Perkins, Randa

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.

Abstract Objectives There is limited research on suicide risk screening (SRS) among head and neck cancer (HNC) patients, a population at increased risk for suicide. To address this gap, this single-site mixed methods study assessed oncology professionals' perspectives about the feasibility, acceptability, and appropriateness of an electronic SRS program that was implemented as a part of routine care for HNC patients. Methods Staff who assisted with SRS implementation completed (e.g., nurses, medical assistants, advanced practice providers, physicians, social workers) a one-time survey (N = 29) and interview (N = 25). Quantitative outcomes were assessed using previously validated feasibility, acceptability, and appropriateness measures. Additional qualitative data were collected to provide context for interpreting the scores. Results Nurses and medical assistants, who were directly responsible for implementing SRS, reported low feasibility, acceptability, and appropriateness, compared with other team members (e.g., physicians, social workers, advanced practice providers). Team members identified potential improvements needed to optimize SRS, such as hiring additional staff, improving staff training, providing different modalities for screening completion among individuals with disabilities, and revising the patient-reported outcomes to improve suicide risk prediction. Conclusion Staff perspectives about implementing SRS as a part of routine cancer care for HNC patients varied widely. Before screening can be implemented on a larger scale for HNC and other cancer patients, additional implementation strategies may be needed that optimize workflow and reduce staff burden, such as staff training, multiple modalities for completion, and refined tools for identifying which patients are at greatest risk for suicide.