Clinical Application of Computational Methods in Precision Oncology: A Review.

Importance: There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective: To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review: The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings: Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance: Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

Novice Oncology Nurse Navigator: Core Elements in Establishing Training Needs and Building on Competencies.

BACKGROUND: Oncology nurse navigation opportunities are rapidly expanding as the value of the role is recognized. However, there is a lack of training opportunities focusing on the unique needs of the oncology nurse navigator (ONN). Most navigator training programs provide only general oncology navigation content. OBJECTIVES: The purpose of this article is to evaluate the current state of training for the novice ONN and begin to identify core elements to inform development of a standardized training program. METHODS: Navigator training programs and literature related to the role and development needs of the novice ONN were reviewed. FINDINGS: Training of the novice ONN varies widely, with little evaluation of the most effective way to prepare for the role. It is clear that the learning needs of the ONN are different than those of other types of navigators and oncology nurses.

Paths to partnership: Veterans Health Administration's Journey in pilot testing breast cancer care quality measures.

Prioritizing personalized, proactive, patient-driven health care is among the Veterans Health Administration's (VHA's) transformational initiatives. As one of the largest integrated healthcare systems, the VHA sets standards for performance measures and outcomes achieved in quality of care. Evidence-based practice (EBP) is a hallmark in oncology nursing care. EBP can be linked to positive outcomes and improving quality that can be influenced directly by nursing interventions. VHA oncology nurses had the opportunity to partner with the Oncology Nursing Society (ONS), ONS Foundation, and the Joint Commission in the multiyear development of a comprehensive approach to quality cancer care. Building on a platform of existing measures and refining measurement sets culminated in testing evidence-based, nursing-sensitive quality measures for reliability through the ONS Foundation-supported Breast Cancer Care (BCC) Quality Measures Set. The BCC Measures afforded the VHA to have its many sites collectively assess documentation of the symptoms of patients with breast cancer, the use of colony-stimulating factors, and education about neutropenia precautions provided. Parallel paths of the groups, seeking evidence-based measures, led to the perfect partnership in the VHA's journey in pilot testing the BCC Measures in veterans with breast cancer. This generated further quality assessments and continuous improvement projects for spread and sustainability throughout the VHA.