Tevaarwerk, Amye J

Traditional home-call managing after-hours pager communications remains a common component of hematology-oncology fellowship and modern practice. Little is known about these patient-related interactions and implications. This project aimed to characterize after-hours pager volumes and evaluate perceived impacts on care, physician wellbeing, and trainee education. We performed a mixed-methods study at a tertiary academic center, analyzing after-hours pager activity from 2020-2024 across Hematology Consult (HCP), Hematology Outpatient (HOP), and Medical Oncology (MOP) pagers. A survey of hematology-oncology fellows evaluated perceptions and attitudes toward the home-call experience using free-text responses and a 5-point Likert scale. After-hours pager burden was substantial and sustained. The MOP received >3,000 pages annually, with medians of 5-6 per weekday and 15-15.5 per weekend day. The HOP volume nearly doubled over the study period (960 to 1,721 annually) with median pager volumes of 1-3 per weekday and 2-7 per weekend day. The HCP volume ranged from 594-768 pages annually. Survey response rate was 58% (19/33). Only 16% of fellows endorsed educational value from patient calls on MOP or HOP, and ≤5% agreed that call tasks required physician-level expertise. Respondents reported that the pager resulted in fatigue, anxiety, and social disruptions. Free text responses revealed themes of low educational value, high non-urgent call volume, and system inefficiencies. Contemporary home-call had substantial pager burdens with limited actionability, adverse effects on wellbeing, and low educational value. Systems engineering, including triage processes and augmented patient education, could help optimize patient care, physician wellbeing, and trainee education.

PURPOSE Incorporation of structured data elements within the electronic health record generates real-time data for many potential uses. However, this requires a change in documentation practices, which may increase documentation effort. The best strategies to support structured documentation interventions have not been thoroughly investigated. Herein, we report the results of extending a data capture tool across multiple disease sites and practice locations. METHODS A note-based data capture tool was developed to collect five data elements using minimum common oncology data elements language. Soft-stop close visit validation (CVV), offloading of data entry tasks to allied health staff, group education, individualized e-mails, and one-on-one assistance were used to support utilization. Baseline and follow-up surveys were collected. For each provider, we compared data capture in the 6 months before and after their CVV go-live. RESULTS Forty-five providers participated (31 physicians, 14 nurse pracationers and physician assistants [NPPAs]), resulting in 23,274 encounters pre-CVV go-live and 23,146 post-CVV go-live visits. Median percentage of all data elements captured increased from 69.5% to 82.6% across all providers. The effect of CVV was immediate and sustained. When surveyed, most respondents agreed with the value propositions of understanding practice population, outcomes, selecting trials to open, and identifying patients for trial enrollment. Most respondents (15/20, 75%) felt the soft stop was a helpful reminder. CONCLUSION Structured data can be successfully collected in provider notes with >80% data capture. Personalized assistance incorporating the structured data elements into users' preexisting templates and soft-stop CVV, along with monitoring the results of implementation and personalized follow-up, was used to support the structured data capture intervention.

PURPOSE This national study was undertaken to determine screening mammography rates among transgender and gender diverse (TGD) individuals. METHODS TGD individuals and cisgender women who qualified to receive screening mammography based on American College of Radiology guidelines were identified in the longitudinal OptumLabs Data Warehouse. The primary end point was the percentage of patients who had high rates of screening (>75% recommended screenings). Multivariable analyses were conducted in a matched population of transgender men to cisgender women to identify and adjust for factors associated with high adherence. Individuals were matched at a 1:4 ratio (transgender men: cisgender women) on age, race/ethnicity, year of enrollment in their health care plan, and duration of follow-up. RESULTS This study focused on 10,478 TGD individuals (3,778 transgender men; 1,294 transgender women; 5,406 with gender dysphoria not meeting other TGD criteria, NMOT) and 6,218,369 cisgender women. For transgender men, transgender women, individuals with gender dysphoria NMOT, and cisgender women, high mammography rates were observed in 41.1% (95% CI, 39.5 to 42.6), 7.4% (95% CI, 6.0 to 8.8), 11.9% (95% CI, 11.0 to 12.7), and 38.3% (95% CI, 38.3 to 38.4), respectively ( P < .0001). When transgender men were compared with 15,112 matched cisgender women, an age-based differential association was observed between sex and high mammography rates (interaction P = .0003); compared with cisgender women, transgender men appeared more likely to manifest high rates of screening at age 40-49 years (odds ratios [ORs], 1.38 [95% CI, 1.28 to 1.50]; P < .0001) and 50-59 years (OR, 1.57 [95% CI, 1.35 to 1.81]; P < .0001) but not in the older age groups. CONCLUSION The relatively high rates of screening mammography in cisgender women and transgender men suggest clinicians are offering mammography to individuals who are at average or high breast cancer risk, as compared with cisgender women, and not to transgender women at low risk.

Abstract The adoption of electronic health records (EHRs) has transformed health care, improving efficiency and chart accessibility. However, the widespread reliance on unstructured data entry and the lack of standardized documentation frameworks have resulted in significant data fragmentation across health care systems. The prevalence of unstructured data in EHRs limits their potential for clinical decision support, trial matching, real‐world evidence (RWE) generation, and quality measurement. Data fragmentation in health care triggers a cascade of challenges that ultimately compromise patient care. Clinicians face an excessive documentation burden and struggle to locate critical information buried in unstructured notes. Researchers encounter difficulties in extracting reliable clinical data. EHR vendors grapple with standardizing unstructured information for interoperability, and payers are unable to process unstructured clinical data efficiently to support value‐based care models. These challenges are particularly acute in oncology, where complex clinical elements like cancer staging, disease status, and treatment changes require precise, structured documentation. Emerging artificial intelligence (AI) technologies, such as large language models (LLMs) and ambient listening, offer a path to automate structured data generation while reducing the workload on providers. Here, the authors propose LLM‐based workflows that balance automation with clinician verification, streamlining data entry without compromising accuracy. Realizing these benefits requires coordinated efforts among clinicians, researchers, EHR vendors, payers, and policymakers to align regulatory frameworks with AI‐driven innovations. This article outlines a strategy to enhance structured data capture within EHRs, ultimately improving patient care, research, and health care efficiency.

AbstractIntroductionStructured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy).MethodsA tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free‐text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go‐live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden.ResultsPre‐intervention (5‐weeks before go‐live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre‐intervention. During a 6‐month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post‐intervention months increased to 64% for stage and 81%–82% for the other data categories post‐intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture.ConclusionsStructured data can be captured (1) in real‐time, (2) sustained over time without (3) undue provider burden using note‐based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.

AbstractBackgroundPatient‐reported outcomes (PROs) are a better tool for evaluating the experiences of patients who have symptomatic, treatment‐associated adverse events (AEs) compared with clinician‐rated AEs. The authors present PROs assessing health‐related quality of life (HRQoL) and treatment‐related neurotoxicity for adjuvant capecitabine versus platinum on the Eastern Cooperative Oncology Group‐American College of Radiology Imaging Network (ECOG‐ACRIN) EA1131 trial (ClinicalTrials.gov identifier NCT02445391).MethodsParticipants completed the National Comprehensive Cancer Network Functional Assessment of Cancer Therapy–Breast Cancer Symptom Index (NFBSI‐16) and the Functional Assessment of Cancer Therapy–Gynecologic Oncology Group neurotoxicity subscale (platinum arm only) at baseline, cycle 3 day 1 (C3D1), 6 months, and 15 months. Because of early termination, power was insufficient to test the hypothesis that HRQoL, as assessed by the NFBSI‐16 treatment side‐effect (TSE) subscale, would be better at 6 and 15 months in the capecitabine arm; all analyses were exploratory. Means were compared by using t‐tests or the Wilcoxon rank‐sum test, and proportions were compared by using the χ2 test.ResultsTwo hundred ninety‐six of 330 eligible patients provided PROs. The mean NFBSI‐16 TSE subscale score was lower for the platinum arm at baseline (p = .02; absolute difference, 0.6 points) and for the capecitabine arm at C3D1 (p = .04; absolute difference, 0.5 points), but it did not differ at other times. The mean change in TSE subscale scores differed between the arms from baseline to C3D1 (platinum arm, 0.15; capecitabine arm, −0.72; p = .03), but not from baseline to later time points. The mean decline in Functional Assessment of Cancer Therapy–Gynecologic Oncology Group neurotoxicity subscale scores exceeded the minimal meaningful change (1.38 points) from baseline to each subsequent time point (all p < .05).ConclusionsDespite the similar frequency of clinician‐rated AEs, PROs identified greater on‐treatment symptom burden with capecitabine and complemented clinician‐rated AEs by characterizing patients’ experiences during chemotherapy.

PURPOSE Multidisciplinary tumor boards (MTBs) support high-quality cancer care. Little is known about the impact of information technology (IT) tools on the operational and technical aspects of MTBs. The National Comprehensive Cancer Network EHR Oncology Advisory Group formed a workgroup to investigate the impact of IT tools such as EHRs and virtual conferencing on MTBs. METHODS The workgroup created a cross-sectional survey for oncology clinicians (eg, pathology, medical, surgical, radiation, etc) participating in MTBs at 31 National Comprehensive Cancer Network member institutions. A standard invitation e-mail was shared with each EHR Advisory Group Member with a hyperlink to the survey, and each member distributed the survey to MTB participants at their institution or identified the appropriate person at their institution to do so. The survey was open from February 26, 2022, to April 26, 2022. Descriptive statistics were applied in the analysis of responses, and a qualitative thematic analysis of open-ended responses was completed. RESULTS Individuals from 27 institutions participated. Almost all respondents (99%, n = 764 of 767) indicated that their MTBs had participants attending virtually. Most indicated increased attendance (69%, n = 514 of 741) after virtualization with the same or improved quality of discussion (75%, n = 557 of 741) compared with in-person MTBs. Several gaps between the current and ideal state emerged regarding EHR integration: 57% (n = 433 of 758) of respondents noted the importance of adding patients for MTB presentation via the EHR, but only 40% (n = 302 of 747) reported being able to do so most of the time. Similarly, 87% (n = 661 of 760) indicated the importance of documenting recommendations in the EHR, but only 53% (n = 394 of 746) reported this occurring routinely. CONCLUSION Major gaps include the lack of EHR integration for MTBs. Clinical workflows and EHR functionalities could be improved to further optimize EHRs for MTB management and documentation.

This editorial reviews the many opportunities and obstacles associated with ePRO integration in oncology

Electronic patient‐reported outcome (ePRO) programs may offer advantages for patients with cancer, clinicians, health care systems, payors, and society in general; but developing and maintaining an ePRO program will require cancer centers to navigate defining meaningful problems, collecting ePROs, implementing action when those ePROs require intervention without over‐burdening clinicians, and monitoring the successes and failures of their ePRO programs. Physician informaticists from the National Comprehensive Cancer Network Electronic Health Record Advisory Group offer 10 guiding principles to consider when contemplating, building, or refining an ePRO program for patients with cancer.