Disclosed herein are systems and methods for identifying radiation therapy treatment data for different patients, such as field geometry. A central server collects patient data, radiation therapy treatment planning data, clinic-specific rules, and other pertinent treatment/medical data associated with a patient. The server then executes one or more machine-learning computer models to predict field geometry variables and weights associated with the patient's treatments. Using the predicted variables and weights, the server execute a clinic-specific set of logic to identify suggested field geometry, such as couch/gantry angles and/or arc attributes. The server then monitors whether end users (e.g., medical professionals) revise the suggested field geometry and trains the model accordingly.

A surgical product supply system includes a cart having a first compartment and a second compartment. The first compartment has first, second, third and fourth walls. The first and second walls are constructed of radio-reflective material and the third and fourth walls are constructed of a radio-absorptive material. The first compartment has a first storage area. A first RFID antenna array is attached to the first wall and is positioned within the first storage area. The first RFID antenna array includes a first plurality of RFID antennas. A second RFID antenna array is attached to the second wall and is positioned within the first storage area. The second RFID antenna array includes a second plurality of RFID antennas. The first RFID antenna is offset relative to the second RFID antenna such that opposing central axes of the first and second RFID antennas are not colinear.

In some embodiments, a clinician programmer device for controlling a deep brain stimulation (DBS) system is adapted to assist a clinician to conduct an electrode screening review for the DBS system including screening of segmented electrodes. The clinician programmer stores software code for conducting a screening review in memory. The software code may comprise: code for providing one or more interface screens for guiding the user of the device through testing of electrode configurations of the implantable stimulation lead, wherein the code for providing applies at least one testing progression for guiding the user of the device through a defined testing order.

In some embodiments, a clinician programmer device for controlling a deep brain stimulation (DBS) system is adapted to assist a clinician to conduct an electrode screening review for the DBS system including screening of segmented electrodes. The clinician programmer stores software code for conducting a screening review in memory. The software code may comprise: code for providing one or more interface screens for guiding the user of the device through testing of electrode configurations of the implantable stimulation lead, wherein the code for providing applies at least one testing progression for guiding the user of the device through a defined testing order.

A system includes an imaging device and a server device in communication with the imaging device. The imaging device is positioned to capture images of a set of inventory items associated with health care tasks and is configured to generate a first signal when an inventory item from among the set of inventory items has been retrieved. The server device includes a processor that is configured to receive an identification of a health care task associated with the inventory item; receive the first signal from the imaging device indicating when the inventory item has been retrieved; update a database record associated with the inventory item within a database responsive to receiving the first signal; and transmit a second signal to a client device including information associated with the updated database record.

A system includes an imaging device and a server device in communication with the imaging device. The imaging device is positioned to capture images of a set of inventory items associated with health care tasks and is configured to generate a first signal when an inventory item from among the set of inventory items has been retrieved. The server device includes a processor that is configured to receive an identification of a health care task associated with the inventory item; receive the first signal from the imaging device indicating when the inventory item has been retrieved; update a database record associated with the inventory item within a database responsive to receiving the first signal; and transmit a second signal to a client device including information associated with the updated database record.

A system for providing a quick response (QR) code associated with an injection system is disclosed. The system includes the injection system and at least one processor. The at least one processor is programmed or configured to: receive data associated with the injection system; generate a network resource based on the data associated with the injection system, wherein, when generating the network resource, the at least one processor is programmed or configured to encode the data associated with the injection system into the network resource; generate a QR code based on the network resource; and display the QR code on a display screen of the injection system.

A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

The present disclosure relates to quality control for point-of-care medical diagnostic systems. In various embodiments, the system includes an on-board storage containing a synthetic quality control material, a plurality of sub-systems having a plurality of operating parameters and including a material analyzer, a database storing quality control results that include results of the material analyzer analyzing the synthetic quality control material over time, one or more processors, and at least one memory storing instructions which, when executed by the one or more processors, cause the system to, automatically without user intervention: generate a control chart based on the quality control results, determine that a parameter of the plurality of operating parameters is out-of-tolerance based on the control chart, and adjust at least one of the plurality of sub-systems without user intervention to bring the out-of-tolerance parameter to within tolerance.