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In 2017, to mark the centennial of Alice Guy Blaché’s visit to Columbia University, I wrote about her two lectures. It was a fairly open-ended post that outlined both what I knew and still did not know about these ephemeral moments in film history. Thanks to new information from WFPP readers, I am now able to share an update.

Nursing home residents are characterized by frailty, multiple chronic illnesses, and high levels of physical and cognitive impairment. More than one-quarter of long-stay nursing home residents are hospitalized each year. These hospitalizations are costly, and many are considered potentially avoidable. Unnecessary hospitalizations cause disruption to residents, risk of complications, and possibility of reduced functioning on return to the nursing home. Reducing avoidable hospitalizations of nursing home residents is an important quality-improvement initiative that may also reduce cost of care. This symposium will include an overview of the Centers for Medicare & Medicaid Services (CMS) Initiative to Reduce Avoidable Hospitalizations among Nursing Facility Residents—Payment Reform (henceforth, the Initiative), and will describe the evaluation design and early findings. The Initiative, begun in 2016, tests a new Medicare Part B payment model that pays participating nursing facilities and practitioners for providing higher-level care on site to eligible long-stay residents instead of transferring them to hospitals. These payments are for care of residents with six qualifying conditions whose changing symptoms could possibly trigger a hospital transfer. Four presentations will focus on: (1) Description of the Initiative and individual state models, (2) An overview of evaluation methods, describing the approach to comparison group selection and quantitative impact analysis; (3) Early primary data (telephone interview) results from the perspective of participating nursing facilities; and (4) Preliminary primary data results concerning participating practitioners (physicians and physician extenders).

Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals' spatio-temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual-collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale-adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone-collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals' life trajectories.