Balancing multiple stakeholder objectives for floodplain reconnection and wetland restoration.

Floodplain reconnection and wetland restoration projects are increasingly implemented to enhance flood resiliency, and these nature-based solutions can also achieve co-benefits of nutrient storage and improved habitats. Considering the multiple and sometimes incompatible objectives of stakeholders for uses of riverside lands, a decision-support tool linked to a hydraulic model would enable planners to simulate floodplain restoration scenarios while also quantifying and assessing the trade-offs between the stakeholder objectives to arrive at optimal restoration designs. We illustrate a simple ranking approach using an n-dimensional objective function to represent key stakeholders engaged in restoration. We applied our approach in a watershed in central Vermont (USA) that has been identified by regional and state-level stakeholders as an important location to mitigate flooding damages but also to improve water quality - all within a context of increasing development pressures on riparian lands and limited financial resources to accomplish restoration. Eleven different floodplain reconnection and wetland restoration modifications were combined in six scenarios and simulated with 2D Hydrologic Engineering Center's River Analysis System (2D HEC-RAS), along with a baseline (no-action) scenario. Only modest attenuation of peak flows for 2-, 25-, 50- and 100-year design storms was achieved by the floodplain restoration scenarios due to the steep setting, and flashy nature of the watershed. Yet, several scenarios of floodplain reconnection projects more than met the necessary annual phosphorus load reductions targeted under a Total Maximum Daily Load implementation plan. Our approach provided planners with a ranking of restoration scenarios that best met multiple stakeholder objectives and allowed effectiveness of alternate design scenarios to be quantified, justified, and visualized to promote consensus decision-making.

An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences.

Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.

Epidemiology of Connectional Silence in specialist serious illness conversations.

CONTEXT: Human connection can reduce suffering and facilitate meaningful decision-making amid the often terrifying experience of hospitalization for advanced cancer. Some conversational pauses indicate human connection, but we know little about their prevalence, distribution or association with outcomes. PURPOSE: To describe the epidemiology of Connectional Silence during serious illness conversations in advanced cancer. METHODS: We audio-recorded 226 inpatient palliative care consultations at two academic centers. We identified pauses lasting 2+ seconds and distinguished Connectional Silences from other pauses, sub-categorized as either Invitational (ICS) or Emotional (ECS). We identified treatment decisional status pre-consultation from medical records and post-consultation via clinicians. Patients self-reported quality-of-life before and one day after consultation. RESULTS: Among all 6769 two-second silences, we observed 328 (4.8%) ECS and 240 (3.5%) ICS. ECS prevalence was associated with decisions favoring fewer disease-focused treatments (ORadj: 2.12; 95% CI: 1.12, 4.06). Earlier conversational ECS was associated with improved quality-of-life (p = 0.01). ICS prevalence was associated with clinicians' prognosis expectations. CONCLUSIONS: Connectional Silences during specialist serious illness conversations are associated with decision-making and improved patient quality-of-life. Further work is necessary to evaluate potential causal relationships. PRACTICE IMPLICATIONS: Pauses offer important opportunities to advance the science of human connection in serious illness decision-making.

Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication.

BACKGROUND: Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations. DISCUSSION: Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts. Using actual conversations from a large direct-observation cohort study, we demonstrate how natural language processing and unsupervised machine learning methods can reveal underlying types of uncertainty stories in serious illness conversations. CONCLUSIONS: Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.