bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical large language model to predict loss to follow up for oncology patients discharged to skilled nursing facilities.
Authors: Liu Tianshi , Chen Ziyi , Wu Yonghui , Guo Yi , Chatzkel Jonathan Alexander , Bian Jiang 
Published: May 2024

e13602 Background: The prognosis of cancer patients is heavily influenced by performance status which also informs an oncologist’s decision for initiating or continuing with cancer treatment. From SEER data, there is evidence that oncology patients who are discharged to a skilled nursing facility are less likely to receive chemotherapy and radiation. Patients with advanced cancer discharged from hospital also have a readmission rate as high as 34 percent. Those that are unlikely to follow up are a vulnerable population that should be identified to discuss how to best optimize post hospitalization care. Methods: University of Florida's Integrated Data Repository (IDR) was queried for established oncology patients at University of Florida Health from 2012 – 2023. The cohort was further narrowed down to patients who had exposures to antineoplastics within 6 months and loss to follow up was defined as not having a clinic visit within 6 months of hospital discharge. The study cohort included 194 patients and 97 patients were not lost to follow up and 97 patients were lost to follow up. Structured health data including ICD-9 and 10 codes, antineoplastic drug exposures, Area Deprivation Index (ADI), discharge vitals were extracted. Dataset was split into 70% training and 30% testing. Using logistic regression, random forest and XGBoost, loss to follow up was predicted with 5 fold cross validation and hyperparameter tuning with GridSearchCV for the best ROC AUC. Results: The median Charlson score for cohort was 11, median length of stay in the hospital was 7 days, median ED visits within 6 months was 8, median LACE score of 21. 86% of the cohort had Medicare, 6% with Medicaid, 6% Blue Cross, 2% Managed Care and 0.5% Other. There were 96 females and 98 males. 66% of the cohort was Caucasian and 31% Black, 3.9% Hispanic, 1% Asian, 0.5% multiracial. XGBoost and random forest performed better than logistic regression, both achieving a AUC ROC of 0.83 vs. 0.78. XGBoost had a higher F1 score of 0.75, sensitivity of 0.71, compared to 0.68 and 0.55 respectively for both Random Forest and logistic regression. The most important features for Random Forest were ED visits within 6 months, followed by diastolic blood pressure, heart rate, temperature then systolic blood pressure on day of discharge. The most important features for XGBoost were diagnoses of pulmonary congestion, pulmonary embolism, pleural effusion and type 2 diabetes with complications. Conclusions: Our XGBoost model achieved metrics which are in line with the higher performances seen with machine learning models predicting readmissions following hospital discharge. This serves as a good baseline model to compare against new state of the art models that incorporate unstructured data such as clinical notes and radiology image reports. These models are currently under development by the same authors using the clinical large language model GatorTron.
Prospective assessment of AI-enabled cost savings, continuous support, and psychosocial enhancement (AI New Era Study).
Authors: Xu Chunwei , Hao Yue , Jian Qijie , Zhu Youcai , Wang Qian , Pang Fei , Zhang Shirong , Wang Dong , Lv Donglai , Chen Hui , Yuan Shaohua , Wang Kai 
Published: May 2024

e13615 Background: Cancer patients (pts) need extensive primary care across their treatment journey, yet the world's primary health systems struggle with patient volume and resource scarcity. This study aims to assess the effectiveness of Artificial Intelligence (AI) medical service model in primary care and clinical service pre-screening, as well as their potential impact on improving the quality of medical services and reducing medical costs. Methods: A total of 17,856 medical consultation inquiries from 7,522 pts with various types of cancer have been collected and categorized. The study employed a novel AI-driven medical service model (based on large language model), amalgamating clinical guidelines with a local oncology database, to prospectively field questions from 454 cancer-afflicted pts. Clinicians evaluated the accuracy and comprehensiveness of the responses to basic medical issues, while pts assessed the intelligibility and satisfaction. Results: Treatment-related inquiries constituted 34.44% (n = 6,149) of the total, with 81.53% of pts asking such questions. Basic medical issues were addressed in 65.56% (n = 11,707) of inquiries, raised by 59.78% of pts. Specific concerns within basic medical issues included test report explanations (20.45%), adverse reaction consultations (19.82%), and medication information requests (17.66%). In light of the prevalence of basic medical issues, the study prospectively explores the potential of AI technology to revolutionize medical models, driving the advent of a new era in healthcare research. As result, the AI model exhibited high performance across several metrics: accuracy (96.87 ± 10.29), comprehensiveness (97.40 ± 9.91), intelligibility (95.72 ± 12.62), and satisfaction (96.34 ± 11.03). Notably, the model demonstrated a proficiency in identifying queries necessitating physician expertise, with a 95.60% accuracy rate upon review. A survey of 50 outpatients indicated an enhancement in service speed (7.78%) and patient satisfaction (14.29%). This increase in satisfaction was primarily ascribed to pts' heightened sense of control and safety. When benchmarked against GPT-4, the AI model's overall performance was significantly superior (95.08 ± 10.05 vs 89.70 ± 12.85, p<0.01), particularly in domestic drug knowledge and gene report analysis. Conclusions: Largely fueled by the advanced capabilities of large language model, AI has the potential to profoundly reshape the entire patient care journey. Our AI medical service model efficiently meets key healthcare needs, addressing over half of clinical issues and cutting costs. It also guides pts to the right specialists for timely care. Early surveys indicate better primary care and improved doctor-patient relations. Further research is needed to understand AI's effects on fairness, access, and outcomes across varied demographic and socioeconomic strata.
Integrating electronic health records (EHRs) to facilitate cancer biomarker testing: Real-world implementation barriers and solutions.
Authors: Huelsman Karen M. , Offit Caroline , Waugh Wendi , McNair Christopher , Kurtin Sandra E. , Enstad Crystal , Rice Courtney , Osterman Travis , Yu Peter Paul , Shepard Gregg Christian , Martin Nikki A. , Kim Joseph , Kisiel Molly , Plotkin Elana 
Published: May 2024

e13649 Background: Effective integration between EHRs and cancer biomarker testing processes is an unmet need across academic and community settings. Despite the benefits of EHR-integrated workflows and the desire for streamlined test results, as supported by a recent ASCO Workforce Survey, many health systems do not currently have this capacity, especially when reference labs are external to the ordering institution. Methods: The Association of Cancer Care Centers (ACCC) convened a live summit on October 4, 2023 to understand facilitators and barriers to integrating biomarker orders and results into the EHR. To capture a comprehensive perspective, ACCC convened 37 oncologists, pathologists, nurse navigators, and administrators from diverse settings using a range of EHRs, patient advocacy groups, and EHR/testing companies. Thematic analysis of the summit transcript and a post-summit assessment was employed to identify common experiences among participants. Results: The key benefits of EHR integration include increased efficiency with test ordering/result retrieval, streamlined communication, and improved clinical decision-making. Factors associated with successful EHR integration include strong clinical champions, creation of a precision medicine oversight committee, buy-in from administrative leadership, IT support to build EHR customizations, support from EHR vendors/reference labs, and coordinated project management. Notable barriers include variations in EHR interoperability, utilization of multiple reference labs, lack of consistent nomenclature, resistance to change, allocation of time for post-integration updates/maintenance, lack of internal IT capabilities/resources, and a need to establish different clinical workflows depending on which clinician places the biomarker order. Conclusions: The rapid expansion of biomarker-driven cancer treatments requires oncologists to have timely access to guideline-concordant clinical information to inform complex clinical decision-making. While EHRs can streamline clinical workflows, these results underscore the need to address operational and human factors to facilitate successful EHR-laboratory integration. Strategies to address barriers and build successful EHR integrations across diverse clinical oncology settings are crucial for optimizing how cancer biomarker tests are ordered, reported and used in clinical practice.
AI frontiers in oncology: Bridging the gap between humanity and machine.
Authors: Sharma Rubal , Di Petro Gaetano , Rajupet Sritha , El-Sayegh Suzanne , Kwok Yue Faat Raymond , Dhar Meekoo , Bershadskiy Alexander , Gut Thomas , Castellanos Mario R. 
Published: May 2024

e13657 Background: Artificial intelligence (AI) is rapidly transforming medical education and how healthcare services are delivered. Oncology healthcare professionals (HCPs) can significantly benefit from the advances of AI including optimized diagnostic capabilities, treatment plans, resource allocation, patient outcomes and much more. The synergy between technology and healthcare is strengthening, heralding opportunities for a revolution in medical education and the broader healthcare industry. We developed an introductory AI workshop and conducted pre- and post-surveys, aiming to inform the design of successive workshops and an AI curriculum, to access knowledge, attitudes and perceived challenges for AI integration into Medical Oncology. Methods: An interactive one-hour workshop was conducted for physicians and faculty of the Hematology and Oncology department. 18 participants, encompassing 6 physicians and 12 other HCPs completed a pre- and post-workshop survey. We evaluated participants' knowledge, opinions, and perceived limitations regarding AI in oncology research, practitioner education, and clinical applications using both open and closed-ended questions, such as multiple-choice and 1-5 Likert scale formats. Results: Survey results included 18 pre-workshop and 17 post-workshop participants, 83% had no formal computer science training: AI knowledge in medicine and awareness of clinical AI applications increased by 100%. Interestingly 24% felt the workshop influenced their view on AI's role in their medical career and better prepared for AI-related challenges. 58% believed AI would assist clinicians within 5 years. However, the belief that AI will play a significant role in their own career did not change after the intervention. Data privacy concerns increased by 25%, while concerns about physician over-reliance on AI decreased by 15%. Notably, 76% were motivated to engage with AI during their future practice after the workshop. Conclusions: The AI initiative significantly enhanced oncology healthcare professionals' understanding and readiness for AI integration, despite most lacking prior formal AI education. A marked increase in AI knowledge and motivation to use AI in practice was noted, although perceptions of AI's impact in their day-to-day work remained unchanged with one workshop, contrasting expert views and predictions. Data privacy concerns rose, but decreased fears of over-reliance on AI indicate a more balanced perspective. These findings stress the need for ongoing AI education in healthcare and underscore the importance of baseline evaluations and feedback for shaping future workshops. Since, AI's integration into society is poised to transform healthcare, an AI curriculum is essential.
Impact of digital tools in oncology: A global analysis of the adoption of ONCOassist and NCCN Chemotherapy Order Templates (NCCN Templates) in collaboration with OncoAlert.
Authors: Gandur-Quiroga Maria Natalia , Krasnapolski Martin Alejandro , Yekeduz Emre , Izzo Matt , Bambury Kevin Dan , Morgan Gilberto 
Published: May 2024

e13662 Background: The integration of digital technology has significantly transformed clinical practice, especially in oncology. ONCOassist, an application that integrates content from the NCCN Templates offers professionals quick and up-to-date access to information crucial for cancer treatment. Methods: To analyze and determine whether factors such as the Human Development Index (HDI) or Gross National Income (GNI) influence the adoption of the NCCN Templates, an in-application survey was distributed to health professionals who accessed NCCN content. The survey asked two questions: (1) would respondents use a specific NCCN protocol for a current patient (2) if not, the reasons why they did not utilize it. Finally, how the acceptance of the NCCN Templates was related to HDI or GNI and specialty was analyzed. Results: The survey was responded to by 2522 health professionals from 122 countries. The NCCN Templates were widely accepted (80%) across different geographic and economic contexts, indicating their global applicability and trust. Generally, GNI is categorized by high income, upper middle income, lower middle income, and low income, while HDI includes very high, high, medium, and low income. For the lowest levels of GNI and HDI, there were few responses (1% by GNI and 8% by HDI), and for purposes of this abstract, they were combined with the next highest level, lower middle income, and medium HDI. Using GNI, the rates of using the NCCN Templates in countries with high income (72%) were lower than those with upper-middle income (83%) and lower middle/low income (86%). In a similar inverse tendency, based on HDI, the utilization rates of the NCCN Templates were 70%, 83%, and 86% in countries with very-high, high, and medium/low HDI, respectively. When stratified by specialty, the absolute numbers showed that most respondents belonged to the field of the breast (36%), followed by gastrointestinal (28%) and lung cancers (10%). However, when we analyzed percentages, we observed that the decision-making process regarding protocol adoption was consistent among specialties despite the variance in specialist population size. When we analyzed the reasons for not utilizing NCCN Templates, “Other reasons” was the primary response (51%). Among the remaining answers, using specific local protocols was the primary reason (47% of the remaining), followed by the lack of drug availability (18% of the remaining) and reimbursement issues (18% of the remaining). Conclusions: There was a general acceptance of the NCCN Templates among healthcare professionals globally. The willingness to utilize NCCN Templates content was not strongly determined by the HDI or GNI, suggesting a perception of their utility beyond economic barriers. The protocol has the potential to be used in daily clinical practice, improving decision-making.
Usability and feasibility of the open-source GeRI platform for remote assessments in older adults with prostate cancer.
Authors: Mir Nabiel Ali , Che Yan , Mendez Megan , Huisingh-Scheetz Megan , Szmulewitz Russell Zelig 
Published: May 2024

e13664 Background: The Geriatric Remote Initiative (GeRI) is an open-source tool to collect patient-generated health data for older adults with cancer. It comprises a WiFi-enabled Raspberry Pi tablet, a Bangle.js 2 wrist accelerometer, and Mi Body Composition Scale 2 for data collection. Following the initial phase where feedback from older cancer survivors and caregivers refined a demonstration GeRI platform, this study deployed a live version of GeRI in advanced prostate cancer patients to evaluate its feasibility and usability. Methods: In this single-arm pilot feasibility and usability study, we evaluated GeRI among ten advanced prostate cancer patients aged ≥65 years undergoing androgen deprivation (ADT) across varied frailty risk categories. Participants interacted with GeRI for ≥12 weeks. Data collection comprised demographics, clinical information, and vulnerability assessment (Cancer and Aging Research Group [CARG] score) from in-clinic surveys. Biometric (wearable/scale) and electronic patient-reported outcomes data from the tablet were securely transmitted to the institution’s HIPAA-compliant cloud. Usability was evaluated using the System Usability Scale (SUS), aiming for an average score of 68 or higher. Feasibility was achieved if ≥6 participants completed at least 50% of all prompted interactions with the system components (surveys, wearable, and weight/bioimpedance) throughout a 12-week study period. Results: Ten men with metastatic hormone-sensitive prostate cancer [median age: 73 years; 40% minority; median of 3 months of ADT exposure (IQR: 2-4.5); 80% had intensified ADT with 60% getting concurrent prostate-directed radiation] engaged with GeRI for a median of 104 days (IQR: 93-131). 40% were vulnerable with a CARG>5. System usability, evaluated by SUS, yielded a mean score of 84 (SD: 12.5). All participants met the feasibility criterion. Deficiencies in obtaining complete 48-hour accelerometry data from 7 participants were noted with only 1 being due to persistent non-wear of the wearable and the remainder due to transmission failure. Conclusions: The GeRI platform exhibited exceptional usability and met all feasibility benchmarks in a geriatric oncology setting, highlighting its potential despite technical hurdles in accelerometry data collection. Further deployment of GeRI is underway [or planned] through an expanded program focusing on other cancer types, including head and neck, and more robust biometric data collection.
Enhancing oncology research through data sharing and re-use: A five-year review of Vivli's global data platform.
Authors: Li Rebecca , Sudlow Rebecca Mary Caroline , OCallaghan Jennifer 
Published: May 2024

e13670 Background: The last decade has seen a paradigm shift in clinical research with the accessibility of trial data, facilitating secondary analyses and the development of novel predictive models. Vivli a non-profit entity established in 2018, epitomizes this movement with its global platform (vivli.org) dedicated to the facilitating access to and re-use of individual participant data from completed clinical trials for use by researchers worldwide. Methods: Commencing with approximately 2,500 trials from 15 members, Vivli has expanded over five years to encompass more than 7,000 trials involving 3.6 million participants across 50 members who contribute trial data from various sectors (industry, academic, government and non-profit foundation). Results: Oncology trials constitute 11% of the Vivli data pool of 7,000 trials, constituting 36% of Vivli’s research proposals due to the therapeutic area's high-impact potential. Researchers accessing data in the Vivli platform have produced a significant body of scientific work, with 74 oncology-focused publications emerging from their analyses. Notably, Roche's data has been a key resource, denoted by the 28% of all Vivli proposals requesting access to Roche data. Fifty-eight publications have resulted from secondary analysis of Roche oncology data. The motivation for secondary analyses in oncology have been predominantly for new research into treatment effectiveness or safety (39%). Other reasons include confirmation of existing research (15%), individual participant data (IPD) meta-analysis (14%), statistical method (10%), clinical trial design (5%), and summary-level meta-analysis (4%), training/testing (7%), other (5%). Conclusions: The Vivli platform has proven to be a key resource in recent years for the oncology community enabling the integration and publication of new insights. The sharing of IPD is in part motivated to ensure reproducibility of findings; our experience demonstrates that a minority of researchers are interested in reproducibility analysis but the majority of research is focused on new insight generation. The platform's success in facilitating the findability, accessibility and reuse of patient level data is signified by ever increasing numbers of scientific publications emerging from analyses on the Vivli platform and this underscores its role in advancing scientific discovery and development within the field of oncology.
Efficacy of a culturally tailored screening campaign to tackle cervical cancer disparities among Hispanic patients.
Authors: Po Justine , Bookstein Arthur , Barahona Rosa , Baezconde-Garbanati Lourdes 
Published: May 2024

e13697 Background: Cultural barriers have been identified as drivers of cervical cancer disparities. Hispanic patients specifically experience a higher incidence of cervical cancer and are more likely to be diagnosed at advanced stages than non-Hispanic White patients, mediated by multiple factors including cultural perceptions. To tackle cultural barriers in cervical cancer screening, this study assessed the efficacy of a community-based media campaign to increase Pap tests within a predominantly Hispanic community in east Los Angeles (LA). Methods: The campaign was implemented through a collaboration of local community members, design and communication experts, and cancer researchers. Prior to campaign design, focus groups were conducted with at-risk Hispanic women aged 18-45. Learnings from these focus groups and additional study of the visual culture of east LA led to selection of the jacaranda tree, which blooms for a short time in LA each spring, as a visual cue to pursue screening. This imagery was accompanied by a scheduling phone number and messaging (“It’s time.” and “You can prevent cervical cancer.”) in Spanish and English. Campaign materials were displayed on bus benches, billboards, and light post banners throughout east LA. To analyze campaign efficacy, intercept surveys were conducted from 2015-2018 by trained interviewers at locations throughout the community. In their preferred language, female participants (n=770) were asked standardized questions assessing campaign recall, message interpretation, and likelihood of scheduling a Pap test after viewing the campaign. Results: Among participants, mean age was 47.4 years, 88.7% primarily spoke Spanish at home, 25.2% were uninsured, 80.6% had not been vaccinated against HPV, and 12.5% had not had a Pap test in the last 3 years. 25.7% of participants recalled having seen campaign materials prior to survey, with 71.7% interpreting the message as a call to prevent and/or get screened for cervical cancer. 89.3% of participants stated that they were Likely or Extremely Likely to schedule a Pap test after viewing the campaign, including 87.2% of those who had not had a Pap test in the last 3 years. Conclusions: These findings support the efficacy of a community-based strategy to increase cervical cancer screening. This campaign showed high screening intent in a sample of participants who were disproportionately uninsured (25.2% v. 8.5% overall in the US in 2018), with similar efficacy among those who were overdue for recommended screenings. These results emphasize the benefit of culturally tailored messaging to improve screening uptake within underserved populations.