Agile Implementation and Expansive Learning: Identifying Contradictions and Their Resolution Using an Activity Theory Perspective
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ABSTRACT:
SUBMITTER: Stray V
PROVIDER: S-EPMC7251609 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundDespite the United States Preventive Services Task Force recommendation to screen adults for unhealthy alcohol use, the implementation of alcohol screening in primary care remains suboptimal.MethodsA pre and post-implementation study design that used Agile implementation process to increase screening for unhealthy alcohol use in adult patients from October 2021 to June 2022 at a large primary care clinic serving minority and underprivileged adults in Indianapolis.ResultsIn comparison to a baseline screening rate of 0%, the agile implementation process increased and sustained screening rates above 80% for alcohol use using the Alcohol Use Disorders Identification Test - Consumption tool (AUDIT-C).ConclusionsUsing the agile implementation process, we were able to successfully implement evidence-based recommendations to screen for unhealthy alcohol use in primary care.
| S-EPMC11238453 | biostudies-literature
Project description:We present two models of how people form beliefs that are based on machine learning theory. We illustrate how these models give insight into observed human phenomena by showing how polarized beliefs can arise even when people are exposed to almost identical sources of information. In our first model, people form beliefs that are deterministic functions that best fit their past data (training sets). In that model, their inability to form probabilistic beliefs can lead people to have opposing views even if their data are drawn from distributions that only slightly disagree. In the second model, people pay a cost that is increasing in the complexity of the function that represents their beliefs. In this second model, even with large training sets drawn from exactly the same distribution, agents can disagree substantially because they simplify the world along different dimensions. We discuss what these models of belief formation suggest for improving people's accuracy and agreement.
| S-EPMC8126847 | biostudies-literature
Project description:BackgroundExploring barriers to the uptake of research based recommendations into practice is an important part of the development of implementation programmes. Techniques to identify barriers can include use of theory-informed questionnaires and qualitative interviews. Conceptualising and measuring theory-informed factors, and engaging health professionals' to uncover all potential barriers, can be a difficult task. This paper presents a case study of the process of trying to identify, systematically, the key factors influencing health professionals' referrals for women diagnosed with mild to moderate postnatal depression for psychological treatment. The paper illustrates how the factors were conceptualised and measured and explores the real world challenges experienced, with implications for future implementation studies.MethodsTheory-informed factors were conceptualised and measured using a questionnaire and interviews. The questionnaire was piloted, before being administered to general practitioners, practice nurses and health visitors working in general practices in one area of the UK NHS. The interviews were conducted with a small sample of general practitioners who had not completed the questionnaire, further exploring factors influencing their referral decisions in the local context.ResultsThe response rate to the questionnaire was low (19%), despite selecting the recommendation to target through engagement with local stakeholders and surveying local health professionals, and despite using two reminders, an incentive prize, and phone calls to practice managers to bolster response rates.ConclusionsTwo significant challenges to achieving higher response rates and successfully exploring local context were identified: the difficulties of developing a robust- but feasible- questionnaire to explore theory-informed factors, and targeting recommendations that are important to policy makers, but which health professionals view as unimportant. This case study highlights the "trade-off" between scientifically rigorous collection of data against the pragmatism and flexibility requirements of "real world" implementation. Future implementation studies should explore different ways of identifying factors influencing the adoption of recommendations to bridge this gulf.
| S-EPMC3537535 | biostudies-literature
Project description:In the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule.We encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features.The predictive abilities of the proposed approach were compared with three classical predictive models without instance-based embedding. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations. To validate the ability of the proposed approach to find bioactive conformers, 12 small molecules with co-crystallized structures were seeded in one data set. 10 out of 12 co-crystallized structures were indeed identified as significant conformers using the proposed approach.The proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers.
| S-EPMC3439725 | biostudies-literature
Project description:BackgroundMachine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new "biomarkers" that will enhance the effectiveness of ML in clinical decision making. Since the interaction of the immune system and cancer is a hallmark of tumor establishment and progression, one potential area for cancer biomarker discovery is through the investigation of cancer-related immune cell signatures. Hence, we hypothesize that blood immune cell signatures can act as a biomarker for cancer progression.MethodsTo probe this, we have developed and tested a multiparameter cell-surface marker screening pipeline, using flow cytometry to obtain high-resolution systemic leukocyte population profiles that correlate with detection and characterization of several cancers in murine syngeneic tumor models.ResultsWe discovered a signature of several blood leukocyte subsets, the most notable of which were monocyte subsets, that could be used to train CATboost ML models to predict the presence and type of cancer present in the animals.ConclusionsOur findings highlight the potential utility of a screening approach to identify robust leukocyte biomarkers for cancer detection and characterization. This pipeline can easily be adapted to screen for cancer specific leukocyte markers from the blood of cancer patient.
| S-EPMC10435879 | biostudies-literature
Project description:Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land-surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large-scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%-44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%-27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large-scale circulation patterns featuring less active upper-level ridge-trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.
| S-EPMC8243942 | biostudies-literature
Project description:BackgroundCommunity engagement is critical to the acceleration of evidence-based interventions into community settings. Harnessing the knowledge and opinions of community leaders increases the likelihood of successful implementation, scale-up, and sustainment of evidence-based interventions. Faith in Action (Fe en Acción) is an evidence-based promotora-led physical activity program designed to increase moderate-to-vigorous physical activity among churchgoing Latina women.MethodsWe conducted in-depth interviews using a semi-structured interview guide based on the Consolidated Framework for Implementation Research (CFIR) at various Catholic and Protestant churches with large Latino membership in San Diego County, California to explore barriers and facilitators to implementation of Faith in Action and identify promising implementation strategies for program scale-up and dissemination. We interviewed 22 pastors and church staff and analyzed transcripts using an iterative-deductive team approach.ResultsPastors and church staff described barriers and facilitators to implementation within three domains of CFIR: characteristics of individuals (lack of self-efficacy for and knowledge of physical activity; influence on churchgoers' behaviors), inner setting (church culture and norms, alignment with mission and values, competing priorities, lack of resources), and outer setting (need for buy-in from senior leadership). From the interviews, we identified four promising implementation strategies for the scale-up of faith-based health promotion programs: (1) health behavior change training for pastors and staff, (2) tailored messaging, (3) developing community collaborations, and (4) gaining denominational support.ConclusionsWhile churches can serve as valuable partners in health promotion, specific barriers and facilitators to implementation must be recognized and understood. Addressing these barriers through targeted implementation strategies at the adopter and organizational level can facilitate improved program implementation and lead the way for scale-up and dissemination.
| S-EPMC7427873 | biostudies-literature
Project description:Despite apparent promise and the availability of numerous examples in the literature, machine learning models are rarely used in practice in ICU units. This mismatch suggests that there are poorly understood barriers preventing uptake, which we aim to identify. We begin with a qualitative study with 29 interviews of 40 Intensive Care Unit-, hospital- and MedTech company staff members. As a follow-up to the study, we attempt to quantify some of the technical issues raised. To perform experiments we selected two models based on criteria such as medical relevance. Using these models we measure the loss of performance in predictive models due to drift over time, change of available patient features, scarceness of data, and deploying a model in a different context to the one it was built in. The qualitative study confirms our assumptions on the potential of AI-driven analytics for patient care, as well as showing the prevalence and type of technical blocking factors that are responsible for its slow uptake. The experiments confirm that each of these issues can cause important loss of predictive model performance, depending on the model and the issue. Based on the qualitative study and quantitative experiments we conclude that more research on practical solutions to enable AI-driven innovation in Intensive Care Units is needed. Furthermore, the general poor situation with respect to public, usable implementations of predictive models would appear to limit the possibilities for both the scientific repeatability of the underlying research and the transfer of this research into practice.
| S-EPMC9768782 | biostudies-literature
Project description:BackgroundAs many as 70% of intensive care unit (ICU) survivors suffer from long-term physical, cognitive, and psychological impairments known as post-intensive care syndrome (PICS). We describe how the first ICU survivor clinic in the United States, the Critical Care Recovery Center (CCRC), was designed to address PICS using the principles of Agile Implementation (AI).MethodsThe CCRC was designed using an eight-step process known as the AI Science Playbook. Patients who required mechanical ventilation or were delirious ≥48 hours during their ICU stay were enrolled in the CCRC. One hundred twenty subjects who completed baseline HABC-M CG assessments and had demographics collected were included in the analysis to identify baseline characteristics that correlated with higher HABC-M CG scores. A subset of patients and caregivers also participated in focus group interviews to describe their perceptions of PICS.ResultsQuantitative analyses showed that the cognitive impairment was a major concern of caregivers. Focus group data also confirmed that caregivers of ICU survivors (n = 8) were more likely to perceive cognitive and mental health symptoms than ICU survivors (n = 10). Caregivers also described a need for ongoing psychoeducation about PICS, particularly cognitive and mental health symptoms, and for ongoing support from other caregivers with similar experiences.ConclusionsOur study demonstrated how the AI Science Playbook was used to build the first ICU survivor clinic in the United States. Caregivers of ICU survivors continue to struggle with PICS, particularly cognitive impairment, months to years after discharge. Future studies will need to examine whether the CCRC model of care can be adapted to other complex patient populations seen by health-care professionals.
| S-EPMC7048643 | biostudies-literature
Project description:In 2010, the Federal Parliament (Bundestag) of Germany passed a new law (Arzneimittelmarktneuordnungsgesetz, AMNOG) on the regulation of medicinal products that applies to all pharmaceutical products with active ingredients that are launched beginning January 1, 2011. The law describes the process to determine the price at which an approved new product will be reimbursed by the statutory health insurance system. The process consists of two phases. The first phase assesses the additional benefit of the new product versus an appropriate comparator (zweckmäßige Vergleichstherapie, zVT). The second phase involves price negotiation. Focusing on the first phase, this paper investigates requirements of benefit assessment of a new product under this law with special attention on the methods applied by the German authorities on issues such as the choice of the comparator, patient relevant endpoints, subgroup analyses, extent of benefit, determination of net benefit, primary and secondary endpoints, and uncertainty of the additional benefit. We propose alternative approaches to address the requirements in some cases and invite other researchers to help develop solutions in other cases.
| S-EPMC4737288 | biostudies-other