The patent's assignee is
News editors obtained the following quote from the background information supplied by the inventors: "A traditional social graph is a social structure made of individuals, groups, entities, or organizations generally referred to as 'nodes.' Which are connected by one or more specific types of interdependency. Social graph analysis views social relationships in terms of network theory consisting of nodes and edges. Nodes are the individual actors or data points and edges are the relationships between the nodes. The resulting network-based structures are often very complex. There can be many kinds of edges between nodes. In its simplest form, a social graph or social network is a map of all of the relevant edges between all the nodes being studied.
"Social graphs are generally hosted on computer systems. The computer systems are connected to various local and wide area computer networks allowing users to interact with the information located on various computer systems. Users may enter personal information, view information about others, search for information, and update information about others."
As a supplement to the background information on this patent application, VerticalNews correspondents also obtained the inventor's summary information for this patent application: "This disclosure relates to a method, a system, and a program that use social networking techniques to identify teams of clinicians within a medical institution.
"The method includes accessing a social graph with a computer system including a clinician node for each clinician in the institution. An edge is created for each organizational (formal) relationship and each professional (informal) relationship between the clinicians. Patient data is collected for each patient treated at the institution and entered into the computer system. The computer system associates each patient with a patient node in the social graph. Then the computer system creates an edge between each patient node and an element node which corresponds to each element of patient data corresponding to the patient. The computer system creates another edge between each patient node and the clinician node corresponding to the treating clinician. The computer system creates another edge between each element node of patient data and each clinician node corresponding to the treating clinician. The computer system also monitors each usage of the patient data by each clinician and creates an edge between each clinician node and the element node of patient data. Next, the computer system assigns a weight to each connection between the nodes. The weight of each connection relates to the number of edges connecting each node either directly or indirectly through intermediate nodes. The weighting may relate to the characteristics of each edge. The computer system can be configured to give greater weight to edges possessing certain characteristics. For example, formal organizational relationships may be weighted more heavily than informal professional relationships. The computer system creates a first data set that scores each clinician node that is connected to a chosen node. The computer system displays each clinician that corresponds to a clinician node with a score over a threshold score. Each displayed node may be ranked by the score.
"The present disclosure can also be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which when loaded in a computer system is able to carry out these methods.
"By identifying teams of clinicians, relationships between the clinicians may be improved from the identification of unknown similarities by particular clinicians. When a team of clinicians is identified, the team may be assembled as a clinical team for a given patient, a group of patients, an advisory board for a particular condition, a panel discussion, or more informal teams such as a luncheon. By identifying teams of clinicians and improving clinician relationships, either professional relationships or non-professional relationships, the communication between clinicians will be improved. The improvement in communication may result in improved clinical knowledge among the clinicians and ultimately improved patient outcomes. The dynamic and organic nature of the development of teams by these methods may result in the creation of teams that transcend formal organizational boundaries to provide a level of care that might otherwise be limited by those formal boundaries. The identification of these teams may provide valuable insights leading to revisions of an institution's formal organizational structure, resulting in new organizational structures that are optimized for patient care and improved outcomes. Identifying teams of clinicians may also allow patients to identify a team of clinicians which have experience or expertise with a particular element of patient data. This identified team may he used to form a specialized team to treat the patient or for the patient to seek a second opinion. Identifying a team of clinicians with experience or expertise with a particular element of patient data may also be used by the institution to form a research team for the element of patient data such as a specific disease.
"Certain embodiments of the present disclosure may include some, all, or none of the above advantages. One or more other technical advantages may be readily apparent to those skilled in the art for the figures, descriptions, and claims included herein. Moreover, while specific advantages have been enumerated above, various embodiments may include all, some, or none of the enumerated advantages.
BRIEF DESCRIPTION OF THE DRAWINGS
"There are shown in the drawings embodiments, which are exemplary, it being understood that the disclosure is not limited to the precise arrangements and instrumentalities shown.
"FIG. 1 is an example of computer system architecture.
"FIG. 2 is an example of integrated system architecture.
"FIG. 3 is an example of network architecture.
"FIG. 4 is an example of a portion of a social graph.
"FIG. 4A is a flowchart illustrating a method with may be used to create the portion of a social graph illustrated in FIG. 4.
"FIG. 5A is an example of another portion of the social graph of FIG. 4.
"FIG. 5B is an example of another portion of the social graph of FIG. 4.
"FIG. 5C is a flowchart illustrating methods with may be used to create the portions of a social graph illustrated in FIGS. 5A and 5B.
"FIG. 6 is an example of another portion of the social graph of FIG. 4.
"FIG. 6A is a flowchart illustrating a method with may be used to create the portion of a social graph illustrated in FIG. 6.
"FIG. 7 is an example of groups of edges connected to a chosen node.
"FIG. 7A is an example of a data set.
"FIG. 7B is a flowchart illustrating a method with may be used to create data set illustrated in FIG. 7B.
"FIG. 8A is an example of a display showing patient data.
"FIG. 8B is another example of a display showing patient data.
"FIG. 8C is an example of a display showing patient data received from a medical device.
"FIG. 9 is an example of a display.
"FIG. 10 is an illustration of an example of a method for identifying a team of clinicians."
For additional information on this patent application, see: Hartman, Robert. Social Network Techniques Applied to the Use of Medical Data. Filed
Keywords for this news article include: .
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