Abstract: A computing system and/or a method may be provided for using a risk assessment to provide a notification. The computing system may comprise a processor. The processor may be configured to perform the method. A biomarker may be received for a patient from a sensing system. A data collection that includes pre-surgical data may be received for a patient. A probability of a patient outcome due to a surgery performed on the patient may be determined using the biomarker and the data collection. A notification may be sent to a user. The notification may indicate that the probability of the surgical complication may exceed a threshold.

Abstract: A computing system for measuring and monitoring patient biomarkers for detecting or predicting a post-surgical hysterectomy complication may be provided. A post-surgical hysterectomy complication may be predicted or detected by comparing measured/processed patient biomarker data with a corresponding determined threshold value. The comparison of the measured/processed patient biomarker data and the corresponding threshold may be performed in association with a context. The context may be based on at least one of a hysterectomy surgery recovery timeline, at least one situational attribute, or at least one environmental attribute. A notification message associated with a predicted or detected post-surgical hysterectomy complication may be sent (e.g., sent in real time) to a patient device or a healthcare provider's device. The notification message may be supplemented by a severity level message.

Abstract: Blood perfusion difficulty complication(s) may be predicted based on biomarker measurements obtained before a surgery and/or during the surgery via one or more sensing systems. For example, a computing system may monitor the patient biomarker(s) including core body temperature, peripheral temperature, blood sugar level, hydration state data, and/or oxygen saturation data. Based on the prediction, the computing system may generate a control signal configured to alter a matter in which a surgical cutting and stapling device and/or a surgical energy operate, to adjust a surgical procedure plan, to adjust a surgical instrument selection, indicate a probability of the blood perfusion difficulty complication, and/or to indicate a suggested adjustment to surgical procedure plan, surgical approach, and/or surgical instrument selection.

Abstract: Patient biomarkers may be monitored before, during, and/or after thoracic surgery to predict complications, detect complications, track recovery, and/or make pre-, in- and/or post-surgery recommendations to avoid predicted complications and/or mitigate detected complications. Complications (e.g., prolonged air leak or esophageal stricture) may be predicted based on patient parameters and/or biomarker measurements. Complications may be predicted or detected based on biomarker measurements compared to thresholds associated with a biomarker (e.g., developed from baselines) generated based on patient parameters, pre- and/or in-surgery biomarker measurements, and/or surgical details (e.g., decrease in lung capacity). Recovery milestones may be tracked based on biomarker measurements compared to predicted biomarker measurements for recovery stages. A recommendation (e.g.

Abstract: Tissue irregularity complication(s) may be predicted based on biomarker measurements obtained before a surgery and/or during the surgery via one or more sensing systems. For example, a computing system may monitor the patient biomarker(s) including tissue perfusion pressure, lactate, oxygen saturation, VO2Max, respiration rate, autonomic tone, sweat rate, heart rate variability, skin conductance, GI motility, edema and/or hydration state. Based on the prediction, the computing system may generate a control signal configured to alter a matter in which a surgical cutting and stapling device and/or a surgical energy operate, to adjust a surgical procedure plan, to adjust a surgical instrument selection, indicate a probability of the tissue irregularity complication, and/or to indicate a suggested adjustment to surgical procedure plan, surgical approach, and/or surgical instrument selection.

Abstract: A computing system for measuring and monitoring patient biomarkers for detecting or predicting a post-surgical colorectal complication may be provided. A post-surgical colorectal complication may be predicted or detected by comparing measured/processed patient biomarker data with a corresponding determined threshold value. The comparison of the measured/processed patient biomarker data and the corresponding threshold may be performed in association with a context. The context may be based on at least one of a colorectal surgery recovery timeline, at least one situational attribute, or at least one environmental attribute. A notification message associated with a predicted or detected post-surgical colorectal complication may be sent (e.g., sent in real time) to a patient device or a healthcare provider's device. The notification message may be supplemented by a severity level message.

Abstract: A computing system may predict a complication based on measurements of related biomarker(s) obtained via one or more sensing systems, and generate an adjustment of a surgical parameter associated with a surgery. The biomarker measurements may include pre-surgical and/or in-surgical measurements. The hemostasis-related biomarker(s) measured pre-surgery may include blood pressure, blood pH, edema, heart rate, blood perfusion rate, coagulation status and/or the like. Based on the prediction, the computing system may generate a control signal configured to alter a matter in which a surgical cutting and stapling device and/or a surgical energy operate, to adjust a surgical procedure plan, to adjust to a surgical instrument selection, indicate a probability of the hemostasis complication, and/or to indicate a suggested adjustment to surgical procedure plan, surgical approach, and/or surgical instrument selection.

Abstract: A computing device may include an input, a processor, and an output. The device may be configured to receive two points of surgical sensor data from different sensors. The sensors may include wearable patient sensors and/or surgical theater environmental sensor system. The processor may determine a surgical device setting (e.g., a closure load for a powered surgical stapler, or for example, a power level of a surgical energy device). And the output may send a signal indicative of the determined setting. A surgical device may receive the signal and perform a surgical action based on the determined setting. Using a combination of patient-specific and/or surgical-environment-specific sensor inputs to determine a more optimal device setting may lead to better device perform and ultimately better patient outcomes.

Abstract: A surgical computing system may receive usage data associated with movement of a surgical instrument and user inputs to the surgical instrument. The surgical computing system may receive motion and biomarker sensor data from sensing systems applied to the operator of the surgical instrument. The surgical computing system may determine, based on at least one of the usage data and/or the sensor data, a control feature for implementation by the surgical instrument. The surgical computing system may communicate the determined control feature(s) to the surgical instrument. The surgical instrument may modify its operation based on the control features.

Abstract: A computing system for measuring and monitoring patient biomarkers for detecting or predicting a post-surgical bariatric complication may be provided. A post-surgical bariatric complication may be predicted or detected by comparing measured/processed patient biomarker data with a corresponding determined threshold value. The comparison of the measured/processed patient biomarker data and the corresponding threshold may be performed in association with a context. The context may be based on at least one of a bariatric surgery recovery timeline, at least one situational attribute, or at least one environmental attribute. A notification message associated with a predicted or detected post-surgical bariatric complication may be sent (e.g., sent in real time) to a patient device or a healthcare provider's device. The notification message may be supplemented by a severity level message.

Abstract: A surgical computing system may receive usage data associated with movement of a surgical instrument and user inputs to the surgical instrument. The surgical computing system may receive motion and biomarker sensor data from sensing systems applied to the operator of the surgical instrument. The surgical computing system may determine, based on at least one of the usage data and/or the sensor data, an evaluation of the actions of the operator of the surgical instrument. The surgical computing system may determine, based on the evaluation, to provide feedback. The feedback may comprise instructions for the surgical instrument to provide haptic feedback and/or to modify its configuration. The feedback may comprise instructions for a display unit to present notifications instructing the healthcare professional. The surgical computing system may communicate instructions for providing the feedback to the surgical instrument and/or the display unit.

Abstract: A surgical computing system may scan for a sensing system located in an operating room. Upon detecting a sensing system in the operating room, the surgical computing system may establish a link with the sensing system. The surgical computing system may receive user role identification data from the sensing system using the established link. The surgical computing system may identify a user role for a user in the operating room based on the received user role identification data. The user role of a user may be or may include at least one of a surgeon, a nurse, a patient, a hospital staff, or a health care professional. Based on the identified user role, the surgical computing system may generate and send surgical aid information for the user in the operating room. The surgical aid information may include information associated with a surgical operation relevant to the identified user role.

Abstract: A surgical computing system may receive usage data associated with movement of a surgical instrument and user inputs to the surgical instrument. The surgical computing system may receive motion and/or biomarker sensor data from sensing systems applied to the operator of the surgical instrument. The surgical computing system may determine, based on at least one of the usage data and/or the sensor data, a control feature for implementation by the surgical instrument. The surgical computing system may communicate the determined control feature(s) to the surgical instrument. The surgical instrument may modify its operation based on the control features.

Abstract: Examples herein may include a computer-implemented method for providing outcome tracking of patients, which may include generating an event trigger for the patient, wherein during a duration of the patient's recovery, the event trigger may correspond to values of a patient biomarker over or under threshold values while the patient is performing a post-surgery activity related to the patient's recovery. The computer-implemented method may include receiving actual patient biomarker data from a patient sensor system for the patient while the patient is performing a post-surgery activity. If the actual patient biomarker data includes values over or under the threshold value while the patient is performing a post-surgery activity, the method may include triggering the event trigger. The method may include generating a notification alert corresponding to the event trigger.

Abstract: A computing system may monitor a patient's biomarkers pre-surgery and/or in-surgery and predict an adhesion complication. The computing system may obtain measurement data associated with one or more patient biomarkers via one or more sensing systems, predict an adhesion complication based on the measurement data associated with the one or more patient biomarkers, and generate an output based on the predicted adhesion complication. The adhesion complication may be predicted by determining a probability of a chronic inflammation response based on the measurement data associated with the one or more patient biomarkers. The generated output may include a control signal configured to indicate an adjustment to an instrument selection for dissecting capability and/or access capability. The generated output may include a control signal configured to notify a surgeon of a probability of an adhesion complication.

Abstract: Examples herein may include a computer-implemented method for providing outcome tracking of a plurality of patients, which may include generating a respective expected patient biomarker dataset for each of the plurality of patients, wherein the expected patient biomarker dataset may represent the expected values of a patient biomarker over the duration of the patient's recovery. The computer implementing method may include receiving respective actual patient biomarker data from respective patient sensor systems for each of the plurality of patients. The method may include aggregating the respective expected patient biomarker dataset and the respective actual patient biomarker data for each of the plurality of patients. The method may include determining differences between the respective expected patient biomarker data and the respective actual patient biomarker data. The method may include generating a treatment notification based on the differences.

Abstract: Examples herein may include a computer-implemented method for contextually transforming data into an aggregated display feed. The method may include determining a first biomarker from a first data stream and a second biomarker from a second data stream. The method may include determining that the first biomarker and the second biomarker are interlinked to a physiologic function or morbidity. The method may include determining one or more cooperative measures related to the physiologic function or morbidity using the first biomarker and the second biomarker. The method may include generating a directional measure to indicate a contextual summary of the one or more cooperative measure. The method may include displaying the directional measure to a health care provider.

Abstract: A computing system and/or a method may be provided for using a risk assessment to provide a notification. The computing system may comprise a processor. The processor may be configured to perform the method. A biomarker may be received for a patient from a sensing system. A data collection that includes pre-surgical data may be received for a patient. A probability of a patient outcome due to a surgery performed on the patient may be determined using the biomarker and the data collection. A notification may be sent to a user. The notification may indicate that the probability of the surgical complication may exceed a threshold.

Abstract: A surgical-data-processing modification command may be triggered based on changing surgical data processing requirements of the surgical procedure. And the surgical-data-processing modification command may direct changes in processing such as output frequency, output resolution, processing resource utilization, operational data transforms, and the like. The surgical-data-processing modification command and the system disclosed herein may be used to implement a variety of processing strategies for surgical sensing, including procedure specific load balancing and sensor prioritization.