tbuckley/cabot-search huggingface dataset:
Engaging student volunteers in non-clinical navigation: ACS community access to resources, education, and support (ACS CARES).
Authors: Bonny Morris, Gabrielle Betty Rocque, Shanthi Sivendran, Emily K. Hill, Ian Bostock, Robert Chong, Noah Federman, Charlotte Waugh, D'Ambra Dent, Cynthia Del Valle, Arif Kamal
Journal: Journal of Clinical Oncology (2024)

e13577 Background: Despite known benefits of oncology patient navigation in improving patient outcomes, implementation is variable and many programs are understaffed. The American Cancer Society (ACS) developed the ACS CARES (Community Access to Resources, Education, and Support) program to expand navigation capacity through the standardized training of students from local universities as volunteers providing non-clinical navigation support. Methods: This pragmatic, real-world hybrid type 2 study utilizes the Consolidated Framework for Implementation Research (CFIR) to evaluate the implementation of student volunteer navigation for patients receiving cancer care at 3 pilot sites. Volunteers meet with patients in clinic, administer a modified NCCN distress thermometer health-related social needs (HRSNs) screen, and administer non-clinical navigation support. Initial process measures from the first 3 months of the pilot data include number and demographics of patients served, distress scores and HRSNs, and time spent by volunteers. Results: Pilot sites spanned diverse geographic regions (rural mid-West, rural-suburban Southeast, urban West) and included clinics with multiple cancer types. From 10/23/23-12/23/23, 22 volunteers navigated 73 patients who were 53% female, 22% Black, and an average age of 57. Of all patients, 46% had a moderate (score 3-6) and 37% had a severe (score 7-10) distress score. ACS CARES volunteers identified 116 HRSNs (56% practical, 18% physical, 17% emotional, 7% social, 2% spiritual). The most common practical HRSNs were finances and transportation. Volunteers spent an average of 78.6 min per patient per in-person meeting, 14 min per patient per follow-up call, 25 min per patient coordinating care and sending encrypted messages, and 90 min per patient identifying and providing resources to address barriers to care. Total time providing non-clinical navigation was over 250 hours. Conclusions: Student volunteers embedded in oncology clinics provided non-clinical navigation support to 73 patients in the first 3 months of the ACS CARES program, further extending the reach and depth of support provided by pilot site care teams. Future analyses will include survey and EMR data to examine program impact on clinical and patient reported outcomes and healthcare utilization. Clinical trial information: 2013209-1.
Optimizing the oncology ecosystem: Addressing barriers between community and academic oncologists.
Authors: Ahmed Khalil, Ghazal Kango, Stephanie L. Graff
Journal: Journal of Clinical Oncology (2024)

e13580 Background: Oncology practice is becoming more complex and collaboration between academic and community practices is necessary to optimize care for patients. This qualitative analysis explores perceived gaps between academic and community practices and proposed solutions to a more cohesive oncology ecosystem for optimal quality and care delivery. Methods: A 10-question mixed-methods quantitative and qualitative survey was developed (SurveyMonkey) to identify practice setting, methods of collaboration, perceived barriers, and strategies for improvement. Both open-ended and multiple-choice questions were used. Survey was distributed via social media outreach on Facebook, X, and email. Individual responses were coded by AK, then compared using two sample z-test for proportions. Results: 68 responses were collected, comprising 45.5% community and 44.1% academic physicians. Non-clinician respondents (2.9%) were excluded. Respondents had been in practice: &gt;20 years (16.42%), 10-20 years (40.3%), 5-9 years (19.4%) and &lt; 5 years (23.9%). Collaboration commonly occurred via conferences (73.68%), shared Electronic Medical Records (EMRs, 59.65%) or research (38.6%). The main barriers perceived are summarized (Table). Community oncologists describe lack of appreciation from academics, time constraints, difficulty communicating. Main barriers to collaboration perceived by academic oncologists include delayed referral, time constraints, difficulty communicating, lack of shared EMRs, fear of patient displacement from the primary referring practice, and administrative red tape. Proposed solutions from both groups include engaging community oncologists in academic tumor boards (n=8/68,11.8%) and regional education events (n=2/68, 2.9%). Improved communication. Additionally, both groups were interested in expanding clinical trials in the community. Both groups noted the need for financial incentives and institutional support for successful cooperation (n=3/68, 4.4%). Conclusions: Our data unveiled the nuances of collaboration. Barriers mainly consist of lack of acknowledgement, poor communication, and barriers in EMR utilization. Both groups struggle with time constraints. This data can inform multi-shareholder groups working for systems-based process improvement, expanding collaboration between community and academic oncology. [Table: see text]
Assessing the landscape of integrative oncology at US comprehensive cancer centers (CCC).
Authors: Elyssa Kim, Jessica Cheng, Jamie Fertal, Frank Munoz, Seyma Saritoprak, Amy Truong, Christy DiCristofano, Sarah Ku, Art Rodriguez, Preeti Soni, Anne Marie Reb, Judy Rose, Richard T. Lee
Journal: Journal of Clinical Oncology (2024)

e13565 Background: Interest in integrative oncology (IO) has grown among patients and its availability has expanded within CCCs. This study assesses the availability of IO programs in U.S. CCCs. Methods: In 2023, a cross-sectional survey was sent to CCCs including NCI-designated CCCs by email regarding IO programs. Participants were asked to describe the importance of IO within their cancer center, the programs and services offered, and how the programs are provided. Results: Of ninety-four survey invitations sent, fifty-eight completed surveys were returned (response rate 62%). Three-fourths of completed surveys (74%) came from NCI-designated CCCs. Over half of respondents (57%) rated IO services to be important (or very important) at their center with 53% stating that their center has a formal IO clinical program. Univariate analysis showed that non-NCI centers were more likely to perceive IO services as being important compared to NCI-designated centers (100% vs. 77%, p=0.04). Respondents stated that their IO program has been in existence for ≥10 years (40%), 5-9 years (26%), and 0-4 years (34%). Among the surveyed cancer centers, availability of IO program was 33% (adult and pediatric) vs. 49% (adult only). The IO programs are available to patients with active cancer (91%), patients in survivorship off-treatment (81%), and healthy patients without a history of cancer (22%). The IO services were offered in an outpatient clinic on the main campus (76%), community sites (29%), or inpatient settings (40%). The most common oncology specific services reported were nutrition (51%), exercise counseling (42%), mind-body medicine (38%), and acupuncture (33%). The clinicians who provided integrative medicine consultations were primarily in internal medicine (31%), family medicine (26%), and medical oncology (19%). In inpatient and outpatient settings, insurance was billed for physical therapy (50% vs. 67%) and diet/nutrition (36% vs. 50%) services, whereas acupuncture (47%) and massage (43%) services were charged by cash/card in an outpatient setting only. Music therapy (33% vs. 47%) and pet therapy (34% vs. 43%) services were offered for free most commonly in both inpatient and outpatient settings, respectively. The highest average out of pocket fee service was acupuncture ($42±27) followed by integrative medicine consultations ($34±29). Conclusions: Despite increasing patient interest and established benefits of integrative therapies as noted by recently published ASCO clinical guidelines, only half of CCCs reported having a formal IO program. More research is needed to understand the gap between patient interest, the growing evidence-base, and how to expand integrative oncology services within existing cancer centers.
Clustering analysis for predicting racial disparities in genitourinary cancer–related financial burden.
Authors: Yanjia Zhang, Atulya Aman Khosla, Sufal Chhabra, Nitya Batra, Mohammad Arfat Ganiyani, Anmol Goyal, Shreyas S Bellur, Rohit Singh, Karan Jatwani, Venkataraghavan Ramamoorthy, Muni Rubens, Anshul Saxena, Rohan Garje
Journal: Journal of Clinical Oncology (2024)

e13612 Background: Healthcare costs in the United States (US) are significantly impacted by genitourinary (GU) cancers. Prostate cancer treatment alone cost the US more than $22 billion in 2020. We sought to utilize machine learning (ML)-based classification to investigate differences in the total cost of healthcare expenditure among patients with GU cancer in the US. Methods: The study included eligible adult GU cases from the 2019–2021 Medical Expenditure Panel Survey. Renal cell, prostate, urothelial, testicular, and penile cancers were identified using ICD-10 CM codes. Patients were clustered using demographic information such as age, race, sex, marital status, highest degree, insurance status, perceived health status, and perceived mental health status. The analysis accounted for the complex survey design and sampling weights. The cluster groups were taken into account when conducting descriptive and regression analyses. GU cancer cases were divided into low-, medium-, and high-risk healthcare expenditure clusters. Results: The study included a representative sample of 9,061,181 cases of GU cancer in the US between 2019 and 2021. The average age of the sample was 71.6 years (95% CI: 70.7, 72.5). There were 7.9% Hispanic (Hisp), 74.8% non-Hispanic White (NHW), 13.8% non-Hispanic Black (NHB), and 3.5% individuals from Asian or other races. Overall, the mean total healthcare expenditures within low-, med-, and high-risk clusters were $17974 (13816, 22133), $20496 (13100, 27893), and $30512 (21389, 39635), respectively. Regression analysis showed that, after adjusting for sampling weight, cluster, gender, family income, age, insurance status, and survey year, the mean total expenditure was higher by $11209.70 and $13100.81 in med- and high-risk clusters when compared to the low-risk cluster (ref.). Within each race or ethnicity, the proportion of Hisp or NHB individuals was higher in med- and high-risk clusters. Med- and high-risk clusters were more likely to contain people with cognitive impairments, poor health status, and financial difficulties in their families. Conclusions: We used an unsupervised clustering algorithm that can distinguish between GU cases with high and low expenditure based solely on sociodemographic variables. We demonstrate that our findings were consistent for all three data cohorts. This approach can predict the disparity in utilization in any insured population. [Table: see text]
Impact of Exigent Research Network’s partnership with the Tempus AI TIME Program on the screening and matching of patients for clinical trials.
Authors: Ajeet Gajra, Samantha Mallahan, Sibel Blau, Julio Antonio Peguero, Mahran Shoukier, Mohamad Kanaan, Chanh Trung Huynh, Daniel N. Efiom-Ekaha, Hema M. Vankayala, Kyle Kitchen, Stephan Disean Kendall, James Michael Orsini, Sunil Babu, Jessica Hellyer, Thomas Weart, Janelle Marie Meyer, Christian Clifford, Danielle Skelly, Emily Patnaude, Matthew M. Cooney
Journal: Journal of Clinical Oncology (2024)

e13599 Background: Exigent Research is comprised of independent community oncology practices with a central patient data repository to support clinical research. The Tempus AI TIME program provides clinical trials solutions including a diverse trial portfolio, TApp an AI powered patient matching platform, patient tracking LINK portal, and a rapid trial activation process. This partnership facilitated Tempus AI to continuously search the data repository and enabled Exigent to increase trial enrollment. Methods: TApp is a clinical trial matching platform that utilizes patient data, trial eligibility criteria, and NLP models to execute AI powered matching of subjects to trials. TApp searches the entire patient population for TIME trial matches. New patient clinical information or changes to trial eligibility criteria triggers a new TApp search and matches are reviewed by Tempus AI nurses. Trials were opened in an accelerated “just in time” manner if a subject was ready immediately or prospectively prior to a first patient being identified. Data collected included trial activations, number of patients, available studies, patient and trial updates, TApp searches, nursing effort, matches, and consents. Interventional Phase I to III trials and observational studies were included. Results: From 2022 - 2023, there were 16 Exigent sites in the TIME network. The patient population screened was 244,986 matched to 189 clinical trials. There were 3,179,079 changes to individual patient records and 2,152 trial updates. This triggered 216,712,093 unique TApp searches and 33,083 matches reviewed by nurses. In total, 1,312 patients matched to observational studies and 6,040 patients for interventional trials. Cumulatively, nurses spent 6,617 hours on patient screening resulting in a $330,830 cost. There were 71 trial activations resulting in 312 patient consents (169 observational, 143 interventional). The average duration for study activation was 12.3 days for a just in time activation and 33.8 days for a prospective activation. Conclusions: Exigent Research leveraged a central data repository with the Tempus AI TIME program to continuously screen a large patient population for 185+ trials. During a two-year period, 215M+ unique TApp searches occurred which resulted in over 310 patient consents with rapid activation times. Future clinical trial strategies should partner AI screening technologies with robust trial infrastructure solutions to maximize enrollment success. [Table: see text]
Improving LDCT screening rates within a large multidisciplinary community based practice using integrated ACR accredited imaging centers.
Authors: Garry Lachhar, Jarett Greenside, Harry Staszewski
Journal: Journal of Clinical Oncology (2024)

e13582 Background: Lung cancer remains the leading cause of cancer-related death in the US, accounting for about 22% of such deaths. Lung cancer detected by low-dose computed tomography (LDCT) screening is discovered at an early stage 64-85% of the time. The benefits of LDCT screening have been demonstrated in numerous trials, but there has been difficulty in establishing programs that screen a high percentage of eligible patients. We present data from the first 18 months of our lung cancer screening program that is designed to provide the highest quality screening, follow-up and access to prevention, diagnosis, and treatment including clinical trials within their local communities. Methods: New York Cancer and Blood Specialists (NYCBS) is a community practice with over 50 locations across NY. While most lung cancer screening programs are hospital-based, NYCBS has a series of American College of Radiology (ACR) accredited imaging centers that perform these screenings and are closely affiliated with oncology, primary care, Ob-Gyn, nephrology, urology and other specialty medical and surgical practices. These centers are strategically located in areas that have high rates of tobacco use as suggested by heat map data from the NY City Community Health Survey . Upon initial patient presentation, all NYCBS and affiliated sites screen patients with tablet-based questionnaires that prompt additional questions during follow-up appointments. Patients with positive findings are discussed during a monthly, system-wide multidisciplinary lung nodule review conference. Follow-up interventions include referrals to pulmonology, interventional radiology, or further imaging with PET/CT. Per ACR guidelines, patients not requiring acute intervention continue with follow-up CT. Results: In 2023, 29,501 patients were screened with an in-office questionnaire. Of those patients, 3,606 were eligible for screening, 225 declined, and 896 (29%) high risk patients proceeded to undergo LDCT screening. In screened patients, 128 underwent follow-up CT, 36 patients had findings requiring immediate intervention, and 6 were diagnosed with lung cancer. 5 of these patients were diagnosed with stage I and 1 patient was diagnosed with stage IV lung cancer. Conclusions: LDCT screening is a proven method to discover early stage lung cancer and improve the survival rate of diagnosed patients but has not been well adopted in practice. With access to a diverse array of multidisciplinary practices across NY and integrated accredited imaging centers, we have increased the accessibility of lung cancer screening and improved transitions to follow-up care. Nationally, only 4.5% of patients at high risk are screened, however, we found that with our approach to patient care, we have achieved among the highest reported screening rates in eligible individuals.
Expanding access to care through physician and medically integrated dispensing: Legal and regulatory considerations.
Authors: Jesse C. Dresser
Journal: Journal of Clinical Oncology (2024)

e13556 Background: Oncology providers face a myriad of challenges, especially in navigating the intricate legal and regulatory aspects of physician dispensing and pharmacy practices. This session will dissect these complexities, offering insights into medically integrated dispensing and its importance in enhancing patient outcomes. We will provide a thorough overview of the current state-of-the-market, highlighting the benefits of a medically integrated model, such as immediate access to patient health records, substantial cost savings, integration of prescription data with patient health information, and the presence of dedicated staff for personalized patient education and monitoring. Methods: This section will elaborate on the structured approach and methodologies employed to navigate and demystify the legal and regulatory challenges in expanding access to care through physician and medically integrated dispensing. It encompasses: 1) Defining the Landscape: Introduces essential terms and concepts to understand legal nuances in oncology. 2) Legal and Regulatory Environment: Examines legal challenges, regulatory influences, and the impact of PBMs on care delivery. 3) Case Studies in Network Access: Presents real-world challenges and legal strategies in oncology performance and medication reimbursement. 4) Coverage &amp; Reimbursement: Discusses complexities and strategies in oncology coverage, reimbursement, and patient steering. 5) Staying Compliant: Highlights compliance with standards like USP 797/800 and the impact of pharmacy regulations on dispensing. 6) Legal Empowerment: Provides legal tools and strategies for effective patient advocacy, network access, and handling disputes. Results: This section will analyze the success of applied methodologies in oncology, highlighting improvements in provider empowerment, patient outcomes, and practice operations within legal boundaries. It will demonstrate how these strategies enhance legal understanding among oncology professionals, improving care delivery and access to health services. Conclusions: Frier Levitt addresses legal issues in oncology dispensing, integrating legal analysis, case studies, and strategies to enhance care delivery. The session focuses on engaging with PBMs, insurers, and regulators for fair treatment and compliance. It aims to empower attendees with practical approaches for improving patient care amidst healthcare changes. The presentation contributes significantly to the Care Delivery/Models of Care track, offering a detailed perspective on legalities in physician dispensing and pharmacy practices.
Practice patterns in prostate cancer therapy among healthcare professionals: Comparison with expert recommendations from an online tool.
Authors: Marie N. Becker, Kristen M. Rosenthal, Tanya B. Dorff, Rana R. McKay, Michael Thomas Schweizer, David James VanderWeele, Alicia K. Morgans
Journal: Journal of Clinical Oncology (2024)

e13549 Background: The treatment landscape of prostate cancer (PC) is complex, and recent approvals of new agents and regimens further challenge healthcare professionals (HCPs) to select optimal therapy for patients with PC. We sought to identify practice patterns among community HCPs for PC by comparing HCP and key opinion leader (KOL) treatment recommendations using an online decision support tool. Methods: The online decision support tool was developed with 5 KOLs who provided therapy recommendations for 96 patient case scenarios encompassing mHSPC, nmCRPC, and mCRPC based on factors including disease volume and symptoms, comorbidities, previous therapy, and biomarker status. HCPs entered specific patient and disease factors into the tool along with their intended treatment plan and were shown the 5 individual KOL recommendations for the patient case. An analysis of HCP selected therapy compared with KOL recommendations was performed. Results: Between September 2023 and January 2024, 286 patient cases were entered by 100 HCPs. KOL consensus defined as ≥3 in agreement. HCP therapy selection discordance with KOL recommendations was 69% for nmCRPC (n = 64 cases), 42% for mHSPC (n = 125 cases), and 46% for mCRPC (n = 97 cases). The level of discordance was 63% (n = 43 cases) for nmCRPC with a PSADT &lt;10 months and 81% (n = 21 cases) with a PSADT ≥10 months (Table). Regardless of PSADT, selection of therapy for nmCRPC without listed comorbidities was highly discordant 82% (n = 34 cases). In mHSPC, the highest level of discordance was seen in cases with de novo, symptomatic, high-volume disease, and diabetes at 73% (n = 11). Discordance was 54% for de novo mHSPC with symptomatic disease (n = 52 cases). HCP selection of 1L, 2L, or 3L therapy for mCRPC was 44% to 47% discordant with KOL recommendations regardless of prior therapy. Selection of therapy for mCRPC with HRR mutations including BRCA (n = 29) was 48% discordant. Among HCPs who initially selected treatment options that diverged from expert recommendations or were uncertain about therapy selection, 48% (n/N = 75/157) intend to change their therapy selection to align with the experts. Detailed data analysis with KOL recommendations will be presented. Conclusions: This analysis suggests differences in clinical practice between HCPs and KOLs for multiple case scenarios in PC and suggests that use of the decision tool may lead to increased alignment of treatment choices with KOLs. [Table: see text]