Abstract: Devices, systems, and methods for monitoring patient hemodynamic status, systemic vascular resistance, reversal of cardiac and respiratory rates, and patient respiratory volume or effort are disclosed. A peripheral venous pressure is measured and used to detect levels, changes, or problems relating to patient blood volume. The peripheral venous pressure measurement is transformed from the time domain to the frequency domain for analysis. A heart rate frequency is identified, and harmonics of the heart rate frequency are detected and evaluated to determine, among other things, hypovolemia or hypervolemia, systemic vascular resistance, and of cardiac and respiratory rates, and patient respiratory volume or effort.

Abstract: The invention provides an IV system for monitoring a patient that is positioned on the patient's body. The IV system includes: 1) a catheter that inserts into the patient's venous system; 2) a pressure sensor connected to the catheter that measures physiological signals indicating a pressure in the patient's venous system; 3) a motion sensor that measures motion signals; and 4) a processing system that: i) receives the physiological signals from the pressure sensor; ii) receives the motion signals from the motion sensor; iii) processes the motion signals by comparing them to a pre-determined threshold value to determine when the patient has a relatively low degree of motion; and iv) process the physiological signals to determine a physiological parameter when the processing system determines that the motion signals are below the pre-determined threshold value.

Abstract: Devices, systems, and methods for monitoring patient hemodynamic status, systemic vascular resistance, reversal of cardiac and respiratory rates, and patient respiratory volume or effort are disclosed. A peripheral venous pressure is measured and used to detect levels, changes, or problems relating to patient blood volume. The peripheral venous pressure measurement is transformed from the time domain to the frequency domain for analysis. A heart rate frequency is identified, and harmonics of the heart rate frequency are detected and evaluated to determine, among other things, hypovolemia or hypervolemia, systemic vascular resistance, and of cardiac and respiratory rates, and patient respiratory volume or effort.

Abstract: Devices, systems, and methods for monitoring patient hemodynamic status, systemic vascular resistance, reversal of cardiac and respiratory rates, and patient respiratory volume or effort are disclosed. A peripheral venous pressure is measured and used to detect levels, changes, or problems relating to patient blood volume. The peripheral venous pressure measurement is transformed from the time domain to the frequency domain for analysis. A heart rate frequency is identified, and harmonics of the heart rate frequency are detected and evaluated to determine, among other things, hypovolemia or hypervolemia, systemic vascular resistance, and of cardiac and respiratory rates, and patient respiratory volume or effort.

Abstract: Devices, systems, and methods for monitoring patient hemodynamic status, systemic vascular resistance, reversal of cardiac and respiratory rates, and patient respiratory volume or effort are disclosed. A peripheral venous pressure is measured and used to detect levels, changes, or problems relating to patient blood volume. The peripheral venous pressure measurement is transformed from the time domain to the frequency domain for analysis. A heart rate frequency is identified, and harmonics of the heart rate frequency are detected and evaluated to determine, among other things, hypovolemia or hypervolemia, systemic vascular resistance, and of cardiac and respiratory rates, and patient respiratory volume or effort.

Abstract: Devices, systems, and methods for monitoring patient hemodynamic status, systemic vascular resistance, reversal of cardiac and respiratory rates, and patient respiratory volume or effort are disclosed. A peripheral venous pressure is measured and used to detect levels, changes, or problems relating to patient blood volume. The peripheral venous pressure measurement is transformed from the time domain to the frequency domain for analysis. A heart rate frequency is identified, and harmonics of the heart rate frequency are detected and evaluated to determine, among other things, hypovolemia or hypervolemia, systemic vascular resistance, and of cardiac and respiratory rates, and patient respiratory volume or effort.

Abstract: Devices, systems, and methods for monitoring patient hemodynamic status, systemic vascular resistance, reversal of cardiac and respiratory rates, and patient respiratory volume or effort are disclosed. A peripheral venous pressure is measured and used to detect levels, changes, or problems relating to patient blood volume. The peripheral venous pressure measurement is transformed from the time domain to the frequency domain for analysis. A heart rate frequency is identified, and harmonics of the heart rate frequency are detected and evaluated to determine, among other things, hypovolemia or hypervolemia, systemic vascular resistance, and of cardiac and respiratory rates, and patient respiratory volume or effort.

Abstract: Devices, systems, and methods for filtering medical device noise artifacts from circulatory waveform signals are disclosed. A circulatory pressure is measured and transformed from the time domain to the frequency domain for analysis to determine patient status. To avoid artifacts of the pumping, the time-domain measurements are filtered to generate a filtered time-domain signal, by removing active pumping periods. The filtered time-domain signal is transformed into a frequency-domain signal, which is analyzed based upon peaks indicating respiratory rate, heart rate, or harmonics thereof. Peaks may be adjusted based on a ratio that considers removed signals. A metric of patient status is then determined from the peaks or corresponding frequencies. The patient status may be related to blood volume of the patient and may be used to control pump operation.

Abstract: Devices, systems, and methods for filtering medical device noise artifacts from venous waveform signals are disclosed. A peripheral venous pressure (PVP) is measured and transformed from the time domain to the frequency domain for analysis to determine patient status. To avoid artifacts of the pumping, the time-domain PVP measurements are filtered to generate a filtered time-domain PVP signal by removing active pumping periods. The filtered time-domain PVP signal is transformed into a frequency-domain PVP signal, which is analyzed based upon peaks indicating respiratory rate, heart rate, or harmonics thereof. A metric of patient status is then determined from the peaks or corresponding frequencies. The patient status may be related to blood volume of the patient and may be used to control pump operation.

Abstract: Devices, systems, and methods for monitoring patient hemodynamic status, systemic vascular resistance, reversal of cardiac and respiratory rates, and patient respiratory volume or effort are disclosed. A peripheral venous pressure is measured and used to detect levels, changes, or problems relating to patient blood volume. The peripheral venous pressure measurement is transformed from the time domain to the frequency domain for analysis. A heart rate frequency is identified, and harmonics of the heart rate frequency are detected and evaluated to determine, among other things, hypovolemia or hypervolemia, systemic vascular resistance, and of cardiac and respiratory rates, and patient respiratory volume or effort.

Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: The described implementations relate to expressing terminologies in a hierarchical form. One implementation can receive a terminology that can include concept-code pairs. For example, each of the concept-code pairs can include a concept and a code that is assigned to the concept by the terminology. The implementation can map the concepts to levels of a hierarchical ontology, and associate some of the concepts across different levels of the hierarchical ontology. The implementation can also provide programmatic access to the concept-code pairs of the terminology via the hierarchical ontology.

Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: A de-identification system is described herein for converting original messages into de-identified messages. The de-identification system leverages original message-inception-functionality which operates as a gateway for providing original messages for use by a production environment. Namely, the de-identification system includes a transformation module that receives the original messages from the original message-inception functionality. The transformation module then converts instances of sensitive information contained in the original messages into non-sensitive information, to produce the de-identified messages. A de-identified environment can consume the de-identified messages with high confidence that the messages have been properly sanitized. This is because the de-identification work has been performed at a well-contained quarantine level of the message processing functionality.

Abstract: The described implementations relate to patient identification. One implementation can acquire binary biometric data and structured data from a patient. This implementation can compare the acquired patient binary biometric data and structured data to binary biometric data and structured data associated with patient files in an electronic master patient index.