key: cord-0065761-xgoewxoi
authors: Seid, Michael; Hartley, David M.; Margolis, Peter A.
title: A science of collaborative learning health systems
date: 2021-06-01
journal: Learn Health Syst
DOI: 10.1002/lrh2.10278
sha: 3bc54a674b329c1942df5216d27bd9f285337f80
doc_id: 65761
cord_uid: xgoewxoi

INTRODUCTION: Improving the U.S. healthcare system and health outcomes is one of the most pressing public health challenges of our time. Previously described Collaborative Learning Health Systems (CLHSs) are a promising approach to outcomes improvement. In order to fully realize this promise, a deeper understanding of this phenomenon is necessary. METHODS: We drew on our experience over the past decade with CLHSs as well as qualitative literature review to answer three questions: What kind of phenomena are CLHSs? and what is an appropriate scientific approach? How might we frame CLHSs conceptually? What are potential mechanisms of action? RESULTS: CLHSs are complex adaptive systems in which all stakeholders are able to collaborate, at scale, to create and share resources to satisfy a variety of needs. This is accomplished by providing infrastructure and services that enable stakeholders to act on their inherent motivations. This framing has implications for both research and practice. CONCLUSION: Articulating this framework and potential mechanisms of action should facilitate research to test and refine hypotheses as well as guide practice to develop and optimize this promising approach to improving healthcare systems.

To achieve optimal outcomes, the healthcare system must deliver the right care to the right person at the right time, every time. This is fundamentally a problem of coordination-of getting the right information and interventions to the right people at the right time and place.

Solving the coordination problem is the motivation behind personalized medicine, shared decision-making, the chronic care model, and reducing medical errors. Nevertheless, it is evident that solving this problem is difficult, due partly to the large variety of needs and array of potential interventions. No standardization by central decision makers can be expected to allocate the right treatment for each person. Nor is it likely that each professional would know, based only on their own experience, what intervention will be best for every patient in their care. To solve the coordination problem requires organizing the health system in such a way that every person can access resources for acting in ways that improve outcomes, contribute to learning what works best, and share that knowledge seamlessly.

The National Academies of Science, Engineering, and Medicine has described a Learning Healthcare System 1 in which clinical care, science, informatics, incentives, and culture are aligned for continuous improvement, innovation, and research; new knowledge is captured as a by-product of care, and evidence is applied reliably and is seamlessly embedded in the delivery process. This is a promising concept for solving the problem of coordination, but until recently, examples of Learning Healthcare Systems were rare. We and colleagues have described Collaborative Learning Health Systems (CLHSs), termed Learning Networks, 2 that use a network organizational architecture to facilitate collaboration at scale in order to improve health outcomes. We have described the CLHS framework and methods 2 and the processes used to replicate these and have shown that CLHSs improve outcomes across multiple diseases and conditions and across participating sites with diverse contexts. [3] [4] [5] More recently, we have recently described a formal methodology for assessing network maturity. 6 As of Spring of 2021, we know of 11 CLHSs 7 (Learning Networks), encompassing almost 700 teams across almost 300 healthcare organizations in 43 States and Washington, D.C., and five countries. Given that CLHSs have been replicated, within pediatrics, across chronic conditions (eg, Reference 4), inpatient safety, [8] [9] [10] [11] preterm birth, 3 and community health, 12 it is appropriate to consider these as a class.

We consider CLHSs to be a subset of LHSs, rather than distinct from LHSs. Camarinha-Matos and Afsarmanesh 13 have distinguished between networking (communicating and exchanging information for mutual benefit), coordination (the act of working together harmoniously), cooperation (not only exchanging and acting on information but also sharing resources for achieving aligned or shared goals) on the one hand and collaboration (a process in which entities share information, resources, and responsibilities to jointly plan, implement, and evaluate a program of activities aimed at achieving shared, aligned goals), on the other. While learning-a major goal of any LHS-can occur as the result of networking, coordination, and/or cooperation alone, CLHSs achieve rapid learning at scale through collaboration as detailed below.

Therefore, although collaboration is assumed to be present to some degree in all LHSs, the reason for highlighting this particular subset as Collaborative LHSs is to highlight the role of collaboration and the organizational approach to facilitate collaboration at scale to solve the coordination problem. Hierarchical and other organizations often do not achieve collaboration at scale due to their structure and limited interconnectivity, which tend to support a small number of line-specific functions rather than shared goals and alignment of purpose across the organization. Networks, due to their distributed, highly connected nature, achieve and sustain cooperation and collaboration broadly. [14] [15] [16] That is, CLHSs use a network organizational form to facilitate the production and sharing of resources so that all stakeholders (patients and families, clinicians, researchers) can collaborate toward solving the coordination problem.

CLHSs hold great promise, given their impact, as one pathway to improving outcomes through the vision of the Learning Health System. To fully realize the promise of CLHSs, however, we need a deeper understanding of them. Here, we seek to explain-not advocate-as a way to increase understanding of CLHSs. What kind of phenomena are CLHSs and what is an appropriate scientific approach? How might we frame CLHSs conceptually? What are potential mechanisms of action? In this commentary, we suggest answers to these questions, with the goal of helping researchers and practitioners better understand and improve CLHSs, including Learning Networks. Our approach to answering these questions is based on our own and colleagues' experiences, as well as qualitative literature review to synthesize a range of scholarship relevant to CLHSs. We incorporate sources from a variety of fields including complexity, organizational dynamics, political science, evolutionary biology, behavioral economics, sociology, innovation, and system design. We use a simplified example to ground our approach and then consider each question above in turn.

Our example uses hypothetical people, composites based on ethnographic research. 17 We build from the perspective of Orleans, a 12-year-old girl newly diagnosed with Crohn's disease. In addition to whatever personal, social, or physiological resources she has, she also has a variety of needs in the face of this diagnosis. Her medical needs include, among others, accurate diagnosis, selecting an appropriate treatment or treatments, and evaluating their effectiveness.

She also has needs relating to how to navigate the healthcare system, her life beyond her illness and symptoms, and making sense of her new status as a person with a chronic illness. Corbin and Strauss 18 refer to these domains as "illness work," "everyday life work," and "biographical work," respectively. Moreover, these needs will change over the course of her illness as both she and her condition change. 19 Orleans and her family need resources such as optimal treatment (eg, medicine, appropriate nutrition), information about how and when to call the doctor in between clinical appointments, accommodations at school such as unrestricted restroom access, and support around body image, managing a colostomy, or how to navigate conversations with friends about the disease and its implications. Dr. Roan, Orleans' gastroenterologist has a full panel of patients, and, while she has access to continuing education and Medline, the "gold standard" randomized controlled trial evidence available to her describes the average effect of a single treatment on a highly selected group of patients. How should she counsel Orleans and her family on the best treatment to choose, not to mention Orleans' nonmedical needs?

Orleans is one patient and Dr. Roan is one clinician. Expanding this example across many patients and many clinicians, one can imagine a huge variety of needs. Correspondingly, one can imagine a reservoir of resources, comprised of information, knowledge, and know-how (IKK), potentially available to meet those needs. Bianca, for example, has just graduated from college. Diagnosed with Crohn's disease at 11 years of age, she has learned to cope with the condition, sees herself as an expert patient, and wants to make a difference for others with the disease. Mitch Kapour, a researcher, has developed a system for patients to track the effects of treatments such as the ones Dr. Roan and Orleans are considering. And Veronica Mars, a nurse, has used Dr. Kapour's system to help patients and clinicians learn together about which treatments might work best for an individual patient. Bianca, Dr. Kapour, and Nurse Mars live and practice in different healthcare organizations in cities other than Orleans', and, though they may have similar motivations and shared goals, they do not collaborate or even know each other. They are three of potentially (hundreds of) thousands of people willing and able to contribute resources to satisfy the needs of people like Orleans and Dr. Roan's.

But unless there is a system-like a CLHS-that enables them to act on their motivations to create and share these resources and that makes it easy for people like Orleans to get what is needed when it is needed (WINWIN), the match between resources and needs-and the effect on Orleans' health and well-being-is only potential. CLHSs can be thought of as complex systems that facilitate collaboration toward best care and outcomes. Below, we unpack this statement to answer the questions posed above.

We view healthcare as a system 20 -a set of related processes that work together to achieve an outcome (as attributed to W.E. Demming). This has several implications, the most important of which are that the people in the healthcare system are enabled and constrained in their efforts and activities by the system itself and that improving outcomes will require us to change the system not just the people in the system. Further, we see healthcare as a complex system, 21,22 more akin to an organism or a society than to a machine.

Complex systems are ubiquitous in biological, physical, and social systems. Cells, organs, human bodies, organizations, societies, ecosystems, stock markets, and climate have been described as complex systems.

Complex systems share several characteristics and understanding these sheds light on how we might think about CLHSs. Complex systems are self-organizing; the parts (in healthcare, the patients, clinicians, administrators, technology) interact and react to one another and eventually form stable patterns. Further, it is the pattern of interactions among the parts that is important in understanding the effects of the system; if Orleans and Dr. Roan never meet, if their interaction is not collaborative, or if Orleans cannot fill her prescriptions, it is less likely Orleans will receive-or act on-an appropriate treatment. Complex systems are nonlinear, with implications for change strategies.

Thus, interventions applied to the system-incentivizing Dr. Roan to increase patient satisfaction scores, for example-are unlikely to have the predicted effect. Fourth, complex systems often contain complex sub-systems (hospitals contain wards and clinics, which contain people, some of whom have something amiss with one or more of their organs, and so on), and these systems interact with and interpenetrate one another. Finally, some complex systems-like the healthcare system-have humans in them. Humans have agency; they act on their environment based on their own understanding of their situation.

Seeing CLHSs as complex systems has implications for understanding and changing them. 26, 27 To make a good match, it is necessary to have welldefined needs and goals, 28 a system for informing, making, 29 and optimizing [30] [31] [32] decisions about the match, accessible resources, and the ability to update this matching as new information or needs arise.

At the level of individuals, matching happens as a result of coproduction of good care involving patients, families, and clinical care teams ( Figure 1 ). [33] [34] [35] A good match relies on the work of both patients and clinicians, like Orleans and Dr. Roan, to collaborate to coproduce IKK-information (eg, data on clinical symptoms), knowledge (eg, which treatments work for whom), and know-how (eg, how to reliably achieve and adhere to evidence-based care) for informing, choosing, and implementing the match.

Value creation configurations can impact the effectiveness of coproduction and matching. 36 Healthcare services take various organizational configurations to produce value for patients. 36 The dominant organizational form in healthcare is a consultative model known as a F I G U R E 1 Schematic illustrating a CLHS. Changing the system, rather than people, unleashes inherent motivation toward a shared purpose. Using an actor-oriented organizational architecture, a CLHS becomes a value network, facilitating the production and sharing of IKK (information, knowledge, know-how) for getting WINWIN ("What I need, when it's needed"). This IKK is available in the clinical encounter (the value shop), facilitating coproduction of a match between needs and resources "value shop," in which value is created through "mobilizing resources and activities to resolve a particular customer problem" (p. 414). 37 When Orleans has a clinic visit with Dr. Roan, the value shop configuration addresses clinical care through highly customized matching of resources to needs by clinicians and care teams, often using sophisti- A potential concern in any value network configuration is the relevance and rigor of the resources available. What if patients (or clinicians for that matter) create and spread bad information?

How might we ensure that misinformation or disinformation is not generated and spread, with potentially calamitous results? First, it is important to recognize that patients and clinicians, as active agents, seek information and other resources from a variety of sources.

Project documented that although clinicians are the first choice for most people for information about health concerns, online sources, including advice from peers, are a significant source of health information in the US. 43 Second, it is instructive to recognize the different types of resources needed by patients and families. 27 

Describing coproduction and the value network logic begs the question of the underlying mechanisms: What makes them work?

Coproduction relies on engagement, by which we mean the degree to which people in all roles (patients, families, clinicians, researchers, others) are involved in the CLHS to utilize, create, and/or share the information, knowledge, and know-how for improving care and outcomes. 47 Engagement varies as people become more and less involved. People can be engaged as participants, using existing information and resources (eg, reading a blog post, using clinical decisionmaking tools, signing up for a research study, tracking on Dr. Kapour's app), as contributors by making the resources better (eg, writing a blog post, refining clinical decision tools as Nurse Mars has done), or take ownership by creating new resources (eg, a new research study, patient resource, or clinical tool). As more people (patients, families, clinicians, researchers) are engaged in contributing to existing resources or creating and sharing new ones, more and better resources will be available.

The value network can be understood as enabled by an actororiented architecture. 40 With enough actors (people, institutions) motivated and able to self-organize; a commons where they create, store, and share resources; and structures, processes, and protocols that facilitate multiactor collaboration, resources are produced and shared across the CLHS, thus facilitating the individual patienttreatment matching process at scale.

The mechanisms underlying the actor-oriented architecture and engagement are similar-unleashing inherent motivation to enable contribution and sharing toward the purpose of better health for individuals and communities. A large body of social and behavioral science suggests that people tend to be prosocial and motivated by affiliation, autonomy, and mastery. 48 People want to make a difference (contribute in positive ways to health) and will do so if their situation allows this. Non-market-based production 49 is a term used to describe the fact that people will do things for reasons other than money. Examples outside healthcare are widespread, from parent-teacher associations to open source software (eg, R, Linux) to Yelp and the Audubon Christmas Bird Count. Building systems (structures and processes) to make it easier for people to collaborate to improve health unleashes this inherent motivation.

The existing healthcare service system limits the ability of people to act upon these motivations. Well-intentioned, competent people currently function within a system that impedes rather than empowers stakeholders to apply their inherent motivation and expertise to solving challenging problems that match resources to needs. 50 Changing outcomes requires changing the system; CLHSs such as Learning Networks intentionally change the system in such a way as to enable participants to better collaborate to make this match.

CLHSs focus relentlessly on outcomes, create infrastructure and processes to facilitate sharing and collaboration, and promulgate methods to supporting continuous improvement and research to provide a way of solving problems and learning together.

CLHSs have been replicated, within pediatrics, across chronic conditions (eg, Reference 4), inpatient safety, [8] [9] [10] [11] preterm birth, 3 and community health, 12 suggesting that these are a class of scientific phenomena that can be studied systematically. We assert that an appropriate scientific approach is to view CLHSs as complex systems. [51] [52] [53] In complex systems, the whole is more than the sum of the parts-the interaction among the parts creates emergent phenomena.

In CLHSs, the system's parts are people (like patients, clinicians, administrators), institutions (like hospitals, insurance, and pharmaceutical companies), and assets (eg, databases, medicines, clinic space).

CLHSs provide an environment and infrastructure that enable these parts to interact in new ways to facilitate better care and outcomes. This is accomplished, we assert, by making it easier for more "actors" to create and share resources that can be used to coproduce better matching of resources to needs. Powering this system is the inherent motivation of all stakeholders; by aligning goals and making it easier to express this inherent motivation, CLHSs enable nonmarket production toward these goals. Implicit to this framework is collaboration; for this reason, we term such organizations Collaborative Learning Healthcare Systems (CLHSs).

The conceptual framework of CLHSs presented in this work is distinct from other descriptions of LHSs, which tend to emphasize big data or vertical health systems within firms. First, they are arrange- 

Social/organizational challenges to CLHS development and scale exist. Governance of CLHSs requires an understanding of the mechanisms underlying value creation, as well as an appreciation of the scholarship and practice involved in stewarding a commons. 54 Policies about privacy, data standards, 55 ethics, 56 intellectual property, 57 and others must be developed to facilitate sharing and collaboration. Potential problems such as free-riding (taking advantage of others' contributions) and other opportunistic behaviors must be addressed. 58 The network organizational form requires norms of collaboration and trust, new ways of leading, and specific leadership competencies. 36 Leaders must promote these norms, lead through influence and by stimulating new connections, and develop new leaders so that such systems can continue to grow and develop.

There are substantial financial and human resources required to enable CLHSs; funding for these and efforts to reduce both fixed and variable costs are required. Scientific norms regarding the degree of evidence required for decision-making or the quality of data required for knowledge creation must be established. The rigor and relevance of resources posted to the commons must be ensured. Increasing the number of people aware of and involved in CLHSs and facilitating increasing levels of contribution is an ongoing effort required to scale up. Equity must be directly addressed, incorporating a range of stakeholders so that these advances do not widen, but rather reduce existing health disparities. Especially important are ways to change the system to make it possible for people with fewer resources or who face systemic racism to be able to benefit from CLHSs.

Technical challenges, likewise, merit consideration. Data needed to generate evidence and reusable technology platforms are required to gain an economy of scale. Systems engineering to maintain and upgrade the infrastructure is key. Data streams from new technologies will require linkages among existing data repositories. New analytic approaches and artificial intelligence will solve and create both technological and social challenges.

If we are to transform the US healthcare system into a CLHS or collection of CLHSs, this conceptual picture suggests a broad agenda spanning basic through translational research. Analogous to NIH's NCATS's translational science spectrum (https://ncats. it matter what is shared or with whom? Does network structure (eg, who is connected to whom) matter, and how?

• Understanding the requirements and limitations of supporting infrastructure. What supporting infrastructure is required for CLHSs to function appropriately? Are there processes and policies (eg, data models, intellectual property policies, ethics, standard contracts) that need to be in place to promote collaboration in such systems?

• Understanding conditions necessary for creating a CLHS. Is there a parsimonious set of "rules" for creating a CLHS in which connectivity and collaboration flourishes? A central challenge is to identify parameters or sets of parameters 59 such as policies and procedures, that might sufficiently change the interactions among components of the healthcare system to induce a shift away from the current system of healthcare toward a CLHS.

• Developing measures and model systems. How do we define "engagement" or "collaboration" and how might we use such measures to observe and better understand CLHSs? How might we construct and use model systems (eg, a computational model 60 • Understanding the impact of the policy environment on CLHSs.

What are the financial incentives for clinicians, healthcare systems, and payers and how might these affect CLHS proliferation? Are there policies that might reduce the risks healthcare systems might incur by sharing data or information or that would encourage evidence generation?

The framework described in this commentary should be considered as a starting point for understanding CLHSs and a set of hypotheses to be tested. It is our hope that articulating this theory will enable more patients, scholars, practitioners, and policy makers to collaborate in achieving the vision of a Learning Healthcare System.

We are grateful to our colleagues who provided feedback and revisions to the conceptual framework. 

The Learning Healthcare System: Workshop Summary

Using a network organisational architecture to support the development of learning healthcare systems

A statewide initiative to reduce inappropriate scheduled births at 36(0/7)-38(6/7) weeks' gestation

Improved outcomes in a quality improvement collaborative for pediatric inflammatory bowel disease

Improvement in Interstage survival in a National Pediatric Cardiology Learning Network

A maturity grid assessment tool for learning networks

Impact of a pressure injury prevention bundle in the solutions for patient safety network

Ohio Children's hospitals' solutions for patient safety: a framework for pediatric patient safety improvement

A statewide collaborative to reduce pediatric surgical site infections

Surgical site infection reduction by the solutions for patient safety hospital engagement network

Development of a child health learning network to improve population health outcomes; presented in honor of Dr Robert Haggerty

Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management. International Federation of Information Processing

Cooperation in evolving social networks

Cooperation, clustering, and assortative mixing in dynamic networks

Dynamics of collective performance in collaboration networks

Using goal-directed design to create a novel system for improving chronic illness care

Managing chronic illness at home: three lines of work

A nursing model for chronic illness management based upon the trajectory framework

Thinking in Systems: A Primer

The Systems View of Life: A Unifying Vision

Complexity Theory and the Social Sciences: The State of the Art

Evidence-based medicine

for the Evidence Based Medicine Renaissance Group

From patients to end users

Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions

After Dr Google: peer-to-peer health care

Measuring patient-centered care: an updated systematic review of how studies define and report concordance between patients' preferences and medical treatments

Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango)

Selecting the most appropriate treatment for each patient

The BestFIT trial: a SMART approach to developing individualized weight loss treatments

Targeting Alzheimer's disease at the right time and the right place: validation of a personalized approach to diagnosis and treatment

Coproduction of healthcare service

Improving primary care for patients with chronic illness: the chronic care model

Organizing care for patients with chronic illness

Networked health care: Rethinking value creation in learning health care systems

Configuring value for competitive advantage: on chains, shops, and networks

A guide to gutsy living": patient-driven development of a pediatric ostomy toolkit

Towards safer, better healthcare: harnessing the natural properties of complex sociotechnical systems

The architecture of collaboration

Co-creating value innovation through resource integration

Health professional networks as a vector for improving healthcare quality and safety: a systematic review

The Social Life of Health Information

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

Culture as infrastructure in learning health systems

Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention

Engagement, peer production, and the learning healthcare system

The Penguin and the Leviathan: The Triumph of Cooperation over Self-Interest

The Wealth of Networks: How Social Production Transforms Markets and Freedom

Why We Revolt: A Patient Revolution for Careful and Kind Care

Understanding health care as a complex system: the foundation for unintended consequences

Complexity science: the challenge of complexity in health care

Changing how we think about healthcare improvement

Governing the Commons

A digital architecture for a network-based learning health system: integrating chronic care management, quality improvement, and research. eGEMs

An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics

Intellectual property. Commons-based strategies and the problems of patents

Agency problems and governance mechanisms in collaborative communities

Architectural frameworks: defining the structures for implementing learning health systems

A collaborative learning health system agent-based model: computational and face validity. Learn Health Syst. 2021;e10261

Effectivenessimplementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact

A science of collaborative learning health systems