Learning Healthcare System Research Articles

Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree.

Improving peri- and postnatal facility-based care in low-resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost-effective, simple, evidence-based interventions. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high-resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS. Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co-develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low-cost hardware and state-of-the-art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree. Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement. Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID-19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects. Human-centred digital development of a QI system for newborn care has demonstrated the potential of a sustainable learning healthcare system to improve newborn care and outcomes in LRS. Pilot implementation evaluation is ongoing in three of the four aforementioned hospitals (two in Zimbabwe and one in Malawi) and a larger scale clinical cost effectiveness trial is planned.

Better together: Integrating biomedical informatics and healthcare IT operations to create a learning health system during the COVID-19 pandemic.

The growing availability of multi‐scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare “data age.” This new era is defined by the explosive growth in bio‐molecular, clinical, and population‐level data that can be readily accessed by researchers, clinicians, and decision‐makers, and utilized for systems‐level approaches to hypothesis generation and testing as well as operational decision‐making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems. While the history of the academic field of BMI includes active engagement in the delivery of operational HIT platforms, in many contemporary settings these efforts have grown distinct. Recent experiences during the COVID‐19 pandemic have demonstrated greater coordination of BMI and HIT activities that have allowed organizations to respond to pandemic‐related changes more effectively, with demonstrable and positive impact as a result. In this position paper, we discuss the challenges and opportunities associated with driving alignment between BMI and HIT, as viewed from the perspective of a learning healthcare system. In doing so, we hope to illustrate the benefits of coordination between BMI and HIT in terms of the quality, safety, and outcomes of care provided to patients and populations, demonstrating that these two groups can be “better together.”

The Future of Critical Care: Optimizing Technologies and a Learning Healthcare System to Potentiate a More Humanistic Approach to Critical Care.

While technological innovations are the invariable crux of speculation about the future of critical care, they cannot replace the clinician at the bedside. This article summarizes the work of the Society of Critical Care Medicine–appointed multiprofessional task for the Future of Critical Care. The Task Force notes that critical care practice will be transformed by novel technologies, integration of artificial intelligence decision support algorithms, and advances in seamless data operationalization across diverse healthcare systems and geographic regions and within federated datasets. Yet, new technologies will be relevant and meaningful only if they improve the very human endeavor of caring for someone who is critically ill.

Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital.

Objective A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation. Methods Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation. Results A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed. Conclusion Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.

Toward an ontology of collaborative learning healthcare systems.

ObjectiveTo establish a basis for a domain ontology ‐ a formal, explicit specification of a shared conceptualization ‐ of collaborative learning healthcare systems (CLHSs) in order to facilitate measurement, explanation, and improvement.MethodsWe adapted the “Methontology” approach to begin building an ontology of CLHSs. We specified the purpose of an ontology, acquired domain knowledge via literature review, conceptualized a common framework of CLHSs using a grounded approach, refined these concepts based on expert panel input, and illustrated concept application via four cases.ResultsThe set of concepts identified as important to include in an ontology includes goals, values, structure, actors, environment, and products. To establish this set of concepts, we gathered input from content experts in two ways. First, expert panel methods were used to elicit feedback on these concepts and to test the elicitation of terms for the vocabulary of the Values concept. Second, from these discussions we developed a mapping exercise to test the intuitiveness of the concepts, requesting that network leaders from four CLHSs complete a mapping exercise to associate characteristics of their networks with the high‐level concepts, building the vocabulary for each concept in a grounded fashion. We also solicited feedback from these participants on the experience of completing the mapping exercise, finding that the exercise is acceptable and could aid in CLHS development and collaboration. Respondents identified opportunities to improve the operational definitions of each concept to ensure that corresponding vocabularies are distinct and non‐overlapping.DiscussionOur results provide a foundation for developing a formal, explicit shared conceptualization of CLHSs. Once developed, such a tool can be useful for measurement, explanation, and improvement. Further work, including alignment to a top‐level ontology, expanding the vocabulary, and defining relations between vocabulary is required to formally build out an ontology for these uses.

Promoting patient and nurse safety: testing a behavioural health intervention in a learning healthcare system: results of the DEMEANOR pragmatic, cluster, cross-over trial

BackgroundBased on clinical staff safety within a learning healthcare system, the purpose of this study was to test an innovative model of care for addressing disruptive behaviour in hospitalised patients...

ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines

BackgroundIdentification of clinically significant genetic alterations involved in human disease has been dramatically accelerated by developments in next-generation sequencing technologies. However, the infrastructure and accessible comprehensive curation tools necessary for analyzing an individual patient genome and interpreting genetic variants to inform healthcare management have been lacking.ResultsHere we present the ClinGen Variant Curation Interface (VCI), a global open-source variant classification platform for supporting the application of evidence criteria and classification of variants based on the ACMG/AMP variant classification guidelines. The VCI is among a suite of tools developed by the NIH-funded Clinical Genome Resource (ClinGen) Consortium and supports an FDA-recognized human variant curation process. Essential to this is the ability to enable collaboration and peer review across ClinGen Expert Panels supporting users in comprehensively identifying, annotating, and sharing relevant evidence while making variant pathogenicity assertions. To facilitate evidence-based improvements in human variant classification, the VCI is publicly available to the genomics community. Navigation workflows support users providing guidance to comprehensively apply the ACMG/AMP evidence criteria and document provenance for asserting variant classifications.ConclusionsThe VCI offers a central platform for clinical variant classification that fills a gap in the learning healthcare system, facilitates widespread adoption of standards for clinical curation, and is available at https://curation.clinicalgenome.org

Celebrating 10 years of the Journal of Comparative Effectiveness Research.

Celebrating 10 years of the Journal of Comparative Effectiveness Research.

Pediatric Epilepsy Learning Healthcare System Quality of Life (PELHS-QOL-2): A novel health-related quality of life prompt for children with epilepsy.

Pediatric epilepsy is often associated with diminished health-related quality of life (HRQOL). Our aim was to establish the validity of the Pediatric Epilepsy Learning Healthcare System Quality of Life (PELHS-QOL-2) questions, a novel two-item HRQOL prompt for children with epilepsy, primarily for use in clinical care. We performed a multicenter cross-sectional study to validate the PELHS-QOL-2. Construct validity was established through bivariate comparisons with four comparator measures and known drivers of quality of life in children with epilepsy, as well as by creating an a priori multivariable model to predict the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55). Validity generalization was established through bivariate comparisons with demographic and clinical information. Content validity and clinical utility were established by assessing how well the PELHS-QOL-2 met eight design criteria for an HRQOL prompt established by a multistakeholder group of experts. The final participant sample included 154 English-speaking caregivers of children with epilepsy (mean age = 9.7years, range = .5-18, 49% female, 70% White). The PELHS-QOL-2 correlated with the four comparator instruments (ρ = .44-.56), was significantly associated with several known drivers of quality of life in children with epilepsy (p<.05), and predicted QOLCE-55scores in the multivariate model (adjusted R2 = .54). The PELHS-QOL-2 item was not associated with the age, sex, and ethnicity of the children nor with the setting and location of data collection, although PELHS-QOL-Medications was significantly associated with race (worse for White race). Following both quantitative and qualitative analysis, the PELHS-QOL-2met seven of eight design criteria. The PELHS-QOL-2 is a valid HRQOL prompt and is well suited for use in clinical care as a mechanism to routinely initiate conversations with caregivers about quality of life in children with epilepsy. The association of PELHS-QOL-Medications with race merits further study.

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.

517. Subcutaneous Sarilumab for the Treatment of Hospitalized patients with Moderate to Severe COVID19 Disease: A Pragmatic, Embedded, Multi-Center Randomized Clinical Trial

BackgroundThe aim of this pragmatic, embedded adaptive trial was to measure the effectiveness of subcutaneous sarilumab in addition to an evolving standard of care for clinical management of inpatients with moderate to severe COVID-19 disease (NCT04359901). The study is also a real-world demonstration of the realization of a prospective learning healthcare system. MethodsTwo-arm, randomized, open-label controlled 5-center trial comparing standard care alone to standard care (SOC), which evolved over time, with addition of subcutaneous sarilumab (200 mg or 400 mg anti-IL6R) among hospitalized patients with moderate to severe COVID-19 not requiring mechanical ventilation. The primary outcome was 14-day incidence of intubation or death. The trial used a randomized play-the-winner design and was fully embedded within the EHR system, including the adaptive randomization process. ResultsAmong 417 patients screened, 162 were eligible based on chart review, 53 consented, and 50 were evaluated for the primary endpoint of intubation or death ( >30% of eligible patients enrolled) (Figure 1). After the second interim review, the unblinded Data Monitoring Committee recommended that the study be stopped due to concern for safety: a high probability that rates of intubation or death were higher with addition of sarilumab to SOC (92.6%), and a very low probability (3.4%) that sarilumab would be found to be superior.Figure 1. Key Study Milestones, Outcomes, and Adaptations ConclusionThis randomized trial of patients hospitalized with COVID-19 and requiring supplemental oxygen but not mechanical ventilation found no evidence of benefit from subcutaneous sarilumab in addition to an evolving standard-of-care. The numbers of patients and events were too low to allow independent conclusions to be drawn, but this study contributes valuable information about the role of subcutaneous IL-6 inhibition in the treatment of patients hospitalized with COVID-19. The major innovation of this trial was the advancement of embedded, point-of-care clinical trials for FDA-approved drugs; this represents a realization of the learning healthcare system. Methods developed and piloted during the conduct of this trial can be used in future investigations to speed the advancement of clinical science. Disclosures Nishant Shah, MD, General Electric (Shareholder)Pfizer, Inc. (Research Grant or Support) Karen Visnaw, RN, Liquidia (Shareholder) Paul Monach, MD,PhD, Celgene (Consultant)ChemoCentryx (Consultant)Kiniksa (Advisor or Review Panel member)

Patient Experiences, Trust, and Preferences for Health Data Sharing.

Scholars have examined patients' attitudes toward secondary use of routinely collected clinical data for research and quality improvement. Evidence suggests that trust in health care organizations and physicians is critical. Less is known about experiences that shape trust and how they influence data sharing preferences. To explore learning health care system (LHS) ethics, democratic deliberations were hosted from June 2017 to May 2018. A total of 217 patients with cancer participated in facilitated group discussion. Transcripts were coded independently. Finalized codes were organized into themes using interpretive description and thematic analysis. Two previous analyses reported on patient preferences for consent and data use; this final analysis focuses on the influence of personal lived experiences of the health care system, including interactions with providers and insurers, on trust and preferences for data sharing. Qualitative analysis identified four domains of patients' lived experiences raised in the context of the policy discussions: (1) the quality of care received, (2) the impact of health care costs, (3) the transparency and communication displayed by a provider or an insurer to the patient, and (4) the extent to which care coordination was hindered or facilitated by the interchange between a provider and an insurer. Patients discussed their trust in health care decision makers and their opinions about LHS data sharing. Additional resources, infrastructure, regulations, and practice innovations are needed to improve patients' experiences with and trust in the health care system. Those who seek to build LHSs may also need to consider improvement in other aspects of care delivery.

Development and Expression of a High-Reliability Organization

Summary Health care organizations have increasingly adopted the principles of high-reliability organizations to improve their safety and quality. These principles must be built into the culture of the organization as well as into the systems and frameworks used to improve processes and evaluate outcomes in a learning health care system. On the basis of its experience responding to disasters — including emerging diseases, tropical storms, and the Covid-19 pandemic — Houston Methodist has established a culture of continual improvement and a learning health care system that supports this goal. In this article, Houston Methodist’s leadership reviews the principles of high-reliability health care organizations, describes the tools and systems that Houston Methodist has used to adopt these principles, and explains how these efforts aided in operations during the pandemic.

Effectiveness of a multidisciplinary rehabilitation program in real-world patients with chronic back pain: A pilot cohort data analysis.

BACKGROUND: Randomized clinical trials (RCT) suggest a multidisciplinary approach to pain rehabilitation is superior to other active treatments in improving pain intensity, function, disability, and pain interference for patients with chronic pain, with small effect size (ds 0.20–0.36) but its effectiveness remains unknown in real-world practice.OBJECTIVE: The current study examined the effectiveness of a multidisciplinary program to a cognitive and behavioral therapy (pain-CBT) in real-world patients with chronic back pain.METHODS: Twenty-eight patients ( 57.6, 82.1% Female) completed a multidisciplinary program that included pain psychology and physical therapy. Eighteen patients ( 58.9, 77.8% Female) completed a CBT-alone program. Using a learning healthcare system, the Pain Catastrophizing Scale, 0–10 Numerical Pain Rating Scale, and Patient-Reported Outcomes Measurement Information System measures were administered before and after the programs.RESULTS: We found significant improvement in mobility and pain behavior only after a multidisciplinary program (’s 0.031; 0.69 and 0.55). We also found significant improvement in pain interference, fatigue, depression, anxiety, social role satisfaction, and pain catastrophizing after pain-CBT or multidisciplinary programs (’s 0.037; s 0.29–0.73). Pain ratings were not significantly changed by either program (’s 0.207).CONCLUSIONS: The effect of a multidisciplinary rehabilitation program observed in RCT would be generalizable to real-world practice.

"Closing the chasm" - guidelines bridge the gap from evidence to implementation.

"Closing the chasm" - guidelines bridge the gap from evidence to implementation.

Health management via telemedicine: Learning from the COVID-19 experience.

At the onset of the COVID-19 (coronavirus disease 2019) pandemic, telemedicine was rapidly implemented to protect patients and healthcare providers from infection. It is unlikely that care delivery will fully return to the pre-COVID form. Telemedicine offers many opportunities to improve care efficiency, accessibility, and patient outcomes, but many challenges exist related to technology interoperability, the digital divide, and usability. We propose that telemedicine evolve to support continuity of care throughout the patient journey, including multidisciplinary care teams and the seamless integration of data into the clinical workflow to support a learning healthcare system. Importantly, evidence is needed to support this paradigm shift in care delivery to ensure the quality and efficacy of care delivered via telemedicine. Here, we highlight gaps and opportunities that need to be addressed by the biomedical informatics community to move forward with safe and effective healthcare delivery via telemedicine.

Learning From What We Do, and Doing What We Learn: A Learning Health Care System in Action.

Different models of learning health systems are emerging. At Vanderbilt University Medical Center, the Learning Health Care System (LHS) Platform was established with the goal of creating generalizable knowledge. This differentiates the LHS Platform from other efforts that have adopted a quality improvement paradigm. By supporting pragmatic trials at the intersection of research, operations, and clinical care, the LHS Platform was designed to yield evidence for advancing content and processes of care through carefully designed, rigorous study. The LHS Platform provides the necessary infrastructure and governance to leverage translational, transdisciplinary team science to inform clinical and operational decision making across the health system. The process transforms a clinical or operational question into a research question amenable to a pragmatic trial. Scientific, technical, procedural, and human infrastructure is maintained for the design and execution of individual LHS projects. This includes experienced pragmatic trialists, project management, data science inclusive of biostatistics and clinical informatics, and regulatory support. Careful attention is paid to stakeholder engagement, including health care providers and the community. Capturing lessons from each new study, the LHS Platform continues to mature with plans to integrate implementation science and to complement clinical and process outcomes with cost and value considerations. The Vanderbilt University Medical Center LHS Platform is now a pillar of the health care system and leads the evolving culture of learning from what we do and doing what we learn.

Importance of nationally coordinated data collection and quality improvement networks

There is an urgent need for a learning health care system that continuously and systematically draws from data to inform improvements in care. Hospital-based quality monitoring is unable to effectively track specialized care, each domain of care being performed by a handful of clinicians in each center, whose definition of standard of care comes from national professional associations and guidelines. In a myriad of domains that make up specialized care in Canada, data-driven quality improvement is hindered by a lack of nationally coordinated, practice-specific, dynamically alterable data collection. To address this deficiency, we suggest three immediately applicable, synergistic recommendations to improve real-time, patient and health system relevant, data-informed healthcare quality improvement and research. Namely, (1) add a small number of open “placeholder” fields to the Canadian Institutes of Health Information hospital discharge database, (2) create nationally coordinated strategic patient-level data collection and research for specific clinical problems and conditions of urgent health importance, and (3) support the creation and maintenance of perpetual national quality improvement and research collectives, enabling nationally-coordinated platforms for continuous standardized data collection, quality improvement and research. Examples of these initiatives within the domains of critical care medicine and thoracic surgery are presented. Continued adoption of a national standardized data-collection and data-driven quality improvement and research networks across a range of clinical disciplines would help deliver national transformation in both quality and efficiency of Canadian healthcare.

What can a learning healthcare system teach us about improving outcomes?

This review describes the learning healthcare system paradigm, recent examples, and future directions. Patients, clinicians, and health systems frequently encounter decisions between available treatments, technologies, and healthcare delivery methods with little or no evidence about the comparative effectiveness and safety of the available options. Learning healthcare systems endeavor to recognize such knowledge gaps, integrate comparative effectiveness research - including clinical trials - into clinical care to address the knowledge gaps, and seamlessly implement the results into practice to improve care and patient outcomes. Recent studies comparing the effectiveness of diagnostic tests and treatments, using information technology to identify patients likely to experience an outcome or benefit from an intervention, and evaluating models of healthcare delivery have demonstrated how a learning healthcare system approach can reduce arbitrary variation in care, decrease cost, and improve patient outcomes. Learning healthcare systems have the potential to answer questions of importance to patients, clinicians, and health system leaders, improve efficiency of healthcare delivery, and improve patient outcomes. Achieving this goal will require realignment of the culture around clinical care, institutional and federal investment, expanded stakeholder engagement, tailored ethical and regulatory guidance, and methodologic advances in information technology and biostatistics.

Adapting coordinated specialty care in the post-COVID-19 era: study protocol for an integrative mixed-methods study

BackgroundCoordinated Specialty Care (CSC) programs provide evidence-based services for young people with a recent onset of a psychotic disorder. OnTrackNY is a nationally recognized model of CSC treatment in New York state. In 2019, OnTrackNY was awarded a hub within the Early Psychosis Intervention Network (EPINET) to advance its learning health care system (LHS). The OnTrackNY network is comprised of 23 CSC teams across New York state. OnTrack Central, an intermediary organization, provides training and implementation support to OnTrackNY teams. OnTrack Central coordinates a centralized data collection protocol for quality improvement and evaluation of program fidelity and a mechanism to support practice based-research. OnTrackNY sites’ breadth coupled with OnTrack Central oversight provides an opportunity to examine the impacts of the COVID-19 crisis in New York State, and supplementary funding was awarded to the OnTrackNY EPINET hub in 2021 for that purpose.MethodsThis project will examine the implications of modifications to service delivery within the OnTrackNY LHS during and after the COVID-19 crisis. We will use the Framework for Reporting Adaptations and Modification-Enhanced (FRAME) to classify systematically, code, and analyze modifications to CSC services and ascertain their impact. We will utilize integrative mixed methods. Qualitative interviews with multi-level stakeholders (program participants, families, providers, team leaders, agency leaders, trainers (OnTrack Central), and decision-makers at the state and local levels) will be used to understand the process of making decisions, information about modifications to CSC services, and their impact. Analysis of OnTrackNY program data will facilitate examining trends in team staffing and functioning, and participant service utilization and outcomes. Study findings will be summarized in a CSC Model Adaptation Guide, which will identify modifications as fidelity consistent or not, and their impact on service utilization and care outcomes.DiscussionA CSC Model Adaptation Guide will inform CSC programs, and the state and local mental health authorities to which they are accountable, regarding modifications to CSC services and the impact of these changes on care process, and participant service utilization and outcomes. The guide will also inform the development of tailored technical assistance that CSC programs may need within OnTrackNY, the EPINET network, and CSC programs nationally.Trial registrationNCT04021719, July 16th, 2019.