Martin, Daniel B.

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.

46 Background: Tumor boards (TBs) are considered a key component for ensuring high quality cancer care. However, little is known about TB operational aspects that impact effectiveness. We aimed to understand how TBs leverage Electronic Health Record (EHR) functionalities and how pandemic-driven transitions from in-person TBs to virtual TBs impacted TB quality and effectiveness. Methods: The National Comprehensive Cancer Network (NCCN) Oncology EHR Advisory Group formed a Workgroup to assess the current state of TBs, obtain providers’ views on ideal TB state, and develop recommendations for improving TB quality and effectiveness. The Workgroup surveyed providers in various specialties at 31 NCCN Member Institutions in March 2022. Results: 846 providers from 27 institutions responded to the survey (n = 76 respondents were excluded from analysis because they reported not participating in TBs). Almost all (n = 764/767, 99%) indicated that one or more participants always or frequently attended TB meetings virtually. When comparing (newly established) virtual to (pre-pandemic) in-person TBs, 69% indicated increased attendance at virtual TBs. 65% reported no change in case volume, and 56% reported unchanged discussion quality while 19% reported increased discussion quality when comparing virtual to in-person TB meetings. In regards to EHR integration, several gaps between current and ideal state were noted. For instance, 40% always or frequently added patients to the TB roster via the EHR, but 57% reported this functionality to be very important or important for TBs. Additionally, 53% always or frequently document TB recommendations within the EHR, while 87% indicated EHR documentation of recommendations as very important or important. Respondents reported that barriers to documentation include resource constraints, concerns with patient access to documentation, legal and liability concerns, and poor EHR functionality. Conclusions: Overall, the majority of responding providers reported virtual TBs to be at least similar in quality to in-person TB meetings. A major gap identified by this study is the lack of documentation of TB recommendations in the EHR. Workflows and EHR functionalities could be improved to fully optimize the EHR for TB management and documentation.[Table: see text]