Systems and Methods for Providing Third-Party Interactions with a Set of Task-Specific Components

1 . A method, comprising:

receiving, at a user interface of a computing device, a user identifier and a prompt related to an identified clinical task;

determining a set of task-specific components and a set of databases to which the user identifier has access;

selecting, by a machine-learning model trained to select from among the set of task-specific components, a task-specific component from among the set of task-specific components based on the prompt;

communicatively coupling the task-specific component to a database from the set of databases based on the prompt;

providing the prompt to the task-specific component;

receiving a response to the prompt, wherein the response is generated by the task-specific component using information from the database; and

providing the response to a user.

2 . The method of claim 1 , further comprising:

receiving a second user identifier and the prompt related to the identified clinical task;

determining a second set of task-specific components and a second set of databases to which the user identifier has access;

selecting, by the machine-learning model, a second task-specific component from among the second set of task-specific components based on the prompt;

communicatively coupling the second task-specific component to a second database from the second set of databases based on the prompt;

providing the prompt to the second task-specific component; and

receiving a second response to the prompt, wherein the second response is generated by the second task-specific component using information from the second database.

3 . The method of claim 1 , wherein the set of task-specific components comprises one or more task-specific agent modules.

4 . The method of claim 1 , wherein the user identifier comprises an authentication token for the user.

5 . The method of claim 1 , wherein the set of databases comprises one or more databases storing data owned by the user.

6 . The method of claim 1 , wherein the machine-learning model is a component of a super agent module.

7 . The method of claim 1 , wherein the task-specific component comprises an interconnected node architecture.

8 . The method of claim 1 , wherein the task-specific component comprises a patient query agent, and wherein the database stores information from medical documents provided by the user.

9 . The method of claim 1 , wherein each task-specific component in the set of task-specific components has a corresponding individual or group-level permission data, and wherein determining the set of task-specific components to which the user identifier has access comprises comparing the user identifier with the permission data.

10 . The method of claim 1 , wherein the task-specific component comprises a care gap agent configured to identify gaps in patient care plans, and wherein the database stores patient care plan data of the user.

11 . A computing system, comprising:

control circuitry;

memory; and

one or more sets of instructions stored in the memory and configured for execution by the control circuitry, the one or more sets of instructions comprising instructions for:

receiving, at a user interface of a computing device, a user identifier and a prompt related to an identified clinical task;

determining a set of task-specific components and a set of databases to which the user identifier has access;

selecting, by a machine-learning model trained to select from among the set of task-specific components, a task-specific component from among the set of task-specific components based on the prompt;

communicatively coupling the task-specific component to a database from the set of databases based on the prompt;

providing the prompt to the task-specific component;

receiving a response to the prompt, wherein the response is generated by the task-specific component using information from the database; and

providing the response to a user.

12 . The computing system of claim 11 , wherein the set of task-specific components comprises one or more task-specific agent modules.

13 . The computing system of claim 11 , wherein the user identifier comprises an authentication token for the user.

14 . The computing system of claim 11 , wherein the set of databases comprises one or more databases storing data owned by the user.

15 . The computing system of claim 11 , wherein the machine-learning model is a component of a super agent module.

16 . The computing system of claim 11 , wherein the task-specific component comprises an interconnected node architecture.

17 . A non-transitory computer-readable storage medium storing one or more sets of instructions configured for execution by a computing device having control circuitry and memory, the one or more sets of instructions comprising instructions for:

receiving, at a user interface of a computing device, a user identifier and a prompt related to an identified clinical task;

determining a set of task-specific components and a set of databases to which the user identifier has access;

selecting, by a machine-learning model trained to select from among the set of task-specific components, a task-specific component from among the set of task-specific components based on the prompt;

communicatively coupling the task-specific component to a database from the set of databases based on the prompt;

providing the prompt to the task-specific component;

receiving a response to the prompt, wherein the response is generated by the task-specific component using information from the database; and

providing the response to a user.

18 . The non-transitory computer-readable storage medium of claim 17 , wherein the set of task-specific components comprises one or more task-specific agent modules.

19 . The non-transitory computer-readable storage medium of claim 17 , wherein the user identifier comprises an authentication token for the user.

20 . The non-transitory computer-readable storage medium of claim 17 , wherein the set of databases comprises one or more databases storing data owned by the user.