A peristaltic pump and related-method are disclosed that includes a cam shaft having a plunger cam, a plunger-cam follower that engages the plunger cam of the cam shaft, a tube receiver, a spring, a plunger, a position sensor, and a processor. The tube receiver receives a tube. The spring provides a bias. The plunger is biased toward the tube by the spring and the plunger is to the plunger-cam follower, such that expansion of the plunger cam along a radial angle intersecting the plunger-cam follower as the cam shaft rotates actuates the plunger away from the tube. The position sensor determines a position of the plunger and the processor estimates fluid flow within the tube utilizing the position of the plunger.

A peristaltic pump and related-method are disclosed that includes a cam shaft having a plunger cam, a plunger-cam follower that engages the plunger cam of the cam shaft, a tube receiver, a spring, a plunger, a position sensor, and a processor. The tube receiver receives a tube. The spring provides a bias. The plunger is biased toward the tube by the spring and the plunger is to the plunger-cam follower, such that expansion of the plunger cam along a radial angle intersecting the plunger-cam follower as the cam shaft rotates actuates the plunger away from the tube. The position sensor determines a position of the plunger and the processor estimates fluid flow within the tube utilizing the position of the plunger.

Systems and methods for intelligently routing a member of an organization to a single point-of-contact within an optimized, secure network to address all the member's healthcare needs are described. The disclosed intelligent routing configurations transform and process, in real-time, vast amounts of member data to generate aggregated diagnoses and a member score specific to each member's household. The scores, among other things, are used to determine an identification of special needs and an appropriate advocate within the organization to route the member, and its account file containing real-time member and household level data.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

Methods, non-transitory computer readable media, and surgical computing devices are illustrated that improve robotic surgical systems. With this technology, one or more machine learning models are trained based on historical state data obtained for a computer-assisted surgical system (CASS) at each of a plurality of time periods during a plurality of historical knee arthroplasty surgical procedures. One or more of the machine learning models are applied to initial state data for a current knee arthroplasty surgical procedure to generate robotic commands required to achieve one or more future states of the CASS. The initial state data comprises a surgical plan. One or more surgical tools of the CASS are then manipulated based on the robotic commands to achieve the one or more future states of the CASS and thereby carry out at least a portion of the surgical plan.

Methods, non-transitory computer readable media, and surgical computing devices are illustrated that improve surgical planning using machine learning. With this technology, a machine learning model is trained based on historical case log data sets associated with patients that have undergone a surgical procedure. The machine learning model is applied to current patient data for a current patient to generate a predictor equation. The current patient data comprises anatomy data for an anatomy of the current patient. The predictor equation is optimized to generate a size, position, and orientation of an implant, and resections required to achieve the position and orientation of the implant with respect to the anatomy of the current patient, as part of a surgical plan for the current patient. The machine learning model is updated based on the current patient data and current outcome

A method and system for performing hip arthroplasty include analyzing images of a patient's hip joint in a plurality of positions to identify preoperative hip geometry. A statistical patient model predicts prosthetic hip implant performance based on the preoperative knee geometry and given prosthetic knee implant implantation parameters for a plurality of selected patient activities, each having a predefined motion profile to calculate an optimized surgical plan for performing the procedure using a computer assisted surgical system, which may use fiducial markers affixed to patient tissue. Hip geometry can be determined by angles between landmarks in the images, including sacral tilt, pelvic incidence, pelvic femoral angle, and ante-inclination angle in x-ray images. Implant performance criteria can include, for example, edge loading and range of motion of implant components.

An augmented reality system and method for assisting surgical staff during an arthroplasty procedure includes a camera system configured to capture images of a surgical scene and a processor configured to determine from the images location and orientation information about one or more patient anatomical features and to maintain a three-dimensional model of these anatomical features within the surgical scene. The system models the field-of-view of augmented reality headsets worn by surgical staff, and maps this to the anatomical model, allowing the system to overlay relevant information to the user about particular anatomical features in the surgical plan.

Methods and systems for performing a knee arthroplasty procedure include analyzing images of a patient's patellofemoral and femoral-tibial joint in a plurality of flexion positions to identify preoperative knee geometry. A statistical patient model predicts prosthetic knee implant performance based on the preoperative knee geometry and given prosthetic knee implant implantation parameters to calculate an optimized surgical plan for performing the procedure using a computer assisted surgical system, which may use fiducial markers affixed to patient tissue. The model can include selectable patient activities to adjust the motion profile for plan optimization.