued research and validation will further refine these models, increasing their reliability and their clinical/clinical research applicability. Citation Format: Akshara Singareeka Raghavendra, Nuria Kotecki, Kristofer Jennings, Ottavia Amato, Debu Tripathy, Ahmad Awada, Nuhad Ibrahim. Nomogram for predicting brain metastasis in early-stage breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-03-03.

Oncology social workers' involvement in palliative care: Secondary data analysis from nationwide oncology social workers survey.

Social workers are vital in delivering psychosocial services in palliative care, yet their specific roles in palliative oncology remain undefined. This study aimed to delineate the current practice role of oncology social workers involvement in palliative care in the United States. This study utilized a cross-sectional design and involved secondary analysis of data from a nationwide survey focused on workforce conditions for oncology social workers. The participants were social workers who were directly involved in providing care to cancer patients and delivering palliative care services. They completed an online survey in which they indicated the relevance of 91 tasks related to their practice. The survey also collected individual demographic and work-related characteristics. Exploratory factor analysis was used to achieve the study objective. Responses from a secondary data set of 243 oncology social workers involved in palliative care results in a 6-factor solution comprising 34 tasks. These factors were identified as: Therapeutic Interventions for Individuals, Couples, and Families; Facilitate Patient Care Decision-making; Care Coordination; Assessment and Emotional Support; Organization and Community Service; and Equity and Justice. All 6 factors demonstrated good internal reliability, as indicated by Cronbach's alpha scores above 0.70. The findings can be used to develop job descriptions and education for social workers employed in palliative cancer care. The clear role descriptions also make social work visible to other professionals in palliative oncology. By clarifying the roles of oncology social workers, this study contributes to the improvement of palliative care delivery and enhances interprofessional collaboration within cancer care teams.

A Review: A Comparative Study of Branded and Generic Anti- Diabetic Drugs

This comprehensive review investigates the market landscape of antihypertensive medications, undertaking a detailed comparative analysis of branded and generic options. In the face of rising global prevalence of hypertension, the study aims to provide a holistic perspective on the efficacy, safety, and market dynamics shaping the use of branded and generic drugs in the management of these chronic conditions. Utilizing data derived from market trends, sales analytics, and consumer preferences, the review scrutinizes the market behaviors and forces influencing the adoption of branded and generic antihypertensive drugs. Factors such as cost-effectiveness, patient adherence, and healthcare provider preferences are explored to shed light on the complexities associated with medication selection in the context of chronic disease management. Through an in-depth examination of industry strategies, regulatory frameworks, and the role of healthcare policies, the review aims to uncover the interplay between market dynamics and clinical considerations. It seeks to inform healthcare professionals, policymakers, and industry stakeholders about the evolving landscape of choices in antihypertensive therapies, fostering a nuanced understanding that can guide optimal decision-making in patient care and resource allocation.

A Review: A Comparative Study of Branded and Generic Anti-Hypertensive Drugs

This comprehensive review investigates the market landscape of antihypertensive medications, undertaking a detailed comparative analysis of branded and generic options. In the face of rising global prevalence of hypertension, the study aims to provide a holistic perspective on the efficacy, safety, and market dynamics shaping the use of branded and generic drugs in the management of these chronic conditions. Utilizing data derived from market trends, sales analytics, and consumer preferences, the review scrutinizes the market behaviors and forces influencing the adoption of branded and generic antihypertensive drugs. Factors such as cost-effectiveness, patient adherence, and healthcare provider preferences are explored to shed light on the complexities associated with medication selection in the context of chronic disease management. Through an in-depth examination of industry strategies, regulatory frameworks, and the role of healthcare policies, the review aims to uncover the interplay between market dynamics and clinical considerations. It seeks to inform healthcare professionals, policymakers, and industry stakeholders about the evolving landscape of choices in antihypertensive therapies, fostering a nuanced understanding that can guide optimal decision-making in patient care and resource allocation.

Redefining the Role of Medical Affairs Professionals as Innovators and Leaders in Industry-Led Medical Education.

Medical affairs professionals are pivotal players at the intersection of medical innovation and practice in the pharmaceutical industry. They are uniquely positioned to translate complex medical knowledge into actionable insights for internal and external stakeholders. Industry-led continuing medical education (CME) programs, guided by these professionals, hold the potential to markedly improve clinicians' application of evidence-based medicine (EBM) in clinical settings, thereby elevating patient care outcomes. However, current CME techniques often overlook the integration of diverse disciplines such as educational theories, cognitive psychology, information mastery, and implementation science, which are important for effective real-time decision-making in patient care. This gap in integrating implementation science is vital, as it is key in ensuring that medical innovations are not just developed but also effectively implemented and efficiently utilized in clinical settings. In this opinion article, we aim to highlight the crucial yet often underrecognized role of medical affairs professionals in shaping robust and practical CME programs within the industry. We explore emerging trends and approaches in medical education and CME based on the principles of adult education. Additionally, we explore how medical affairs professionals can effectively drive the adoption of EBM in clinical practice. This exploration aims to provide insights into enhancing CME programs, with medical affairs professionals at the forefront of innovation and leadership in bridging gaps in clinical practice.

Why is it Important for Family Physicians to Identify Reliable Sources of Information in the Digital Age?

Dear Editor, Accessing reliable information in the 21st century can be challenging, particularly with the ease of access to data through technology. For both society and health professionals, the integrity of information is of utmost importance. Evidence-based medicine (EBM) offers a framework for evaluating the reliability of sources through rigorous scientific methods, including experimentation, falsification, and data accumulation (1). Medical knowledge has historically evolved through continuous refinement, with evidence being established via repeated experiments and analyses. Reliable information stems from multiple confirmations rather than isolated findings. EBM uses a hierarchy of evidence to categorize research by its reliability, facilitating informed clinical decision-making. It presents a structured approach to medical diagnosis, treatment, and follow-up strategies based on evidence levels (Grade Practice Recommendations, A-D) (2). The importance of EBM is emphasized in the digital era, promoting a systematic and reliable presentation of information. To combat misinformation, it is important to exercise critical thinking and skepticism toward information sources. Accessing reliable medical information is of crucial importance, especially in times of crisis such as the COVID-19 pandemic (3). Primary sources, including peer-reviewed journals, respected international guidelines, scientific reports, and reputable textbooks play a pivotal role in providing accurate and trustworthy information. For example, The National Institute for Health and Clinical Excellence (NICE) and the Cochrane Library offer evidence-based information that has undergone peer review (5,6). The pandemic has highlighted the significance of open access to scientific publications, which has facilitated global information sharing and accelerated vaccine development, ultimately reducing morbidity and mortality rates. This event has underscored the necessity of reliable information sources and the importance of continued collaboration and cooperation in the scientific community. Misinformation, particularly on social media, can pose significant challenges when it comes to verifying the accuracy of online information. To differentiate between truth and falsehood, it is crucial to employ critical evaluation methods, such as cross-referencing multiple sources and examining evidence consistency. Educational initiatives on media and health literacy from a young age, as well as platforms dedicated to verifying claims, can play a vital role in countering misinformation (6). In the digital age, family physicians face both opportunities and challenges due to the abundance of online information. It is very important to acknowledge that quick access to a wide range of data is invaluable for informed decision-making in patient care. However, the vast amount of information available also poses the risk of encountering unreliable or outdated sources. Therefore, it is crucial to utilize reputable medical literature and evidence-based resources (7). For family physicians, it is important to remain up-to-date with the latest research and guidelines through respected indexes such as Web of Science, PubMed, and Scopus. This practice not only helps in delivering high-quality patient care but also supports lifelong learning and professional development (8). Additionally, recent global health crises have emphasized the need for quick access to accurate and reliable medical information to effectively address public health emergencies (6). Therefore, discerning and accessing reliable information sources are indispensable skills for family physicians in the digital age. Keywords: evidence-based medicine, information sources, family practice, practice guideline, resource guides

Identifying pathological groups from MRI in prostate cancer using graph representation learning

Multiparametric magnetic resonance imaging (mpMRI) plays a critical role in prostate cancer (PCa) diagnosis, aiding in clinical trial evaluation and personalized treatment planning. We propose a novel prediction approach based on graph representation learning (GRL) integrating mpMRI images for classifying International Society of Urological Pathology (ISUP) grade groups. We initially constructed a mpMRI image graph representation learning (MMIGRL) model using a graph-based representation fusion approach. After constructing a heterogeneous graph integrating T2-weighted and apparent diffusion coefficient (ADC) images, we applied an adaptive method that dynamically adjusted the edges within the graph to reveal relationships within the mpMRI images. Subsequently, we developed a residual graph convolution network (Res-GCN) model to establish the mapping relationship between the mpMRI fusion graph and ISUP grade groups. The results of 5-fold cross-validation demonstrated excellent overall performance, and the prediction results using independent testing datasets confirmed the model's generalizability. The results of ablation studies highlighted the positive impact of integrating T2-weighted and ADC images and the effectiveness of residual connections in learning hierarchical features within the graph structure. The results of comparative experiments validated the superiority of the proposed MMIGRL model compared with convolutional neural network (CNN) and vision transformer (ViT) models. This approach holds promise for accurate ISUP grade group classification in PCa diagnosis, contributing to improved patient care and clinical decision making.

Abstract 7390: Learning Llama Agents for medical record analysis and standardization

Abstract Introduction: The purpose of this study is to develop custom, open source, LLM enabled pipelines and agents for the extraction and standardization of data from free text medical records. Procedures: This project aims to create a versatile tool for healthcare data analysis, with initial development utilizing our extensive collection (over 10,000 documents) of renal cell carcinoma pathology reports. These reports are an ideal starting point as they cover over 20 years of care and consist of a racially and ethnically diverse cohort with over a quarter of patients being from traditionally underrepresented backgrounds. The jargon-abundant and highly technical nature of kidney cancer pathology represents a major challenge. We will accomplish this objective using an approach that balances the harnessing advanced AI capabilities with ensuring practicality and efficiency in real-world applications. First, we will leverage the capabilities of GPT-3.5/4 to generate a comprehensive labeled dataset (referred to as pseudo labels - machine-applied labels that will be used for training a newmodel) tailored for pathology, radiology, and clinician reports of kidney cancer patients. Second, we will harness the pseudo labeled datasets to train a smaller, open-source, yet highly efficient model for our specific needs (termed model distillation). LLAMA 2, known for its high performance in diverse tasks, is our model of choice. Its open-source nature and relatively modest computational requirements make it ideal for deployment within medical institutions. The ability to run locally ensures the protection of patient health information and reduces reliance on costlier, proprietary models. The rationale is threefold: first, to benefit from the superior performance and reasoning capabilities of GPT models; second, to create a streamlined model that is resource-efficient and tailored to our specific requirements; third, to enable the final streamlined model to be freely available by utilizing open source LLMs (e.g., Llama 2). To assess the feasibility of our methodology, we evaluated the ability of GPT-3.5 to label our data with high accuracy. Data Summary: Our pilot set of pathology reports consisted of 109 documents and 3 extraction fields: histology type, biopsy site, and biopsy procedure. Using GPT 3.5 we achieved an accuracy of over 95% in each extracted field type thereby demonstrating its ability to act as a proficient “teacher” model for our future open source “student” model. Conclusions: Our preliminary results suggest that current state of the art LLMs are highly proficient in extracting and standardizing information from clinical reports. Building out a full, open source, LLM enabled pipeline will increase the accuracy, flexibility, and efficiency of medical data extraction across institutions and lead to quicker and more informed decision-making in patient care and research. Citation Format: David Hein, Alana Christie, Hua Zhong, Ellen Araj, James Brugarolas, Lindsay Cowell, Payal Kapur, Andrew Jamieson. Learning Llama Agents for medical record analysis and standardization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7390.

Toward Informed Selection and Interpretation of Clinical Genomic Tests in Prostate Cancer.

Clinical genomic testing of patient germline, tumor tissue, or plasma cell-free DNA can enable a personalized approach to cancer management and treatment. In prostate cancer (PCa), broad genotyping tests are now widely used to identify germline and/or somatic alterations in BRCA2 and other DNA damage repair genes. Alterations in these genes can confer cancer sensitivity to poly (ADP-ribose) polymerase inhibitors, are linked with poor prognosis, and can have potential hereditary cancer implications for family members. However, there is huge variability in genomic tests and reporting standards, meaning that for successful implementation of testing in clinical practice, end users must carefully select the most appropriate test for a given patient and critically interpret the results. In this white paper, we outline key pre- and post-test considerations for choosing a genomic test and evaluating reported variants, specifically for patients with advanced PCa. Test choice must be based on clinical context and disease state, availability and suitability of tumor tissue, and the genes and regions that are covered by the test. We describe strategies to recognize false positives or negatives in test results, including frameworks to assess low tumor fraction, subclonal alterations, clonal hematopoiesis, and pathogenic versus nonpathogenic variants. We assume that improved understanding among health care professionals and researchers of the nuances associated with genomic testing will ultimately lead to optimal patient care and clinical decision making.