Effectiveness of knowledge brokering and recommendation dissemination for influencing healthcare resource allocation decisions: A cluster randomised controlled implementation trial
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SUBMITTER: Sarkies M
PROVIDER: S-EPMC8570499 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Standard guidance for allocating healthcare resources based on cost-effectiveness recommends using different decision rules for independent and mutually exclusive alternatives, although there is some confusion around the definition of "mutually exclusive." This paper reviews the definitions used in the literature and shows that interactions (i.e., non-additive effects, whereby the effect of giving 2 interventions simultaneously does not equal the sum of their individual effects) are the defining feature of mutually exclusive alternatives: treatments cannot be considered independent if the costs and/or benefits of one treatment are affected by the other treatment. The paper then identifies and categorizes the situations in which interventions are likely to have non-additive effects, including interventions targeting the same goal or clinical event, or life-saving interventions given to overlapping populations. We demonstrate that making separate decisions on interventions that have non-additive effects can prevent us from maximizing health gained from the healthcare budget. In contrast, treating combinations of independent options as though they were "mutually exclusive" makes the analysis more complicated but does not affect the conclusions. Although interactions are considered by the World Health Organization, other decision makers, such as the National Institute for Health and Care Excellence (NICE), currently make independent decisions on treatments likely to have non-additive effects. We propose a framework by which interactions could be considered when selecting, prioritizing, and appraising healthcare technologies to ensure efficient, evidence-based decision making.
| S-EPMC5949981 | biostudies-literature
Project description:BackgroundWhole disease models (WDM) are large-scale, system-level models which can evaluate multiple decision questions across an entire care pathway. Whilst this type of model can offer several advantages as a platform for undertaking economic analyses, the availability and quality of existing WDMs is unknown.ObjectivesThis systematic review aimed to identify existing WDMs to explore which disease areas they cover, to critically assess the quality of these models and provide recommendations for future research.MethodsAn electronic search was performed on multiple databases (MEDLINE, EMBASE, the NHS Economic Evaluation Database and the Health Technology Assessment database) on 23rd July 2023. Two independent reviewers selected studies for inclusion. Study quality was assessed using the National Institute for Health and Care Excellence (NICE) appraisal checklist for economic evaluations. Model characteristics were descriptively summarised.ResultsForty-four WDMs were identified, of which thirty-two were developed after 2010. The main disease areas covered by existing WDMs are heart disease, cancer, acquired immune deficiency syndrome and metabolic disease. The quality of included WDMs is generally low. Common limitations included failure to consider the harms and costs of adverse events (AEs) of interventions, lack of probabilistic sensitivity analysis (PSA) and poor reporting.ConclusionsThere has been an increase in the number of WDMs since 2010. However, their quality is generally low which means they may require significant modification before they could be re-used, such as modelling AEs of interventions and incorporation of PSA. Sufficient details of the WDMs need to be reported to allow future reuse/adaptation.
| S-EPMC10501624 | biostudies-literature
Project description:BackgroundKnowledge brokering is a knowledge translation strategy used in healthcare settings to facilitate the implementation of evidence into practice. How healthcare providers perceive and respond to various knowledge translation approaches is not well understood. This qualitative study used the Theoretical Domains Framework to examine healthcare providers' experiences with receiving one of two knowledge translation strategies: a remote knowledge broker (rKB); or monthly emails, for encouraging delivery of mood management interventions to patients enrolled in a smoking cessation program.MethodsSemi-structured interviews were conducted with 21 healthcare providers recruited from primary care teams. We used stratified purposeful sampling to recruit participants who were allocated to receive either the rKB, or a monthly email-based knowledge translation strategy as part of a cluster randomized controlled trial. Interviews were structured around domains of the Theoretical Domains Framework (TDF) to explore determinants influencing practice change. Data were coded into relevant domains.ResultsBoth knowledge translation strategies were considered helpful prompts to remind participants to deliver mood interventions to patients presenting depressive symptoms. Neither strategy appeared to have influenced the health care providers on the domains we probed. The domains pertaining to knowledge and professional identity were perceived as facilitators to implementation, while domains related to beliefs about consequences, emotion, and environmental context acted as barriers and/or facilitators to healthcare providers implementing mood management interventions.ConclusionBoth strategies served as reminders and reinforced providers' knowledge regarding the connection between smoking and depressed mood. The TDF can help researchers better understand the influence of specific knowledge translation strategies on healthcare provider behavior change, as well as potential barriers and facilitators to implementation of evidence-informed interventions. Environmental context should be considered to address challenges and facilitate the movement of knowledge into clinical practice.
| S-EPMC10557557 | biostudies-literature
Project description:ObjectiveTo examine nonprofit hospitals' financial and spending allocations when the private sector payment rate is higher than the Medicare's payment rate.Data sourcesHospital financial data for 2014-2018 from Center for Medicare and Medicaid Services Hospital Cost Reports, hospital characteristics from the American Hospital Association (AHA) Annual Survey.Study designHospital and year level fixed effects regressions modeling each hospital's (1) operating net income per discharge equivalent (DE); (2) administrative cost per DE; (3) patient care cost per DE; (4) registered nurse per bed; charity care cost per DE; and (5) provision of unprofitable services as a function of the private sector to Medicare payment ratio (PMR).Data collection/extraction methodsHospital/year-level data from hospital cost reports merged with AHA data. Samples included general short-term hospitals with nonprofit ownership, excluding critical access hospitals.Principal findingsThe final sample included a total of 8862 hospital-year observations, with a mean PMR of 1.62. Nonprofit hospitals having a 0.1 higher PMR were associated with $257 (95% CI: $181-$334) increase in operating net income per DE; $66 (95% CI: $32-$99) increase in administrative cost per DE; $170 (95% CI: $120-$220) increase in patient care cost per DE; and $18 (95% CI: $10-$25) increase in charity care cost per DE. We found hospitals hired 0.86 (95% CI: -0.08 to 1.81) more registered nurses per 100 beds, but no evidence on providing more beds for unprofitable services, such as obstetric care, burn care, alcohol/drug abuse treatment, or psychiatric care.ConclusionsHigher private sector prices led primarily to greater surplus and administrative cost for nonprofit hospitals and smaller increases in spending on services that will directly benefit patients.
| S-EPMC8928020 | biostudies-literature
Project description:Cost-effectiveness analysis, routinely used in health care to inform funding decisions, can be extended to consider impact on health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates socioeconomic differences in model parameters to capture how an intervention would affect both overall population health and differences in health between population groups. In DCEA, uncertainty analysis can consider the decision uncertainty around on both impacts (i.e., the probability that an intervention will increase overall health and the probability that it will reduce inequality). Using an illustrative example assessing smoking cessation interventions (2 active interventions and a "no-intervention" arm), we demonstrate how the uncertainty analysis could be conducted in DCEA to inform policy recommendations. We perform value of information (VOI) analysis and analysis of covariance (ANCOVA) to identify what additional evidence would add most value to the level of confidence in the DCEA results. The analyses were conducted for both national and local authority-level decisions to explore whether the conclusions about decision uncertainty based on the national-level estimates could inform local policy. For the comparisons between active interventions and "no intervention," there was no uncertainty that providing the smoking cessation intervention would increase overall health but increase inequality. However, there was uncertainty in the direction of both impacts when comparing between the 2 active interventions. VOI and ANCOVA show that uncertainty in socioeconomic differences in intervention effectiveness and uptake contributes most to the uncertainty in the DCEA results. This suggests potential value of collecting additional evidence on intervention-related inequalities for this evaluation. We also found different levels of decision uncertainty between settings, implying that different types and levels of additional evidence are required for decisions in different localities.
| S-EPMC8295967 | biostudies-literature
Project description:BackgroundA knowledge brokering (KB) intervention was implemented in Burkina Faso. By creating partnerships with health system actors in one district, the broker was expected to assess their knowledge needs, survey the literature to provide the most recent research evidence, produce various knowledge translation tools, and support them in using research to improve their actions. The purpose of this study was to analyze the key factors that influenced the KB project and to make recommendations for future initiatives.MethodsThe qualitative design involved a single case study in which the KB intervention implementation was evaluated retrospectively. Data came from interviews with the intervention team (n = 4) and with various actors involved in the intervention (n = 16). Data from formative evaluations conducted during the KB implementation and observation data from a two-month field mission were also used. Two conceptual frameworks were combined to guide the analysis: the Consolidated Framework for Implementation Research (Damschroder et al., 2009) and the Ecological Framework (Durlak & DuPre, 2008).ResultsVarious KB activities were conducted during the first two years of implementation at the local level. The project came to an early end following vain efforts to relocate the intervention at the central level in order to further influence the policy process. Certain shortcomings in the implementation team negatively influenced the implementation: inadequate leadership, no shared vision regarding the reorientation of the intervention, challenges related to the KB role, and lack of frank communications internally. Other impediments to the intervention's deployment included local actors' lack of decision-making authority, the unavailability of resources and of organizational incentives for involvement in the KB intervention, and contextual challenges in accessing the central level. However, the KB strategy presented several strengths: collaborative development, support provided to local partners by the broker, and training opportunities and support provided to the broker.ConclusionsMore attention must be paid to intervention planning, partners' engagement, human, financial and technical resources availability, continuous development of skills and of communications within the KB team, and periodic assessment of potential obstacles related to the complexity of the system within which the intervention has been implemented. Using implementation science frameworks when developing KB strategies in the West African context should be promoted.
| S-EPMC6660220 | biostudies-literature
Project description:BackgroundThis is the third in a series of papers reporting a program of Sustainability in Health care by Allocating Resources Effectively (SHARE) in a local healthcare setting. Leaders in a large Australian health service planned to establish an organisation-wide, systematic, integrated, evidence-based approach to disinvestment. In order to introduce new systems and processes for disinvestment into existing decision-making infrastructure, we aimed to understand where, how and by whom resource allocation decisions were made, implemented and evaluated. We also sought the knowledge and experience of staff regarding previous disinvestment activities.MethodsStructured interviews, workshops and document analysis were used to collect information from multiple sources in an environmental scan of decision-making systems and processes. Findings were synthesised using a theoretical framework.ResultsSixty-eight respondents participated in interviews and workshops. Eight components in the process of resource allocation were identified: Governance, Administration, Stakeholder engagement, Resources, Decision-making, Implementation, Evaluation and, where appropriate, Reinvestment of savings. Elements of structure and practice for each component are described and a new framework was developed to capture the relationships between them. A range of decision-makers, decision-making settings, type and scope of decisions, criteria used, and strengths, weaknesses, barriers and enablers are outlined. The term 'disinvestment' was not used in health service decision-making. Previous projects that involved removal, reduction or restriction of current practices were driven by quality and safety issues, evidence-based practice or a need to find resource savings and not by initiatives where the primary aim was to disinvest. Measuring resource savings is difficult, in some situations impossible. Savings are often only theoretical as resources released may be utilised immediately by patients waiting for beds, clinic appointments or surgery. Decision-making systems and processes for resource allocation are more complex than assumed in previous studies.ConclusionThere is a wide range of decision-makers, settings, scope and type of decisions, and criteria used for allocating resources within a single institution. To our knowledge, this is the first paper to report this level of detail and to introduce eight components of the resource allocation process identified within a local health service.
| S-EPMC5423420 | biostudies-literature
Project description:ObjectiveTo develop a resource allocation model to optimize health departments' Centers for Disease Control and Prevention (CDC)-funded HIV prevention budgets to prevent the most new cases of HIV infection and to evaluate the model's implementation in 4 health departments.Design, settings, and participantsWe developed a linear programming model combined with a Bernoulli process model that allocated a fixed budget among HIV prevention interventions and risk subpopulations to maximize the number of new infections prevented. The model, which required epidemiologic, behavioral, budgetary, and programmatic data, was implemented in health departments in Philadelphia, Chicago, Alabama, and Nebraska.Main outcome measuresThe optimal allocation of funds, the site-specific cost per case of HIV infection prevented rankings by intervention, and the expected number of HIV cases prevented.ResultsThe model suggested allocating funds to HIV testing and continuum-of-care interventions in all 4 health departments. The most cost-effective intervention for all sites was HIV testing in nonclinical settings for men who have sex with men, and the least cost-effective interventions were behavioral interventions for HIV-negative persons. The pilot sites required 3 to 4 months of technical assistance to develop data inputs and generate and interpret the results. Although the sites found the model easy to use in providing quantitative evidence for allocating HIV prevention resources, they criticized the exclusion of structural interventions and the use of the model to allocate only CDC funds.ConclusionsResource allocation models have the potential to improve the allocation of limited HIV prevention resources and can be used as a decision-making guide for state and local health departments. Using such models may require substantial staff time and technical assistance. These model results emphasize the allocation of CDC funds toward testing and continuum-of-care interventions and populations at highest risk of HIV transmission.
| S-EPMC4887404 | biostudies-literature
Project description:BACKGROUND:Knowledge brokers are increasingly used by policy agencies, yet little is known about how they engage with policy-makers and facilitate discussions with them about their research needs. This study examines knowledge brokers' behaviour in one-off interactions with policy-makers commissioning rapid reviews. It describes how knowledge brokers engage with policy-makers, build trust and gain agreement about the review's parameters. METHODS:We observed and transcribed 15 structured knowledge brokering sessions and used line-by-line analysis to derive, test and refine a coding schedule. The final coding schedule was applied to all transcripts. We assigned 35 codes to three tasks identified in the data, namely eliciting information, exploring the policy context and negotiating the content of reviews. RESULTS:The knowledge brokers we observed were skilled facilitators who built trust by their open stance, neutrality, and knowledge of research and policy contexts. Trust engendered an interplay of expertise in which review questions and scope were clarified and contextual factors evaluated. Negotiation about the content of the review focused on understanding how it would contribute to the policy process, comparing options and assessing feasibility. Key functions of knowledge brokers included eliciting and clarifying information, linking the review questions to the context and purpose, moving fluidly between policy and research perspectives, and weighing up review options against policy objectives. Four knowledge brokering roles were identified, namely diagnostic, facilitative, deliberative and interpretative. CONCLUSIONS:This study identified ways in which knowledge brokers established rapport with policy-makers who commissioned reviews, enabled disclosure of essential information and explored contextual factors that affected the review's purpose and intended use. Knowledge brokers were competent in the discourse and conventions of both policy and research and were skilled in negotiating complex policy and political environments, assisting policy-makers to evaluate options and craft a review proposal that was targeted, responsive and feasible. Mutuality, respect and an interplay of expertise were integral to the knowledge brokering process. Future research might usefully examine whether other rapid review programmes using knowledge brokers have similar results as well as the transferability of the four knowledge brokering roles to other contexts and settings.
| S-EPMC6292028 | biostudies-literature
Project description:BackgroundWe know that patient care can be improved by implementing evidence-based innovations and applying research findings linked to good practice. Successfully implementing innovations in complex organisations, such as the UK's National Health Service (NHS), is often challenging as multiple contextual dynamics mediate the process. Research studies have explored the challenges of introducing innovations into healthcare settings and have contributed to a better understanding of why potentially useful innovations are not always implemented in practice, even if backed by strong evidence. Mediating factors include health policy and health system influences, organisational factors, and individual and professional attitudes, including decision makers' perceptions of innovation evidence. There has been limited research on how different forms of evidence are accessed and utilised by organisational decision makers during innovation adoption. We also know little about how diverse healthcare professionals (clinicians, administrators) make sense of evidence and how this collective sensemaking mediates the uptake of innovations.MethodsThe study will involve nine comparative case study sites of acute care organisations grouped into three regional clusters across England. Each of the purposefully selected sites represents a variety of trust types and organisational contexts. We will use qualitative methods, in-depth interviews, observation of key meetings, and systematic analysis of relevant secondary data to understand the rationale and challenges involved in sourcing and utilising innovation evidence in the empirical setting of infection prevention and control. We will use theories of innovation adoption and sensemaking in organisations to interpret the data. The research will provide lessons for the uptake and continuous use of innovations in the English and international health systems.DiscussionUnlike most innovation studies, which involve single-level analysis, our study will explore the innovation-adoption process at multiple embedded levels: micro (individual), meso (organisational), and macro (interorganisational). By comparing and contrasting across the nine sites, each with different organisational contexts, local networks, leadership styles, and different innovations considered for adoption, the findings of the study will have wide relevance. The research will produce actionable findings responding to the political and economic need for healthcare organisations to be innovation-ready.
| S-EPMC3325880 | biostudies-literature