Hyper-Acute Stroke Systems of Care and Workflow.

PURPOSE OF REVIEW: Recent stroke treatment advances have necessitated agile, broad-scale healthcare system redesign, to achieve optimal patient outcomes and access equity. Optimised hyperacute stroke care requires integrated pre-hospital, emergency department, stroke specialist, radiology, neurosurgical and endovascular neurointervention services, guided by a population-wide needs analysis. In this review, we survey system integration efforts, providing case studies, and identify common elements of successful initiatives. RECENT FINDINGS: Different regions and nations have evolved varied acute stroke systems depending on geography, population density and workforce. However, common facilitators to these solutions have included stroke unit care as a foundation, government-clinician synergy, pre-hospital pathway coordination, service centralisation, and stroke data guiding system improvement. Further technological advantages will minimize the geographical distance disadvantages and facilitate virtual expertise redistribution to remote areas. Continued treatment advances necessitate an integrated, adaptable, population-wide trans-disciplinary approach. A well-designed clinician-led and government-supported system can facilitate hyperacute care and scaffold future system enhancements.

Comparison of the Response to an Electronic Versus a Traditional Informed Consent Procedure in Terms of Clinical Patient Characteristics: Observational Study.

BACKGROUND: Electronic informed consent (eIC) is increasingly used in clinical research due to several benefits including increased enrollment and improved efficiency. Within a learning health care system, a pilot was conducted with an eIC for linking data from electronic health records with national registries, general practitioners, and other hospitals. OBJECTIVE: We evaluated the eIC pilot by comparing the response to the eIC with the former traditional paper-based informed consent (IC). We assessed whether the use of eIC resulted in a different study population by comparing the clinical patient characteristics between the response categories of the eIC and former face-to-face IC procedure. METHODS: All patients with increased cardiovascular risk visiting the University Medical Center Utrecht, the Netherlands, were eligible for the learning health care system. From November 2021 to August 2022, an eIC was piloted at the cardiology outpatient clinic. Prior to the pilot, a traditional face-to-face paper-based IC approach was used. Responses (ie, consent, no consent, or nonresponse) were assessed and compared between the eIC and face-to-face IC cohorts. Clinical characteristics of consenting and nonresponding patients were compared between and within the eIC and the face-to-face cohorts using multivariable regression analyses. RESULTS: A total of 2254 patients were included in the face-to-face IC cohort and 885 patients in the eIC cohort. Full consent was more often obtained in the eIC than in the face-to-face cohort (415/885, 46.9% vs 876/2254, 38.9%, respectively). Apart from lower mean hemoglobin in the full consent group of the eIC cohort (8.5 vs 8.8; P=.0021), the characteristics of the full consenting patients did not differ between the eIC and face-to-face IC cohorts. In the eIC cohort, only age differed between the full consent and the nonresponse group (median 60 vs 56; P=.0002, respectively), whereas in the face-to-face IC cohort, the full consent group seemed healthier (ie, higher hemoglobin, lower glycated hemoglobin [HbA1c], lower C-reactive protein levels) than the nonresponse group. CONCLUSIONS: More patients provided full consent using an eIC. In addition, the study population remained broadly similar. The face-to-face IC approach seemed to result in a healthier study population (ie, full consenting patients) than the patients without IC, while in the eIC cohort, the characteristics between consent groups were comparable. Thus, an eIC may lead to a better representation of the target population, increasing the generalizability of results.

Early Lessons from the Implementation of Genomic Medicine Programs.

Massively parallel sequencing is emerging from research settings into clinical practice, helping the vision of precision medicine to become a reality. The most successful applications are using the tools of implementation science within the framework of the learning health-care system. This article examines the application of massively parallel sequencing to four clinical scenarios: pharmacogenomics, diagnostic testing, somatic testing for molecular tumor characterization, and population screening. For each application, it highlights an exemplar program to illustrate the enablers and challenges of implementation. International examples are also presented. These early lessons will allow other programs to account for these factors, helping to accelerate the implementation of precision medicine and health.

Invited Commentary: Baby Steps to a Learning Mental Health-Care System-Can We Do the Work?

There are an infinite number of small decisions to be made in routine clinical practice, and most will never be the subject of prospective research studies. Rather than making these decisions arbitrarily, learning health-care systems leverage experience represented by electronic health record data and other sources to inform decision-making and improve clinical practice. While this approach has been elusive in mental health, Coulombe et al. (Am J Epidemiol. 2021;190(7):1210-1219) use UK National Health Service data to evaluate a decision rule for antidepressant choice created using dynamic weighted survival modeling. Although the results are equivocal in this use case, the work suggests a path forward for data-driven decision-making in routine mental health care. Such approaches will be required to set the stage for a learning mental health care system.

Research versus practice: The dilemmas of research ethics in the era of learning health-care systems.

In this article we attempt to answer the question of how the ethical and conceptual framework (ECF) for a learning health-care system (LHS) affects some of the main controversies in research ethics by addressing five key problems of research ethics: (a) What is the difference between practice and research? (b) What is the relationship between research ethics and clinical ethics? (c) What is the ethical relevance of the principle of clinical equipoise? (d) Does participation in research require a higher standard of informed consent than the practice of medicine? and (e) What ethical principle should take precedence in medicine? These questions allow us to construct two opposite idealized positions on the distinction between research and practice: the integration model and the segregation model of research and practice. We then compare the ECF for an LHS with these two idealized positions. We argue that the ECF for a LHS does not, in fact, solve these problems, but that it is a third, separate position in the relationship between research ethics and clinical ethics. Moreover, we suggest that the ECF for a LHS raises new ethical problems that require additional ethical analysis and justification. Our article contributes to the discussion on the relationship between research ethics and clinical ethics, revealing that although a learning health-care system may significantly change the landscape of health care, some ethical dilemmas still require resolving on both theoretical and policy-making levels.

Role of the Internet in Solving the Last Mile Problem in Medicine.

Internet-augmented medicine has a strong role to play in ensuring that all populations benefit equally from discoveries in the medical sciences. Yet, data from the Centers for Disease Control and Prevention collected from 1999 to 2014 suggested that during the first phase of internet diffusion, progress against mortality has stalled, and in some cases, receded in rural areas that are traditionally underserved by medical and broadband resources. This problem of failing to extend the benefits of extant medical knowledge equitably to all populations regardless of geography can be framed as the "last mile problem in health care." In theory, the internet should help solve the last mile problem by making the best knowledge in the world available to everyone worldwide at a low cost and no delay. In practice, the antiquated supply chains of industrial age medicine have been slow to yield to the accelerative forces of evolving internet capacity. This failure is exacerbated by the expanding digital divide, preventing residents of isolated, geographically distant communities from taking full advantage of the digital health revolution. The result, according to the Federal Communications Commission's (FCC's) Connect2Health Task Force, is the unanticipated emergence of "double burden counties," ie, counties for which the mortality burden is high while broadband access is low. The good news is that a convergence of trends in internet-enabled health care is putting medicine within striking distance of solving the last mile problem both in the United States and globally. Specific trends to monitor over the next 25 years include (1) using community-driven approaches to bridge the digital divide, (2) addressing structural disconnects in care through P4 Medicine, (3) meeting patients at "point-of-need," (4) ensuring that no one is left behind through population management, and (5) self-correcting cybernetically through the learning health care system.

Randomized program evaluation of the Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM): A research and clinical operations partnership to examine effectiveness.

The United States is facing an opioid crisis in which overdose is the leading cause of injury death-misuse of opioids constitutes the vast majority of those deaths. In 2016 alone, over 42,000 people died from opioid overdose, an increase of 27% from the prior year. Deployment of the Stratification Tool for Opioid Risk Mitigation (STORM), a clinical decision support tool to improve opioid safety, is one response by the Veterans Health Administration (VHA) to the opioid crisis. STORM identifies VHA patients at very high risk of opioid-related adverse events and lists potential risk mitigation strategies. Deployment of STORM also helps VHA meet certain requirements of the Comprehensive Addiction and Recovery Act of 2016. In alignment with the VHA's learning health care system initiative, a multidisciplinary team designed a randomized evaluation of a policy approach to mandating case reviews of very-high-risk patients identified by STORM and the impacts of patient inclusion versus exclusion in mandated STORM case reviews using a stepped-wedge design. The STORM evaluation involves drafting the policy notice, shepherding it through the VHA approval process, and implementing the cluster randomized design. This mixed-methods evaluation includes (1) a qualitative assessment of medical center implementation strategies with the aim of understanding of how STORM is incorporated into practice, and (2) quantitative analyses of the relations between policy mandates and STORM inclusion on opioid-related adverse events. The findings from this synergistic research design will yield critical insights for VHA leadership to refine opioid prescribing-related policy and practice.

Taiwan renal care system: A learning health-care system.

Taiwan renal care system is an evolving learning health-care system. There are four facets of this system. From the early history of dialysis and Taiwan Renal Registry Data System, it facilitates the generation of data to knowledge. National multidisciplinary pre-end-stage renal disease care project and outcome enhances knowledge to practice. Early chronic kidney disease (CKD) programs and 2015 Taiwan CKD clinical guidelines implicate the practice to customer, and then explore the causes of CKD help to resume customer to data. A learning health-care system allows better and safer care at lower cost, enhancement of public health and patient empowerment. The successful development of a learning health-care system was to collect, accumulate and analyze data, interpret results, deliver tailored message and take action to change practice. Through the established database and data analysis, an integrated care system would be able to improve clinical outcomes and achieve the most cost-effectiveness care. Acute kidney injury, CKD with unknown origin, palliative care and kidney transplant are our new focuses to struggle.

Moving From Discovery to System-Wide Change: The Role of Research in a Learning Health Care System: Experience from Three Decades of Health Systems Research in the Veterans Health Administration.

The Veterans Health Administration is unique, functioning as an integrated health care system that provides care to more than six million veterans annually and as a home to an established scientific enterprise that conducts more than $1 billion of research each year. The presence of research, spanning the continuum from basic health services to translational research, has helped the Department of Veterans Affairs (VA) realize the potential of a learning health care system and has contributed to significant improvements in clinical quality over the past two decades. It has also illustrated distinct pathways by which research influences clinical care and policy and has provided lessons on challenges in translating research into practice on a national scale. These lessons are increasingly relevant to other health care systems, as the issues confronting the VA-the need to provide timely access, coordination of care, and consistent high quality across a diverse system-mirror those of the larger US health care system.

Who is willing to participate in low-risk pragmatic clinical trials without consent?

PURPOSE: General notification offers a possible alternative to written informed consent for pragmatic randomized controlled trials (pRCTs). It involves patients being informed through brochures, posters, and letters that research is being conducted simultaneously to providing clinical care and that patients will be enrolled in pRCTs without study-specific consent. A previous survey found that a substantial minority of respondents endorsed general notification. We aimed to know who is willing to enroll in this type of trials using general notification rather than written consent. METHODS: The previous study was a cross-sectional, probability-based survey, with a 2 × 2 factorial design. Two scenarios were assessed: two low-risk pRCTs in hypertension, one comparing two drugs with similar benefit/risk ratio and the other taking the same drug in the morning or at night. Each scenario had two routes: written consent vs verbal consent and written consent vs general notification. In this study, we were interested in the latter route in both scenarios. Respondents' preferences were measured based on their recommendation to the research ethics committee and the respondent's personal preference. We aimed to investigate the characteristics of those supporting general notification in either outcome or the variables explaining consistency and inconsistency between their personal preference and their recommendation. Based on the results of the original survey, we aimed to have at least 200 inconsistent respondents; to this end, the sample size was increased accordingly in a second wave of the survey. RESULTS: One thousand six hundre and ten respondents were included; 1003 from the original survey and 607 new ones belonging to the second wave. Thirty-nine percent of respondents chose general notification as personal preference and/or recommendation. Respondents with lower education levels were more prone to accept general notification than those holding a university degree [OR (95% CI)], primary school [2.959 (2.069-4.232)], secondary school [2.899 (2.09-4.021)], or high school [1.620 (1.184-2.217)]. Also unemployed [1.372 (1.064-1.770)] and retired [1.445 (1.049-1.990)], but not students, showed preference for general notification in comparison with those employed. Individuals more than 24 years old and having received high school or university (or postgraduate) education were statistically significantly more consistent in their decisions. CONCLUSIONS: Thirty-nine percent of respondents is open to not to be asked for their informed consent in low-risk pRCTs; of these, those being less educated and not having current job or being retired are significantly more open to general notification. The use of this alternative method to written consent for simultaneous conduct of pRCTs and care should be considered and educational programs settled up to, in the case of public acceptance, ensure its ethical appropriateness.

Proposals to Conduct Randomized Controlled Trials Without Informed Consent: a Narrative Review.

BACKGROUND: Individual informed consent from all participants is required for most randomized clinical trials (RCTs). However, some exceptions-for example, emergency research-are widely accepted. METHODS: The literature on various approaches to randomization without consent (RWOC) has never been systematically reviewed. Our goal was to provide a survey and narrative synthesis of published proposals for RWOC. We focused on proposals to randomize at least some participants in a study without first obtaining consent to randomization. This definition included studies that omitted informed consent entirely, omitted informed consent for selected patients (e.g., the control group), obtained informed consent to research but not to randomization, or only obtained informed consent to randomization after random assignment had already occurred. It omitted oral and staged consent processes that still obtain consent to randomization from all participants before randomization occurs. RESULTS: We identified ten different proposals for RWOC: two variants of cluster randomization, two variants of the Zelen design, consent to postponed information, two-stage randomized consent, cohort multiple RCT, emergency research, prompted optional randomization trials, and low-risk pragmatic RCTs without consent. CONCLUSION: Of all designs discussed here, only cluster randomized designs and emergency research are routinely used, with the justification that informed consent is infeasible in those settings. Other designs have raised concerns that they do not appropriately respect patient autonomy. Recent proposals have emphasized the importance for RWOC of demonstrating such respect through systematic patient engagement, transparency, and accountability, potentially in the context of learning health care systems.

The pragmatic clinical trial in a learning health care system.

BACKGROUND/AIMS: A learning health care system ideally incorporates the ability to adapt to the pace of change, the incorporation of new clinical research paradigms, and leverages electronic health record systems and clinical decision support systems to narrow the divide between research and clinical practice. METHODS: An adaptive clinical trial can be embedded into the sites and practice of clinical care in a highly pragmatic way to simultaneously generate high-quality data on treatment efficacy and improve the care of patients. This approach can be expanded into a pragmatic platform trial, meaning a trial that is intended to evaluate multiple treatments for a disease or diseases, possibly in combination, and with the available treatments potentially changing over time. This strategy is illustrated using a trial currently being implemented in Europe and funded by the European Union, evaluating three different "domains" of treatments for patients with severe community-acquired pneumonia requiring intensive care. RESULTS: Simulation studies demonstrate that this approach has the potential to save lives while identifying the best treatment strategies for this critically ill population. CONCLUSION: Patients are likely to benefit if we can merge clinical trials and decision support into a single continuous learning process.

Exploring the ethical and regulatory issues in pragmatic clinical trials.

The need for high-quality evidence to support decision making about health and health care by patients, physicians, care providers, and policy-makers is well documented. However, serious shortcomings in evidence persist. Pragmatic clinical trials that use novel techniques including emerging information and communication technologies to explore important research questions rapidly and at a fraction of the cost incurred by more "traditional" research methods promise to help close this gap. Nevertheless, while pragmatic clinical trials can bridge clinical practice and research, they may also raise difficult ethical and regulatory challenges. In this article, the authors briefly survey the current state of evidence that is available to inform clinical care and other health-related decisions and discuss the potential for pragmatic clinical trials to improve this state of affairs. They then propose a new working definition for pragmatic research that centers upon fitness for informing decisions about health and health care. Finally, they introduce a project, jointly undertaken by the National Institutes of Health Health Care Systems Research Collaboratory and the National Patient-Centered Clinical Research Network (PCORnet), which addresses 11 key aspects of current systems for regulatory and ethical oversight of clinical research that pose challenges to conducting pragmatic clinical trials. In the series of articles commissioned on this topic published in this issue of Clinical Trials, each of these aspects is addressed in a dedicated article, with a special focus on the interplay between ethical and regulatory considerations and pragmatic clinical research aimed at informing "real-world" choices about health and health care.

Pragmatic Comparative Effectiveness Trials and Learning Health Systems in Pain Medicine: Opportunities and Challenges.

Large randomized clinical trials or aggregates of clinical trials represent the highest levels of clinical evidence because they minimize different sources of confounding and bias. The current review provides an in-depth discussion of the challenges faced and methods we can use to overcome these obstacles to tailor novel designs of pragmatic effectiveness trials to pain medicine. The authors describe their experiences with an open-source learning health system to collect high-quality evidence and conduct pragmatic clinical trials within a busy academic pain center.

Pediatric Critical Care in the Twenty-first Century and Beyond.

Pediatric critical care addresses prevention, diagnosis, and treatment of organ dysfunction in the setting of increasingly complex patients, therapies, and environments. Soon burgeoning data science will enable all aspects of intensive care: driving facilitated diagnostics, empowering a learning health-care environment, promoting continuous advancement of care, and informing the continuum of critical care outside the intensive care unit preceding and following critical illness/injury. Although novel technology will progressively objectify personalized critical care, humanism, practiced at the bedside, defines the essence of pediatric critical care now and in the future.

Navigating Informed Consent Requirements and Expectations in Cluster Randomized Trials: Research Ethics Board Members' and Researchers' Views.

Informed consent is a cornerstone of ethical human research. However, as cluster randomized trials (CRTs) are increasingly popular to evaluate health service interventions, especially as health systems aspire toward the learning health system, questions abound how research teams and research ethics boards (REBs) should navigate intertwining consent and data-use considerations. Methodological and ethical questions include who constitute the participants, whose and what types of consent are necessary, and how data from people who have not consented to participation should be managed to optimize the balance of trust in the research enterprise, respect for persons, the promotion of data integrity, and the pursuit of the public good in the research arena. In this paper, we report the findings and lessons learned from a qualitative study examining how researchers and REB members consider the ethical dimensions of when data can be collected and used in CRTs in the evolving research landscape.

Exploring Patient Journey Mapping and the Learning Health System: Scoping Review.

BACKGROUND: Journey maps are visualization tools that can facilitate the diagrammatical representation of stakeholder groups by interest or function for comparative visual analysis. Therefore, journey maps can illustrate intersections and relationships between organizations and consumers using products or services. We propose that some synergies may exist between journey maps and the concept of a learning health system (LHS). The overarching goal of an LHS is to use health care data to inform clinical practice and improve service delivery processes and patient outcomes. OBJECTIVE: The purpose of this review was to assess the literature and establish a relationship between journey mapping techniques and LHSs. Specifically, in this study, we explored the current state of the literature to answer the following research questions: (1) Is there a relationship between journey mapping techniques and an LHS in the literature? (2) Is there a way to integrate the data from journey mapping activities into an LHS? (3) How can the data gleaned from journey map activities be used to inform an LHS? METHODS: A scoping review was conducted by querying the following electronic databases: Cochrane Database of Systematic Reviews (Ovid), IEEE Xplore, PubMed, Web of Science, Academic Search Complete (EBSCOhost), APA PsycInfo (EBSCOhost), CINAHL (EBSCOhost), and MEDLINE (EBSCOhost). Two researchers applied the inclusion criteria and assessed all articles by title and abstract in the first screen, using Covidence. Following this, a full-text review of included articles was done, with relevant data extracted, tabulated, and assessed thematically. RESULTS: The initial search yielded 694 studies. Of those, 179 duplicates were removed. Following this, 515 articles were assessed during the first screening phase, and 412 were excluded, as they did not meet the inclusion criteria. Next, 103 articles were read in full, and 95 were excluded, resulting in a final sample of 8 articles that satisfied the inclusion criteria. The article sample can be subsumed into 2 overarching themes: (1) the need to evolve service delivery models in health care, and (2) the potential value of using patient journey data in an LHS. CONCLUSIONS: This scoping review demonstrated the gap in knowledge regarding integrating the data from journey mapping activities into an LHS. Our findings highlighted the importance of using the data from patient experiences to enrich an LHS and provide holistic care. To satisfy this gap, the authors intend to continue this investigation to establish the relationship between journey mapping and the concept of LHSs. This scoping review will serve as phase 1 of an investigative series. Phase 2 will entail the creation of a holistic framework to guide and streamline data integration from journey mapping activities into an LHS. Lastly, phase 3 will provide a proof of concept to demonstrate how patient journey mapping activities could be integrated into an LHS.

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.

Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.

A Privacy-Preserving Audit and Feedback System for the Antibiotic Prescribing of General Practitioners: Survey Study.

BACKGROUND: Antibiotic resistance is a worldwide public health problem that is accelerated by the misuse and overuse of antibiotics. Studies have shown that audits and feedback enable clinicians to compare their personal clinical performance with that of their peers and are effective in reducing the inappropriate prescribing of antibiotics. However, privacy concerns make audits and feedback hard to implement in clinical settings. To solve this problem, we developed a privacy-preserving audit and feedback (A&F) system. OBJECTIVE: This study aims to evaluate a privacy-preserving A&F system in clinical settings. METHODS: A privacy-preserving A&F system was deployed at three primary care practices in Norway to generate feedback for 20 general practitioners (GPs) on their prescribing of antibiotics for selected respiratory tract infections. The GPs were asked to participate in a survey shortly after using the system. RESULTS: A total of 14 GPs responded to the questionnaire, representing a 70% (14/20) response rate. The participants were generally satisfied with the usefulness of the feedback and the comparisons with peers, as well as the protection of privacy. The majority of the GPs (9/14, 64%) valued the protection of their own privacy as well as that of their patients. CONCLUSIONS: The system overcomes important privacy and scaling challenges that are commonly associated with the secondary use of electronic health record data and has the potential to improve antibiotic prescribing behavior; however, further study is required to assess its actual effect.

A Learning Health System Framework to Operationalize Health Data to Improve Quality Care: An Australian Perspective.

Our healthcare system faces a burgeoning aging population, rising complexity, and escalating costs. Around 10% of healthcare is harmful, and evidence is slow to implement. Innovation to deliver quality and sustainable health systems is vital, and the methods are challenging. The aim of this study is to describe the process and present a perspective on a coproduced Learning Health System framework. The development of the Framework was led by publicly funded, collaborative, Academic Health Research Translation Centres, with a mandate to integrate research into healthcare to deliver impact. The focus of the framework is "learning together for better health," with coproduction involving leadership by an expert panel, a systematic review, qualitative research, a stakeholder workshop, and iterative online feedback. The coproduced framework incorporates evidence from stakeholders, from research, from data (practice to data and data to new knowledge), and from implementation, to take new knowledge to practice. This continuous learning approach aims to deliver evidence-based healthcare improvement and is currently being implemented and evaluated.