Correction: The Science of Learning Health Systems: Scoping Review of Empirical Research.
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ABSTRACT:
SUBMITTER: Ellis LA
PROVIDER: S-EPMC9389390 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
Correction: The Science of Learning Health Systems: Scoping Review of Empirical Research.
Ellis Louise A LA Sarkies Mitchell M Churruca Kate K Dammery Genevieve G Meulenbroeks Isabelle I Smith Carolynn L CL Pomare Chiara C Mahmoud Zeyad Z Zurynski Yvonne Y Braithwaite Jeffrey J
JMIR medical informatics 20220804 8
[This corrects the article DOI: 10.2196/34907.]. ...[more]
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Project description:BackgroundThe development and adoption of a learning health system (LHS) has been proposed as a means to address key challenges facing current and future health care systems. The first review of the LHS literature was conducted 5 years ago, identifying only a small number of published papers that had empirically examined the implementation or testing of an LHS. It is timely to look more closely at the published empirical research and to ask the question, Where are we now? 5 years on from that early LHS review.ObjectiveThis study performed a scoping review of empirical research within the LHS domain. Taking an "implementation science" lens, the review aims to map out the empirical research that has been conducted to date, identify limitations, and identify future directions for the field.MethodsTwo academic databases (PubMed and Scopus) were searched using the terms "learning health* system*" for papers published between January 1, 2016, to January 31, 2021, that had an explicit empirical focus on LHSs. Study information was extracted relevant to the review objective, including each study's publication details; primary concern or focus; context; design; data type; implementation framework, model, or theory used; and implementation determinants or outcomes examined.ResultsA total of 76 studies were included in this review. Over two-thirds of the studies were concerned with implementing a particular program, system, or platform (53/76, 69.7%) designed to contribute to achieving an LHS. Most of these studies focused on a particular clinical context or patient population (37/53, 69.8%), with far fewer studies focusing on whole hospital systems (4/53, 7.5%) or on other broad health care systems encompassing multiple facilities (12/53, 22.6%). Over two-thirds of the program-specific studies utilized quantitative methods (37/53, 69.8%), with a smaller number utilizing qualitative methods (10/53, 18.9%) or mixed-methods designs (6/53, 11.3%). The remaining 23 studies were classified into 1 of 3 key areas: ethics, policies, and governance (10/76, 13.2%); stakeholder perspectives of LHSs (5/76, 6.6%); or LHS-specific research strategies and tools (8/76, 10.5%). Overall, relatively few studies were identified that incorporated an implementation science framework.ConclusionsAlthough there has been considerable growth in empirical applications of LHSs within the past 5 years, paralleling the recent emergence of LHS-specific research strategies and tools, there are few high-quality studies. Comprehensive reporting of implementation and evaluation efforts is an important step to moving the LHS field forward. In particular, the routine use of implementation determinant and outcome frameworks will improve the assessment and reporting of barriers, enablers, and implementation outcomes in this field and will enable comparison and identification of trends across studies.
| S-EPMC8908194 | biostudies-literature
Project description:Mental health is influenced by multiple complex and interacting genetic, psychological, social, and environmental factors. As such, developing state-of-the-art mental health knowledge requires collaboration across academic disciplines, including environmental science. To assess the current contribution of environmental science to this field, a scoping review of the literature on environmental influences on mental health (including conditions of cognitive development and decline) was conducted. The review protocol was developed in consultation with experts working across mental health and environmental science. The scoping review included 202 English-language papers, published between 2010 and 2020 (prior to the COVID-19 pandemic), on environmental themes that had not already been the subject of recent systematic reviews; 26 reviews on climate change, flooding, air pollution, and urban green space were additionally considered. Studies largely focused on populations in the USA, China, or Europe and involved limited environmental science input. Environmental science research methods are primarily focused on quantitative approaches utilising secondary datasets or field data. Mental health measurement was dominated by the use of self-report psychometric scales. Measures of environmental states or exposures were often lacking in specificity (e.g., limited to the presence or absence of an environmental state). Based on the scoping review findings and our synthesis of the recent reviews, a research agenda for environmental science's future contribution to mental health scholarship is set out. This includes recommendations to expand the geographical scope and broaden the representation of different environmental science areas, improve measurement of environmental exposure, prioritise experimental and longitudinal research designs, and giving greater consideration to variation between and within communities and the mediating pathways by which environment influences mental health. There is also considerable opportunity to increase interdisciplinarity within the field via the integration of conceptual models, the inclusion of mixed methods and qualitative approaches, as well as further consideration of the socio-political context and the environmental states that can help support good mental health. The findings were used to propose a conceptual model to parse contributions and connections between environmental science and mental health to inform future studies.
| S-EPMC10094550 | biostudies-literature
Project description:BackgroundLearning 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.MethodsWe 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.ResultsWe 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.ConclusionsWe 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.
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Project description:ObjectiveThis systematic review aims to analyze current capabilities, challenges, and impact of self-directed mobile health (mHealth) research applications such as those based on the ResearchKit platform.Materials and methodsA systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. English publications were included if: 1) mobile applications were used in the context of large-scale collection of data for biomedical research, and not as medical or behavioral intervention of any kind, and 2) all activities related to participating in research and data collection methods were executed remotely without any face-to-face interaction between researchers and study participants.ResultsThirty-six unique ResearchKit apps were identified. The majority of the apps were used to conduct observational studies on general citizens and generate large datasets for secondary research. Nearly half of the apps were focused on chronic conditions in adults.DiscussionThe ability to generate large biomedical datasets on diverse populations that can be broadly shared and re-used was identified as a promising feature of mHealth research apps. Common challenges were low participation retention, uncertainty regarding how use patterns influence data quality, need for data validation, and privacy concerns.ConclusionResearchKit and other mHealth-based studies are well positioned to enhance development and validation of novel digital biomarkers as well as generate new biomedical knowledge through retrospective studies. However, in order to capitalize on these benefits, mHealth research studies must strive to improve retention rates, implement rigorous data validation strategies, and address emerging privacy and security challenges.
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Project description:Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
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Project description:Use of systems science can improve the dissemination and implementation (D&I) process. However, little is known about use of systems science in nutrition D&I research. The purpose of this article is to synthesize the ways in which systems science methodology is applied in nutrition D&I research. Scoping review methodology involved searching 6 academic databases for full-text, peer-reviewed, English articles published between 1970 and 2020 that employed systems science within nutrition D&I research. Data extraction included intervention type, population, study aim, methods, theoretical approach, outcomes, and results. Descriptive statistics and qualitative thematic analysis followed. Thirty-four retained articles qualitatively identified benefits (successful planning and organization of complex interventions) and challenges (limited resources, trainings, and lack of knowledge) to utilizing systems science in nutrition D&I research. Future research should work toward building knowledge capacity among nutrition practitioners by increasing available trainings and resources to enhance the utilization of systems science in nutrition D&I research.
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Project description:Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.
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Project description:BackgroundTo date there is no established consensus of assessment criteria for evaluating research ethics review.MethodsWe conducted a scoping review of empirical research assessing ethics review processes in order to identify common elements assessed, research foci, and research gaps to aid in the development of assessment criteria. Electronic searches of Ovid Medline, PsychInfo, and the Cochrane DSR, ACP Journal Club, DARE, CCTR, CMR, HTA, and NHSEED, were conducted. After de-duplication, 4234 titles and abstracts were reviewed. Altogether 4036 articles were excluded following screening of titles, abstracts and full text. A total of 198 articles included for final data extraction.ResultsFew studies originated from outside North America and Europe. No study reported using an underlying theory or framework of quality/effectiveness to guide study design or analyses. We did not identify any studies that had involved a controlled trial--randomised or otherwise--of ethics review procedures or processes. Studies varied substantially with respect to outcomes assessed, although tended to focus on structure and timeliness of ethics review.DiscussionOur findings indicate a lack of consensus on appropriate assessment criteria, exemplified by the varied study outcomes identified, but also a fragmented body of research. To date research has been largely quantitative, with little attention given to stakeholder experiences, and is largely cross sectional. A lack of longitudinal research to date precludes analyses of change or assessment of quality improvement in ethics review.
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Project description:Sport management scholars have called for the application of broader research approaches, including critical social science. Such approaches help uncover the less-desirable aspects of sport and, therefore, offer a basis for positive change. While there have been advancements in the use of innovative research approaches over time, there remains little understanding of how these calls have been addressed. The purpose of this scoping review is to examine trends, gaps, and the use of critical social science and associated approaches in sport management scholarship. Two hundred sixteen relevant articles were identified through a database search (i.e., five platforms), complemented with a manual search of 419 journals. Results indicate that an increase in critical research published in sport management journals was evident following 2005. Findings suggest that there remains space for increased publication of critical social science work in sport management journals and for researchers to better articulate their research approaches in scholarly outputs.
| S-EPMC8831802 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has presented many multi-faceted challenges to the maintenance of service quality and safety, highlighting the need for resilient and responsive healthcare systems more than ever before. This review examined empirical investigations of Resilient Health Care (RHC) in response to the COVID-19 pandemic with the aim to: identify key areas of research; synthesise findings on capacities that develop RHC across system levels (micro, meso, macro); and identify reported adverse consequences of the effort of maintaining system performance on system agents (healthcare workers, patients).MethodsThree academic databases were searched (Medline, EMBASE, Scopus) from 1st January 2020 to 30th August 2022 using keywords pertaining to: systems resilience and related concepts; healthcare and healthcare settings; and COVID-19. Capacities that developed and enhanced systems resilience were synthesised using a hybrid inductive-deductive thematic analysis.ResultsFifty publications were included in this review. Consistent with previous research, studies from high-income countries and the use of qualitative methods within the context of hospitals, dominated the included studies. However, promising developments have been made, with an emergence of studies conducted at the macro-system level, including the development of quantitative tools and indicator-based modelling approaches, and the increased involvement of low- and middle-income countries in research (LMIC). Concordant with previous research, eight key resilience capacities were identified that can support, develop or enhance resilient performance, namely: structure, alignment, coordination, learning, involvement, risk awareness, leadership, and communication. The need for healthcare workers to constantly learn and make adaptations, however, had potentially adverse physical and emotional consequences for healthcare workers, in addition to adverse effects on routine patient care.ConclusionsThis review identified an upsurge in new empirical studies on health system resilience associated with COVID-19. The pandemic provided a unique opportunity to examine RHC in practice, and uncovered emerging new evidence on RHC theory and system factors that contribute to resilient performance at micro, meso and macro levels. These findings will enable leaders and other stakeholders to strengthen health system resilience when responding to future challenges and unexpected events.
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