Pandemic promises: Interrogating espoused data‐informed decision‐making
Ontology highlight
ABSTRACT:
SUBMITTER: Breslin J
PROVIDER: S-EPMC8652806 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.
| S-EPMC7080550 | biostudies-literature
Project description:BackgroundWith opioid misuse, opioid use disorder (OUD), and opioid overdose deaths persisting at epidemic levels in the U.S., the largest implementation study in addiction research-the HEALing Communities Study (HCS)-is evaluating the impact of the Communities That Heal (CTH) intervention on reducing opioid overdose deaths in 67 disproportionately affected communities from four states (i.e., "sites"). Community-tailored dashboards are central to the CTH intervention's mandate to implement a community-engaged and data-driven process. These dashboards support a participating community's decision-making for selection and monitoring of evidence-based practices to reduce opioid overdose deaths.Methods/designA community-tailored dashboard is a web-based set of interactive data visualizations of community-specific metrics. Metrics include opioid overdose deaths and other OUD-related measures, as well as drivers of change of these outcomes in a community. Each community-tailored dashboard is a product of a co-creation process between HCS researchers and stakeholders from each community. The four research sites used a varied set of technical approaches and solutions to support the scientific design and CTH intervention implementation. Ongoing evaluation of the dashboards involves quantitative and qualitative data on key aspects posited to shape dashboard use combined with website analytics.DiscussionThe HCS presents an opportunity to advance how community-tailored dashboards can foster community-driven solutions to address the opioid epidemic. Lessons learned can be applied to inform interventions for public health concerns and issues that have disproportionate impact across communities and populations (e.g., racial/ethnic and sexual/gender minorities and other marginalized individuals).Trial registrationClinicalTrials.gov (NCT04111939).
| S-EPMC7528750 | biostudies-literature
Project description:BackgroundHealth insurance enrollment is a difficult financial decision with large health impacts. Challenges such as low health insurance literacy and lack of knowledge about choosing a plan further complicate this decision-making process. Therefore, to support consumers in their choice of a health insurance plan, it is essential to understand how individuals go about making this decision.ObjectiveThis study aims to understand the sources of information used by individuals to support their employer-provided health insurance enrollment decisions. It seeks to describe how individual descriptive factors lead to choosing a particular type of information source.MethodsAn introduction was presented on health insurance plan selection and the sources of information used to support these decisions from the 1980s to the present. Subsequently, an electronic survey of 151 full-time faculty and staff members was conducted. The survey consisted of four sections: demographics, sources of information, health insurance literacy, and technology acceptance. Descriptive statistics were used to show the demographic characteristics of the 126 eligible respondents and to study the response behaviors in the remaining survey sections. Proportion data analysis was performed using the Cochran-Armitage trend test to understand the strength of the association between our variables and the types of sources used by the respondents.ResultsIn terms of demographics, most of the respondents were women (103/126, 81.7%), represented a small household (1-2 persons; 87/126, 69%), and used their insurance 3-12 times a year (52/126, 41.3%). They assessed themselves as having moderate to high health insurance literacy and high acceptance of technology. The most selected and top-ranked sources were Official employer or state websites and Official Human Resources Virtual Benefits Counselor Alex. From our data analysis, we found that the use of official primary sources was constant across age groups and health insurance use groups. Meanwhile, the use of friends or family as a primary source slightly decreased as age and use increased.ConclusionsIn this exploratory study, we identified the main sources of health insurance information among full-time employees from a large state university and found that most of the respondents needed 2-3 sources to gather all the information that they desired. We also studied and identified the relationships between individual factors (such as age, gender, and literacy) and 2 dependent variables on the types of primary sources of information. We encountered several limitations, which will be addressed in future studies.
| S-EPMC8391737 | biostudies-literature
Project description:BackgroundPublic health professionals are increasingly expected to engage in evidence-informed decision making to inform practice and policy decisions. Evidence-informed decision making involves the use of research evidence along with expertise, existing public health resources, knowledge about community health issues, the local context and community, and the political climate. The National Collaborating Centre for Methods and Tools has identified a seven step process for evidence-informed decision making. Tools have been developed to support public health professionals as they work through each of these steps. This paper provides an overview of tools used in three Canadian public health departments involved in a study to develop capacity for evidence-informed decision making.MethodsAs part of a knowledge translation and exchange intervention, a Knowledge Broker worked with public health professionals to identify and apply tools for use with each of the steps of evidence-informed decision making. The Knowledge Broker maintained a reflective journal and interviews were conducted with a purposive sample of decision makers and public health professionals. This paper presents qualitative analysis of the perceived usefulness and usability of the tools.ResultsTools were used in the health departments to assist in: question identification and clarification; searching for the best available research evidence; assessing the research evidence for quality through critical appraisal; deciphering the 'actionable message(s)' from the research evidence; tailoring messages to the local context to ensure their relevance and suitability; deciding whether and planning how to implement research evidence in the local context; and evaluating the effectiveness of implementation efforts. Decision makers provided descriptions of how the tools were used within the health departments and made suggestions for improvement. Overall, the tools were perceived as valuable for advancing and sustaining evidence-informed decision making.ConclusionTools are available to support the process of evidence-informed decision making among public health professionals. The usability and usefulness of these tools for advancing and sustaining evidence-informed decision making are discussed, including recommendations for the tools' application in other public health settings beyond this study. Knowledge and awareness of these tools may assist other health professionals in their efforts to implement evidence-informed practice.
| S-EPMC4223550 | biostudies-literature
Project description:BackgroundCareful patient-clinician shared decision-making about dialysis initiation has been promoted, but few studies have addressed patient perspectives on the extent of information provided and how decisions to start dialysis are made.MethodsNinety-nine maintenance dialysis patients recruited from 15 outpatient dialysis centers in North Carolina completed semistructured interviews on information provision and communication about the initiation of dialysis. These data were examined with content analysis. In addition, informed decision-making (IDM) scores were created by summing patient responses (yes/no) to 10 questions about the decision-making.ResultsThe mean IDM score was 4.4 (of 10; SD = 2.0); 67% scored 5 or lower. Age at the time of decision-making (r = -0.27, P = 0.006), years of education (r = 0.24, P = 0.02) and presence of a warning about progressing to end-stage kidney disease (t = 2.9, P = 0.005) were significantly associated with IDM scores. Nearly 70% said that the risks and burdens of dialysis were not mentioned at all, and only one patient recalled that the doctor offered the option of not starting dialysis. While a majority (67%) said that they felt they had no choice about starting dialysis (because the alternative would be death) or about dialysis modality, only 21.2% said that they had felt rushed to make a decision. About one-third of the patients perceived that the decision to start dialysis and modality was already made by the doctor.ConclusionsA majority of patients felt unprepared and ill-informed about the initiation of dialysis. Improving the extent of IDM about dialysis may optimize patient preparation prior to starting treatment and their perceptions about the decision-making process.
| S-EPMC3811056 | biostudies-literature
Project description:INTRODUCTION:It is increasingly considered important that people make an autonomous and informed decision concerning colorectal cancer (CRC) screening. However, the realisation of autonomy within the concept of informed decision-making might be interpreted too narrowly. Additionally, relatively little is known about what the eligible population believes to be a 'good' screening decision. Therefore, we aimed to explore how the concepts of autonomous and informed decision-making relate to how the eligible CRC screening population makes their decision and when they believe to have made a 'good' screening decision. METHODS:We conducted 27 semi-structured interviews with the eligible CRC screening population (eighteen CRC screening participants and nine non-participants). The general topics discussed concerned how people made their CRC screening decision, how they experienced making this decision and when they considered they had made a 'good' decision. RESULTS:Most interviewees viewed a 'good' CRC screening decision as one based on both reasoning and feeling/intuition, and that is made freely. However, many CRC screening non-participants experienced a certain social pressure to participate. All CRC screening non-participants viewed making an informed decision as essential. This appeared to be the case to a lesser extent for CRC screening participants. For most, experiences and values were involved in their decision-making. CONCLUSION:Our sample of the eligible CRC screening population viewed aspects related to the concepts of autonomous and informed decision-making as important for making a 'good' CRC screening decision. However, in particular the existence of a social norm may be affecting a true autonomous decision-making process. Additionally, the present concept of informed decision-making with its strong emphasis on making a fully informed and well-considered decision does not appear to be entirely reflective of the process in practice. More efforts could be made to attune to the diverse values and factors that are involved in deciding about CRC screening participation.
| S-EPMC7259584 | biostudies-literature
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.
| S-EPMC7676870 | biostudies-literature
Project description:BackgroundThe current state of evidence regarding measures that assess evidence-informed decision-making (EIDM) competence attributes (i.e., knowledge, skills, attitudes/beliefs, behaviours) among nurses is unknown. This systematic review provides a narrative synthesis of the psychometric properties and general characteristics of EIDM competence attribute measures in nursing.MethodsThe search strategy included online databases, hand searches, grey literature, and content experts. To align with the Cochrane Handbook of Systematic Reviews, psychometric outcome data (i.e., acceptability, reliability, validity) were extracted in duplicate, while all remaining data (i.e., study and measure characteristics) were extracted by one team member and checked by a second member for accuracy. Acceptability data was defined as measure completion time and overall rate of missing data. The Standards for Educational and Psychological Testing was used as the guiding framework to define reliability, and validity evidence, identified as a unified concept comprised of four validity sources: content, response process, internal structure and relationships to other variables. A narrative synthesis of measure and study characteristics, and psychometric outcomes is presented across measures and settings.ResultsA total of 5883 citations were screened with 103 studies and 35 unique measures included in the review. Measures were used or tested in acute care (n?=?31 measures), public health (n?=?4 measures), home health (n?=?4 measures), and long-term care (n?=?1 measure). Half of the measures assessed a single competence attribute (n?=?19; 54.3%). Three measures (9%) assessed four competence attributes of knowledge, skills, attitudes/beliefs and behaviours. Regarding acceptability, overall missing data ranged from 1.6-25.6% across 11 measures and completion times ranged from 5 to 25?min (n?=?4 measures). Internal consistency reliability was commonly reported (21 measures), with Cronbach's alphas ranging from 0.45-0.98. Two measures reported four sources of validity evidence, and over half (n?=?19; 54%) reported one source of validity evidence.ConclusionsThis review highlights a gap in the testing and use of competence attribute measures related to evidence-informed decision making in community-based and long-term care settings. Further development of measures is needed conceptually and psychometrically, as most measures assess only a single competence attribute, and lack assessment and evidence of reliability and sources of established validity evidence.RegistrationPROSPERO #CRD42018088754.
| S-EPMC7254762 | biostudies-literature
Project description:The aims of this study were to investigate the role of the veterinarian characteristics (e.g., age, gender, self-estimation, use of the internet), and their attitudes concerning animal owners seeking self-information. A particular focus was laid on any association between shared decision making (SDM), age and gender. In an online survey, 527 German veterinarians were asked about their attitude regarding SDM principles and their experiences with self-informed animal owners. The factors associated with veterinarians' perception of SDM were investigated in a multivariable linear regression model. A recently published structural equation model consolidated the application of SDM, empathic behavior, and veterinarians' evaluation of self-education as latent factors. Interconnected questionnaire items were processed using an exploratory factor analysis to 11 interpretable factors. Veterinarians who assumed therapy failure was associated with themselves had significantly higher rates of SDM (p = 0.002). In contrast, SDM was significantly lower (p = 0.002) if they assumed that therapy failure was due to the animal's owners. SDM was negatively associated with the perceived quality of the pet owners' self-information (p < 0.001) and if skepticism was perceived as the reason for seeking the self-information (p = 0.001). Veterinarians who advised against self-information (p = 0.006) and those who assumed that self-information of animal owners goes along with uncertainty (p = 0.001) had low SDM values (p = 0.006). Asking the animal owner for self-information (p = 0.001), and recommendations of good information sources (p = 0.022) were positively associated with SDM. Looking at the influence of age and gender on the application of SDM, older people and males rated higher. However, the evaluation of the latent factor SDM was based on the self-estimation of the participants. Assuming that younger women were less self-confident, we cannot exclude that young female participants self-evaluated their SDM skills lower than older male participants, although both groups would objectively have the same SDM level. Practitioners who have a positive attitude toward animal owners, who enjoy contact with animal owners and welcome their interest in further (self-)information, show empathic behavior, and have a positive attitude toward SDM are more likely to have better veterinarian-animal owner-relationships.
| S-EPMC8564114 | biostudies-literature
Project description:Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response.
| S-EPMC9997955 | biostudies-literature