Examining how organizational leaders perceive internet-delivered cognitive behavioural therapy for public safety personnel using the RE-AIM implementation framework

Background

Within Canada, internet-delivered cognitive behavioural therapy (ICBT) has recently been tailored by PSPNET to meet the needs of public safety personnel (PSP) to help address high rates of mental health problems within this population. Perceptions and outcomes of ICBT among PSP are promising, but it remains unknown how PSPNET is perceived by PSP organizational leaders. It is important to assess this gap because these leaders have significant potential to influence the uptake of ICBT.

Methods

In the current study, PSP leaders (n = 10) were interviewed to examine their perceptions of PSPNET and opportunities to improve ICBT implementation. The RE-AIM evaluation framework was used to assess PSP leaders' perceptions of PSPNET in terms of reach, effectiveness, adoption, implementation, and maintenance.

Results

The results evidenced that leaders perceived PSPNET as effective in reaching and serving PSP and PSP organizations. PSP leaders reported perceiving ICBT as effectively implemented, especially for being freely offered to individual PSP and for improving PSP's access to experienced therapists specifically trained to work with PSP. Participants indicated organizations have promoted and will continue promoting PSPNET longer-term, facilitating adoption and maintenance. Factors perceived as facilitating successful service delivery included building relationships and trust with PSP organizations and general support for PSP leadership mental health initiatives. PSP leaders identified perceived areas for improving ICBT implementation (e.g., ensuring leaders have access to data on PSPNET uptake and outcomes, creating promotional videos, expanding availability of PSPNET to other provinces, offering additional options for receiving therapist support).

Implications

Overall, the study provides insights into PSP leaders' perceptions of the implementation of ICBT among PSP and ideas for optimizing implementation efforts.

Assessing Alternative Approaches for Wound Closure in a National Pediatric Learning Health System

INTRODUCTION

Our objective was to perform a feasibility study using real-world data from a learning health system (LHS) to describe current practice patterns of wound closure and explore differences in outcomes associated with the use of tissue adhesives and other methods of wound closure in the pediatric surgical population to inform a potentially large study.

METHODS

A multi-institutional cross-sectional study was performed of a random sample of patients <18 y-old who underwent laparoscopic appendectomy, open or laparoscopic inguinal hernia repair, umbilical hernia repair, or repair of traumatic laceration from January 1, 2019, to December 31, 2019. Sociodemographic and operative characteristics were obtained from 6 PEDSnet (a national pediatric LHS) children's hospitals and OneFlorida Clinical Research Consortium (a PCORnet collaboration across 14 academic health systems). Additional clinical data elements were collected via chart review.

RESULTS

Of the 692 patients included, 182 (26.3%) had appendectomies, 155 (22.4%) inguinal hernia repairs, 163 (23.6%) umbilical hernia repairs, and 192 (27.8%) traumatic lacerations. Of the 500 surgical incisions, sutures with tissue adhesives were the most frequently used (n = 211, 42.2%), followed by sutures with adhesive strips (n = 176, 35.2%), and sutures only (n = 72, 14.4%). Most traumatic lacerations were repaired with sutures only (n = 127, 64.5%). The overall wound-related complication rate was 3.0% and resumption of normal activities was recommended at a median of 14 d (interquartile ranges 14-14).

CONCLUSIONS

The LHS represents an efficient tool to identify cohorts of pediatric surgical patients to perform comparative effectiveness research using real-world data to support medical and surgical products/devices in children.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions

BACKGROUND

The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods.

MAIN TEXT

This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly.

CONCLUSIONS

Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Qualitative Interviews to Add Patient Perspectives in Colorectal Cancer Screening: Improvements in a Learning Health System

Health systems are interested in increasing colorectal cancer (CRC) screening rates as CRC is a leading cause of preventable cancer death. Learning health systems are ones that use data to continually improve care. Data can and should include qualitative local perspectives to improve patient and provider education and care. This study sought to understand local perspectives on CRC screening to inform future strategies to increase screening rates across our integrated health system. Health insurance plan members who were eligible for CRC screening were invited to participate in semi-structured phone interviews. Qualitative content analysis was conducted using an inductive approach. Forty member interviews were completed and analyzed. Identified barriers included ambivalence about screening options (e.g., "If it had the same performance, I'd rather do home fecal sample test. But I'm just too skeptical [so I do the colonoscopy]."), negative prior CRC screening experiences, and competing priorities. Identified facilitators included a positive general attitude towards health (e.g., "I'm a rule follower. There are certain things I'll bend rules. But certain medical things, you just got to do."), social support, a perceived risk of developing CRC, and positive prior CRC screening experiences. Study findings were used by the health system leaders to inform the selection of CRC screening outreach and education strategies to be tested in a future simulation model. For example, the identified barrier related to ambivalence about screening options led to a proposed revision of outreach materials that describe screening types more clearly.

Exploration of the Voice of the Patient in Learning Health Systems: A Socio-Technical Perspective

Despite learning health systems' focus on including the patients in improving healthcare services, research shows they are still considered participants, not partners. This article aims to provide practical guidance for recognizing and including the Voice of the Patient (VoP) as data in a continuous LHS by describing how the VoP can present itself, how it can be incorporated into the LHS and the barriers and enablers for doing so. Five key domains were identified to consider when including the patient perspective. The use of technology could be a facilitator for patients to provide their perspectives. However, there is a risk of increased health inequity by reducing the VoP of patients with low health or digital literacy.

Designing an Interprofessional Online Course to Foster Learning Health Systems

The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.

Learning health systems and evidence ecosystems: a perspective on the future of evidence-based medicine and evidence-based guideline development

Despite forming the cornerstone of modern clinical practice for decades, implementation of evidence-based medicine at scale remains a crucial challenge for health systems. As a result, there has been a growing need for conceptual models to better contextualise and pragmatize the use of evidence-based medicine, particularly in tandem with patient-centred care. In this commentary, we highlight the emergence of the learning health system as one such model and analyse its potential role in pragmatizing both evidence-based medicine and patient-centred care. We apply the learning health system lens to contextualise the key activity of evidence-based guideline development and implementation, and highlight how current inefficiencies and bottlenecks in the evidence synthesis phase of evidence-based guideline development threaten downstream adherence. Lastly, we introduce the evidence ecosystem as a complementary model to learning health systems, and propose how innovative developments from the evidence ecosystem may be integrated with learning health systems to better enable health impact at speed and scale.

Development and use of a novel tool for assessing and improving researcher embeddedness in learning health systems and applied system improvements

This paper outlines the development, deployment and use, and testing of a tool for measuring and improving healthcare researcher embeddedness - i.e., being connected to and engaged with key leverage points and stakeholders in a health system. Despite the widely acknowledged importance of embeddedness for learning health systems and late-stage translational research, we were not aware of useful tools for addressing and improving embeddedness in scholar training programs. We developed the MN-LHS Embeddedness Tool covering connections to committees, working groups, leadership, and other points of contact across four domains: patients and caregivers; local practice (e.g., operations and workflows); local institutional research (e.g., research committees and agenda- or initiative-setting groups); and national (strategic connections within professional groups, conferences, etc.). We used qualitative patterns and narrative findings from 11 learning health system training program scholars to explore variation in scholar trajectories and the embeddedness tool's usefulness in scholar professional development. Tool characteristics showed moderate evidence of construct validity; secondarily, we found significant differences in embeddedness, as a score, from baseline through program completion. The tool has demonstrated simple, practical utility in making embeddedness an explicit (rather than hidden) part of applied and learning health system researcher training, alongside emerging evidence for validity.

Exploring the impact of evaluation on learning and health innovation sustainability: protocol for a realist synthesis

BACKGROUND

Within the Learning Health System (LHS) model, learning routines, including evaluation, allow for continuous incremental change to take place. Within these learning routines, evaluation assists in problem identification, data collection, and data transformation into contextualized information, which is then re-applied to the LHS environment. Evaluation that catalyzes learning and improvement may also contribute to health innovation sustainability. However, there is little consensus as to why certain evaluations seem to support learning and sustainability, while others impede it. This realist synthesis seeks to understand the contextual factors and underlying mechanisms or drivers that best support health systems learning and sustainable innovation.

METHODS

This synthesis will be guided by Pawson and colleagues' 2005 and Emmel and colleagues' 2018 guidelines for conducting realist syntheses. The review process will encompass five steps: (1) scoping the review, (2) building theories, (3) identifying the evidence, (4) evidence selection and appraisal, and (5) data extraction and synthesis. An Expert Committee comprised of leaders in evaluation, innovation, sustainability, and realist methodology will guide this synthesis. Review findings will be reported using the RAMESES guidelines.

DISCUSSION

The use of a realist review will allow for exploration and theorizing about the contextual factors and underlying mechanisms that make evaluations 'work' (or 'not work') to support learning and sustainability. Depending on results, we will attempt to synthesize findings into a series of recommendations for evaluations with the intention to support health systems learning and sustainability. Finalized results will be presented at national and international conferences, as well as disseminated via a peer-reviewed publication.

SYSTEMATIC REVIEW REGISTRATION

This realist synthesis protocol has been registered with PROSPERO ( https://www.crd.york.ac.uk/prospero/  ID 382690).

Organisation and integrated healthcare approaches for people living with HIV, multimorbidity, or both: a systematic review

BACKGROUND

Universal recommendation for antiretroviral drugs and their effectiveness has put forward the challenge of assuring a chronic and continued care approach to PLHIV (People Living with HIV), pressured by aging and multimorbidity. Integrated approaches are emerging which are more responsive to that reality. Studying those approaches, and their relation to the what of delivery arrangements and the how of implementation processes, may support future strategies to attain more effective organizational responses.

METHODS

We reviewed empirical studies on either HIV, multimorbidity, or both. The studies were published between 2011 and 2020, describing integrated approaches, their design, implementation, and evaluation strategy. Quantitative, qualitative, or mixed methods were included. Electronic databases reviewed cover PubMed, SCOPUS, and Web of Science. A narrative analysis was conducted on each study, and data extraction was accomplished according to the Effective Practice and Organisation of Care taxonomy of health systems interventions.

RESULTS

A total of 30 studies, reporting 22 different interventions, were analysed. In general, interventions were grounded and guided by models and frameworks, and focused on specific subpopulations, or priority groups at increased risk of poorer outcomes. Interventions mixed multiple integrated components. Delivery arrangements targeted more frequently clinical integration (n = 13), and care in proximity, community or online-telephone based (n = 15). Interventions reported investments in the role of users, through self-management support (n = 16), and in coordination, through multidisciplinary teams (n = 9) and continuity of care (n = 8). Implementation strategies targeted educational and training activities (n = 12), and less often, mechanisms of iterative improvement (n = 3). At the level of organizational design and governance, interventions mobilised users and communities through representation, at boards and committees, and through consultancy, along different phases of the design process (n = 11).

CONCLUSION

The data advance important lessons and considerations to take steps forward from disease-focused care to integrated care at two critical levels: design and implementation. Multidisciplinary work, continuity of care, and meaningful engagement of users seem crucial to attain care that is comprehensive and more proximal, within or cross organizations, or sectors. Promising practices are advanced at the level of design, implementation, and evaluation, that set integration as a continued process of improvement and value professionals and users' knowledge as assets along those phases.

TRIAL REGISTRATION

PROSPERO number CRD42020194117.

How alignment between health systems and their embedded research units contributes to system learning

BACKGROUND

There is growing interest in the contributions of embedded, learning health system (LHS), research within healthcare delivery systems. We examined the organization of LHS research units and conditions affecting their contributions to system improvement and learning.

METHODS

We conducted 12 key-informant and 44 semi-structured interviews in six delivery systems engaged in LHS research. Using rapid qualitative analysis, we identified themes and compared: successful versus challenging projects; LHS units and other research units in the same system; and LHS units in different systems.

RESULTS

LHS units operate both independently and as subunits within larger research centers. Contributions of LHS units to improvements and learning are influenced by alignment of facilitating factors within units, within the broader system, and between unit and host system. Key alignment factors were availability of internal (system) funding directing researchers' work toward system priorities; researchers' skills and experiences that fit a system's operational needs; LHS unit subculture supporting system improvement and collaboration with clinicians and other internal stakeholders; applications of external funding to system priorities; and executive leadership for system-wide learning. Mutual understanding and collaboration between researchers, clinicians, and leaders was fostered through direct consultation between LHS unit leaders and system executives and engagement of researchers in clinical and operational activities.

CONCLUSIONS

Embedded researchers face significant challenges to contributing to system improvement and learning. Nevertheless, when appropriately led, organized, and supported by internal funding, they may learn to collaborate effectively with clinicians and system leaders in advancing care delivery toward the learning health system ideal.

The science of clinical quality registries

Clinical quality registries can be a transformational tool to improve healthcare delivery. Clinical registries with an incorporated quality emphasis identify evidence-practice gaps, inform quality improvement, and provide foundational research data to examine and improve health-related outcomes. For registries to create an impact it is essential that clinicians and researchers understand historical context, importance, advantages, and key criticisms. This methodological paper highlights the skills and capabilities required to build and maintain a robust clinical quality registry. This includes key measures to ensure data security, quality control, ongoing operational components, and benchmarking of care outcomes.

The learning health system for pediatric nephrology: building better systems to improve health

Learning health systems (LHS) align science, informatics, incentives, and culture for continuous improvement and innovation. In this organizational system, best practices are seamlessly embedded in the delivery process, and new knowledge is captured as an integral byproduct of the care delivery experience aimed to transform clinical practice and improve patient outcomes. The objective of this review is to describe how building better health systems that integrate clinical care, improvement, and research as part of an LHS can improve care within pediatric nephrology. This review will provide real-world examples of how this system can be established in a single center and across multiple centers as learning health networks.

Paediatric intensive care 'do not do' recommendations in Spain: Selection by Delphi method

INTRODUCTION

Health care is not free of ineffective, unsafe or inefficient diagnostic and therapeutic practices. To address this, different scientific societies and health authorities have proposed 'do not do' recommendations (DNDRs). Our goal was the selection by consensus of a set of DNDRs for paediatric intensive care in Spain.

MATERIAL AND METHOD

The research was carried out in 2 phases: first, gathering potential DNDRs; second, selecting the most important ones, using the Delphi method, based on the prevalence of the practice to be modified, the severity of its potential risks and the ease with which it could be modified. Proposals and evaluations were both made by members of working groups of the Sociedad Española de Cuidados Intensivos Pediátricos (SECIP, Spanish Society of Paediatric Intensive Care), coordinated by email. The initial set of DNDRs was reduced based on the coefficient of variation (<80%) of the corresponding evaluations.

RESULTS

A total of 182 DNDRs were proposed by 30 intensivists. The 14 Delphi evaluators managed to pare down the initial set to 85 DNDRs and, after a second round, to the final set of 26 DNDRs. The care quality dimensions most represented in the final set are clinical effectiveness and patient safety.

CONCLUSIONS

This study allowed the selection by consensus of a series of recommendations to avoid unsafe, inefficient or ineffective practices in paediatric intensive care in Spain, which could be useful for improving the quality of clinical care in our field.

Integrated rapid-cycle comparative effectiveness trials using flexible point of care randomisation in electronic health record systems

Whilst the Randomised Controlled Trial remains the gold standard for deriving robust causal estimates of treatment efficacy, too often a traditional design proves prohibitively expensive or cumbersome when it comes to assessing questions regarding the comparative effectiveness of routinely used treatments. As a result, patients experience variation in practice as clinicians lack the evidence needed to personalise treatments effectively. This variation may be classified as unwarranted, where existing evidence is ignored, or legitimate where in the absence of evidence, clinicians rely on experience, expert opinion, and inferred principles from basic science to make decisions. We argue that within the right ethical and technological framework, legitimate variation can be transformed into a mechanism for evidence generation and learning. Learning Health Systems which harness existing variation in practice, represent a novel approach for generating evidence from everyday clinical practice. The development of these systems has gained traction due to the increased availability of modern Electronic Health Record Systems. However, despite their promise, overcoming hurdles to successfully integrating clinical trials within Learning Health Systems has proven challenging. This article describes the origins of integrated clinical trials and explores two main barriers to their further implementation - how best to obtain informed consent from patients to participate in routine comparative effectiveness research, and how to automate and integrate randomisation into a clinical workflow. Having described these barriers, we present a potential solution in the form of a research pipeline using a novel form of flexible point-of-care randomisation to allow clinicians and patients to participate in studies where there is clinical equipoise.

How a point-of-care dashboard facilitates co-production of health care and health for and with individuals with psychotic disorders: a mixed-methods case study

BACKGROUND

Individuals with psychotic disorders experience widespread treatment failures and risk early death. Sweden's largest department specializing in psychotic disorders sought to improve patients' health by developing a point-of-care dashboard to support joint planning and co-production of care. The dashboard was tested for 18 months and included more than 400 patients at two outpatient clinics.

METHODS

This study evaluates the dashboard by addressing two questions: 1) Can differences in health-related outcome measures be attributed to the use of the dashboard? 2) How did the case managers experience the accessibility, use, and usefulness of the dashboard for co-producing care with individuals with psychotic disorders? This mixed-method case study used both Patient-Reported Outcome Measures (PROM) and data from a focus group interview with case managers. Data collection and analysis were framed by the Clinical Adoption Meta Model (CAMM) phases: i) accessibility, ii) system use, iii) behavior, and iv) clinical outcomes. The PROM used was the 12-item World Health Organization Disability Assessment Schedule (WHODAS 2.0), which assesses functional impairment and disability. Patients at clinics using the dashboard were matched with patients at clinics not using the dashboard. PROM data were compared using non-parametric statistics due to skewness in distribution. The focus group included five case managers who had experience using the dashboard with patients.

RESULTS

Compared to patients from clinics that did not use the dashboard, patients from clinics that did use the dashboard improved significantly overall (p = 0.045) and in the domain self-care (p = 0.041). Focus group participants reported that the dashboard supported data feedback-informed care and a proactive stance related to changes in patients' health. The dashboard helped users identify critical changes and enabled joint planning and evaluation.

CONCLUSION

Dashboard use was related to better patient health (WHODAS scores) when compared with matched patients from clinics that did not use the dashboard. In addition, case managers had a positive experience using the dashboard. Dashboard use might have lowered the risk for missing critical changes in patients' health while increasing the ability to proactively address needs. Future studies should investigate how to enhance patient co-production through use of supportive technologies.

Health system learning with Indigenous communities: a study protocol for a two-eyed seeing review and multiple case study

Background

It is well documented that Canadian healthcare does not fully meet the health needs of First Nations, Inuit or Métis peoples. In 1996, the Royal Commission on Aboriginal Peoples concluded that Indigenous peoples’ healthcare needs had to be met by strategies and systems that emerged from Indigenous worldviews and cultures. In 2015, the Truth and Reconciliation Commission also called on health organizations to learn from Indigenous “knowledges” and integrate Indigenous worldviews alongside biomedicine and other western ways of knowing. These calls have not yet been met. Meanwhile, the dynamic of organizational learning from knowledges and evidence within communities is poorly understood—particularly when learning is from communities whose ways of knowing differ from those of the organization. Through an exploration of organizational and health system learning, this study will explore how organizations learn from the Indigenous communities they serve and contribute to (re-)conceptualizing the learning organization and learning health system in a way that privileges Indigenous knowledges and ways of knowing.

Methods

This study will employ a two-eyed seeing literature review and embedded multiple case study. The review, based on Indigenous and western approaches to reviewing and synthesizing knowledges, will inform understanding of health system learning from different ways of knowing. The multiple case study will examine learning by three distinct government organizations in Northwest Territories, a jurisdiction in northern Canada, that have roles to support community health and wellness: Tłı̨chǫ Government, Gwich’in Tribal Council, and Government of Northwest Territories. Case study data will be collected via interviews, talking circles, and document analysis. A steering group, comprising Tłı̨chǫ and Gwich’in Elders and representatives from each of the three partner organizations, will guide all aspects of the project.

Discussion

Examining systems that create health disparities is an imperative for Canadian healthcare. In response, this study will help to identify and understand ways for organizations to learn from and respectfully apply knowledges and evidence held within Indigenous communities so that their health and wellness are supported. In this way, this study will help to guide health organizations in the listening and learning that is required to contribute to reconciliation in healthcare.

WITHDRAWN: Designing a professional development online short course to foster Learning Healthcare Systems

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been withdrawn at the request of the editor and publisher. The publisher regrets that an error occurred which led to the premature publication of this paper. This error bears no reflection on the article or its authors. The publisher apologizes to the authors and the readers for this unfortunate error.

Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty

Background

Practice based research and learning networks (PBRLNs) are groups of learning communities that focus on improving delivery and quality of care. Accurate data from primary care electronic medical records (EMRs) is crucial in forming the backbone for PBRLNs. The purpose of this work is to: (1) report on descriptive findings from recent frailty work, (2) describe strategies for working across PBRLNs in primary care, and (3) provide lessons learned for engaging PBRLNs.

Methods

We carried out a participatory based descriptive study that engaged five different PBRLNs. We collected Clinical Frailty Scale scores from a sample of participating physicians within each PBRLN. Descriptive statistics were used to analyze frailty scores and patients’ associated risk factors and demographics. We used the Consolidated Framework for Implementation Research to inform thematic analysis of qualitative data (meeting minutes, notes, and conversations with co-investigators of each network) in recognizing challenges of working across networks.

Results

One hundred nine physicians participated in collecting CFS scores across the five provinces (n = 5466). Percentages of frail (11-17%) and not frail (82-91%) patients were similar in all networks, except Ontario who had a higher percentage of frail patients (25%). The majority of frail patients were female (65%) and had a significantly higher prevalence of hypertension, dementia, and depression. Frail patients had more prescribed medications and numbers of healthcare encounters. There were several noteworthy challenges experienced throughout the research process related to differences across provinces in the areas of: numbers of stakeholders/staff involved and thus levels of burden, recruitment strategies, data collection strategies, enhancing engagement, and timelines.

Discussion

Lessons learned throughout this multi-jurisdictional work included: the need for continuity in ethics, regular team meetings, enhancing levels of engagement with stakeholders, the need for structural support and recognizing differences in data sharing across provinces.

Conclusion

The differences noted across CPCSSN networks in our frailty study highlight the challenges of multi-jurisdictional work across provinces and the need for consistent and collaborative healthcare planning efforts.

Insights into internet-delivered cognitive behavioural therapy for public safety personnel: Exploration of client experiences during and after treatment

Canadian public safety personnel (PSP) experience high rates of mental health problems and barriers to receiving care. Internet-delivered cognitive behavioural therapy (ICBT) may help reduce barriers to care; however, there is no literature involving qualitative analyses of client feedback to describe PSP experiences with ICBT. Identifying these experiences is important because it can inform future use of ICBT with this group that has unique needs. The current study was designed to explore how clients (N = 82) experienced ICBT that had been tailored to meet their needs; specifically, the study assessed their perceptions of program impacts, what clients found helpful, and client suggestions for improvements. The ICBT course included five core lessons, client stories, and nine initial additional resources, as well as flexible frequencies (optional, once weekly, or twice weekly) and durations (8 to 16 weeks) of therapist support. A qualitative reliability thematic analysis was used to analyze client communications and feedback. Responses to a Treatment Satisfaction Questionnaire administered at eight weeks post-enrollment were available for 57 clients. Client emails with therapists were also examined among all clients, including an additional 25 clients who did not complete the Treatment Satisfaction Questionnaire. Themes identified in the qualitative analyses were related to: reported impacts and hindering events, helpful and challenging course skills and content, helpful aspects of the course, and areas for improvement. Clients who completed the Treatment Satisfaction Questionnaire and those who did not reported beneficial impacts from the program, with the most commonly endorsed themes being skill development and normalizing mental health issues. Hindering events were experienced by both groups and included timeline challenges, technology challenges, and negative effects. Comments from both groups suggested that clients had more success than challenges when practicing the skills. Thought challenging was the skill most frequently identified as helpful. Clients described many aspects of the program as helpful with the most frequently endorsed themes being the course format and content, the flexible nature of the course, access to additional materials and case stories, and therapist assistance. Clients also provided suggestions for improving the course (e.g., case stories, additional resources, timelines audio and videos). Overall, client communications suggest that ICBT is accepted and perceived as beneficial among PSP. These results informed rapid improvements to the ICBT program tailored for PSP and may inform others seeking to provide digital mental health services to PSP.

Learning health systems in primary care: a systematic scoping review

BACKGROUND

Learning health systems have been gaining traction over the past decade. The purpose of this study was to understand the spread of learning health systems in primary care, including where they have been implemented, how they are operating, and potential challenges and solutions.

METHODS

We completed a scoping review by systematically searching OVID Medline®, Embase®, IEEE Xplore®, and reviewing specific journals from 2007 to 2020. We also completed a Google search to identify gray literature.

RESULTS

We reviewed 1924 articles through our database search and 51 articles from other sources, from which we identified 21 unique learning health systems based on 62 data sources. Only one of these learning health systems was implemented exclusively in a primary care setting, where all others were integrated health systems or networks that also included other care settings. Eighteen of the 21 were in the United States. Examples of how these learning health systems were being used included real-time clinical surveillance, quality improvement initiatives, pragmatic trials at the point of care, and decision support. Many challenges and potential solutions were identified regarding data, sustainability, promoting a learning culture, prioritization processes, involvement of community, and balancing quality improvement versus research.

CONCLUSIONS

We identified 21 learning health systems, which all appear at an early stage of development, and only one was primary care only. We summarized and provided examples of integrated health systems and data networks that can be considered early models in the growing global movement to advance learning health systems in primary care.

Trialists perspectives on sustaining, spreading, and scaling-up of quality improvement interventions

BACKGROUND

Quality improvement (QI) evaluations rarely consider how a successful intervention can be sustained long term, nor how to spread or scale to other locations. A survey of authors of randomized trials of diabetes QI interventions included in an ongoing systematic review found that 78% of trials reported improved quality of care, but 40% of these trials were not sustained. This study explores why and how the effective interventions were sustained, spread, or scaled.

METHODS

A qualitative approach was used, focusing on case examples. Diabetes QI program trial authors were purposefully sampled and recruited for telephone interviews. Authors were eligible if they had completed the author survey, agreed to follow-up, and had a completed a diabetes QI trial they deemed "effective." Snowball sampling was used if the participant identified someone who could provide a different perspective on the same trial. Interviews were transcribed verbatim. Inductive thematic analysis was conducted to identify barriers and facilitators to sustainability, spread, and/or scale of the QI program, using case examples to show trajectories across projects and people.

RESULTS

Eleven of 44 eligible trialists participated in an interview. Four reported that the intervention was "sustained" and nine were "spread," however, interviews highlighted that these terms were interpreted differently over time and between participants. Participant stories highlighted the varied trajectories of how projects evolved and how some research careers adapted to increase impact. Three interacting themes, termed the "3C's," helped explain the variation in sustainability, spread, and scale: (i) understanding the concepts of implementation, sustainability, sustainment, spread, and scale; (ii) having the appropriate competencies; and (iii) the need for individual, organizational, and system capacity.

CONCLUSIONS

Challenges in defining sustainability, spread and scale make it difficult to fully understand impact. However, it is clear that from the beginning of intervention design, trialists need to understand the concepts and have the competency and capacity to plan for feasible and sustainable interventions that have potential to be sustained, spread and/or scaled if found to be effective.

Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions

PURPOSE OF REVIEW

The past few decades have seen significant technologic innovation for the treatment and diagnosis of cardiovascular diseases. The subsequent growing complexity of modern medicine, however, is causing fundamental challenges in our healthcare system primarily in the spheres of patient involvement, data generation, and timely clinical implementation. The Institute of Medicine advocated for a learning health system (LHS) in which knowledge generation and patient care are inherently symbiotic. The purpose of this paper is to review how the advances in technology and big data have been used to further patient care and data generation and what future steps will need to occur to develop a LHS in cardiovascular disease.

RECENT FINDINGS

Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback. Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.

Learning health systems using data to drive healthcare improvement and impact: a systematic review

Background

The transition to electronic health records offers the potential for big data to drive the next frontier in healthcare improvement. Yet there are multiple barriers to harnessing the power of data. The Learning Health System (LHS) has emerged as a model to overcome these barriers, yet there remains limited evidence of impact on delivery or outcomes of healthcare.

Objective

To gather evidence on the effects of LHS data hubs or aligned models that use data to deliver healthcare improvement and impact. Any reported impact on the process, delivery or outcomes of healthcare was captured.

Methods

Systematic review from CINAHL, EMBASE, MEDLINE, Medline in-process and Web of Science PubMed databases, using learning health system, data hub, data-driven, ehealth, informatics, collaborations, partnerships, and translation terms. English-language, peer-reviewed literature published between January 2014 and Sept 2019 was captured, supplemented by a grey literature search. Eligibility criteria included studies of LHS data hubs that reported research translation leading to health impact.

Results

Overall, 1076 titles were identified, with 43 eligible studies, across 23 LHS environments. Most LHS environments were in the United States (n = 18) with others in Canada, UK, Sweden and Australia/NZ. Five (21.7%) produced medium-high level of evidence, which were peer-reviewed publications.

Conclusions

LHS environments are producing impact across multiple continents and settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06215-8.

Developing Real-world Evidence-Ready Datasets: Time for Clinician Engagement

PURPOSE OF REVIEW

Real-world data (RWD) applications in healthcare that support learning health systems and pragmatic clinical trials are gaining momentum, largely due to legislation supporting real-world evidence (RWE) for drug approvals. Clinical notes are thought to be the cornerstone of RWD applications, particularly for conditions with limited effective treatments, extrapolation of treatments from other conditions, or heterogenous disease biology and clinical phenotypes.

RECENT FINDINGS

Here, we discuss current issues in applying RWD captured at the point-of-care and provide a framework for clinicians to engage in RWD collection. To achieve clinically meaningful results, RWD must be reliably captured using consistent terminology in the description of our patients. RWD complements traditional clinical trials and research by informing the generalizability of results, generating new hypotheses, and creating a large data network for scientific discovery. Effective clinician engagement in the development of RWD applications is necessary for continued progress in the field.

A practical model for research with learning health systems: Building and implementing effective complex case management

For researchers to contribute meaningfully to the creation of learning health systems, practical tools are required to operationalize existing conceptual frameworks. We describe a model currently in use by the University of Wisconsin Health Innovation Program (HIP). The HIP model consolidates and enhances existing learning health system frameworks by defining specific steps needed to create sustainable change based on research conducted within the health system. As an example of the model's application, we describe its use to improve patient identification for the University of Wisconsin health system's case management program. Our case study shows the importance of culture, infrastructure, and strong leadership support in realizing a learning health systems research project and creating sustainable change within the health system. By articulating the foundational elements and steps to conduct research with learning health systems, our model supports researchers in achieving the challenge of moving learning health systems from concept to action.

A qualitative study of design stakeholders' views of developing and implementing a registry-based learning health system

BACKGROUND

New opportunities to record, collate, and analyze routine patient data have prompted optimism about the potential of learning health systems. However, real-life examples of such systems remain rare and few have been exposed to study. We aimed to examine the views of design stakeholders on designing and implementing a US-based registry-enabled care and learning system for cystic fibrosis (RCLS-CF).

METHODS

We conducted a two-phase qualitative study with stakeholders involved in designing, implementing, and using the RCLS-CF. First, we conducted semi-structured interviews with 19 program personnels involved in design and delivery of the program. We then undertook 11 follow-up interviews. Analysis of interviews was based on the constant comparative method, supported by NVivo software.

RESULTS

The organizing principle for the RCLS-CF was a shift to more partnership-based relationships between patients and clinicians, founded in values of co-production, and facilitated by technology-enabled data sharing. Participants proposed that, for the system to be successful, the data it collects must be both clinically useful and meaningful to patients and clinicians. They suggested that the prerequisites included a technological infrastructure capable of supporting data entry and joint decision-making in an accessible way, and a set of social conditions, including willingness from patients and clinicians alike to work together in new ways that build on the expertise of both parties. Follow-up interviews highlighted some of the obstacles, including technical challenges and practical constraints on refiguring relationships between clinicians and patients.

CONCLUSIONS

The values and vision underlying the RCLS-CF were shared and clearly and consistently articulated by design stakeholders. The challenges to realization were often not at the level of principle, but were both practical and social in character. Lessons from this study may be useful to other systems looking to harness the power of "big data" registries, including patient-reported data, for care, research, and quality improvement.

A framework for value-creating learning health systems

Background

Interest in value-based healthcare, generally defined as providing better care at lower cost, has grown worldwide, and learning health systems (LHSs) have been proposed as a key strategy for improving value in healthcare. LHSs are emerging around the world and aim to leverage advancements in science, technology and practice to improve health system performance at lower cost. However, there remains much uncertainty around the implementation of LHSs and the distinctive features of these systems. This paper presents a conceptual framework that has been developed in Canada to support the implementation of value-creating LHSs.

Methods

The framework was developed by an interdisciplinary team at the Institut national d’excellence en santé et en services sociaux (INESSS). It was informed by a scoping review of the scientific and grey literature on LHSs, regular team discussions over a 14-month period, and consultations with Canadian and international experts.

Results

The framework describes four elements that characterise LHSs, namely (1) core values, (2) pillars and accelerators, (3) processes and (4) outcomes. LHSs embody certain core values, including an emphasis on participatory leadership, inclusiveness, scientific rigour and person-centredness. In addition, values such as equity and solidarity should also guide LHSs and are particularly relevant in countries like Canada. LHS pillars are the infrastructure and resources supporting the LHS, whereas accelerators are those specific structures that enable more rapid learning and improvement. For LHSs to create value, such infrastructures must not only exist within the ecosystem but also be connected and aligned with the LHSs’ strategic goals. These pillars support the execution, routinisation and acceleration of learning cycles, which are the fundamental processes of LHSs. The main outcome sought by executing learning cycles is the creation of value, which we define as the striking of a more optimal balance of impacts on patient and provider experience, population health and health system costs.

Conclusions

Our framework illustrates how the distinctive structures, processes and outcomes of LHSs tie together with the aim of optimising health system performance and delivering greater value in health systems.

Electronic supplementary material

The online version of this article (10.1186/s12961-019-0477-3) contains supplementary material, which is available to authorized users.

Decision Support to Enhance Automated Laboratory Testing by Leveraging Analytical Capabilities

To achieve effective laboratory automation, analytical capabilities must be developed to support data analysis. This allows for effective development and deployment of decision support strategies within the automated laboratory. Practically, these take the form of dashboards, static and real time; workflow processes, such as autoverification; reflex protocols; and testing cascades, which reduce errors of omission and commission. This requires data from the LIS and middleware that enable sophisticated laboratory automation lines. This article addresses the historical, current, and future state of laboratory analytics using examples and offering a framework to organize thinking around analytical capabilities.

Simulation as an ethical imperative and epistemic responsibility for the implementation of medical guidelines in health care

Guidelines orient best practices in medicine, yet, in health care, many real world constraints limit their optimal realization. Since guideline implementation problems are not systematically anticipated, they will be discovered only post facto, in a learning curve period, while the already implemented guideline is tweaked, debugged and adapted. This learning process comes with costs to human health and quality of life. Despite such predictable hazard, the study and modeling of medical guideline implementation is still seldom pursued. In this article we argue that to systematically identify, predict and prevent medical guideline implementation errors is both an epistemic responsibility and an ethical imperative in health care, in order to properly provide beneficence, minimize or avoid harm, show respect for persons, and administer justice. Furthermore, we suggest that implementation knowledge is best achieved technically by providing simulation modeling studies to anticipate the realization of medical guidelines, in multiple contexts, with system and scenario analysis, in its alignment with the emerging field of implementation science and in recognition of learning health systems. It follows from both claims that it is an ethical imperative and an epistemic responsibility to simulate medical guidelines in context to minimize (avoidable) harm in health care, before guideline implementation.

Future Directions for Pain Management: Lessons from the Institute of Medicine Pain Report and the National Pain Strategy

According to the Institute of Medicine Relieving Pain in America Report and the soon to be released National Pain Strategy, pain affects over 100 million Americans and costs our country in over $500 billion per year. We have a greater appreciation for the complex nature of pain and that it can develop into a disease in itself. As such, we need more efforts on prevention of chronic pain and for interdisciplinary approaches. For precision pain medicine to be successful, we need to link learning health systems with pain biomarkers (eg, genomics, proteomics, patient reported outcomes, brain markers) and its treatment.

An Integrated Model for Patient Care and Clinical Trials (IMPACT) to support clinical research visit scheduling workflow for future learning health systems

We describe a clinical research visit scheduling system that can potentially coordinate clinical research visits with patient care visits and increase efficiency at clinical sites where clinical and research activities occur simultaneously. Participatory Design methods were applied to support requirements engineering and to create this software called Integrated Model for Patient Care and Clinical Trials (IMPACT). Using a multi-user constraint satisfaction and resource optimization algorithm, IMPACT automatically synthesizes temporal availability of various research resources and recommends the optimal dates and times for pending research visits. We conducted scenario-based evaluations with 10 clinical research coordinators (CRCs) from diverse clinical research settings to assess the usefulness, feasibility, and user acceptance of IMPACT. We obtained qualitative feedback using semi-structured interviews with the CRCs. Most CRCs acknowledged the usefulness of IMPACT features. Support for collaboration within research teams and interoperability with electronic health records and clinical trial management systems were highly requested features. Overall, IMPACT received satisfactory user acceptance and proves to be potentially useful for a variety of clinical research settings. Our future work includes comparing the effectiveness of IMPACT with that of existing scheduling solutions on the market and conducting field tests to formally assess user adoption.