SOCIAL NETWORK TECHNIQUES APPLIED TO THE USE OF MEDICAL DATA

• US 20140236625A1
• Filed: 02/15/2013
• Published: 08/21/2014
• Est. Priority Date: 02/15/2013
• Status: Abandoned Application

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Hartman, Robert
• Application Number:
  US13/768,419
• Publication Number:
  US 20140236625A1
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/63 for local operation

Abstract

A method for indentifying a clinician team using a computer system including accessing a social graph with nodes for each clinician in a medial institution and collecting patient data from patients treated by the medical institution. Edges are created between patient nodes and element nodes corresponding to patient data. Edges are also created between each element node the clinician node of each treating clinician. Additional edges are created by monitoring the usage of patient data by each clinician. A weight is assigned to each connection between each of the nodes. A data set is created which scores each clinician node with respect to a chosen node. Each clinician with a clinician node with a score over a threshold score is displayed. The display may automatically form a team of clinicians.

Claims

1. A method for identifying clinician teams comprising:

accessing, by one or more computer systems, a social graph with a plurality of nodes having a clinician node for each clinician of an institution and at least an edge between each clinician node corresponding to each organizational relationship and each professional relationship between each clinician;

collecting at least one element of patient data for each of a plurality of patients treated by the institution;

associating, by the one or more computer systems, each of the plurality of patients with a patient node and creating an edge between each patient node and an element node corresponding to each element of patient data, the one or more computer systems creating an edge between each element node of corresponding patient data with each clinician node corresponding to each clinician treating the patient;

monitoring, by the one or more computer systems, each usage of the patient data by each clinician and creating an edge between each clinician node corresponding to the clinician using the patient data and each element node of patient data used;

assigning, by the one or more computer systems, a weight to each connection between each of the plurality of nodes relating at least to the number of edges between each of the plurality of nodes;

creating, by the one or more computer systems, a first data set that scores each clinician node that is connected to at least one chosen node of the plurality of nodes by the weight of the connection between the clinician node and the at least one chosen node; and

displaying, by the one or more computer systems, each clinician corresponding to a clinician node having a score over a threshold score in the first data set.

2. The method according to claim 1, wherein the patient data includes at least one of a treating clinician, a reported condition, a set of physiological data, a set of medical device settings, a course of treatment, or a reaction to the course of treatment.

3. The method according to claim 2, wherein at least one of the set of physiological data and the set of medical device settings are automatically received from at least one medical device.

4. The method according to claim 1, wherein the each usage includes at least one of a prescribed course of treatment for a patient, an update to the patient data, a search of the patient data, or an inquiry of the patient data.

5. The method according to claim 1, wherein the threshold score varies by the weight of the connection between at least two clinician nodes within the first data set.

6. The method according to claim 1, wherein a clinician team is automatically formed from each clinician displayed.

7. The method according to claim 1, wherein displaying includes at least one of emailing, outputting to a display device, or texting.

8. A system for identifying clinician teams comprising:

a processor;

a mass storage component coupled to the processor and storing a social graph with a plurality of nodes having a clinician node for each clinician of an institution and at least an edge for each organizational relationship and for each professional relationship between each clinician;

at least one interface which collects at least one element of patient data for each of a plurality of patients treated by the institution, the processor associates each of the plurality of patients with a patient node and creates an edge between each patient node and an element node corresponding to the element of patient data, the processor creates an edge between each element node and the clinician node of each clinician treating the patient; and

a display,

wherein the at least one interface is monitored by the processor which creates an edge between each clinician node corresponding to the clinician using the patient data and each element node of patient data used, the processor assigns a weight to each connection between each of the plurality of nodes by at least the number of edges between each of the plurality of nodes, the processor creates a first data set that scores each clinician node that is connected to at least one chosen node of the plurality of nodes by the weight of the connection between the clinician node and at least one chosen node, the processor displays each clinician corresponding to each clinician node over a threshold score in the first data set.

9. The system according to claim 8, furthering including an integrated system coupled to the processor for automatically collecting the patient data.

10. The system according to claim 9, wherein the integrated system is coupled to the processor by a network.

11. The system according to claim 10, wherein the network includes a first trusted network which is connected to a second trusted network by a wide-area network, wherein only the first trusted network and the second network can decipher data transmitted through the wide-area network.

12. The system according to claim 11, wherein the processor is in the first trusted network and the mass storage component is in the second trusted network.

13. The system according to claim 8, wherein the processor displays each clinician on a display device.

14. The system according to claim 8, wherein the processor runs an algorithm that creates a second data set that scores each connection between each clinician node and every other clinician node, the algorithm identifies at least two clinician nodes each having a score in the second data set that is below a desired score, the algorithm searches the social graph for a chosen node having a first data set where each of the at least two clinician nodes are over the threshold score, wherein the processor displays each of the clinicians corresponding to that at least two clinician nodes and the chosen node having a first data set where each of the at least two clinician nodes are over the threshold score.

15. A computer readable medium having embodied thereon a program, the program being executable by a processor for performing a method for identifying a clinician team, the method comprising:

accessing, by one or more computer systems, a social graph with a plurality of nodes having a clinician node for each clinician of an institution and at least an edge between each clinician node corresponding to each organizational relationship and each professional relationship between each clinician;

collecting at least one element of patient data for each of a plurality of patients treated by the institution;

associating, by the one or more computer systems, each of the plurality of patients with a patient node and creating an edge between each patient node and an element node corresponding to each element of patient data, the one or more computer systems creating an edge between each element node of corresponding patient data with each clinician node corresponding to each clinician treating the patient;

monitoring, by the one or more computer systems, each usage of the patient data by each clinician and creating an edge between each clinician node corresponding to the clinician using the patient data and each element node of patient data used;

assigning, by the one or more computer systems, a weight to each connection between each of the plurality of nodes relating at least to the number of edges between each of the plurality of nodes;

creating, by the one or more computer systems, a first data set that scores each clinician node that is connected to at least one chosen node of the plurality of nodes by the weight of the connection between the clinician node and the at least one chosen node; and

displaying, by the one or more computer systems, each clinician corresponding to a clinician node having a score over a threshold score in the first data set.

16. The computer readable medium of claim 15, wherein the patient data includes at least one of a treating clinician, a reported condition, a set of physiological data, a set of medical device settings, a course of treatment, or a reaction to the course of treatment.

17. The computer readable medium of claim 15, wherein the each usage includes at least one of a prescribed course of treatment for a patient, an update to the patient data, a search of the patient data, or an inquiry of the patient data.

18. The computer readable medium of claim 15, wherein assigning the weight further includes multiplying each edge by a factor for the amount of time that has passed since the edge was created.

19. The computer readable medium of claim 18, wherein the factor is reduced as the amount of time increases.

20. The computer readable medium of claim 19, wherein the factor ranges from 1 to 0.