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Clinical Studies

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  • Assessing Electronic Chart Review Practices and Historical Information Needs during New Patient Encounters in the ICU Rochester, Minn.

    The purpose of this study is to develop an interactive medical record timeline based on physician preferences and perceived clinical needs, to improve the chart review process for new patient encounters in the ICU.
  • AWARE Family Communication Tool Rochester, Minn.

    The purpose of the study is to identify the daily patient-specific information needs of intensive care unit patients and their families in order to develop an effective communication tool for use on electronic devices.
  • Electronic Stethoscope Breathing Sound Recognition and Reasoning: Initial Data Collection and Models Development Rochester, Minn.

    The purpose of this study is to develop an effective recording sound system combined with a deep learning model for rapid and accurate recognition of normal lung sounds.
  • Evaluating an Interactive Medical Record Timeline (MeRLin) to Facilitate Historical Chart Review During New Patient Encounters in the ICU Rochester, Minn.

    The purpose of this study is to identify how an interactive medical record timeline may improve the chart-review process for clinicians evaluating unfamiliar patients with complex medical/surgical histories in the ICU.
  • Prevention of Severe Acute Respiratory Failure in Patients with PROOFcheck - an Electronic Checklist to Prevent Organ Failure (PROOFcheck) Rochester, Minn.

    Severe acute respiratory failure requiring prolonged mechanical ventilation is the most common form of acute organ dysfunction in the hospital, and is often associated with multiple organ failure, high mortality, and functional impairment. The purpose of this study is to improve the outcomes of patients at high risk with early intervention using an electronic medical records checklist aimed at preventing the lung injury that commonly leads to organ failure.
  • Real-time Bedside Video Recognition and Reasoning: Initial Data Collection and Models Development (ICU Video Recognition) Rochester, Minn., Jacksonville, Fla., Scottsdale/Phoenix, Ariz.

    The purpose of this study is to enable the recognition ability of a camera by providing its training in advance to detect breathing tubes and oxygen masks on any person’s face and reliably recognize signature features of acute brain failure displayed by the patient.

    The specific aim for this study is to develop a Computer Vision model using a camera to automatically detect the presence of an endotracheal tube (ETT), an oxygen mask, or a noninvasive ventilation device on patients in the ICU.Digital Video Recordings (DVRs) have played an important role in patient care as preventive and remote monitoring. One of the examples of the DVR application is its use to help patients learn from the recording of their gaits which detects their unsafe walking habits. Other examples of DVR use in healthcare include: supporting daily work in critical care medicine for the purposes of reducing medical errors, lowering the cost of care, and improving the quality of care by reducing the workload of providers. Recently, advances in deep learning technology especially video analytics have helped computers see the world the way humans do. This opens up the potential for automated observations and object detection in healthcare.
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