Citations

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.

It is an exciting time for big data efforts in radiation oncology. The use of big data to help aid both outcomes and decision‐making research is becoming a reality. However, there are true challenges that exist in the space of gathering and utilizing performance and outcomes data. Here, we summarize the current state of big data in radiation oncology with respect to outcomes and discuss some of the efforts and challenges in radiation oncology big data.

The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.

300 Background: The National Radiation Oncology Registry (NROR) is developing an IT infrastructure to support the efficient collection, aggregation, and analysis of data concerning cancer patients treated with radiotherapy across the United States. Methods: Detailed requirements were developed to emphasize the following distinguishing goals for the system: 1) streamline the process of data collection by minimizing manual data entry; 2) support the collection of patient-reported outcomes (PROs) and their linkage with clinician-observed outcomes; 3) facilitate data integration within the NROR and between the NROR and other registries; 4) allow for evolution of the system in response to new learning; and 5) promote sustainability and ongoing improvement through an open standard of interoperability. Results: An innovative technical architecture has been designed to meet the goals described. The system maximizes efficiency through automated extraction and electronic transfer of data from radiation oncology information systems. PROs are collected directly from patients via a web-based portal and linked to data submitted from participating institutions. A data dictionary has been developed based on national standards and controlled vocabularies to facilitate data integration. Contemporary forms-based database technology is used in combination with traditional relational technology to permit both flexible evolution of the system and efficient data analysis. A unique “gateway” architecture and open transmission standard makes it possible for commercial products to interoperate with the registry in the future. Conclusions: The NROR prototype system will be launched later this year to collect data for a pilot registry of prostate cancer patients. Its performance and usability will be important factors in the success of the pilot. If effective, the platform will support the expansion of the NROR to benefit patients, clinicians, researchers, payors, device manufacturers, and policy-makers by capturing reliable, population-based information on radiation treatment delivery and health outcomes.

Purpose Polycythemia vera (PV) is a myeloproliferative neoplasm (MPN) associated with disabling symptoms and a heightened risk of life-threatening complications. Recent studies have demonstrated the effectiveness of JAK inhibitor therapy in patients with PV patients who have a history of prior hydroxyurea (HU) use (including resistance or intolerance), phlebotomy requirements, and palpable splenomegaly. We aimed to determine how these features contribute alone and in aggregate to the PV symptom burden. Patients and Methods Through prospective evaluation of 1,334 patients with PV who had characterized symptom burden, we assessed patient demographics, laboratory data, and the presence of splenomegaly by disease feature (ie, known HU use, known phlebotomy requirements, splenomegaly). Results The presence of each feature in itself is associated with a moderately high symptom burden (MPN symptom assessment form [SAF] total symptom score [TSS] range, 27.7 to 29.2) that persists independent of PV risk category. In addition, symptoms incrementally increase in severity with the addition of other features. Patients with PV who had all three features (PV-HUPS) faced the highest total score (MPN-SAF TSS, 32.5) but had similar individual symptom scores to patients with known HU use (PV-HU), known phlebotomy (PV-P), and splenomegaly (PV-S). Conclusion The results of this study suggest that patients with PV who have any one of the features in question (known HU use, known phlebotomy, or splenomegaly) have significant PV-associated symptoms. Furthermore, it demonstrates that many PV symptoms remain severe independent of the number of features present.