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The influence of complexity: a bibliometric analysis of complexity science in healthcare.

ABSTRACT: OBJECTIVES:To analyse trends in the academic literature applying complexity science to healthcare, focusing specifically on bibliometric characteristics and indicators of influence. DESIGN:This study reports a bibliometric analysis via a systematic search of the academic literature applying complexity science to healthcare. METHOD:A search of four academic databases was performed on 19 April 2018. Article details were downloaded and screened against inclusion criteria (peer-reviewed journal articles applying complexity science to healthcare). Publication and content data were then collected from included articles, with analysis focusing on trends over time in the types and topics of articles, and where they are published. We also analysed the influence of this body of work through citation and network analyses. RESULTS:Articles on complexity science in healthcare were published in 268 journals, though a much smaller subset was responsible for a substantial proportion of this literature. USA contributed the largest number of articles, followed by the UK, Canada and Australia. Over time, the number of empirical and review articles increased, relative to non-empirical contributions. However, in general, non-empirical literature was more influential, with a series of introductory conceptual papers being the most influential based on both overall citations and their use as index references within a citation network. The most common topics of focus were health systems and organisations generally, and education, with recent uptake in research, policy, and change and improvement. CONCLUSIONS:This study identified changes in the types of articles on complexity science in healthcare published over time, and their content. There was evidence to suggest a shift from conceptual work to the application of concrete improvement strategies and increasingly in-depth examination of complex healthcare systems. We also identified variation in the influence of this literature at article level, and to a lesser extent by topic of focus.

SUBMITTER: Churruca K

PROVIDER: S-EPMC6475366 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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The influence of complexity: a bibliometric analysis of complexity science in healthcare.

Churruca Kate K Pomare Chiara C Ellis Louise A LA Ellis Louise A LA Long Janet C JC Braithwaite Jeffrey J

BMJ open 20190323 3

<h4>Objectives</h4>To analyse trends in the academic literature applying complexity science to healthcare, focusing specifically on bibliometric characteristics and indicators of influence.<h4>Design</h4>This study reports a bibliometric analysis via a systematic search of the academic literature applying complexity science to healthcare.<h4>Method</h4>A search of four academic databases was performed on 19 April 2018. Article details were downloaded and screened against inclusion criteria (peer ...[more]

PMID: 30904877

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Project description:BackgroundLogic models are commonly used in evaluations to represent the causal processes through which interventions produce outcomes, yet significant debate is currently taking place over whether they can describe complex interventions which adapt to context. This paper assesses the logic models used in healthcare research from a complexity perspective. A typology of existing logic models is proposed, as well as a formal methodology for deriving more flexible and dynamic logic models.AnalysisVarious logic model types were tested as part of an evaluation of a complex Patient Experience Toolkit (PET) intervention, developed and implemented through action research across six hospital wards/departments in the English NHS. Three dominant types of logic model were identified, each with certain strengths but ultimately unable to accurately capture the dynamics of PET. Hence, a fourth logic model type was developed to express how success hinges on the adaption of PET to its delivery settings. Aspects of the Promoting Action on Research Implementation in Health Services (PARIHS) model were incorporated into a traditional logic model structure to create a dynamic "type 4" logic model that can accommodate complex interventions taking on a different form in different settings.ConclusionLogic models can be used to model complex interventions that adapt to context but more flexible and dynamic models are required. An implication of this is that how logic models are used in healthcare research may have to change. Using logic models to forge consensus among stakeholders and/or provide precise guidance across different settings will be inappropriate in the case of complex interventions that adapt to context. Instead, logic models for complex interventions may be targeted at facilitators to enable them to prospectively assess the settings they will be working in and to develop context-sensitive facilitation strategies. Researchers should be clear as to why they are using a logic model and experiment with different models to ensure they have the correct type.

Project description:ObjectiveMulticriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain.MethodsA bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords.ResultsA total of 410 publications were identified with an average yearly growth rate of 32% (1999-2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Université de Montréal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research.ConclusionThis study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).

Project description:INTRODUCTION:Translating scientific advances in genomic medicine into evidence-based clinical practice is challenging. Studying the natural translation of genomics into 'early-adopting' health system sectors is essential. We will (a) examine 29 health systems (Australian and Melbourne Genomics Health Alliance flagships) integrating genomics into practice and (b) combine this learning to co-design and test an evidence-based generalisable toolkit for translating genomics into healthcare. METHODS AND ANALYSIS:Twenty-nine flagships integrating genomics into clinical settings are studied as complex adaptive systems to understand emergent and self-organising behaviours among inter-related actors and processes. The Effectiveness-Implementation Hybrid approach is applied to gather information on the delivery and potential for real-world implementation. Stages '1' and '2a' (representing hybrid model 1) are the focus of this protocol. The Translation Science to Population Impact (TSci Impact) framework is used to study policy decisions and service provision, and the Theoretical Domains Framework (TDF) is used to understand individual level behavioural change; both frameworks are applied across stages 1 and 2a. Stage 1 synthesises interview data from 32 participants involved in developing the genomics clinical practice systems and approaches across five 'demonstration-phase' (early adopter) flagships. In stage 2a, stakeholders are providing quantitative and qualitative data on process mapping, clinical audits, uptake and sustainability (TSci Impact), and psychosocial and environmental determinants of change (TDF). Findings will be synthesised before codesigning an intervention toolkit to facilitate implementation of genomic testing. Study methods to simultaneously test the comparative effectiveness of genomic testing and the implementation toolkit (stage 2b), and the refined implementation toolkit while simply observing the genomics intervention (stage 3) are summarised. ETHICS AND DISSEMINATION:Ethical approval has been granted. The results will be disseminated in academic forums and used to refine interventions to translate genomics evidence into healthcare. Non-traditional academic dissemination methods (eg, change in guidelines or government policy) will also be employed.

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The influence of complexity: a bibliometric analysis of complexity science in healthcare.

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The influence of complexity: a bibliometric analysis of complexity science in healthcare.

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