Methods and systems for grouping informed advisor pairings

1 . A system for grouping informed advisor pairings, the system comprising:

a computing device, wherein the computing device is configured to:

obtain a first user feature;

obtain an element of user geolocation that identifies the geographical location of the user;

determine a first prognostic user feature, wherein determining a first prognostic user feature comprises:

training a prognostic user feature machine learning model on a training dataset including a plurality of example user features as inputs correlated to a plurality of example prognostic user features as outputs; and

generating the first prognostic user feature as a function of the first user feature using the trained prognostic user feature machine learning model;

determine an informed advisor element as a function of the first prognostic user feature and the geographical location of the user, wherein determining the informed advisor element comprises training, using element training data comprising a plurality of prognostic user features correlated to a plurality of informed advisor elements, a machine-learning model configured to receive the first prognostic user feature as an input and output the informed advisor element, wherein the computing device is configured to iteratively update the element training data to reflect geographical variances among correlations between the plurality of prognostic user features and the plurality of informed advisor elements;

group a user with an informed advisor as a function of the informed advisor element based on the first prognostic user feature;

update a user medical profile as a function of the first prognostic user feature;

transmit the updated user medical profile to a remote device operated by the informed advisor; and

display the updated medical profile on the remote device.

2 . The system of claim 1 , wherein obtaining the first user feature comprises:

transmitting, to a user device operated by the user, a feedback prompt;

receiving, from the user device, a feedback response entered by the user based on the feedback prompt; and

transmitting, to a remote device operated by the informed advisor, the feedback response.

3 . The system of claim 1 , wherein obtaining the first user feature comprises:

using at least a microphone, generating an interaction recording by recording a verbal interaction between an informed advisor and the user; and

transcribing the verbal interaction recording using an automatic speech recognition system.

4 . The system of claim 1 , wherein the computing device is configured to determine the informed advisor element as a function of a review of the informed advisor.

5 . The system of claim 1 , wherein the computing device is configured to update the user medical profile such that the user medical profile includes a medical session datum.

6 . The system of claim 1 , wherein the computing device is further configured to:

obtain a second user feature subsequent to the grouping of the user with the informed advisor;

determine a second prognostic user feature as a function of the second user feature using the trained prognostic user feature machine learning model;

transmit the second prognostic user feature to a remote device operated by the informed advisor; and

adjust the user medical profile as a function of a comparison between the first prognostic user feature and the second prognostic user feature.

7 . The system of claim 1 , wherein:

the first prognostic user feature indicates that the user is likely to develop a medical condition;

the informed advisor element comprises a competency of an informed advisor; and

the competency includes treatment of the medical condition.

8 . The system of claim 1 , wherein:

the first user feature comprises a user preference datum; and

grouping the user with the informed advisor comprises scheduling an interaction between the user and the informed advisor as a function of the user preference datum.

9 . A method of grouping informed advisor pairings, the method comprising:

using at least a processor, obtaining a first user feature;

using at least the processor, obtain an element of user geolocation that identifies the geographical location of the user;

determining, by at least the processor, a first prognostic user feature as a function of the first user feature, wherein determining a first prognostic user feature comprises:

training a prognostic user feature machine learning model on a training dataset including a plurality of example user features as inputs correlated to a plurality of example prognostic user features as outputs; and

generating the first prognostic user feature as a function of the first user feature using the trained prognostic user feature machine learning model;

determining, by at least the processor, an informed advisor element as a function of the first prognostic user feature and the geographical location of the user, wherein determining the informed advisor element comprises training, using element training data comprising a plurality of prognostic user features correlated to a plurality of informed advisor elements, a machine-learning model configured to receive the first prognostic user feature as an input and output the informed advisor element, wherein the computing device is configured to iteratively update the element training data to reflect geographical variances among correlations between the plurality of prognostic user features and the plurality of informed advisor elements;

grouping, by at least the processor, a user with an informed advisor as a function of the informed advisor element based on the first prognostic user feature;

updating, by at least the processor, a user medical profile as a function of the first prognostic user feature;

transmitting, by at least the processor, the updated user medical profile to a remote device operated by the informed advisor; and

displaying the updated medical profile on the remote device.

10 . The method of claim 9 , wherein obtaining the first user feature comprises:

transmitting, to a user device operated by the user, a feedback prompt;

receiving, from the user device, a feedback response entered by the user based on the feedback prompt; and

transmitting, to a remote device operated by the informed advisor, the feedback response.

11 . The method of claim 9 , wherein obtaining the first user feature comprises:

using at least a microphone, generating an interaction recording by recording a verbal interaction between an informed advisor and the user; and

transcribing the verbal interaction recording using an automatic speech recognition system.

12 . The method of claim 9 , wherein the informed advisor element is determined as a function of a review of the informed advisor.

13 . The method of claim 9 , wherein the method further comprises, using the at least a processor, updating the user medical profile such that the user medical profile includes a medical session datum.

14 . The method of claim 9 , wherein the method further comprises:

using the at least a processor, obtaining a second user feature subsequent to the grouping of the user with the informed advisor;

using the at least a processor, determining a second prognostic user feature as a function of the second user feature using the trained prognostic user feature machine learning model;

using the at least a processor, transmitting the second prognostic user feature to a remote device operated by the informed advisor; and

using the at least a processor, adjusting the user medical profile as a function of a comparison between the first prognostic user feature and the second prognostic user feature.

15 . The method of claim 9 , wherein:

the first prognostic user feature indicates that the user is likely to develop a medical condition;

the informed advisor element comprises a competency of an informed advisor; and

the competency includes treatment of the medical condition.

16 . The method of claim 9 , wherein:

the first user feature comprises a user preference datum; and

grouping the user with the informed advisor comprises scheduling an interaction between the user and the informed advisor as a function of the user preference datum.