Abstract: A system and method that permits a user to utilize a means of capturing and managing one or more images containing protected health information (PHI) on a mobile device, as disclosed herein. The user of the system and method may be required to undergo authentication and authorization to access the system and use the method.

Abstract: A system includes a plurality of tracking devices, such as RFID tags, affixed to items, such as vehicles, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and systems send attributes of new events to the backend devices. The backend devices can track the items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Abstract: A baggage system includes a plurality of RFID tags affixed to baggage items, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and baggage terminal systems send attributes of new events to the backend devices. The backend devices can track the baggage items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Abstract: A system includes a data collection engine, a plurality of items including radio-frequency identification chips, a plurality of third party data and insight sources, a plurality of interfaces, client devices, a server and method thereof for preventing suicide. The server includes trained machine learning models, business logic and attributes of a plurality of patient events. The data collection engine sends attributes of new patient events to the server. The server can predict a suicide risk of the new patient events based upon the attributes of the new patient events utilizing the trained machine learning models. Using business logic, data visualization and the trained machine learning models, the server can also make recommendations to reduce the risk of suicides.

Abstract: A system for providing a patient list manager includes a client device for point of care capture and access of patient data and a server for providing cloud based hosting. One or more modules can leverage the patient data to help users improve and facilitate the efficiency and safety of patient management, diagnostic imaging management, clinical work flows, charge capture, revenue cycle management, accreditation reporting, quality and outcomes monitoring and improvement, among other things.

Abstract: A system that includes a plurality of RFID tags affixed to medical items, and a plurality of data collection engine devices, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of patient transactions. A plurality of data collection engines and hospital information systems send attributes of new patient transactions to the backend devices. The backend devices can predict particular outcomes of new patient transactions based upon the attributes of the new patient transactions utilizing the trained machine learning models. Using business logic and the trained machine learning models, the backend devices can also make recommendations to optimize the patient flow in healthcare provider organizations.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a device reader such as a data collection engine device, and a server device. The data collection engine wirelessly transmits power to an RFID chips and receives first medical data from the RFID chip while the RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item such as position and risk states based upon the first medical data.

Abstract: A baggage system includes a plurality of RFID tags affixed to baggage items, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and baggage terminal systems send attributes of new events to the backend devices. The backend devices can track the baggage items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Abstract: A baggage system includes a plurality of RFID tags affixed to baggage items, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and baggage terminal systems send attributes of new events to the backend devices. The backend devices can track the baggage items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Abstract: A system includes a plurality of tracking devices, such as RFID tags, affixed to items, such as vehicles, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and systems send attributes of new events to the backend devices. The backend devices can track the items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a data collection engine device, and a server device. The data collection engine wirelessly transmits power to a first one of the RFID chips and receives first medical data from the first RFID chip while the first RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item such as position and risk states based upon the first medical data.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a data collection engine device, and a server device. The data collection engine wirelessly transmits power to a first one of the RFID chips and receives first medical data from the first RFID chip while the first RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item based upon the first medical data.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a device reader such as a data collection engine device, and a server device. The data collection engine wirelessly transmits power to an RFID chips and receives first medical data from the RFID chip while the RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item such as position and risk states based upon the first medical data.

Abstract: A system includes a data collection engine, a plurality of items including radio-frequency identification chips, a plurality of third party data and insight sources, a plurality of interfaces, client devices, a server and method thereof for preventing suicide. The server includes trained machine learning models, business logic and attributes of a plurality of patient events. The data collection engine sends attributes of new patient events to the server. The server can predict a suicide risk of the new patient events based upon the attributes of the new patient events utilizing the trained machine learning models. Using business logic, data visualization and the trained machine learning models, the server can also make recommendations to reduce the risk of suicides.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a data collection engine device, and a server device. The data collection engine wirelessly transmits power to a first one of the RFID chips and receives first medical data from the first RFID chip while the first RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item based upon the first medical data.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a data collection engine device, and a server device. The data collection engine wirelessly transmits power to a first one of the RFID chips and receives first medical data from the first RFID chip while the first RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item such as position and risk states based upon the first medical data.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a data collection engine device, and a server device. The data collection engine wirelessly transmits power to a first one of the RFID chips and receives first medical data from the first RFID chip while the first RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item such as position and risk states based upon the first medical data.

Abstract: A system includes a plurality of tracking devices, such as RFID tags, affixed to items, such as vehicles, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and systems send attributes of new events to the backend devices. The backend devices can track the items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Abstract: A baggage system includes a plurality of RFID tags affixed to baggage items, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and baggage terminal systems send attributes of new events to the backend devices. The backend devices can track the baggage items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

Abstract: A system includes a plurality of RFID chips affixed to a medical item, a device reader such as a data collection engine device, and a server device. The data collection engine wirelessly transmits power to an RFID chips and receives first medical data from the RFID chip while the RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item such as position and risk states based upon the first medical data.