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Navigating transitions between planned and unexpected events is a familiar challenge for organizations, and yet little is known about the sensemaking processes by which organizational members coordinate action to fit unexpected events within temporally defined plans and schedules. Drawing on an ethnographic study conducted at a local U.S. television station (codenamed “Local TV”), we elaborate on how workers in the news department plan their stories on a daily basis and adjust their plans when new stories break. We find that newsmaking is shaped by expectancy frameworks, which define the baseline of what is expected to occur during the news day, and typifications, which allow newsworkers to categorize incoming events based on relevance and update expectancy frameworks accordingly. Taken together, these provide newsworkers with sensemaking resources for responding regularly to unexpected events. Our study contributes to the understanding of sensemaking processes in three main areas: the effect of time-based dynamics on the control and coordination of work, the interaction of routine and mindful processes in response to unexpected events, and the structural influences of expectations and typifications on sensemaking.

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Severe mitral regurgitation (MR) is a cardiac disease that can lead to fatal consequences. MitraClip (MC) intervention is a percutaneous procedure whereby the mitral valve (MV) leaflets are connected along the edge using MCs. The outcomes of the MC intervention are not known in advance, i.e., the outcomes are quite variable. Artificial intelligence (AI) can be used to guide the cardiologist in selecting optimal MC scenarios. In this study, we describe an atlas of shapes as well as different scenarios for MC implantation for such an AI analysis. We generated the MV geometrical data from three different sources. First, the patients' 3-dimensional echo images were used. The pixel data from six key points were obtained from three views of the echo images. Using PyGem, an open-source morphing library in Python, these coordinates were used to create the geometry by morphing a template geometry. Second, the dimensions of the MV, from the literature were used to create data. Third, we used machine learning methods, principal component analysis, and generative adversarial networks to generate more shapes. We used the finite element (FE) software ABAQUS to simulate smoothed particle hydrodynamics in different scenarios for MC intervention. The MR and stresses in the leaflets were post-processed. Our physics-based FE models simulated the outcomes of MC intervention for different scenarios. The MR and stresses in the leaflets were computed by the FE models for a single clip at different locations as well as two and three clips. Results from FE simulations showed that the location and number of MCs affect subsequent residual MR, and that leaflet stresses do not follow a simple pattern. Furthermore, FE models need several hours to provide the results, and they are not applicable for clinical usage where the predicted outcomes of MC therapy are needed in real-time. In this study, we generated the required dataset for the AI models which can provide the results in a matter of seconds.