National Cancer Institute-Funded Social Risk Research in Cancer Care Delivery: Opportunities for Future Research.

BACKGROUND: Cancer patients and survivors with food insecurity, housing instability, and transportation-related barriers face challenges in access and utilization of quality cancer care thereby adversely impacting their health outcomes. This portfolio analysis synthesized and described National Cancer Institute (NCI)-supported social risk research focused on assessing food insecurity, housing instability, and transportation-related barriers among individuals diagnosed with cancer. METHODS: We conducted a query using the National Institutes of Health iSearch tool to identify NCI-awarded extramural research and training grants (2010-2022). Grant abstracts, specific aims, and research strategies were coded for research characteristics, study population, and outcomes. RESULTS: Of the 30 grants included in this analysis, most assessed transportation-related barriers as patient-level social needs. Grants focused on community-level social risks, food insecurity, and housing instability were largely absent. Most grants included activities that identified the presence of social risks and/or needs (n = 24), connected patients to social care resources (n = 10), and engaged community members or organizations to inform the research study (n = 9). Of the grants, 18 focused on a single type of cancer, primarily breast cancer, and more than half focused on the treatment and survivorship phases. CONCLUSIONS: In the last decade, there has been limited NCI-funded social risk research grants focused on food insecurity and housing instability. Findings highlight opportunities for future cancer care delivery research, including community and health system-level approaches that integrate social and clinical care to address social risks and social needs. Such efforts can help improve outcomes of populations that experience cancer health and health-care disparities.

Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network.

PURPOSE: Patients with cancer are predisposed to developing chronic, comorbid conditions that affect prognosis, quality of life, and mortality. While treatment guidelines and care variations for these comorbidities have been described for the general noncancer population, less is known about real-world treatment patterns in patients with cancer. We sought to characterize the prevalence and distribution of initial treatment patterns across a large-scale data network for depression, hypertension, and type II diabetes mellitus (T2DM) among patients with cancer. METHODS: We used the Observational Health Data Sciences and Informatics network, an international collaborative implementing the Observational Medical Outcomes Partnership Common Data Model to standardize more than 2 billion patient records. For this study, we used 8 databases across 3 countries-the United States, France, and Germany-with 295,529,655 patient records. We identified patients with cancer using SNOMED (Systematized Nomenclature of Medicine) codes validated via manual review. We then characterized the treatment patterns of these patients initiating treatment of depression, hypertension, or T2DM with persistent treatment and at least 365 days of observation. RESULTS: Across databases, wide variations exist in treatment patterns for depression (n = 1,145,510), hypertension (n = 3,178,944), and T2DM (n = 886,766). When limited to 6-node (6-drug) sequences, we identified 61,052 unique sequences for depression, 346,067 sequences for hypertension, and 40,629 sequences for T2DM. These variations persisted across sites, databases, countries, and conditions, with the exception of metformin (73.8%) being the most common initial T2DM treatment. The most common initial medications were sertraline (17.5%) and escitalopram (17.5%) for depression and hydrochlorothiazide (20.5%) and lisinopril (19.6%) for hypertension. CONCLUSION: We identified wide variations in the treatment of common comorbidities in patients with cancer, similar to the general population, and demonstrate the feasibility of conducting research on patients with cancer across a large-scale observational data network using a common data model.

Designing a "Thinking System" to Reduce the Human Burden of Care Delivery.

Cancer patients interact with clinicians who are distributed across locations and organizations. This makes it difficult to coordinate care and adds to the burden of cancer care delivery. Failures in care coordination can harm patients. The rapid growth in the number of cancer survivors and the increasing complexity of cancer care has kindled an interest in new care delivery models. Information technology (IT) is an important component of care delivery. While IT can potentially enhance collaborative work among people distributed across locations, organizations and time, the current design and implementation of health IT adds to the human burden and often makes it a part of the problem instead of the solution. A new paradigm is needed, therefore, to drive innovations that reframe health IT as an enabler (and a component) of a "thinking system," in which patients, caregivers, and clinicians, even when distributed across locations and time, can collaborate to deliver high-quality care while decreasing the burden of care delivery. In a thinking system, the design of collaborative work in health care delivery is based on an understanding of complex interplay among social and technological components. We propose six core design properties for a thinking system: task coordination; information curation; creative and flexible organizing; establishing a common ground; continuity and connection; and co-production. A thinking system is needed to address the complexity of coordination, meet the rising expectation of personalized care, relieve the human burden in care delivery, and to deliver the best quality care that modern science can provide.

Information technology-enabled team-based, patient-centered care: The example of depression screening and management in cancer care.

The existing healthcare delivery systems across the world need to be redesigned to ensure high-quality care is delivered to all patients. This redesign needs to ensure care is knowledge-based, patient-centered and systems-minded. The rapid advances in the capabilities of information and communication technology and its recent rapid adoption in healthcare delivery have ensured this technology will play a vital role in the redesign of the healthcare delivery system. This commentary highlights promising new developments in health information technology (IT) that can support patient engagement and self-management as well as team-based, patient-centered care. Collaborative care is an effective approach to screen and treat depression in cancer patients and it is a good example of the benefits of team-based and patient-centered care. However, this approach was developed prior to the widespread adoption and use of health IT. We provide examples to illustrate how health IT can improve prevention and treatment of depression in cancer patients. We found several knowledge gaps that limit our ability to realize the full potential of health IT in the context of cancer and comorbid depression care. These gaps need to be filled to improve patient engagement; enhance the reach and effectiveness of collaborative care and web-based programs to prevent and treat depression in cancer patients. We also identify knowledge gaps in health IT design and implementation. Filling these gaps will help shape policies that enable clinical teams to deliver high-quality cancer care globally.

Utilizing data consortia to monitor safety and effectiveness of biosimilars and their innovator products.

BACKGROUND: The Biologics Price Competition and Innovation Act, introduced as part of the Affordable Care Act, directed the FDA to create an approval pathway for biologic products shown to be biosimilar or interchangeable with an FDA-approved innovator drug. These biosimilars will not be chemically identical to the reference agent. Investigational studies conducted with biosimilar agents will likely provide limited real-world evidence of their effectiveness and safety. How do we best monitor effectiveness and safety of biosimilar products once approved by the FDA and used more extensively by patients? OBJECTIVE: To determine the feasibility of developing a distributed research network that will use health insurance plan and health delivery system data to detect biosimilar safety and effectiveness signals early and be able to answer important managed care pharmacy questions from both the government and managed care organizations. METHODS: Twenty-one members of the AMCP Task Force on Biosimilar Collective Intelligence Systems met November 12, 2013, to discuss issues involved in designing this consortium and to explore next steps. RESULTS: The task force concluded that a managed care biosimilars research consortium would be of significant value. Task force members agreed that it is best to use a distributed research network structurally similar to existing DARTNet, HMO Research Network, and Mini-Sentinel consortia. However, for some surveillance projects that it undertakes, the task force recognizes it may need supplemental data from managed care and other sources (i.e., a "hybrid" structure model). CONCLUSIONS: The task force believes that AMCP is well positioned to lead the biosimilar-monitoring effort and that the next step to developing a biosimilar-innovator collective intelligence system is to convene an advisory council to address organizational governance.

Building electronic data infrastructure for comparative effectiveness research: accomplishments, lessons learned and future steps.

There are large gaps in our knowledge on the potential impact of diagnostics and therapeutics on outcomes of patients treated in the real world. Comparative effectiveness research aims to fill these gaps to maximize effectiveness of these interventions. Health information technology has the potential to dramatically improve the practice of medicine and of research. This is an overview of about US$100 million of American Recovery and Reinvestment Act investment in 12 projects managed by the Agency for Healthcare Research and Quality to build an electronic clinical data infrastructure that connects research with healthcare delivery. The achievements and lessons learned from these projects provided a foundation for the National Patient-Centered Clinical Research Network (PCORnet)and will help to guide future infrastructure development needed to build an efficient, scalable and sustainable learning health system.

Characterizing genetic variants for clinical action.

Genome-wide association studies, DNA sequencing studies, and other genomic studies are finding an increasing number of genetic variants associated with clinical phenotypes that may be useful in developing diagnostic, preventive, and treatment strategies for individual patients. However, few variants have been integrated into routine clinical practice. The reasons for this are several, but two of the most significant are limited evidence about the clinical implications of the variants and a lack of a comprehensive knowledge base that captures genetic variants, their phenotypic associations, and other pertinent phenotypic information that is openly accessible to clinical groups attempting to interpret sequencing data. As the field of medicine begins to incorporate genome-scale analysis into clinical care, approaches need to be developed for collecting and characterizing data on the clinical implications of variants, developing consensus on their actionability, and making this information available for clinical use. The National Human Genome Research Institute (NHGRI) and the Wellcome Trust thus convened a workshop to consider the processes and resources needed to: (1) identify clinically valid genetic variants; (2) decide whether they are actionable and what the action should be; and (3) provide this information for clinical use. This commentary outlines the key discussion points and recommendations from the workshop.

Sustainability considerations for health research and analytic data infrastructures.

INTRODUCTION: The United States has made recent large investments in creating data infrastructures to support the important goals of patient-centered outcomes research (PCOR) and comparative effectiveness research (CER), with still more investment planned. These initial investments, while critical to the creation of the infrastructures, are not expected to sustain them much beyond the initial development. To provide the maximum benefit, the infrastructures need to be sustained through innovative financing models while providing value to PCOR and CER researchers. SUSTAINABILITY FACTORS: Based on our experience with creating flexible sustainability strategies (i.e., strategies that are adaptive to the different characteristics and opportunities of a resource or infrastructure), we define specific factors that are important considerations in developing a sustainability strategy. These factors include assets, expansion, complexity, and stakeholders. Each factor is described, with examples of how it is applied. These factors are dimensions of variation in different resources, to which a sustainability strategy should adapt. SUMMARY OBSERVATIONS: We also identify specific important considerations for maintaining an infrastructure, so that the long-term intended benefits can be realized. These observations are presented as lessons learned, to be applied to other sustainability efforts. We define the lessons learned, relating them to the defined sustainability factors as interactions between factors. CONCLUSION AND NEXT STEPS: Using perspectives and experiences from a diverse group of experts, we define broad characteristics of sustainability strategies and important observations, which can vary for different projects. Other descriptions of adaptive, flexible, and successful models of collaboration between stakeholders and data infrastructures can expand this framework by identifying other factors for sustainability, and give more concrete directions on how sustainability can be best achieved.

Moving to a user-driven research paradigm.

The traditional "bench-to-bedside" paradigm for clinical research has been successfully used for many decades. This model of knowledge generation has led to discoveries that have enhanced the quality and length of life. The combination of changes in research practice and in health care delivery, growing complexity in decision-making, increasing use of electronic health records (EHR), and growing resource constraints necessitate a shift to a user-driven research paradigm to generate new knowledge. This conceptual framework was created to clarify the perspective of the decision makers as well as the range of factors and the variability in thresholds used to make decisions. This framework may help researchers in creating actionable information to meet the needs of decision makers, which is needed for the transition to a user-driven research paradigm. Further, it is important to create an appropriate set of incentives to facilitate this transition to a user-driven research paradigm.

Building sustainable multi-functional prospective electronic clinical data systems.

A better alignment in the goals of the biomedical research enterprise and the health care delivery system can help fill the large gaps in our knowledge of the impact of clinical interventions on patient outcomes in the real world. There are several initiatives underway to align the research priorities of patients, providers, researchers, and policy makers. These include Agency for Healthcare Research and Quality (AHRQ)-supported projects to build flexible prospective clinical electronic data infrastructure that meet the needs of these diverse users. AHRQ has previously supported the creation of 2 distributed research networks as a new approach to conduct comparative effectiveness research (CER) while protecting a patient's confidential information and the proprietary needs of a clinical organization. It has applied its experience in building these networks in directing the American Recovery and Reinvestment Act funds for CER to support new clinical electronic infrastructure projects that can be used for several purposes including CER, quality improvement, clinical decision support, and disease surveillance. In addition, AHRQ has funded a new Electronic Data Methods forum to advance the methods in clinical informatics, research analytics, and governance by actively engaging investigators from the American Recovery and Reinvestment Act-funded projects and external stakeholders.

Observational studies in systematic [corrected] reviews of comparative effectiveness: AHRQ and the Effective Health Care Program.

OBJECTIVE: Systematic reviewers disagree about the ability of observational studies to answer questions about the benefits or intended effects of pharmacotherapeutic, device, or procedural interventions. This study provides a framework for decision making on the inclusion of observational studies to assess benefits and intended effects in comparative effectiveness reviews (CERs). STUDY DESIGN AND SETTING: The conceptual model and recommendations were developed using a consensus process by members of the methods workgroup of the Effective Health Care Program of the Agency for Healthcare Research and Quality. RESULTS: In considering whether to use observational studies in CERs for addressing beneficial effects, reviewers should answer two questions: (1) Are there gaps in the evidence from randomized controlled trials (RCTs)? (2) Will observational studies provide valid and useful information? The latter question involves the following: (a) refocusing the study questions on gaps in the evidence from RCTs, (b) assessing the risk of bias of the body of evidence of observational studies, and (c) assessing whether available observational studies address the gap review questions. CONCLUSIONS: Because it is unusual to find sufficient evidence from RCTs to answer all key questions concerning benefit or the balance of benefits and harms, comparative effectiveness reviewers should routinely assess the appropriateness of inclusion of observational studies for questions of benefit. Furthermore, reviewers should explicitly state the rationale for inclusion or exclusion of observational studies when conducting CERs.

Cancer pharmacogenomics and pharmacoepidemiology: setting a research agenda to accelerate translation.

Recent advances in genomic research have demonstrated a substantial role for genomic factors in predicting response to cancer therapies. Researchers in the fields of cancer pharmacogenomics and pharmacoepidemiology seek to understand why individuals respond differently to drug therapy, in terms of both adverse effects and treatment efficacy. To identify research priorities as well as the resources and infrastructure needed to advance these fields, the National Cancer Institute (NCI) sponsored a workshop titled "Cancer Pharmacogenomics: Setting a Research Agenda to Accelerate Translation" on July 21, 2009, in Bethesda, MD. In this commentary, we summarize and discuss five science-based recommendations and four infrastructure-based recommendations that were identified as a result of discussions held during this workshop. Key recommendations include 1) supporting the routine collection of germline and tumor biospecimens in NCI-sponsored clinical trials and in some observational and population-based studies; 2) incorporating pharmacogenomic markers into clinical trials; 3) addressing the ethical, legal, social, and biospecimen- and data-sharing implications of pharmacogenomic and pharmacoepidemiologic research; and 4) establishing partnerships across NCI, with other federal agencies, and with industry. Together, these recommendations will facilitate the discovery and validation of clinical, sociodemographic, lifestyle, and genomic markers related to cancer treatment response and adverse events, and they will improve both the speed and efficiency by which new pharmacogenomic and pharmacoepidemiologic information is translated into clinical practice.

Comparative effectiveness research and genomic medicine: an evolving partnership for 21st century medicine.

The American Recovery and Reinvestment Act has provided resources for comparative effectiveness research that will lead to evidence-based decisions about health and health care choices. Some have voiced concerns that evidence-based comparative effectiveness research principles are only relevant to "average" patients and not as much to individuals with unique combinations of genes, exposures and disease outcomes, intrinsic to genomic medicine. In this commentary, we argue that comparative effectiveness research and genomic medicine not only can and should coexist but also they will increasingly benefit from each other. The promise and success of genomic medicine will depend on rigorous comparative effectiveness research to compare outcomes for genome-based applications in practice to traditional non-genome-based approaches. In addition, the success of comparative effectiveness research will depend on developing new methods and clinical research infrastructures to integrate genome-based personalized perspectives into point of care decisions by patients and providers. There is a need to heal the apparent schism between genomic medicine and comparative effectiveness research to enhance knowledge-driven practice of medicine in the 21st century.

The genomic applications in practice and prevention network.

The authors describe the rationale and initial development of a new collaborative initiative, the Genomic Applications in Practice and Prevention Network. The network convened by the Centers for Disease Control and Prevention and the National Institutes of Health includes multiple stakeholders from academia, government, health care, public health, industry and consumers. The premise of Genomic Applications in Practice and Prevention Network is that there is an unaddressed chasm between gene discoveries and demonstration of their clinical validity and utility. This chasm is due to the lack of readily accessible information about the utility of most genomic applications and the lack of necessary knowledge by consumers and providers to implement what is known. The mission of Genomic Applications in Practice and Prevention Network is to accelerate and streamline the effective integration of validated genomic knowledge into the practice of medicine and public health, by empowering and sponsoring research, evaluating research findings, and disseminating high quality information on candidate genomic applications in practice and prevention. Genomic Applications in Practice and Prevention Network will develop a process that links ongoing collection of information on candidate genomic applications to four crucial domains: (1) knowledge synthesis and dissemination for new and existing technologies, and the identification of knowledge gaps, (2) a robust evidence-based recommendation development process, (3) translation research to evaluate validity, utility and impact in the real world and how to disseminate and implement recommended genomic applications, and (4) programs to enhance practice, education, and surveillance.

A health services research agenda for cellular, molecular and genomic technologies in cancer care.

BACKGROUND: In recent decades, extensive resources have been invested to develop cellular, molecular and genomic technologies with clinical applications that span the continuum of cancer care. METHODS: In December 2006, the National Cancer Institute sponsored the first workshop to uniquely examine the state of health services research on cancer-related cellular, molecular and genomic technologies and identify challenges and priorities for expanding the evidence base on their effectiveness in routine care. RESULTS: This article summarizes the workshop outcomes, which included development of a comprehensive research agenda that incorporates health and safety endpoints, utilization patterns, patient and provider preferences, quality of care and access, disparities, economics and decision modeling, trends in cancer outcomes, and health-related quality of life among target populations. CONCLUSIONS: Ultimately, the successful adoption of useful technologies will depend on understanding and influencing the patient, provider, health care system and societal factors that contribute to their uptake and effectiveness in 'real-world' settings.

Payer perspectives on pharmacogenomics testing and drug development.

A series of questions about hypothetical drugs and pharmacogenomic tests was posed to a panel of representatives from the health plan, government and employer sectors in order to elicit suggestions for input on data or study design considerations important for coverage determination. The panel suggested seven areas for drug developers to strongly consider. These areas were to include comparative information on new tests versus usual care, assess the negative predictive value of new tests, measure and report on cost offsets, balance relative risk improvement with absolute risk, consider the policy implications of the products or tests, report percentage responders in addition to group mean improvements, and to include specific pharmacogenomic information in US FDA approved labels. The panel was generally enthusiastic about the promise of the field to improve drug selection or dosing.

The meaning of the U.S. Preventive Services Task Force grade I recommendation: screening for hepatitis C virus infection.

The U.S. Preventive Services Task Force (USPSTF) formulates evidence-based recommendations for clinical preventive services. These recommendations are communicated by letter grades that reflect the quality of evidence and the magnitude of net health benefit expected from delivering the preventive service. When the USPSTF finds insufficient evidence to determine the balance of health benefits or harms of delivering a preventive service, because of a lack of studies, poor-quality studies, or good-quality studies with conflicting results, the USPSTF assigns the service an I letter grade. The USPSTF found insufficient evidence to recommend for or against screening for hepatitis C virus infection in high-risk individuals (I letter grade). This recommendation reflects the need for further research that would provide adequate evidence to assess the net health benefit for persons screened for hepatitis C virus infection.