Emergency departments need to continuously calculate quality indicators in order to perform structural improvements, improvements in the daily routine, and ad-hoc improvements in everyday life. However, many different actors across multiple disciplines collaborate to provide emergency care. Hence, patient-related data is stored in several information systems, which in turn makes the calculation of quality indicators more difficult. To address this issue, we aim to link and use routinely collected data of the different actors within the emergency care continuum. In order to assess the feasibility of linking and using routinely collected data for quality indicators and whether this approach adds value to the assessment of emergency care quality, we conducted a single case study in a German academic teaching hospital. We analyzed the available data of the existing information systems in the emergency continuum and linked and pre-processed the data. Based on this, we then calculated four quality indicators (Left Without Been Seen, Unplanned Reattendance, Diagnostic Efficiency, and Overload Closure). Lessons learned from the calculation and results of the discussions with staff members that had multiple years of work experience in the emergency department provide a better understanding of the quality of the emergency department, the related challenges during the calculation, and the added value of linking routinely collected data.
Data and sensor fusion can enable clinical healthcare systems to improve conditions of a patient. However, hospitals are not the only application field of connected medical devices. Domestic monitoring gets more important day by day and applies Internet of Things with mobile sensors, like wearables. Through data processing data is transferred to smart data and personalized recommendations are improvable, if sensors can be chosen individually. Therefore, we developed a generic medical sensor framework which is able to merge any needed sensor and collect data to improve personalized health of an individual. To evaluate our framework and to prove the added value of sensor fusion we present a sensor-based stress detection game.
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