The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System

OBJECTIVES

We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases.

METHODS AND RESULTS

We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system.

CONCLUSIONS

The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.

The Ethics of Consent in a Shifting Genomic Ecosystem

The collection and use of human genetic data raise important ethical questions about how to balance individual autonomy and privacy with the potential for public good. The proliferation of local, national, and international efforts to collect genetic data and create linkages to support large-scale initiatives in precision medicine and the learning health system creates new demands for broad data sharing that involve managing competing interests and careful consideration of what constitutes appropriate ethical trade-offs. This review describes these emerging ethical issues with a focus on approaches to consent and issues related to justice in the shifting genomic research ecosystem.

Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research

OBJECTIVE

Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data from those systems.

MATERIALS AND METHODS

In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes.

RESULTS

Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting.

DISCUSSION

Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart.

CONCLUSION

By applying data warehousing principles to federate data at the "study" level, the RINS project reduced data fragmentation and promoted research systems integration.

The Indiana Learning Health System Initiative: Early experience developing a collaborative, regional learning health system

INTRODUCTION

Learning health systems (LHSs) are usually created and maintained by single institutions or healthcare systems. The Indiana Learning Health System Initiative (ILHSI) is a new multi-institutional, collaborative regional LHS initiative led by the Regenstrief Institute (RI) and developed in partnership with five additional organizations: two Indiana-based health systems, two schools at Indiana University, and our state-wide health information exchange. We report our experiences and lessons learned during the initial 2-year phase of developing and implementing the ILHSI.

METHODS

The initial goals of the ILHSI were to instantiate the concept, establish partnerships, and perform LHS pilot projects to inform expansion. We established shared governance and technical capabilities, conducted a literature review-based and regional environmental scan, and convened key stakeholders to iteratively identify focus areas, and select and implement six initial joint projects.

RESULTS

The ILHSI successfully collaborated with its partner organizations to establish a foundational governance structure, set goals and strategies, and prioritize projects and training activities. We developed and deployed strategies to effectively use health system and regional HIE infrastructure and minimize information silos, a frequent challenge for multi-organizational LHSs. Successful projects were diverse and included deploying a Fast Healthcare Interoperability Standards (FHIR)-based tool across emergency departments state-wide, analyzing free-text elements of cross-hospital surveys, and developing models to provide clinical decision support based on clinical and social determinants of health. We also experienced organizational challenges, including changes in key leadership personnel and varying levels of engagement with health system partners, which impacted initial ILHSI efforts and structures. Reflecting on these early experiences, we identified lessons learned and next steps.

CONCLUSIONS

Multi-organizational LHSs can be challenging to develop but present the opportunity to leverage learning across multiple organizations and systems to benefit the general population. Attention to governance decisions, shared goal setting and monitoring, and careful selection of projects are important for early success.

Are Communities Willing to Transition Into Learning Health Care Communities? A Community-Based Participatory Evaluation of Stakeholders' Receptivity

This article aims to determine receptivity for advancing the Learning Healthcare System (LHS) model to a novel evidence-based health care delivery framework-Learning Health Care Community (LHCC)-in Baltimore, as a model for a national initiative. Using community-based participatory, qualitative approach, we conducted 16 in-depth interviews and 15 focus groups with 94 participants. Two independent coders thematically analyzed the transcripts. Participants included community members (38%), health care professionals (29%), patients (26%), and other stakeholders (7%). The majority considered LHCC to be a viable model for improving the health care experience, outlining certain parameters for success such as the inclusion of home visits, presentation of research evidence, and incorporation of social determinants and patients' input. Lessons learned and challenges discussed by participants can help health systems and communities explore the LHCC aspiration to align health care delivery with an engaged, empowered, and informed community.

Culture as infrastructure in learning health systems

Building Learning Health Systems requires the combination of information, regulatory, and cultural infrastructures that create communities focused on changing health outcomes through the application of quality improvement methodology, focused data collection, closed feedback loops, and community-participatory techniques. Accomplishing the vision of the Learning Health System relies on building robust infrastructures, and teaching a wide variety of stakeholders to participate in these novel socio-technical systems. In this commentary, I draw on empirical examples from fieldwork with Learning Networks to describe how social scientists view culture and what this concept might hold for learning health sciences.

Sustainable generation of patient-led resources in a learning health system

BACKGROUND

Patient and Family Advisory Councils (PFACs) are an emerging mechanism to integrate patient and family voices into healthcare. One such PFAC is the Patient Advisory Council (PAC) of the ImproveCareNow (ICN) network, a learning health system dedicated to advancing the care of individuals with pediatric inflammatory bowel disease (IBD). Using quality improvement techniques and co-production, the PAC has made great strides in developing novel patient-led resources.

METHODS

This paper, written by patients and providers from ICN, reviews current ICN data on PAC-generated resources, including creation processes and download statistics.

RESULTS

Looking at different iterations of PAC infrastructure, this paper highlights specific leadership approaches used to increase patient involvement and improve resource creation. Emerging data suggests that the larger ICN learning health system has had limited interactions with these resources.

CONCLUSION

ICN provides a novel approach for meaningful integration of patient partners into learning health systems. This paper points to the incredible value of PFAC expertise in the resource creation process. Future work should seek to support PFAC development across other diseases and address the challenges of integrating patient-led resources into clinical care.

Developing a rapid evidence response to COVID-19: The collaborative approach of Saskatchewan, Canada

Introduction

The COVID‐19 Evidence Support Team (CEST) was a provincial initiative that combined the support of policymakers, researchers, and clinical practitioners to initiate a new learning health cycle (LHS) in response to the pandemic. The primary aim of CEST was to produce and sustain the best available COVID‐19 evidence to facilitate decision‐making in Saskatchewan, Canada. To achieve this objective, four provincial organizations partnered to establish a single, data‐driven system.

Methods

The CEST partnership was driven by COVID‐19 questions from Emergency Operational Committee (EOC) of the Saskatchewan Health Authority. CEST included three processes: (a) clarifying the nature and priority of COVID‐19 policy and clinical questions; (b) providing Rapid Reviews (RRR) and Evidence Search Reports (ESR); and (c) seeking the requestors' evaluation of the product. A web‐based repository, including a dashboard and database, was designed to house ESRs and RRRs and offered a common platform for clinicians, academics, leaders, and policymakers to find COVID‐19 evidence.

Results

In CEST's first year, 114 clinical and policy questions have been posed resulting in 135 ESRs and 108 RRRs. While most questions (41.3%) originated with the EOC, several other teams were assembled to address a myriad of questions related to areas such as long‐term care, public health and prevention, infectious diseases, personal protective equipment, vulnerable populations, and Indigenous health. Initial challenges were mobilization of diverse partners and teams, remote work, lack of public access, and quality of emerging COVID‐19 literature. Current challenges indicate the need for institutional commitment for CEST sustainability. Despite these challenges, the CEST provided the Saskatchewan LHS with a template for successful collaboration.

Conclusions

The urgency of COVID‐19 pandemic and the implementation of the CEST served to catalyze collaboration between different levels of a Saskatchewan LHS.

The Reliability of Patient Self-reported Utilization in an Inflammatory Bowel Diseases Learning Health System

Background

Inflammatory bowel disease (IBD) care is beset with substantial practice variation. Learning health systems (LHSs) aim to learn from this variation and improve quality of care by sharing feedback and improvement strategies within the LHS. Obtaining accurate information on outcomes and quality of care is a priority for LHS, which often includes patients' self-reported data. While prior work has shown that patients can accurately report their diagnosis and surgical history, little is known about their ability to self-report recent healthcare utilization, medication use, and vaccination status.

Methods

We compared patient self-reported data within the IBD Qorus LHS regarding recent IBD-related emergency department (ED) visits, hospitalizations, computerized tomography (CT) scans, corticosteroid use, opioid use, influenza vaccinations, and pneumococcal vaccinations with electronic health record (EHR) data.

Results

We compared 328 patient self-reports to data extracted from the EHR. Sensitivity was moderate-to-high for ED visits, hospitalizations, and CT scans (76%, 87%, and 87%, respectively), sensitivity was lower for medication use with 71% sensitivity for corticosteroid use and only 50% sensitivity for self-reported use of opioids. Vaccinations were reported with high sensitivity, but overall agreement was low as many patients reported vaccinations that were not registered in the EHR.

Conclusions

Self-reported IBD-related ED visits, hospitalizations, and CT scans are reported with high sensitivity and accuracy. Medication use, and in particular opioid use, is less reliably reported. Vaccination self-report is likely more accurate than EHR data as many vaccinations are not accurately registered.

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

Digitalization in nephrology has progressed in a manner that is disparate and siloed, even though learning (under a broader Learning Health System initiative) has been manifested in all the main areas of clinical application. Most applications based on artificial intelligence/machine learning (AI/ML) are still in the initial developmental stages and are yet to be adequately validated and shown to contribute to positive patient outcomes. There is also no consistent or comprehensive digitalization plan, and insufficient data are a limiting factor across all of these areas. In this article, we first consider how digitalization along nephrology care pathways relates to the Learning Health System initiative. We then consider the current state of AI/ML-based software and devices in nephrology and the ethical and regulatory challenges in scaling them up toward broader clinical application. We conclude with our proposal to establish a dedicated ethics and governance framework that is centered around health care providers in nephrology and the AI/ML-based software to which their work relates. This framework should help to integrate ethical and regulatory values and considerations, involve a wide range of stakeholders, and apply across normative domains that are conventionally demarcated as clinical, research, and public health.

Pharmacists leadership in a medication shortage response: Illustrative examples from a health system response to the COVID-19 crisis

As medication experts, clinical pharmacists play an active and dynamic role in a medication shortage response. Supplementing existing guidelines with an actionable framework of discrete activities to support effective medication shortage responses can expand the scope of pharmacy practice and improve patient care. Dissemination of best practices and illustrative, networked examples from health systems can support the adoption of innovative solutions. In this descriptive report, we document the translation of published shortage mitigation guidelines into system success through broad pharmacist engagement and the adaption and implementation of targeted strategies. The profound, wide‐reaching medication shortages that accompanied the coronavirus disease 2019 (COVID‐19) pandemic are used to highlight coordinated but distinct practices and how they have been combined to expand the influence of the pharmacy enterprise.

Improving team coordination in primary-care settings via multifaceted team-based feedback: a non-randomised controlled trial study

Background

Coordination is critical to successful team-based health care. Most clinicians, however, are not trained in effective coordination or teamwork. Audit and feedback (A&F) could improve team coordination, if designed with teams in mind.

Aim

The effectiveness of a multifaceted, A&F-plus-debrief intervention was tested to establish whether it improved coordination in primary care teams compared with controls.

Design & setting

Case-control trial within US Veterans Health Administration medical centres.

Method

Thirty-four primary care teams selected from four geographically distinct hospitals were compared with 34 administratively matched control teams. Intervention-arm teams received monthly A&F reports about key coordination behaviours and structured debriefings over 7 months. Control teams were followed exclusively via their clinical records. Outcome measures included a coordination composite and its component indicators (appointments starting on time, timely recall scheduling, emergency department utilisation, and electronic patient portal enrolment). Predictors included intervention arm, extent of exposure to intervention, and degree of multiple team membership (MTM).

Results

Intervention teams did not significantly improve over control teams, even after adjusting for MTM. Follow-up analyses indicated cross-team variability in intervention fidelity; although all intervention teams received feedback reports, not all teams attended all debriefings. Compared with their respective baselines, teams with high debriefing exposure improved significantly. Teams with high debriefing exposure improved significantly more than teams with low exposure. Low exposure teams significantly increased patient portal enrolment.

Conclusion

Team-based A&F, including adequate reflection time, can improve coordination; however, the effect is dose dependent. Consistency of debriefing appears more critical than proportion of team members attending a debriefing for ensuring implementation fidelity and effectiveness.

Is a nudge all we need to promote deliberate clinical inertia and thoughtful clinical decision making?

Deliberate clinical inertia is the art of doing nothing as a positive response. Individual clinicians can promote deliberate clinical inertia through teaching, re-framing the act of 'doing nothing' as 'doing something' and engaging in shared decision making. Behaviour change on a larger scale requires a systematic approach. Nudging is a subtle change to the decision-making context to prompt specific choices. A nudge unit is a team of relevant professionals who engage with various multidisciplinary teams within a health service who help test and implement nudge interventions in a clinical environment. A nudge unit could be used to design environments to prompt clinicians to re-think before ordering unnecessary tests or treatments. Nudge units could improve knowledge translation, support continuous quality improvement and help build a learning health system. They could also boost collaboration and empower staff to evaluate their workplace decision-making frameworks.

A maturity grid assessment tool for learning networks

BACKGROUND

The vision of learning healthcare systems (LHSs) is attractive as a more effective model for health care services, but achieving the vision is complex. There is limited literature describing the processes needed to construct such multicomponent systems or to assess development.

METHODS

We used the concept of a capability maturity matrix to describe the maturation of necessary infrastructure and processes to create learning networks (LNs), multisite collaborative LHSs that use an actor-oriented network organizational architecture. We developed a network maturity grid (NMG) assessment tool by incorporating information from literature review, content theory from existing networks, and expert opinion to establish domains and components. We refined the maturity grid in response to feedback from network leadership teams. We followed NMG scores over time for nine LNs and plotted scores for each domain component with respect to SD for one participating network. We sought subjective feedback on the experience of applying the NMG to individual networks.

RESULTS

LN leaders evaluated the scope, depth, and applicability of the NMG to their networks. Qualitative feedback from network leaders indicated that changes in NMG scores over time aligned with leaders' reports about growth in specific domains; changes in scores were consistent with network efforts to improve in various areas. Scores over time showed differences in maturation in the individual domains of each network. Scoring patterns, and SD for domain component scores, indicated consistency among LN leaders in some but not all aspects of network maturity. A case example from a participating network highlighted the value of the NMG in prompting strategic discussions about network development and demonstrated that the process of using the tool was itself valuable.

CONCLUSIONS

The capability maturity grid proposed here provides a framework to help those interested in creating Learning Health Networks plan and develop them over time.

Geisinger's effort to realize its potential as a learning health system: A progress report

OBJECTIVES

In the last two decades, several organizational initiatives have moved Geisinger into closer alignment with the key characteristics of the learning health system (LHS) model. The intent of this experience report is to provide a firsthand view of the potential of the model and of the complex, multifaceted nature of any endeavor designed and implemented to realize that potential.

METHODS

After describing Geisinger, we offer a critical self-assessment of our progress toward the goal of becoming an LHS, followed by an account of the challenges.

RESULTS

Geisinger has made incremental but measurable progress in implementing the LHS model, especially in two key domains: in patient-clinician engagement and science and informatics. Other challenges, however, present significant opportunities for additional forward movement, especially with respect to incentives, culture, and leadership.

CONCLUSION

Becoming a fully realized LHS is and will be a long-term challenge for any organization that embraces this aspiration.

Implementation of a "real-world" learning health system: Results from the evaluation of the Connected Health Cities programme

BACKGROUND

The "learning health system" has been proposed to deliver better outcomes for patients and communities by analyzing routinely captured health information and feeding back results to clinical staff. This approach has been piloted in the Connected Health Cities (CHC) programme in four regions in the North of England. This paper presents the results of the evaluation of this program conducted between February and December 2018.

METHODS

Fifty nine semistructured interviews were completed with a mix of CHC programme staff and external partners who had contributed to the delivery of the CHC programme. Interviews were audio recorded and transcribed verbatim. This also included the review of project documentation including project reports and minutes of project group meetings, in addition to a short online survey that was completed by 31 members of CHC programme staff. Data were analyzed thematically.

RESULTS

Two overarching themes emerged through the thematic analysis of participant interview: (a) challenges in the implementation of learning health system pathways, and (b) benefits to the CHC approach for both staff and patients. In particular, time constraints in delivering an ambitious program of work, data quality, and accessibility, as well as the long-term sustainability of the CHC programme were noted as key challenges in implementing a LHS at scale.

CONCLUSIONS

The findings from this evaluation provide valuable insight into creating learning health system at scale, including the potential benefits and likely challenges.

Impact of a learning health system on acute care and medical complications after intracerebral hemorrhage

INTRODUCTION

Patients with stroke often experience pneumonia during the acute stage after stroke onset. Oral care may be effective in reducing the risk of stroke-associated pneumonia (SAP). We aimed to determine the changes in oral care, as well as the incidence of SAP, in patients with intracerebral hemorrhage, following implementation of a learning health system in our hospital.

METHODS

We retrospectively analyzed the data of 1716 patients with intracerebral hemorrhage who were hospitalized at a single stroke center in Japan between January 2012 and December 2018. Data were stratified on the basis of three periods of evolving oral care: period A, during which conventional, empirically driven oral care was provided (n = 725); period B, during which standardized oral care was introduced, with SAP prophylaxis based on known risk factors (n = 469); and period C, during which oral care was risk-appropriate based on learning health system data (n = 522). Logistic regression analysis was performed to evaluate associations between each of the three treatment approaches and the risk of SAP.

RESULTS

Among the included patients, the mean age was 71.3 ± 13.6 years; 52.6% of patients were men. During the course of each period, the frequency of oral care within 24 hours of admission increased (P < .001), as did the adherence rate to oral care ≥3 times per day (P < .001). After adjustment for confounding factors, a change in the risk of SAP was not observed in period B; however, the risk significantly decreased in period C (odds ratio 0.61; 95% confidence interval 0.43-0.87) compared with period A. These associations were maintained for SAP diagnosed using strict clinical criteria or after exclusion of 174 patients who underwent neurosurgical treatment.

CONCLUSIONS

Risk-appropriate care informed by the use of learning health system data could improve care and potentially reduce the risk of SAP in patients with intracerebral hemorrhage in the acute stage.

Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR)

BACKGROUND

The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic.

OBJECTIVE

We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization.

METHODS

Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository-the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context.

RESULTS

The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources.

CONCLUSIONS

A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

Prognostic models will be victims of their own success, unless…

Predictive analytics have begun to change the workflows of healthcare by giving insight into our future health. Deploying prognostic models into clinical workflows should change behavior and motivate interventions that affect outcomes. As users respond to model predictions, downstream characteristics of the data, including the distribution of the outcome, may change. The ever-changing nature of healthcare necessitates maintenance of prognostic models to ensure their longevity. The more effective a model and intervention(s) are at improving outcomes, the faster a model will appear to degrade. Improving outcomes can disrupt the association between the model's predictors and the outcome. Model refitting may not always be the most effective response to these challenges. These problems will need to be mitigated by systematically incorporating interventions into prognostic models and by maintaining robust performance surveillance of models in clinical use. Holistically modeling the outcome and intervention(s) can lead to resilience to future compromises in performance.

Putting the "why" in "EHR": capturing and coding clinical cognition

Complaints about electronic health records, including information overload, note bloat, and alert fatigue, are frequent topics of discussion. Despite substantial effort by researchers and industry, complaints continue noting serious adverse effects on patient safety and clinician quality of life. I believe solutions are possible if we can add information to the record that explains the “why” of a patient’s care, such as relationships between symptoms, physical findings, diagnostic results, differential diagnoses, therapeutic plans, and goals. While this information may be present in clinical notes, I propose that we modify electronic health records to support explicit representation of this information using formal structure and controlled vocabularies. Such information could foster development of more situation-aware tools for data retrieval and synthesis. Informatics research is needed to understand what should be represented, how to capture it, and how to benefit those providing the information so that their workload is reduced.

The Competing Demands of Patient Privacy and Clinical Research

Privacy and confidentiality of personal medical information are cornerstones of ethical clinical care and ethical research. But real-world research has challenged traditional ways of thinking about privacy and confidentiality of information. In today's world of "big data" and learning health care systems, researchers and others are combining multiple sources of information to address complex problems. We present a case study that highlights the ethical concerns that arise when a patient who is employed by an academic medical center learns through a research invitational letter that her private information was accessed at this center without her consent. We discuss the ethical challenges of balancing patient privacy with advancing clinical research and ask, what level of privacy and confidentiality can and should patients expect from their clinician providers, fellow research colleagues, and institutions?

Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm

OBJECTIVES

We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites.

MATERIALS AND METHODS

ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System. The estimation accuracy was evaluated by comparing it with the estimator based on the combined individual participant data or pooled data (ie, gold standard).

RESULTS

Our simulation studies revealed that the relative estimation bias of ODAL1 compared with the pooled estimates was <3%, and the ratio of standard errors was <1.25 for all scenarios. ODAL2 achieved higher accuracy (with relative bias <0.1% and ratio of standard errors <1.05). In real data analysis, we investigated the associations of 100 medications with fetal loss during pregnancy. We found that ODAL1 provided estimates with relative bias <10% for 85% of medications, and ODAL2 has relative bias <10% for 99% of medications. For communication cost, ODAL1 requires transferring p numbers from each site to the local site and ODAL2 requires transferring (p×p+p) numbers from each site to the local site, where p is the number of parameters in the regression model.

CONCLUSIONS

This study demonstrates that ODAL is privacy-preserving and communication-efficient with small bias and high statistical efficiency.

Achieving Optimal Population Cardiovascular Health Requires an Interdisciplinary Team and a Learning Healthcare System: A Scientific Statement From the American Heart Association

Population cardiovascular health, or improving cardiovascular health among patients and the population at large, requires a redoubling of primordial and primary prevention efforts as declines in cardiovascular disease mortality have decelerated over the past decade. Great potential exists for healthcare systems-based approaches to aid in reversing these trends. A learning healthcare system, in which population cardiovascular health metrics are measured, evaluated, intervened on, and re-evaluated, can serve as a model for developing the evidence base for developing, deploying, and disseminating interventions. This scientific statement on optimizing population cardiovascular health summarizes the current evidence for such an approach; reviews contemporary sources for relevant performance and clinical metrics; highlights the role of implementation science strategies; and advocates for an interdisciplinary team approach to enhance the impact of this work.

Putting the network to work: Learning networks in rapid response situations

INTRODUCTION

The rapid response to COVID-19 has necessitated infrastructural development and reorientation in order to safely meet patient care needs.

METHODS

A qualitative case study was constructed within a larger ethnographic field study. Document collection and fieldnotes and recordings from nonparticipant observation of network activities were compiled and chronologically ordered to chart the network's response to changes in epilepsy care resulting from COVID-19 and the rapid transition to telemedicine.

RESULTS

The network's response to COVID-19 was characterized by a predisposition to action, the role of sharing as both a group practice and shared value, and the identification of improvement science as the primary contribution of the group within the larger epilepsy community's response to COVID-19. The findings are interpreted as an example of how group culture can shape action via a transparent and mundane shared infrastructure.

CONCLUSIONS

The case of one multi-stakeholder epilepsy Learning Network provides an example of the use of infrastructure that is shaped by the group's culture. These findings contribute to the development of a social theory of infrastructure within Learning Health Systems.

Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research

The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS.