Dataset Information

Inside the Green House "Black Box": Opportunities for High-Quality Clinical Decision Making.

ABSTRACT: To develop a conceptual model that explained common and divergent care processes in Green House (GH) nursing homes with high and low hospital transfer rates.Eighty-four face-to-face, semistructured interviews were conducted with direct care, professional, and administrative staff with knowledge of care processes in six GH organizations in six states.The qualitative grounded theory method was used for data collection and analysis. Data were analyzed using open, axial, and selective coding. Data collection and analysis occurred iteratively.Elements of the GH model created significant opportunities to identify, communicate, and respond to early changes in resident condition. Staff in GH homes with lower hospital transfer rates employed care processes that maximized these opportunities. Staff in GH homes with higher transfer rates failed to maximize, or actively undermined, these opportunities.Variations in how the GH model was implemented across GH homes suggest possible explanations for inconsistencies found in past research on the care outcomes, including hospital transfer rates, in culture change models. The findings further suggest that the details of culture change implementation are important considerations in model replication and policies that create incentives for care improvements.

SUBMITTER: Bowers B

PROVIDER: S-EPMC4939731 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

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Inside the Green House "Black Box": Opportunities for High-Quality Clinical Decision Making.

Bowers Barbara B Roberts Tonya T Nolet Kimberly K Ryther Brenda B

Health services research 20151227

<h4>Objective</h4>To develop a conceptual model that explained common and divergent care processes in Green House (GH) nursing homes with high and low hospital transfer rates.<h4>Data sources/settings</h4>Eighty-four face-to-face, semistructured interviews were conducted with direct care, professional, and administrative staff with knowledge of care processes in six GH organizations in six states.<h4>Study design/data collection</h4>The qualitative grounded theory method was used for data collec ...[more]

PMID: 26708135

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Project description:State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods.

Project description:BackgroundDigital innovations have shown promise for improving maternal health service delivery. However, low- and middle-income countries are still at the adoption-utilization stage. Evidence on mobile health has been described as a black box, with gaps in theoretical explanations that account for the ecosystem of health care and their effect on adoption mechanisms. Bliss4Midwives, a modular integrated diagnostic kit to support antenatal care service delivery, was piloted for 1 year in Northern Ghana. Although both users and beneficiaries valued Bliss4Midwives, results from the pilot showed wide variations in usage behavior and duration of use across project sites.ObjectiveTo strengthen the design and implementation of an improved prototype, the study objectives were two-fold: to identify causal factors underlying the variation in Bliss4Midwives usage behavior and understand how to overcome or leverage these in subsequent implementation cycles.MethodsUsing a multiple case study design, a realist evaluation of Bliss4Midwives was conducted. A total of 3 candidate program theories were developed and empirically tested in 6 health facilities grouped into low and moderate usage clusters. Quantitative and qualitative data were collected and analyzed using realist thinking to build configurations that link intervention, context, actors, and mechanisms to program outcomes, by employing inductive and deductive reasoning. Nonparametric t test was used to compare the perceived usefulness and perceived ease of use of Bliss4Midwives between usage clusters.ResultsWe found no statistically significant differences between the 2 usage clusters. Low to moderate adoption of Bliss4Midwives was better explained by fear, enthusiasm, and high expectations for service delivery, especially in the absence of alternatives. Recognition from pregnant women, peers, supervisors, and the program itself was a crucial mechanism for device utilization. Other supportive mechanisms included ownership, empowerment, motivation, and adaptive responses to the device, such as realignment and negotiation. Champion users displayed high adoption-utilization behavior in contexts of participative or authoritative supervision, yet used the device inconsistently. Intervention-related (technical challenges, device rotation, lack of performance feedback, and refresher training), context-related (staff turnover, competing priorities, and workload), and individual factors (low technological self-efficacy, baseline knowledge, and internal motivation) suppressed utilization mechanisms.ConclusionsThis study shed light on optimal conditions necessary for Bliss4Midwives to thrive in a complex social and organizational setting. Beyond usability and viability studies, advocates of innovative technologies for maternal care need to consider how implementation strategies and contextual factors, such as existing collaborations and supervision styles, trigger mechanisms that influence program outcomes. In addition to informing scale-up of the Bliss4Midwives prototype, our results highlight the need for interventions that are guided by research methods that account for complexity.

Dataset Information

Inside the Green House "Black Box": Opportunities for High-Quality Clinical Decision Making.

Publications

Inside the Green House "Black Box": Opportunities for High-Quality Clinical Decision Making.

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