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Background Many patients with rare diseases are still lacking a timely diagnosis and approved therapies for their condition despite the tremendous efforts of the research community, biopharmaceutical, medical device industries, and patient support groups. The development of clinical research networks for rare diseases offers a tremendous opportunity for patients and multi-disciplinary teams to collaborate, share expertise, gain better understanding on specific rare diseases, and accelerate clinical research and innovation. Clinical Research Networks have been developed at a national or continental level, but global collaborative efforts to connect them are still lacking. The International Rare Diseases Research Consortium set a Task Force on Clinical Research Networks for Rare Diseases with the objective to analyse the structure and attributes of these networks and to identify the barriers and needs preventing their international collaboration. The Task Force created a survey and sent it to pre-identified clinical research networks located worldwide. Results A total of 34 responses were received. The survey analysis demonstrated that clinical research networks are diverse in their membership composition and emphasize community partnerships including patient groups, health care providers and researchers. The sustainability of the networks is mostly supported by public funding. Activities and research carried out at the networks span the research continuum from basic to clinical to translational research studies. Key elements and infrastructures conducive to collaboration are well adopted by the networks, but barriers to international interoperability are clearly identified. These hurdles can be grouped into five categories: funding limitation; lack of harmonization in regulatory and contracting process; need for common tools and data standards; need for a governance framework and coordination structures; and lack of awareness and robust interactions between networks. Conclusions Through this analysis, the Task Force identified key elements that should support both developing and established clinical research networks for rare diseases in implementing the appropriate structures to achieve international interoperability worldwide. A global roadmap of actions and a specific research agenda, as suggested by this group, provides a platform to identify common goals between these networks.

Background Healthcare represents a paradox. While change is everywhere, performance has flatlined: 60% of care on average is in line with evidence- or consensus-based guidelines, 30% is some form of waste or of low value, and 10% is harm. The 60-30-10 Challenge has persisted for three decades. Main body Current top-down or chain-logic strategies to address this problem, based essentially on linear models of change and relying on policies, hierarchies, and standardisation, have proven insufficient. Instead, we need to marry ideas drawn from complexity science and continuous improvement with proposals for creating a deep learning health system. This dynamic learning model has the potential to assemble relevant information including patients’ histories, and clinical, patient, laboratory, and cost data for improved decision-making in real time, or close to real time. If we get it right, the learning health system will contribute to care being more evidence-based and less wasteful and harmful. It will need a purpose-designed digital backbone and infrastructure, apply artificial intelligence to support diagnosis and treatment options, harness genomic and other new data types, and create informed discussions of options between patients, families, and clinicians. While there will be many variants of the model, learning health systems will need to spread, and be encouraged to do so, principally through diffusion of innovation models and local adaptations. Conclusion Deep learning systems can enable us to better exploit expanding health datasets including traditional and newer forms of big and smaller-scale data, e.g. genomics and cost information, and incorporate patient preferences into decision-making. As we envisage it, a deep learning system will support healthcare’s desire to continually improve, and make gains on the 60-30-10 dimensions. All modern health systems are awash with data, but it is only recently that we have been able to bring this together, operationalised, and turned into useful information by which to make more intelligent, timely decisions than in the past.

Background Opioid use and opioid-related overdose continue to rise among racial/ethnic minorities. Social determinants of health negatively impact these communities, possibly resulting in poorer treatment outcomes. Research is needed to investigate how to overcome the disproportionate and deleterious impact of social determinants of health on treatment entry, retention, drug use and related outcomes among racial/ethnic minorities. The current commentary provides recommendations that may help researchers respond more effectively to reducing health disparities in substance use treatment. We begin with recommendations of best research practices (e.g., ensuring adequate recruitment of racial/ethnic minorities in research, central components of valid analysis, and adequate methods for assessing effect sizes for racial/ethnic minorities). Then, we propose that more NIDA research focuses on issues disproportionately affecting racial/ethnic minorities. Next, techniques for increasing the number of underrepresented racial/ethnic treatment researchers are suggested. We then recommend methods for infusing racial/ethnic expertise onto funding decision panels. This commentary ends with a case study that features NIDA’s National Drug Abuse Treatment Clinical Trials Network (CTN). Conclusions The proposed recommendations can serve as guidelines for substance use research funders to promote research that has the potential to reduce racial/ethnic disparities in substance use treatment and to increase training opportunities for racial/ethnic minority researchers.

The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)–funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

Over the past two decades, the National Drug Abuse Treatment Clinical Trials Network (CTN), a program of the National Institute on Drug Abuse (NIDA), has expanded from the initial six Nodes to 16 Nodes, as a nationwide consortium of research scientists and treatment providers working together to improve care for substance use in the nation’s communities. Encompassing both specialty care programs and general medical settings, the Network has become a unique resource for expertise on clinically focused research, bridging the gap between research and treatment delivery. Over 22 years, the CTN has completed 101 studies, resulting in 650 publications. In response to the opioid epidemic, a CTN task force generated a comprehensive list of research priorities in the areas of prevention, treatment, knowledge dissemination, and workforce training, to form the basis of the Network’s opioid portfolio. The Network’s opioid portfolio currently includes five main categories of studies: (1) large multi-site studies; (2) studies aimed at closing the treatment gap; (3) expansion of ongoing studies to improve service delivery and implementation; (4) studies to explore the use of substance use data in electronic health record systems; (5) training and dissemination projects to expand the research/health care provider workforce. With funding from the Helping to End Addiction Long-Term Initiative SM (HEAL), the CTN established five new Nodes, which, along with the pre-existing Nodes, are distributed in every region of the nation and engage researchers and clinicians in areas that have been among the hardest hit by the opioid epidemic. Through this expanded network and its commitment to developing personalized, evidence-based treatments, the CTN is poised to address and provide solutions for the ongoing epidemic of opioid use and addiction.

Networks are everywhere. Health systems and public health settings are experimenting with multifarious forms. Governments and providers are heavily investing in networks with an expectation that they will facilitate the delivery of better services and improve health outcomes. Yet, we lack a suitable conceptual framework to evaluate the effectiveness and sustainability of clinical and health networks. This paper aims to present such a framework to assist with rigorous research and policy analysis. The framework was designed as part of a project to evaluate the effectiveness and sustainability of health networks. We drew on systematic reviews of the literature on networks and communities of practice in health care, and on theoretical and evidence-based studies of the evaluation of health and non-health networks. Using brainstorming and mind-mapping techniques in expert advisory group sessions, we assessed existing network evaluation frameworks and considered their application to extant health networks. Feedback from stakeholders in network studies that we conducted was incorporated. The framework encompasses network goals, characteristics and relationships at member, network and community levels, and then looks at network outcomes, taking into account intervening variables. Finally, the short-term, medium-term and long-term effectiveness of the network needs to be assessed. The framework provides an overarching contribution to network evaluation. It is sufficiently comprehensive to account for many theoretical and evidence-based contributions to the literature on how networks operate and is sufficiently flexible to assess different kinds of health networks across their life-cycle at community, network and member levels. We outline the merits and limitations of the framework and discuss how it might be further tested.

To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale. Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet. Within the Clinical Research Networks, electronic health data are currently collected from 337 hospitals, 169,695 physicians, 3,564 primary care practices, 338 emergency departments, and 1,024 community clinics. Patients can be recruited for prospective studies from any of these clinical sites. The Clinical Research Networks have accumulated data from 80 million patients with at least one visit from 2009 to 2018. The PCORnet Health Plan Research Network population of individuals with a valid enrollment segment from 2009 to 2019 exceeds 60 million individuals, who on average have 2.63 years of follow-up. PCORnet’s infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases. •PCORnet is a national network-of-networks developed to conduct patient-centered outcomes research.•There are nine Clinical Research Networks and two Health Plan Research Networks within PCORnet.•The Clinical Research Networks have collected EHR data for a cohort of 80 million individuals, and the Health Plan Network have collected enrollment and claims files on over 60 million individuals.•PCORnet infrastructure can support large-scale pragmatic clinical trials and observational research using its distributed data network.

Background Healthcare systems are increasingly complex given devolution of powers, decentralization of decision-making, and escalating fragmentation of effort. This indicates a role for governments in how they can bring this together, oversee and improve across the system. Goals pertaining to the safety and quality of clinical care are advanced by governments using stewardship and agent-based strategies that increasingly place reliance on being evidence based. In Australia, clinical networks are a safety and quality stewardship model that convenes multidisciplinary and intersectoral actors to define evidence-based expectations for clinical practice, management and policy. Yet understandings of the role that evidence is intended to play within clinical networks remain under investigated. Despite vast literature examining the contribution of stewardship, networks and evidence use in healthcare, research on these topics have occurred in relative isolation. There lacks an integrative approach to these concepts to advance stewardship research and practice. Methods By employing a theory generating approach, this research outlines the determinants of evidence use by Australian clinical networks as safety and quality stewards. A conceptual framework is developed, informed by examination of the theoretical and empirical literature, findings from mixed methods research involving interviews, documentary analysis and Q-methodology, and validation of findings with research partners. Results The Clinical Network Safety and Quality Stewardship Conceptual Framework situates the determinants of evidence use by networks within understandings of safety and quality stewardship. It encompasses three layers: (i) the model of health system coordination and governance, (ii) network inputs and understandings of evidence, and (iii) points of influence. Within each layer, several attributes are identified that are explained with reference to agency and stewardship theory. We describe the interactions within and between the layers incorporated in the framework that are of importance in order to explain how evidence can shape the decisions that contribute to safety and quality. Conclusions Theoretical contributions offer greater conceptual clarity with regard to the role of evidence within the context of networked stewardship models and systems where foundations are in agent-based approaches of coordination and governance. The conceptual framework seeks to advance scholarly research and practice. Merits, limitations and considerations for further testing of the framework are outlined.

Background To explore the current models of practice in centres delivering specialist care for children with disorders of sex development (DSD), an international survey of 124 clinicians, identified through DSDnet and the I-DSD Registry, was performed in the last quarter of 2014. Results A total of 78 (63 %) clinicians, in 75 centres, from 38 countries responded to the survey. A formal national network for managing DSD was reported to exist in 12 (32 %) countries. The paediatric specialists routinely involved in the initial evaluation of a newborn included: endocrinologist (99 %), surgeon/urologist (95 %), radiologist (93 %), neonatologist (91 %), clinical geneticist (81 %) and clinical psychologist (69 %). A team consisting of paediatric specialists in endocrinology, surgery/urology, clinical psychology, and nursing was only possible in 31 (41 %) centres. Of the 75 centres, 26 (35 %) kept only a local DSD registry and 40 (53 %) shared their data in a multicentre DSD registry. Attendance in local, national and international DSD-related educational programs was reported by 69, 78 and 84 % clinicians, respectively. Participation in audits/quality improvement exercises in DSD care was reported by 14 (19 %) centres. In addition to complex biochemistry and molecular genetic investigations, 40 clinicians (51 %) also had access to next generation sequencing. A genetic test was reported to be more preferable than biochemical tests for diagnosing 5-alpha reductase deficiency and 17-beta hydroxysteroid dehydrogenase 3 deficiency by 50 and 55 % clinicians, respectively. Conclusion DSD centres report a high level of interaction at an international level, have access to specialist staff and are increasingly relying on molecular genetics for routine diagnostics. The quality of care provided by these centres locally requires further exploration.

Background Reorganisation of healthcare services into networks of clinical experts is increasing as a strategy to promote the uptake of evidence based practice and to improve patient care. This is reflected in significant financial investment in clinical networks. However, there is still some question as to whether clinical networks are effective vehicles for quality improvement. The aim of this systematic review was to ascertain the effectiveness of clinical networks and identify how successful networks improve quality of care and patient outcomes. Methods A systematic search was undertaken in accordance with the PRISMA approach in Medline, Embase, CINAHL and PubMed for relevant papers between 1 January 1996 and 30 September 2014. Established protocols were used separately to examine and assess the evidence from quantitative and qualitative primary studies and then integrate findings. Results A total of 22 eligible studies (9 quantitative; 13 qualitative) were included. Of the quantitative studies, seven focused on improving quality of care and two focused on improving patient outcomes. Quantitative studies were limited by a lack of rigorous experimental design. The evidence indicates that clinical networks can be effective vehicles for quality improvement in service delivery and patient outcomes across a range of clinical disciplines. However, there was variability in the networks’ ability to make meaningful network- or system-wide change in more complex processes such as those requiring intensive professional education or more comprehensive redesign of care pathways. Findings from qualitative studies indicated networks that had a positive impact on quality of care and patients outcomes were those that had adequate resources, credible leadership and efficient management coupled with effective communication strategies and collaborative trusting relationships. Conclusions There is evidence that clinical networks can improve the delivery of healthcare though there are few high quality quantitative studies of their effectiveness. Our findings can provide policymakers with some insight into how to successfully plan and implement clinical networks by ensuring strong clinical leadership, an inclusive organisational culture, adequate resourcing and localised decision-making authority.