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Factors influencing application of behavioural science evidence by public health decision-makers and practitioners, and implications for practice.

ABSTRACT: The National Institute of Health and Care Excellence (NICE) in the UK recommends behavioural science evidence underpins public health improvement services. In practice, level of implementation varies. This study is the first to explore factors affecting use of behaviour-specific evidence by public health decision-makers and practitioners for design and delivery of health improvement services. Twenty semi-structured interviews were conducted, along with a review of the commissioning cycle with public health decision-makers and practitioners across a range of health improvement fields (e.g. weight management). Interviews were informed and analysed using the Theoretical Domains Framework (TDF). Limited comprehension of behaviour change, challenges identifying specific behaviour change strategies and translating research into practice were prevalent. Local authority processes encouraged uptake of evidence to justify solutions as opposed to evidence-driven decision-making. Some decision-makers perceived research evidence may stifle innovation and overwhelm practitioners. Potential facilitators of research use included: ensuring uptake and implementation of evidence is compulsory within commissioning and its potential to show value for money. A strong belief in local evidence and achieving outcomes were identified as barriers to research evidence uptake. Social and environmental challenges included cultural, political, and workload pressures and journal article accessibility. Embedding behavioural science systematically into public health practice requires changes throughout the public health system; from priorities set by national public health leaders to the way in which relevant evidence is disseminated. Framing factors affecting use of behavioural science evidence using the TDF is helpful for identifying the range of interventions and support needed to affect change.

SUBMITTER: Curtis K

PROVIDER: S-EPMC6140308 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Factors influencing application of behavioural science evidence by public health decision-makers and practitioners, and implications for practice.

Curtis Kristina K Fulton Emmie E Brown Katherine K

Preventive medicine reports 20180901

The National Institute of Health and Care Excellence (NICE) in the UK recommends behavioural science evidence underpins public health improvement services. In practice, level of implementation varies. This study is the first to explore factors affecting use of <i>behaviour</i>-specific evidence by public health decision-makers and practitioners for design and delivery of health improvement services. Twenty semi-structured interviews were conducted, along with a review of the commissioning cycle ...[more]

PMID: 30233998

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Project description:BackgroundSystems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context.MethodsSemi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development.ResultsThe 'co-production' aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening.ConclusionThese findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings.

Project description:BackgroundProviding care for mental health problems concerns General Practitioners (GPs), Private Psychiatrists (PrPs) and Public Psychiatrists (PuPs). As patient distribution and patterns of practice among these professionals are not well known, a survey was planned prior to a re-organisation of mental health services in an area close to ParisMethodsAll GPs (n = 492), PrPs (n = 82) and PuPs (n = 78) in the South-Yvelines area in France were informed of the implementation of a local mental health program. Practitioners interested in taking part were invited to include prospectively all patients with mental health problem they saw over an 8-day period and to complete a 6-month retrospective questionnaire on their mental health practice. 180 GPs (36.6%), 45 PrPs (54.9%) and 63 PuPs (84.0%) responded.ResultsGPs and PrPs were very similar but very different from PuPs for the proportion of patients with anxious or depressive disorders (70% v. 65% v. 38%, p < .001), psychotic disorders (5% v. 7% v. 30%, p < .001), previous psychiatric hospitalization (22% v. 26 v. 61%, p < .001) and receiving disability allowance (16% v. 18% v. 52%, p < .001). GPs had fewer patients with long-standing psychiatric disorders than PrPs and PuPs (52%, 64% v. 63%, p < .001). Time-lapse between consultations was longest for GPs, intermediate for PuPs and shortest for PrPs (36 days v. 26 v. 18, p < .001). Access to care had been delayed longer for Psychiatrists (PrPs, PuPs) than for GPs (61% v. 53% v. 25%, p < .001). GPs and PuPs frequently felt a need for collaboration for their patients, PrPs rarely (42% v. 61%. v. 10%, p < .001). Satisfaction with mental health practice was low for all categories of physicians (42.6% encountered difficulties hospitalizing patients and 61.4% had patients they would prefer not to cater for). GPs more often reported unsatisfactory relationships with mental health professionals than did PrPs and PuPs (54% v. 15% v. 8%, p < .001).ConclusionGP patients with mental health problems are very similar to patients of private psychiatrists; there is a lack of the collaboration felt to be necessary, because of psychiatrists' workload, and because GPs have specific needs in this respect. The "Yvelines-Sud Mental Health Network" has been created to enhance collaboration.

Project description:BackgroundBehavioural sciences have been shown to support the development of more effective interventions aimed at promoting healthy lifestyles. However, the operationalization of this knowledge seems to be sub-optimal in public health. Effective knowledge transfer strategies are thus needed to optimize the use of knowledge from behavioural sciences in this field. To this end, the present study examined public health practitioners' perceptions and use of theories and frameworks from behavioural sciences to design health promotion interventions.MethodsThis study adopted an exploratory qualitative design. Semi-structured interviews were conducted among 27 public health practitioners from across Canada to explore current intervention development processes, the extent to which they integrate theory and framework from behavioural sciences, and their perceptions regarding the use of this knowledge to inform intervention design. Practitioners from the public sector or non-profit/private organizations who were involved in the development of interventions aimed at promoting physical activity, healthy eating, or other healthy lifestyle habits (e.g., not smoking) were eligible to participate.ResultsPublic health practitioners generally agreed that behaviour change is an important goal of public health interventions. On the other hand, behavioural science theories and frameworks did not appear to be fully integrated in the design of public health interventions. The main reasons were (1) a perceived lack of fit with current professional roles and tasks; (2) a greater reliance on experiential-produced knowledge rather than academic knowledge (mainly for tailoring interventions to local setting characteristics); (3) the presence of a fragmented knowledge base; (4) the belief that theories and frameworks require too much time and resources to be operationalized; and 4) the belief that using behavioural sciences might undermine partnership building.ConclusionsThis study provided valuable insights that may inform knowledge transfer strategies that could be optimally designed to support the integration of behavioural sciences theories and frameworks into public health practices.

Project description:CONTEXT:Relationships between researchers and decision-makers have demonstrated positive potential to influence research, policy and practice. Over time, interest in better understanding the relationships between the two parties has grown as demonstrated by a plethora of studies globally. However, what remains elusive is the evolution of these vital relationships and what can be learned from them with respect to advancing evidence-informed decision-making. We therefore explored the nuances around the initiation, maintenance and dissolution of academic-government relationships. METHODS:We conducted in-depth interviews with 52 faculty at one school of public health and 24 government decision-makers at city, state, federal and global levels. Interviews were transcribed and coded deductively and inductively using Atlas.Ti. Responses across codes and respondents were extracted into an Excel matrix and compared in order to identify key themes. FINDINGS:Eight key drivers to engagement were identified, namely (1) decision-maker research needs, (2) learning, (3) access to resources, (4) student opportunities, (5) capacity strengthening, (6) strategic positioning, (7) institutional conditionalities, and (8) funder conditionalities. There were several elements that enabled initiation of relationships, including the role of faculty members in the decision-making process, individual attributes and reputation, institutional reputation, social capital, and the role of funders. Maintenance of partnerships was dependent on factors such as synergistic collaboration (i.e. both benefit), mutual trust, contractual issues and funding. Dissolution of relationships resulted from champions changing/leaving positions, engagement in transactional relationships, or limited mutual trust and respect. CONCLUSIONS:As universities and government agencies establish relationships and utilise opportunities to share ideas, envision change together, and leverage their collaborations to use evidence to inform decision-making, a new modus operandi becomes possible. Embracing the individual, institutional, networked and systems dynamics of relationships can lead to new practices, alternate approaches and transformative change. Government agencies, schools of public health and higher education institutions more broadly, should pay deliberate attention to identifying and managing the various drivers, enablers and disablers for relationship initiation and resilience in order to promote more evidence-informed decision-making.

Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

Project description:BackgroundNew artificial intelligence (AI) tools are being developed at a high speed. However, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely because of the high implementation complexity of AI, legacy IT infrastructure, and unclear business cases, thus complicating AI adoption. Research has recently started to identify the factors influencing AI readiness of organizations.ObjectiveThis study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also assessed the status quo regarding the dissemination of AI tools in hospitals. We focused on IT decision makers, a seldom studied but highly relevant group.MethodsWe created a web-based survey based on recent AI readiness and implementation literature. Participants were identified through a publicly accessible database and contacted via email or invitational leaflets sent by mail, in some cases accompanied by a telephonic prenotification. The survey responses were analyzed using descriptive statistics.ResultsWe contacted 609 possible participants, and our database recorded 40 completed surveys. Most participants agreed or rather agreed with the statement that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). Participants were asked whether their hospitals used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered "yes." Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lack of resources (staff, knowledge, and financial). Relevant possible opportunities for using AI were increase in efficiency owing to time-saving effects, competitive advantages, and increase in quality of care. Most AI tools in use or in planning have been developed with external partners.ConclusionsFew tools have been implemented in routine care, and many hospitals do not use or plan to use AI in the future. This can likely be explained by missing or unclear business cases or the need for a modern IT infrastructure to integrate AI tools in a usable manner. These shortcomings complicate decision-making and resource attribution. As most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers should assess their hospitals' readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and readiness factors to determine whether improvements can be made over time. This monitoring is especially important with regard to government-supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to further explore the reasons for slow AI.

Dataset Information

Factors influencing application of behavioural science evidence by public health decision-makers and practitioners, and implications for practice.

Publications

Factors influencing application of behavioural science evidence by public health decision-makers and practitioners, and implications for practice.

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