Problem statement To define the Oncology Nursing Society Research Agenda for 2019-2022. Design Multimethod, consensus-building approach by members of the Research Agenda Project Team. Data sources Expert opinion, literature review, surveys, interviews, focus groups, town hall, and review of research priorities from other cancer care organizations and funding agencies. Analysis Content analysis and descriptive statistics were used to synthesize research priority themes that emerged. Findings Three priority areas for scientific development were identified. Implications for nursing The Research Agenda can be used to focus oncology nurses' research, scholarship, leadership, and health policy efforts to advance quality cancer care, inform research funding priorities, and align initiatives and resources across the ONS enterprise.

Research Agenda of the Oncology Nursing Society: 2019-2022.

Effectiveness of Implemented Interventions on Pathologic Nodal Staging of Non-Small Cell Lung Cancer

Background Decision-making in lung cancer is complex due to a rapidly increasing amount of diagnostic data and treatment options. The need for timely and accurate diagnosis and delivery of care demands high-quality multidisciplinary team (MDT) collaboration and coordination. Clinical decision support systems (CDSSs) can potentially support MDTs in constructing a shared mental model of a patient case. This enables the team to assess the strength and completeness of collected diagnostic data, stratification for the right personalized therapy driven by clinical stage and other treatment-influencing factors, and adapt care management strategies when needed. Current CDSSs often have a suboptimal fit into the decision-making workflow, which hampers their impact in clinical practice. Methods A CDSS for multidisciplinary decision-making in lung cancer was designed to support the abovementioned goals through presentation of relevant clinical data in line with existing mental model structures of the MDT members. The CDSS was tested in a simulated multidisciplinary tumor board meeting for primary diagnosis and treatment selection, based on de-identified primary lung cancer cases (n=8). Decision course analysis, eye-tracking data and questionnaires were used to assess the impact of the CDSS on constructing shared mental models to improve the decision-making process and outcome. Results The CDSS supported the team in their self-correcting capacity for accurate diagnosis and TNM classification. It enabled cross-validation of diagnostic findings, surfaced discordance between diagnostic tests and facilitated cancer staging according the diagnostic evidence, as well as spotting contra-indications for personalized treatment selection. Conclusions This study shows the potential of CDSS on clinical decision making, when these systems are properly designed in line with clinical thinking. The presented setup enables assessment of the impact of CDSS design on clinical decision making and optimization of CDSSs to maximize their effect on decision quality and confidence.

/pdf/looking-through-the-eyes-of-the-multidisciplinary-team-the-2g6m6vzz2h.pdf

Looking through the eyes of the multidisciplinary team: the design and clinical evaluation of a decision support system for lung cancer care

Introduction We compared NSCLC treatment and survival within and outside a multidisciplinary model of care from a large community health care system. Methods We implemented a rigorously benchmarked "enhanced" Multidisciplinary Thoracic Oncology Conference (eMTOC) and used Tumor Registry data (2011–2017) to evaluate guideline-concordant care. Because eMTOC was located in metropolitan Memphis, we separated non-MTOC patient by metropolitan and regional location. We categorized National Comprehensive Cancer Network guideline-concordant treatment as "preferred," or "appropriate" (allowable under certain circumstances). We compared demographic and clinical characteristics across cohorts using chi-square tests and survival using Cox regression, adjusted for multiple testing. We also performed propensity-matched and adjusted survival analyses. Results Of 6259 patients, 14% were in eMTOC, 55% metropolitan non-MTOC, and 31% regional non-MTOC cohorts. eMTOC had the highest rates of African Americans (34% versus 28% versus 22%), stages I to IIIB (63 versus 40 versus 50), urban residents (81 versus 78 versus 20), stage-preferred treatment (66 versus 57 versus 48), guideline-concordant treatment (78 versus 70 versus 63), and lowest percentage of nontreatment (6 versus 21 versus 28); all p values were less than 0.001. Compared with eMTOC, hazard for death was higher in metropolitan (1.5, 95% confidence interval: 1.4–1.7) and regional (1.7, 1.5–1.9) non-MTOC; hazards were higher in regional non-MTOC versus metropolitan (1.1, 1.0–1.2); all p values were less than 0.05 after adjustment. Results were generally similar after propensity analysis with and without adjusting for guideline-concordant treatment. Conclusions Multidisciplinary NSCLC care planning was associated with significantly higher rates of guideline-concordant care and survival, providing evidence for rigorous implementation of this model of care.

Survival Impact of an Enhanced Multidisciplinary Thoracic Oncology Conference in a Regional Community Health Care System.

https://ascopubs.org/doi/pdf/10.1200/JOP.2016.018226

Team Science in Cancer Care: Questions, an Observation, and a Caution

The complexity of lung cancer care mandates interaction between clinicians with different skill sets and practice cultures in the routine delivery of care. Using team science principles and a case-based approach, we exemplify the need for the development of real care teams for patients with lung cancer to foster coordination among the multiple specialists and staff engaged in routine care delivery. Achieving coordinated lung cancer care is a high-priority public health challenge because of the volume of patients, lethality of disease, and well-described disparities in quality and outcomes of care. Coordinating mechanisms need to be cultivated among different types of specialist physicians and care teams, with differing technical expertise and practice cultures, who have traditionally functioned more as coactively working groups than as real teams. Coordinating mechanisms, including shared mental models, high-quality communication, mutual trust, and mutual performance monitoring, highlight the challenge of achieving well-coordinated care and illustrate how team science principles can be used to improve quality and outcomes of lung cancer care. To develop the evidence base to support coordinated lung cancer care, research comparing the effectiveness of a diverse range of multidisciplinary care team approaches and interorganizational coordinating mechanisms should be promoted.

https://ascopubs.org/doi/pdf/10.1200/JOP.2016.013813

Deploying Team Science Principles to Optimize Interdisciplinary Lung Cancer Care Delivery: Avoiding the Long and Winding Road to Optimal Care.

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Deploying Team Science Principles to Optimize Interdisciplinary Lung Cancer Care Delivery: Avoiding the Long and Winding Road to Optimal Care.