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Our Approach

Our solutions provide "Actionable Knowledge" to improve decision making during two distinct time periods in the patient treatment process:

  • At the Point of Care, in real-time
  • Operational oversight of care delivery, based on monitoring and feedback for continuous improvment

    Point of Care

    Advances in medical devices (example: electrocardiographs) are providing clinicians with an abundance of additional data about the current patient condition. This information often requires a highly trained individual to interpret this data to determine a decision on treatment. To accurately simplify this information in to actionable knowledge, our solutions use Time-Insensitive Predictive Instruments (TIPIs).

    What is a TIPI?

    It is a "clinical predictive instrument" based on a mathematical formula that generates a 0-100% probability prediction of a given diagnosis (e.g., heart attack) or medical outcome (e.g., death). TIPIs are embedded in medical devices that aquire the needed data for the TIPI in order to translate complex patient data into simple-to-use 0-100% predictions of interest for a patient's care.

    Operational oversight of delivery of care

    However, simplifying the patient data for real-time clinical use is not enough. Overall management of clinical care requires additional issues to be addressed:

  • Are we providing the best care possible?
  • Are we providing timely and appropriate care?

    Delivery of high quality care requires insight into the day-to-day performance on key metrics, both for internal use and for monitoring entities. This is achieved by providing new reporting and visibility tools to simplify the operational oversight decision making on provided through retrospective reports based on TIPI data that support quality improvement in patient treatment and outcomes, provided by the TIPI-Information System (TIPI-IS).

    How does TIPI-IS work? Aggregation of key clinical data into a collective repository and applying analytics, TIPI predictions, and other methods to provide new views and insights. The aggregation includes, from EMS ambulance care, prehospital patient data, prehospital medical device data, prehospital operational data, hospital patient data, hospital medical device data, hospital operational data, all linked together to allow a cohesive picture and monitoring system from a single repository.

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