Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory data to provide useful notifications and/or recommendations. Indeed, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions or the effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate adjustments that may address certain patient conditions or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the occurrence of high-delivered VT during various types of ventilation. According to embodiments, a ventilator may be configured to monitor and evaluate diverse ventilatory parameters to detect an occurrence of and potential causes for high-delivered VT and may subsequently issue suitable notifications and/or recommendations.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory data to provide useful notifications and/or recommendations. Indeed, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions or the effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate adjustments that may address certain patient conditions or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the occurrence of high-delivered VT, during various types of ventilation. According to embodiments, a ventilator may be configured to monitor and evaluate diverse ventilatory parameters to detect an occurrence of and potential causes for high-delivered VT, and may subsequently issue suitable notifications and/or recommendations.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent a myriad of ventilatory parameters. However, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of Auto-PEEP during volume ventilation of a non-triggering patient.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of double triggering during ventilation.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of double triggering during ventilation.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of double triggering during ventilation.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent a myriad of ventilatory parameters. However, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of Auto-PEEP during volume ventilation of a non-triggering patient.

Abstract: This disclosure describes systems and methods for monitoring ventilatory parameters, analyzing ventilatory data associated with those parameters, and providing useful notifications and/or recommendations to clinicians. For example, many clinicians may not easily identify or recognize data patterns and correlations indicative of a fluctuation in resistance during mechanical ventilation of a patient. Furthermore, clinicians may not easily determine potential causes for the fluctuation in resistance and/or steps for mitigating the fluctuation in resistance. According to embodiments, a ventilator may be configured to monitor and evaluate diverse ventilatory parameters to detect fluctuations in resistance and may issue suitable notifications and recommendations to the clinician based on potential causes of the fluctuation, ventilatory and/or patient data, etc.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent a myriad of ventilatory parameters. However, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of Auto-PEEP during volume ventilation of a non-triggering patient.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing ventilatory data associated with those parameters, and providing useful notifications and/or recommendations to clinicians. Modern ventilators monitor, evaluate, and graphically represent a myriad of ventilatory parameters. However, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of Auto-PEEP during various types of pressure ventilation.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of Auto-PEEP during volume ventilation of a triggering patient exhibiting an obstructive component.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing ventilatory data associated with those parameters, and providing useful notifications and/or recommendations to clinicians. Modern ventilators monitor, evaluate, and graphically represent a myriad of ventilatory parameters. However, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of Auto-PEEP during various types of pressure ventilation of a patient exhibiting an obstructive component.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of Auto-PEEP during volume ventilation of a non-triggering patient exhibiting an obstructive component.

Abstract: This disclosure describes systems and methods for monitoring ventilatory parameters, analyzing ventilatory data associated with those parameters, and providing useful notifications and/or recommendations to clinicians. For example, many clinicians may not easily identify or recognize data patterns and correlations indicative of a fluctuation in resistance during mechanical ventilation of a patient. Furthermore, clinicians may not easily determine potential causes for the fluctuation in resistance and/or steps for mitigating the fluctuation in resistance. According to embodiments, a ventilator may be configured to monitor and evaluate diverse ventilatory parameters to detect fluctuations in resistance and may issue suitable notifications and recommendations to the clinician based on potential causes of the fluctuation, ventilatory and/or patient data, etc.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory data to provide useful notifications and/or recommendations. Indeed, many clinicians may not easily identify or recognize data patterns and correlations indicative of certain patient conditions or the effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate adjustments that may address certain patient conditions or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the occurrence of high-delivered VT during various types of ventilation. According to embodiments, a ventilator may be configured to monitor and evaluate diverse ventilatory parameters to detect an occurrence of and potential causes for high-delivered VT and may subsequently issue suitable notifications and/or recommendations.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of inadequate flow during ventilation.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of double triggering during ventilation.

Abstract: This disclosure describes systems and methods for monitoring and evaluating ventilatory parameters, analyzing those parameters and providing useful notifications and recommendations to clinicians. That is, modern ventilators monitor, evaluate, and graphically represent multiple ventilatory parameters. However, many clinicians may not easily recognize data patterns and correlations indicative of certain patient conditions, changes in patient condition, and/or effectiveness of ventilatory treatment. Further, clinicians may not readily determine appropriate ventilatory adjustments that may address certain patient conditions and/or the effectiveness of ventilatory treatment. Specifically, clinicians may not readily detect or recognize the presence of double triggering during ventilation.

Abstract: This disclosure describes systems and methods for monitoring ventilatory parameters, analyzing ventilatory data associated with those parameters, and providing useful notifications and/or recommendations to clinicians. For example, many clinicians may not easily identify or recognize data patterns and correlations indicative of a fluctuation in resistance during mechanical ventilation of a patient. Furthermore, clinicians may not easily determine potential causes for the fluctuation in resistance and/or steps for mitigating the fluctuation in resistance. According to embodiments, a ventilator may be configured to monitor and evaluate diverse ventilatory parameters to detect fluctuations in resistance and may issue suitable notifications and recommendations to the clinician based on potential causes of the fluctuation, ventilatory and/or patient data, etc.

Abstract: This disclosure describes systems and methods for issuing a prompt in response to one or more proposed settings adjustments. Specifically, the prompt may include a projected impact of the one or more proposed settings adjustments on patient condition and/or patient treatment. The prompt may further provide an impact level and/or an alert associated with the projected impact. According to embodiments, one or more recommendations for alternative settings adjustments may be provided on the prompt or on an extension of the prompt. According to embodiments, a clinician may scroll through a plurality of potential settings adjustments until a desired impact level is displayed on the prompt (e.g., a positive impact level). According to still other embodiments, a plurality of proposed settings adjustments may be received. In this case, the projected impact displayed on the prompt may represent a combined projected impact of the plurality of proposed settings adjustments received.