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Determinants of successful clinical networks: the conceptual framework and study protocol.

ABSTRACT: BACKGROUND: Clinical networks are increasingly being viewed as an important strategy for increasing evidence-based practice and improving models of care, but success is variable and characteristics of networks with high impact are uncertain. This study takes advantage of the variability in the functioning and outcomes of networks supported by the Australian New South Wales (NSW) Agency for Clinical Innovation's non-mandatory model of clinical networks to investigate the factors that contribute to the success of clinical networks. METHODS/DESIGN: The objective of this retrospective study is to examine the association between external support, organisational and program factors, and indicators of success among 19 clinical networks over a three-year period (2006-2008). The outcomes (health impact, system impact, programs implemented, engagement, user perception, and financial leverage) and explanatory factors will be collected using a web-based survey, interviews, and record review. An independent expert panel will provide judgements about the impact or extent of each network's initiatives on health and system impacts. The ratings of the expert panel will be the outcome used in multivariable analyses. Following the rating of network success, a qualitative study will be conducted to provide a more in-depth examination of the most successful networks. DISCUSSION: This is the first study to combine quantitative and qualitative methods to examine the factors that contribute to the success of clinical networks and, more generally, is the largest study of clinical networks undertaken. The adaptation of expert panel methods to rate the impacts of networks is the methodological innovation of this study. The proposed project will identify the conditions that should be established or encouraged by agencies developing clinical networks and will be of immediate use in forming strategies and programs to maximise the effectiveness of such networks.

SUBMITTER: Haines M

PROVIDER: S-EPMC3328243 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Determinants of successful clinical networks: the conceptual framework and study protocol.

Haines Mary M Brown Bernadette B Craig Jonathan J D'Este Catherine C Elliott Elizabeth E Klineberg Emily E McInnes Elizabeth E Middleton Sandy S Paul Christine C Redman Sally S Yano Elizabeth M EM

Implementation science : IS 20120313

<h4>Background</h4>Clinical networks are increasingly being viewed as an important strategy for increasing evidence-based practice and improving models of care, but success is variable and characteristics of networks with high impact are uncertain. This study takes advantage of the variability in the functioning and outcomes of networks supported by the Australian New South Wales (NSW) Agency for Clinical Innovation's non-mandatory model of clinical networks to investigate the factors that contr ...[more]

PMID: 22414246

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Project description:BackgroundClinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence-enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice.ObjectiveThe purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence-enabled clinical decision support system evaluation framework.MethodsAn artificial intelligence-enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents.ResultsThe Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension.ConclusionsUser acceptance is the central dimension of artificial intelligence-enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

Project description:Background Homeless people have a higher risk of COVID-19 infection, linked to several social, economic and environmental determinants, frequent comorbidities, obstacles to exercising their constitutional social and health rights, poor medical cover, and insufficient use of the healthcare system. Data on COVID-19 vaccine uptake and its main determinants are lacking for this underserved population.Objectives To construct and test a conceptual framework to model structural social determinants of COVID-19 vaccine uptake among underserved homeless populations, and to test this model to identify the determinants of COVID-19 vaccine uptake on the homeless population living in two metropolitan areas in France.Methods We implemented a multicenter cross-sectional survey from 15/11/2021 to 22/12/2021 in homeless adults in the city of Marseille and in the greater Paris area. Persons sheltered in migrant worker hostels or in emergency social shelters, members of the COVID HOMELESS cohort study in Marseille, and Travelers living in traditional housing were all eligible. A standardized face-to-face questionnaire was administered to the participants where they lived in various languages by trained interviewers. We used structural equation modeling to analyze the structural social determinants of COVID-19 vaccine uptake, the latter defined as receiving at least one dose.Results The participation rate was 64 %, accounting for 3811 participants. There were three main factors associated with greater vaccine uptake: i) opportunity, which included having a personal general practitioner (β = 0.05, p < 0.05), healthcare cover (β = 0.05, p < 0.05), and somebody to accompany the participant for medical appointments (β = -0.04, p < 0.05); ii) motivation, which included attitudes towards vaccination (β = 0.55, p < 0.05), press- and poster-based information (β = 0.03, p < 0.05), and vaccination history (β = 0.03, p < 0.05); iii) type of housing (β = 0.13, p < 0.05) and housing stability (β = 0.04, p < 0.05).Conclusion Our results highlight that housing exclusion is a structural social determinant of COVID-19 vaccine uptake in homeless people in France. They also underline the role which opportunity and motivation play in improving uptake in this underserved homeless population.

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Determinants of successful clinical networks: the conceptual framework and study protocol.

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Determinants of successful clinical networks: the conceptual framework and study protocol.

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