Patient-Reported Outcome Dashboards Within the Electronic Health Record to Support Shared Decision-making: Protocol for Co-design and Clinical Evaluation With Patients With Advanced Cancer and Chronic Kidney Disease

BACKGROUND

Patient-reported outcomes-symptoms, treatment side effects, and health-related quality of life-are important to consider in chronic illness care. The increasing availability of health IT to collect patient-reported outcomes and integrate results within the electronic health record provides an unprecedented opportunity to support patients' symptom monitoring, shared decision-making, and effective use of the health care system.

OBJECTIVE

The objectives of this study are to co-design a dashboard that displays patient-reported outcomes along with other clinical data (eg, laboratory tests, medications, and appointments) within an electronic health record and conduct a longitudinal demonstration trial to evaluate whether the dashboard is associated with improved shared decision-making and disease management outcomes.

METHODS

Co-design teams comprising study investigators, patients with advanced cancer or chronic kidney disease, their care partners, and their clinicians will collaborate to develop the dashboard. Investigators will work with clinic staff to implement the co-designed dashboard for clinical testing during a demonstration trial. The primary outcome of the demonstration trial is whether the quality of shared decision-making increases from baseline to the 3-month follow-up. Secondary outcomes include longitudinal changes in satisfaction with care, self-efficacy in managing treatments and symptoms, health-related quality of life, and use of costly and potentially avoidable health care services. Implementation outcomes (ie, fidelity, appropriateness, acceptability, feasibility, reach, adoption, and sustainability) during the co-design process and demonstration trial will also be collected and summarized.

RESULTS

The dashboard co-design process was completed in May 2020, and data collection for the demonstration trial is anticipated to be completed by the end of July 2022. The results will be disseminated in at least one manuscript per study objective.

CONCLUSIONS

This protocol combines stakeholder engagement, health care coproduction frameworks, and health IT to develop a clinically feasible model of person-centered care delivery. The results will inform our current understanding of how best to integrate patient-reported outcome measures into clinical workflows to improve outcomes and reduce the burden of chronic disease on patients and health care systems.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/38461.

Eliciting patients' healthcare goals and concerns: Do questions influence responses?

There is increasing interest in asking patients questions before their visits to elicit goals and concerns, which is part of the move to support the concept of coproducing care. The phrasing and delivery of such questions differs across settings and is likely to influence responses. This report describes a study that (i) used a three-level model to categorize the goals and concerns elicited by two different pre-visit questions, and (ii) describes associations between responses elicited and the phrasing and delivery of the two questions. The questions were administered to patients with rheumatic disease, and patients with inflammatory bowel disease (IBD). Paper-based responses from 150 patients with rheumatic disease and 338 patients with IBD were analyzed (163 paper, 175 electronic). The goals and concerns elicited were primarily disease or symptom-specific. The specific goal and concern examples featured in one pre-visit question were more commonly reported in responses to that question, compared to the question without examples. Questions completed electronically before the visit were associated with longer responses than those completed on paper in the waiting room. In conclusion, how and when patients' goals and concerns are elicited appears to have an impact on responses and warrants further investigation.

Partnering to Advance Health Equity and a Welcome Opportunity to Gather: Proceedings From the 28th Annual Conference of the Health Care Systems Research Network

In April 2022, the Health Care Systems Research Network (HCSRN) - a consortium of 20 research institutions affiliated with large health systems spread across the United States (and one in Israel) - held its 28th annual conference in Pasadena, California, with 275 researchers, health care colleagues, and external academic partners in attendance. With a conference theme of "Promoting Collaboration and Partnerships to Advance Health Equity," the scientific program was assembled by a multisite planning committee with input from representatives of informal local host Kaiser Permanente Southern California. Objectives of the annual conference are to showcase scientific findings from HCSRN projects and to spur collaboration on research initiatives that improve health and health care for individuals and populations. To those ends, the NIH Pragmatic Trials Collaboratory sponsored a preconference workshop on the essentials of embedded pragmatic clinical trials, and more than a dozen scientific interest groups and active research project teams held ancillary sessions throughout the conference. This welcome opportunity for network members to meet in-person followed a 2-year hiatus necessitated by the COVID-19 pandemic, during which HCSRN conference proceedings were conducted through virtual and written communication platforms.

Creating a provincial post COVID-19 interdisciplinary clinical care network as a learning health system during the pandemic: Integrating clinical care and research

Introduction

Coronavirus Disease-2019 (COVID-19) affects multiple organ systems in the acute phase and also has long-term sequelae. Research on the long-term impacts of COVID-19 is limited. The Post COVID-19 Interdisciplinary Clinical Care Network (PC-ICCN), conceived in July 2020, is a provincially funded resource that is modelled as a Learning Health System (LHS), focused on those people with persistent symptoms post COVID-19 infection.

Methods

The PC-ICCN emerged through collaboration among over 60 clinical specialists, researchers, patients, and health administrators. At the core of the network are the post COVID-19 Recovery Clinics (PCRCs), which provide direct patient care that includes standardized testing and education at regular follow-up intervals for a minimum of 12 months post enrolment. The PC-ICCN patient registry captures data on all COVID-19 patients with confirmed infection, by laboratory testing or epi-linkage, who have been referred to one of five post COVID-19 Recovery Clinics at the time of referral, with data stored in a fully encrypted Oracle-based provincial database. The PC-ICCN has centralized administrative and operational oversight, multi-stakeholder governance, purpose built data collection supported through clinical operations geographically dispersed across the province, and research operations including data analytics.

Results

To date, 5364 patients have been referred, with an increasing number and capacity of these clinics, and 2354 people have had at least one clinic visit. Since inception, the PC-ICCN has received over 30 research proposal requests. This is aligned with the goal of creating infrastructure to support a wide variety of research to improve care and outcomes for patients experiencing long-term symptoms following COVID-19 infection.

Conclusions

The PC-ICCN is a first-in-kind initiative in British Columbia to enhance knowledge and understanding of the sequelae of COVID-19 infection over time. This provincial initiative serves as a model for other national and international endeavors to enable care as research and research as care.

A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback: Proposal for a Randomized Controlled Trial

BACKGROUND

Health care delivery organizations lack evidence-based strategies for using quality measurement data to improve performance. Audit and feedback (A&F), the delivery of clinical performance summaries to providers, demonstrates the potential for large effects on clinical practice but is currently implemented as a blunt one size fits most intervention. Each provider in a care setting typically receives a performance summary of identical metrics in a common format despite the growing recognition that precisionizing interventions hold significant promise in improving their impact. A precision approach to A&F prioritizes the display of information in a single metric that, for each recipient, carries the highest value for performance improvement, such as when the metric's level drops below a peer benchmark or minimum standard for the first time, thereby revealing an actionable performance gap. Furthermore, precision A&F uses an optimal message format (including framing and visual displays) based on what is known about the recipient and the intended gist meaning being communicated to improve message interpretation while reducing the cognitive processing burden. Well-established psychological principles, frameworks, and theories form a feedback intervention knowledge base to achieve precision A&F. From an informatics perspective, precision A&F requires a knowledge-based system that enables mass customization by representing knowledge configurable at the group and individual levels.

OBJECTIVE

This study aims to implement and evaluate a demonstration system for precision A&F in anesthesia care and to assess the effect of precision feedback emails on care quality and outcomes in a national quality improvement consortium.

METHODS

We propose to achieve our aims by conducting 3 studies: a requirements analysis and preferences elicitation study using human-centered design and conjoint analysis methods, a software service development and implementation study, and a cluster randomized controlled trial of a precision A&F service with a concurrent process evaluation. This study will be conducted with the Multicenter Perioperative Outcomes Group, a national anesthesia quality improvement consortium with >60 member hospitals in >20 US states. This study will extend the Multicenter Perioperative Outcomes Group quality improvement infrastructure by using existing data and performance measurement processes.

RESULTS

The proposal was funded in September 2021 with a 4-year timeline. Data collection for Aim 1 began in March 2022. We plan for a 24-month trial timeline, with the intervention period of the trial beginning in March 2024.

CONCLUSIONS

The proposed aims will collectively demonstrate a precision feedback service developed using an open-source technical infrastructure for computable knowledge management. By implementing and evaluating a demonstration system for precision feedback, we create the potential to observe the conditions under which feedback interventions are effective.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/34990.

Learning environments, reliability enhancing work practices, employee engagement, and safety climate in VA cardiac catheterization laboratories

OBJECTIVE

To characterize the relationship between learning environments (the educational approaches, cultural context, and settings in which teaching and learning happen) and reliability enhancing work practices (hiring, training, decision making) with employee engagement, retention, and safety climate.

DATA SOURCE

We collected data using the Learning Environment and High Reliability Practices Survey (LEHRs) from 231 physicians, nurses, and technicians at 67 Veterans Affairs cardiac catheterization laboratories who care for high-risk Veterans.

STUDY DESIGN

The association between the average LEHRs score and employee job satisfaction, burnout, intent to leave, turnover, and safety climate were modeled in separate linear mixed effect models adjusting for other covariates.

DATA COLLECTION

Participants responded to a web-only survey from August through September 2020.

PRINCIPAL FINDINGS

There was a significant association between higher average LEHRs scores and (1) higher job satisfaction (2) lower burnout, (3) lower intent to leave, (4) lower cath lab turnover in the previous 12 months, and (5) higher perceived safety climate.

CONCLUSIONS

Learning environments and use of reliability enhancing work practices are potential new avenues to support satisfaction and safety climate while lowering burnout, intent to leave, and turnover in a diverse US health care workforce that serves a vulnerable and marginalized population.

Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature

The "learning health system" (LHS) concept has been defined in broad terms, which makes it challenging for health system leaders to determine exactly what is required to transform their organization into an LHS. This study provides a conceptual map of the LHS landscape by identifying the activities, principles, tools, and conditions that LHS researchers have associated with the concept. Through a multi-step screening process, two researchers identified 79 publications from PubMed (published before January 2020) that contained information relevant to the question, "What work is required of a healthcare organization that is operating as an LHS?" Those publications were coded as to whether or not they referenced each of 94 LHS elements in the taxonomy developed by the study team. This taxonomy, named the Learning Health Systems Consolidated Framework (LHS-CF), organizes the elements into five "bodies of work" (organizational learning, translation of evidence into practice, building knowledge, analyzing clinical data, and engaging stakeholders) and four "enabling conditions" (workforce skilled for LHS work, data systems and informatics technology in place, organization invests resources in LHS work, and supportive organizational culture). We report the frequency that each of the 94 elements was referenced across the 79 publications. The four most referenced elements were: "organization builds knowledge or evidence," "quality improvement practices are standard practice," "patients and family members are actively engaged," and "organizational culture emphasizes and supports learning." By dissecting the LHS construct into its component elements, the LHS-CF taxonomy can serve as a useful tool for LHS researchers and practitioners in defining the aspects of LHS they are addressing. By assessing how often each element is referenced in the literature, the study provides guidance to health system leaders as to how their organization needs to evolve in order to become an LHS - while also recognizing that each organization should emphasize elements that are most aligned with their mission and goals.

Streamlined versus traditional consent for low-risk comparative effectiveness trials: a randomized experimental study to measure patients' and public attitudes

Aim: Streamlining consent for low-risk comparative effectiveness research (CER) could facilitate research, while safeguarding patients' rights. Materials & methods: 2618 adults were randomized to one of seven consent approaches (six streamlined and one traditional) for a hypothetical, low-risk CER study. A survey measured understanding, voluntariness, and feelings of respect. Results: Participants in all arms had a high understanding of the trial and positive attitudes toward the consent interaction. Highest satisfaction was with a streamlined approach showing a video before the medical appointment. Participants in streamlined were more likely to mistakenly think a signature was required. Conclusion: Streamlined consent was no less acceptable than traditional, signed consent. Streamlined and traditional approaches achieved similar levels of understanding, voluntariness and a feeling that the doctor-patient interaction was respectful.

Standardized Health data and Research Exchange (SHaRE): promoting a learning health system

Aggregate de-identified data from electronic health records (EHRs) provide a valuable resource for research. The Standardized Health data and Research Exchange (SHaRE) is a diverse group of US healthcare organizations contributing to the Cerner Health Facts (HF) and Cerner Real-World Data (CRWD) initiatives. The 51 facilities at the 7 founding organizations have provided data about more than 4.8 million patients with 63 million encounters to HF and 7.4 million patients and 119 million encounters to CRWD. SHaRE organizations unmask their organization IDs and provide 3-digit zip code (zip3) data to support epidemiology and disparity research. SHaRE enables communication between members, facilitating data validation and collaboration as we demonstrate by comparing imputed EHR module usage to actual usage. Unlike other data sharing initiatives, no additional technology installation is required. SHaRE establishes a foundation for members to engage in discussions that bridge data science research and patient care, promoting the learning health system.

Supporting the Utilization of Community-Based Primary Health Care Implementation Research in Ghana

Ever since the 1990s, implementation research in Ghana has guided the development of policies and practices that are essential to establishing community-based primary health care. In response to evidence emerging from this research, the Community-based Health Planning and Services (CHPS) policy was promulgated in 1999 to scale-up results. However, during the first decade of CHPS operation, national monitoring showed that its pace of coverage expansion was unacceptably slow. In 2010, the Ghana Health Service launched a five-year plausibility trial of CHPS reform for testing ways to accelerate scale-up. This initiative, known as the Ghana Essential Health Intervention Program (GEHIP), included a knowledge management component for establishing congruence of knowledge generation and flow with the operational system that GEHIP evidence was intended to reform. Four Upper East Region districts served as trial areas while seven districts were comparison areas. Interventions tested means of developing the upward flow of information based on perspectives of district managers, sub-district supervisors, and community-level workers. GEHIP also endeavored to improve procedures for the downward flow and utilization of policy guidelines. Field exchanges were convened for providing national, regional, and district leaders with opportunities for participatory learning about GEHIP implementation innovations. This systems approach facilitated the process of augmenting the communication of evidence with practical field experience. Scientific rigor associated with the production of evidence was thereby integrated into management decision-making processes in ways that institutionalized learning at all levels. The GEHIP knowledge management system functioned as a prototype for guiding the planning of a national knowledge management strategy. A follow-up project transferred its mechanisms from the Upper East Regional Health Administration to the Policy Planning Monitoring and Evaluation Division of the Ghana Health Service in Accra.

Developing organizational learning for scaling-up community-based primary health care in Ghana

INTRODUCTION

Achieving effective community-based primary health care requires evidence for guiding strategic decisions that must be made. However, research processes often limit data collection to particular organizational levels or disseminate results to specific audiences. Decision-making that emerges can fail to account for the contrasting perspectives and needs of managers at each organizational level. The Ghana Health Service (GHS) addressed this problem with a multilevel and sequential research and action approach that has provided two decades of implementation learning for guiding community-based primary health care development.

METHOD

The GHS implementation research initiatives progressed from (i) a participatory pilot investigation to (ii) an experimental trial of strategies that emerged to (iii) replication research for testing scale-up, culminating in (iv) evidence-based scale-up of a national community-based primary health care program. A reform process subsequently repeated this sequence in a manner that involved stakeholders at the community, sub-district, district, and regional levels of the system. The conduct, interpretation, and dissemination of results that emerged comprised a strategy for achieving systems learning by conducting investigations in phases in conjunction with bottom-up knowledge capture, lateral exchanges for fostering peer learning at each system level, and top-down processes for communicating results as policy. Continuous accumulation of qualitative data on stakeholder reactions to operations at each organizational level was conducted in conjunction with quantitative monitoring of field operations.

RESULTS

Implementation policies were enhanced by results associated with each phase. A quasi-experiment for testing the reform process showed that scale-up of community-based primary health care was accelerated, leading to improvements in childhood survival and reduced fertility.

CONCLUSION

Challenges to system learning were overcome despite severe resource constraints. The integration of knowledge generation with ongoing management processes institutionalized learning for achieving evidence-driven program action.

Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus Nephritis: Development and Validation of Computable Phenotypes

BACKGROUND AND OBJECTIVES

Performing adequately powered clinical trials in pediatric diseases, such as SLE, is challenging. Improved recruitment strategies are needed for identifying patients.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Electronic health record algorithms were developed and tested to identify children with SLE both with and without lupus nephritis. We used single-center electronic health record data to develop computable phenotypes composed of diagnosis, medication, procedure, and utilization codes. These were evaluated iteratively against a manually assembled database of patients with SLE. The highest-performing phenotypes were then evaluated across institutions in PEDSnet, a national health care systems network of >6.7 million children. Reviewers blinded to case status used standardized forms to review random samples of cases (n=350) and noncases (n=350).

RESULTS

Final algorithms consisted of both utilization and diagnostic criteria. For both, utilization criteria included two or more in-person visits with nephrology or rheumatology and ≥60 days follow-up. SLE diagnostic criteria included absence of neonatal lupus, one or more hydroxychloroquine exposures, and either three or more qualifying diagnosis codes separated by ≥30 days or one or more diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 100% (95% confidence interval [95% CI], 99 to 100), specificity was 92% (95% CI, 88 to 94), positive predictive value was 91% (95% CI, 87 to 94), and negative predictive value was 100% (95% CI, 99 to 100). Lupus nephritis diagnostic criteria included either three or more qualifying lupus nephritis diagnosis codes (or SLE codes on the same day as glomerular/kidney codes) separated by ≥30 days or one or more SLE diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 90% (95% CI, 85 to 94), specificity was 93% (95% CI, 89 to 97), positive predictive value was 94% (95% CI, 89 to 97), and negative predictive value was 90% (95% CI, 84 to 94). Algorithms identified 1508 children with SLE at PEDSnet institutions (537 with lupus nephritis), 809 of whom were seen in the past 12 months.

CONCLUSIONS

Electronic health record-based algorithms for SLE and lupus nephritis demonstrated excellent classification accuracy across PEDSnet institutions.

Cultivating informatics capacity for multimorbidity: A learning health systems use case

Background

The aim of this study was to characterize patterns of multimorbidity across patients and identify opportunities to strengthen the informatics capacity of learning health systems that are used to characterize multimorbidity across patients.

Methods

Electronic health record (EHR) data on 225,710 multimorbidity patients were extracted from the Arkansas Clinical Data Repository as a use case. Hierarchical cluster analysis identified the most frequently occurring combinations of chronic conditions within the learning health system's captured data.

Results

Results revealed multimorbidity was highest among patients ages 60 to 74, Caucasians, females, and Medicare payors. The largest numbers of chronic conditions occurred in the smallest numbers of patients (i.e., 70,262 (31%) patients with two conditions, two (<1%) patients with 22 chronic conditions). The results revealed urgent needs to improve EHR systems and processes that collect and manage multimorbidity data (e.g., creating new, multimorbidity-centric data elements in EHR systems, detailed longitudinal tracking of compounding disease diagnoses).

Conclusions

Without additional capacity to collect and aggregate large-scale data, multimorbidity patients cannot benefit from the recent advancements in informatics (i.e., clinical data registries, emerging data standards) that are abundantly working to improve the outcomes of patients with single chronic conditions. Additionally, robust socio-technical system studies of clinical workflows are needed to assess the feasibility of integrating the collection of risk factor data elements (i.e., psycho-social, cultural, ethnic, and socioeconomic attributes of populations) into primary care encounters. These approaches to advancing learning health systems for multimorbidity could substantially reduce the constraints of current technologies, data, and data-capturing processes.

Collaborative conversations during the time of COVID-19: Building a "meta"-learning community

PROBLEM

COVID-19 created new research, clinical, educational, and personal challenges, while simultaneously separating work teams who were under work-from-home restrictions. Addressing these challenges required new forms of collaborative groups.

APPROACH

To support the department community and the rapid sharing of new research, educational, clinical, and personal efforts, a Core Team from the Department of Learning Health Sciences at the University of Michigan developed a meeting series called the COVID Conversations. This Experience Report shares the organizational structure of the COVID Conversations, proposes a comparison to traditional Learning Communities, and reports the results of a questionnaire that gathered details about department members' COVID-related activities.

OUTCOMES

We identify and describe salient similarities and differences between the COVID Conversations and the characteristics of Learning Communities. We also developed and piloted a taxonomy for characterizing LHS research projects that may be further developed for use in Learning Community planning, in conjunction with other maturity grids and ontologies. We propose the term "Meta-Learning Community" to describe the structure and function of the COVID Conversations.

NEXT STEPS

In academic medicine, remote work, telemedicine, and virtual learning may be here to stay. The COVID Conversations constitute a distinct and innovative form of collaborative work in which separate teams addressing distinct goals, yet sharing a common passion to tackle the issues brought by the pandemic, are able to share experiences and learn from one other. The challenges of COVID-19 have made evident the need for multiple forms of organizing teamwork, and our study contributes the notion of a "Meta"-Learning Community as a new form of collaborative work.

An Implementation Science Laboratory as One Approach to Whole System Improvement: A Canadian Healthcare Perspective

Implementation science (IS) has emerged as an integral component for evidence-based whole system improvement. IS studies the best methods to promote the systematic uptake of evidence-based interventions into routine practice to improve the quality and effectiveness of health service delivery and patient care. IS laboratories (IS labs) are one mechanism to integrate implementation science as an evidence-based approach to whole system improvement and to support a learning health system. This paper aims to examine if IS labs are a suitable approach to whole system improvement. We retrospectively analyzed an existing IS lab (Alberta, Canada’s Implementation Science Collaborative) to assess the potential of IS labs to perform as a whole system approach to improvement and to identify key activities and considerations for designing IS labs specifically to support learning health systems. Results from our evaluation show the extent to which IS labs support learning health systems through enabling infrastructures for system-wide improvement and research.

GA4GH: International policies and standards for data sharing across genomic research and healthcare

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

A roadmap to operationalize and evaluate impact in a learning health system

BACKGROUND

Many health systems invest in initiatives to accelerate translation of knowledge into practice. However, organizations lack guidance on how to develop and operationalize such Learning Health System (LHS) programs and evaluate their impact. Kaiser Permanente Washington (KPWA) launched our LHS program in June 2017 and developed a logic model as a foundation to evaluate the program's impact.

OBJECTIVE

To develop a roadmap for organizations that want to establish an LHS program, understand how LHS core components relate to one another when operationalized in practice, and evaluate and improve their progress.

METHODS

We conducted a narrative review on LHS models, key model components, and measurement approaches.

RESULTS

The KPWA LHS Logic Model provides a broad set of constructs relevant to LHS programs, depicts their relationship to LHS operations, harmonizes terms across models, and offers measurable operationalizations of each construct to guide other health systems. The model identifies essential LHS inputs, provides transparency into LHS activities, and defines key outcomes to evaluate LHS processes and impact. We provide reflections on the most helpful components of the model and identify areas that need further improvement using illustrative examples from deployment of the LHS model during the COVID-19 pandemic.

CONCLUSION

The KPWA LHS Logic Model is a starting point for future LHS implementation research and a practical guide for healthcare organizations that are building, operationalizing, and evaluating LHS initiatives.

A learning health systems approach to integrating electronic patient-reported outcomes across the health care organization

INTRODUCTION

Foundational to a learning health system (LHS) is the presence of a data infrastructure that can support continuous learning and improve patient outcomes. To advance their capacity to drive patient-centered care, health systems are increasingly looking to expand the electronic capture of patient data, such as electronic patient-reported outcome (ePRO) measures. Yet ePROs bring unique considerations around workflow, measurement, and technology that health systems may not be poised to navigate. We report on our effort to develop generalizable learnings that can support the integration of ePROs into clinical practice within an LHS framework.

METHODS

Guided by action research methodology, we engaged in iterative cycles of planning, acting, observing, and reflecting around ePRO use with two primary goals: (1) mobilize an ePRO community of practice to facilitate knowledge sharing, and (2) establish guidelines for ePRO use in the context of LHS practice. Multiple, emergent data collection activities generated generalizable guidelines that document the tangible best practices for ePRO use in clinical care. We organized guidelines around thematic areas that reflect LHS structures and stakeholders.

RESULTS

Three core thematic areas (and 24 guidelines) emerged. The theme of governance reflects the importance of leadership, knowledge management, and facilitating organizational learning around best practice models for ePRO use. The theme of integration considers the intersection of workflow, technology, and human factors for ePROs across areas of care delivery. Lastly, the theme of reporting reflects critical considerations for curating data and information, designing system functions and interactions, and presentation of ePRO data to support the translation of knowledge to action.

CONCLUSIONS

The guidelines produced from this work highlight the complex, multidisciplinary nature of implementing change within LHS contexts, and the value of action research approaches to enable rapid, iterative learning that leverages the knowledge and experience of communities of practice.

General orders for the embedded researcher: Moorings for a developing profession

Learning health systems increasingly welcome embedded researchers as stakeholders poised to inform evidence-based practice. While care systems are potentially familiar with the embedded researcher tools and techniques, care systems may less frequently consider embedded research as a vocation. This insensitivity potentially reduces embedded researchers merely to instruments, as opposed to professional partners in transdisciplinary research. This discussion outlines "general orders" for embedded researchers. The general orders outline embedded researchers' fundamental identity and guide conduct as a means to encourage a shared identity among embedded researchers and clarify embedded researchers' roles in learning health system teams. Students and embedded researchers newly engaging learning health systems may particularly benefit from this rudimentary order list.

The triple helix of clinical, research, and education missions in academic health centers: A qualitative study of diverse stakeholder perspectives

INTRODUCTION

Academic health centers are poised to improve health through their clinical, education, and research missions. However, these missions often operate in silos. The authors explored stakeholder perspectives at diverse institutions to understand challenges and identify alignment strategies.

METHODS

Authors used an exploratory qualitative design and thematic analysis approach with data obtained from electronic surveys sent to participants at five U.S. academic health centers (2017-18), with four different types of medical school/health system partnerships. Participants included educators, researchers, system leaders, administrators, clinical providers, resident/fellow physicians, and students. Investigators coded data using constant comparative analysis, met regularly to reconcile uncertainties, and collapsed/combined categories.

RESULTS

Of 175 participants invited, 113 completed the survey (65%). Three results categories were identified. First, five higher-order themes emerged related to aligning missions, including (a) shared vision and strategies, (b) alignment of strategy with community needs, (c) tension of economic drivers, (d) coproduction of knowledge, and (e) unifying set of concepts spanning all missions. Second, strategies for each mission were identified, including education (new competencies, instructional methods, recruitment), research (shifting agenda, developing partnerships, operations), and clinical operations (delivery models, focus on patient factors/needs, value-based care, well-being). Lastly, strategies for integrating each dyadic mission pair, including research-education, clinical operations education, and research-clinical operations, were identified.

CONCLUSIONS

Academic health centers are at a crossroads in regard to identity and alignment across the tripartite missions. The study's results provide pragmatic strategies to advance the tripartite missions and lead necessary change for improved patient health.

Identifying strategies to overcome roadblocks to utilising near real-time healthcare and administrative data to create a Scotland-wide learning health system

Creating a learning health system could help reduce variations in quality of care. Success is dependent on timely access to health data. To explore the barriers and facilitators to timely access to patients' data, we conducted in-depth semi-structured interviews with 37 purposively sampled participants from government, the NHS and academia across Scotland. Interviews were analysed using the framework approach. Participants were of the view that Scotland could play a leading role in the exploitation of routine data to drive forward service improvements, but highlighted major impediments: (i) persistence of paper-based records and a variety of information systems; (ii) the need for a proportionate approach to managing information governance; and (iii) the need for support structures to facilitate accrual, processing, linking, analysis and timely use and reuse of data for patient benefit. There is a pressing need to digitise and integrate existing health information infrastructures, guided by a nationwide proportionate information governance approach and the need to enhance technological and human capabilities to support these efforts.

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.