Abstract: Systems and methods for managing surgical sponges. A dispenser assembly coupled to a main support includes an internal or first storage location to removably receive a carton of sponge sorters, and an external or second storage location to removably support a carton of surgical draping. At least one arm may be supported on the housing and movable between an undeployed position and a deployed position. The arm may include weighing means to measure a weight of the surgical sponge being disposed in a sponge sorter supported by the arm. The arm may be a cantilever and abeam load cell may be disposed within a channel of a beam. A processor may determine fluid weight based on measured weight and dry weight correlated with the surgical sponge being counted out as detected with a data reader. The processor may estimate blood loss to be displayed on a display.

Abstract: Methods, systems, and computer readable medium for a multi-source item creation system. The method includes receiving, through a user interface and from a requesting member of a digital platform, manufacturing parameters for an item to be manufactured, dynamically altering the user interface based on the manufacturing parameters to include different user interface elements that correspond to a subset of attributes, determining, based on interaction with the different user interface elements, additional manufacturing parameters, generating, using one or more machine learning models, a set of manufacturers based on the manufacturing parameters and the additional manufacturing parameters, wherein the one or more machine learning models have been trained to generate the set of manufacturers based on a level of uniqueness of the item to be manufactured and characteristics of the manufacturers, and returning, to the requesting member, a subset of the set of manufacturers.

Abstract: Methods, systems, and computer readable medium for a multi-source item creation system. The method includes receiving, through a user interface and from a requesting member of a digital platform, manufacturing parameters for an item to be manufactured, dynamically altering the user interface based on the manufacturing parameters to include different user interface elements that correspond to a subset of attributes, determining, based on interaction with the different user interface elements, additional manufacturing parameters, generating, using one or more machine learning models, a set of manufacturers based on the manufacturing parameters and the additional manufacturing parameters, wherein the one or more machine learning models have been trained to generate the set of manufacturers based on a level of uniqueness of the item to be manufactured and characteristics of the manufacturers, and returning, to the requesting member, a subset of the set of manufacturers.

Abstract: Methods, systems, and computer readable medium for a multi-source item creation system. The method includes receiving, through a user interface and from a requesting member of a digital platform, manufacturing parameters for an item to be manufactured, dynamically altering the user interface based on the manufacturing parameters to include different user interface elements that correspond to a subset of attributes, determining, based on interaction with the different user interface elements, additional manufacturing parameters, generating, using one or more machine learning models, a set of manufacturers based on the manufacturing parameters and the additional manufacturing parameters, wherein the one or more machine learning models have been trained to generate the set of manufacturers based on a level of uniqueness of the item to be manufactured and characteristics of the manufacturers, and returning, to the requesting member, a subset of the set of manufacturers.

Abstract: Methods, systems, and computer readable medium for a multi-source item creation system. The method includes receiving, through a user interface and from a requesting member of a digital platform, manufacturing parameters for an item to be manufactured, dynamically altering the user interface based on the manufacturing parameters to include different user interface elements that correspond to a subset of attributes, determining, based on interaction with the different user interface elements, additional manufacturing parameters, generating, using one or more machine learning models, a set of manufacturers based on the manufacturing parameters and the additional manufacturing parameters, wherein the one or more machine learning models have been trained to generate the set of manufacturers based on a level of uniqueness of the item to be manufactured and characteristics of the manufacturers, and returning, to the requesting member, a subset of the set of manufacturers.