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Context can influence cancer-related outcomes. For example, health-care organization characteristics, including ownership, leadership, and culture, can affect care access, communication, and patient outcomes. Health-care organization characteristics and other contextual factors can also influence whether and how clinical discoveries reduce cancer incidence, morbidity, and mortality. Importantly, policy, market, and technology changes are transforming health-care organization design, culture, and operations across the cancer continuum. Consequently, research is essential to examine when, for whom, and how organizational characteristics influence person-level, organization-level, and population-level cancer outcomes. Understanding organizational characteristics-the structures, processes, and other features of entities involved in health care delivery-and their dynamics is an important yet understudied area of care delivery research across the cancer continuum. Research incorporating organizational characteristics is critical to address health inequities, test care delivery models, adapt interventions, and strengthen implementation. The field lacks conceptual grounding, however, to help researchers identify germane organizational characteristics. We propose a framework identifying organizational characteristics relevant for cancer care delivery research based on conceptual work in health services, organizational behavior, and management science and refined using a systematic review and key informant input. The proposed framework is a tool for organizing existing research and enhancing future cancer care delivery research. Following a 2012 Journal of the National Cancer Institute monograph, this work complements National Cancer Institute efforts to stimulate research addressing the relationship between cancer outcomes and contextual factors at the patient, provider, team, delivery organization, community, and health policy levels., (Published by Oxford University Press 2024.)

Appropriate models of survivorship care for the growing number of adult survivors of childhood cancer are unclear. We conducted a realist review to describe how models of care that include primary care and relevant resources (eg, tools, training) could be effective for adult survivors of childhood cancer. We first developed an initial program theory based on qualitative literature (studies, commentaries, opinion pieces) and stakeholder consultations. We then reviewed quantitative evidence and consulted stakeholders to refine the program theory and develop and refine context-mechanism-outcome hypotheses regarding how models of care that include primary care could be effective for adult survivors of childhood cancer. Effectiveness for both resources and models is defined by survivors living longer and feeling better through high-value care. Intermediate measures of effectiveness evaluate the extent to which survivors and providers understand the survivor's history, risks, symptoms and problems, health-care needs, and available resources. Thus, the models of care and resources are intended to provide information to survivors and/or primary care providers to enable them to obtain/deliver appropriate care. The variables from our program theory found most consistently in the literature include oncology vs primary care specialty, survivor and provider knowledge, provider comfort treating childhood cancer survivors, communication and coordination between and among providers and survivors, and delivery/receipt of prevention and surveillance of late effects. In turn, these variables were prominent in our context-mechanism-outcome hypotheses. The findings from this realist review can inform future research to improve childhood cancer survivorship care and outcomes., (© The Author(s) 2022. Published by Oxford University Press.)

Importance: Process quality measure performance has improved significantly with public reporting, requiring reevaluation of process-outcome relationships and the emerging role of patient perspectives on care., Objective: To evaluate associations between heart failure patient perspectives of care and publicly reported processes and outcomes., Design: Cross-sectional study, July 2008-June 2011., Setting: US hospitals in the Press Ganey database., Participants: Heart failure inpatients., Measures: Outcomes were Hospital Compare hospital-level risk-adjusted 30-day heart failure mortality and readmissions. Predictors included Hospital Compare heart failure processes of care, a weighted process composite and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) domains for heart failure. Hospital characteristics included volume of heart failure patients and race, health status and education., Results: Among 895 included hospitals, performance on process measures was high (median by hospital for composite, 95.6%); the median HCAHPS overall rating was 86.9. Median mortality was 11.3% and readmissions was 24.8%. No process measures were statistically significantly associated with lower mortality or readmissions in adjusted analyses. Higher ratings on HCAHPS patient perspectives of care were significantly correlated with lower readmissions in adjusted analyses, particularly those publicly reported domains conceptually related to readmissions. The magnitude was small (1.8 points higher on a 100-point scale between the highest and lowest quartiles of hospital readmissions)., Conclusions: Publicly reported process quality measures were no longer associated with outcomes, but higher patient perspectives of care were associated with lower heart failure readmissions. These associations support continued reevaluation of these measures and increased emphasis on patient experience and outcomes, as planned for Value-Based Purchasing., (© The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Improving analytical throughput is the focus of many quantitative workflows being developed for early drug discovery. For drug candidate screening, it is common practice to use ultra-high performance liquid chromatography (U-HPLC) coupled with triple quadrupole mass spectrometry. This approach certainly results in short analytical run time; however, in assessing the true throughput, all aspects of the workflow needs to be considered, including instrument optimization and the necessity to re-run samples when information is missed. Here we describe a high-throughput metabolic stability assay with a simplified instrument set-up which significantly improves the overall assay efficiency. In addition, as the data is acquired in a non-biased manner, high information content of both the parent compound and metabolites is gathered at the same time to facilitate the decision of which compounds to proceed through the drug discovery pipeline.