System and method for automated patient interaction

1. A computer-implemented method for automated patient interaction, comprising:

parsing, with at least one processor, a patient complaint comprising a plurality of words relating to a health of a patient;

determining, with at least one processor and at least one machine-learning algorithm, a subset of patient queries from a plurality of patient queries stored in a patient query database based on the patient complaint and patient data, each patient query of the subset of patient queries configured to prompt the patient or another user on behalf of the patient to input information;

communicating the subset of patient queries to a first computing device operated by the patient or another user on behalf of the patient;

receiving, from the first computing device, user input from the patient or another user on behalf of the patient, the user input comprising separate responses to each patient query of at least a portion of the subset of patient queries;

generating, with at least one processor, output data based on the subset of patient queries and the separate responses;

communicating the output data to a second computing device;

receiving, from the second computing device, an input corresponding to at least one patient query of the subset of patient queries; and

training, with at least one processor and based on the input, the at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

2. The computer-implemented method of claim 1 , wherein the plurality of patient queries is arranged in at least one data structure, and wherein communicating the subset of patient queries to the first computing device and receiving the separate responses to the at least a portion of the subset of patient queries comprises:

communicating at least one query of the subset of patient queries to the first computing device;

receiving a response to the at least one query from the first computing device;

selecting a next query of the subset of patient queries based on the response;

communicating the next query to the first computing device; and

receiving a next response to the next query from the first computing device.

3. The computer-implemented method of claim 1 , wherein the input corresponding to the at least one patient query comprises removing at least one patient query from the subset of patient queries.

4. The computer-implemented method of claim 1 , wherein the input corresponding to the at least one patient query comprises adding at least one query to the subset of patient queries from the plurality of patient queries.

5. The computer-implemented method of claim 1 , wherein the input corresponding to the at least one patient query comprises prioritizing at least one query of the subset of patient queries with respect to at least one other query of the subset of patient queries.

6. The computer-implemented method of claim 1 , wherein communicating the subset of patient queries is performed by a chat bot application configured to interact with the patient via voice and/or text.

7. The computer-implemented method of claim 1 , wherein the patient data comprises at least one of a gender and an age.

8. The computer-implemented method of claim 1 , wherein determining the subset of patient queries is based further on at least one response received from the first computing device to at least one initial query determined based on the patient complaint.

9. The computer-implemented method of claim 1 , further comprising:

after communicating the output data to the second computing device, facilitating voice and/or text communication between an operator of the second computing device and the patient using the first computing device.

10. The computer-implemented method of claim 9 , wherein the input corresponding to the at least one patient query received from the second computing device comprises the voice and/or text communication.

11. The computer-implemented method of claim 1 , wherein the at least one machine-learning algorithm applies a neural network to the patient complaint and the patient data.

12. The computer-implemented method of claim 11 , wherein the neural network is based on word embeddings.

13. The computer-implemented method of claim 1 , wherein the at least one machine-learning algorithm applies at least one model to the patient complaint and the patient data, the method further comprising:

generating training data based on a plurality of patient complaints and training input data corresponding to the plurality of patient complaints; and

training the at least one model based on the training data.

14. The computer-implemented method of claim 13 , further comprising:

communicating the plurality of patient complaints and the plurality of patient queries to a third computing device; and

receiving, from the third computing device, a selected subset of patient queries from the plurality of patient queries, wherein the training input data comprises the selected subset of patient queries.

15. A system for automated patient interaction, comprising:

at least one data storage device comprising a plurality of patient queries; and

at least one processor in communication with the at least one data storage device, the at least one processor programmed or configured to:

parse a patient complaint comprising a plurality of words relating to a health of a patient;

determine, with at least one machine-learning algorithm, a subset of patient queries from the plurality of patient queries based on the patient complaint and patient data, each patient query of the subset of patient queries configured to prompt the patient or another user on behalf of the patient to input information;

communicate the subset of patient queries to a first computing device operated by the patient or another user on behalf of the patient;

receive, from the first computing device, user input from the patient or another user on behalf of the patient, the user input comprising separate responses to each patient query of at least a portion of the subset of patient queries;

generate output data based on the subset of patient queries and the separate responses;

communicate the output data to a second computing device;

receive, from the second computing device, an input corresponding to at least one patient query of the subset of patient queries; and

train the at least one machine-learning algorithm based on the input, the at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

16. The system of claim 15 , wherein the plurality of patient queries is arranged in at least one data structure, and wherein communicating the subset of patient queries to the first computing device and receiving the separate responses to the at least a portion of the subset of patient queries comprises:

communicating at least one query of the subset of patient queries to the first computing device;

receiving a response to the at least one query from the first computing device;

selecting a next query of the subset of patient queries based on the response;

communicating the next query to the first computing device; and

receiving a next response to the next query from the first computing device.

17. The system of claim 15 , wherein the input corresponding to the at least one patient query comprises removing at least one patient query from the subset of patient queries.

18. The system of claim 15 , wherein the input corresponding to the at least one patient query comprises adding at least one query to the subset of patient queries from the plurality of patient queries.

19. The system of claim 15 , wherein the input corresponding to the at least one patient query comprises prioritizing at least one query of the subset of patient queries with respect to at least one other query of the subset of patient queries.

20. The system of claim 15 , wherein communicating the subset of patient queries is performed by a chat bot application configured to interact with the patient via voice and/or text.

21. The system of claim 15 , wherein the patient data comprises at least one of a gender and an age.

22. The system of claim 15 , wherein determining the subset of patient queries is based further on at least one response received from the first computing device to at least one initial query determined based on the patient complaint.

23. The system of claim 15 , wherein the at least one processor is further programmed or configured to:

after communicating the output data to the second computing device, facilitate voice and/or text communication between an operator of the second computing device and the patient using the first computing device.

24. The system of claim 23 , wherein the input corresponding to the at least one patient query received from the second computing device comprises the voice and/or text communication.

25. The system of claim 15 , wherein the at least one machine-learning algorithm applies a neural network to the patient complaint and the patient data.

26. The system of claim 25 , wherein the neural network is based on word embeddings.

27. The system of claim 15 , wherein the at least one machine-learning algorithm applies at least one model to the patient complaint and the patient data, and wherein the at least one processor is further programmed or configured to:

generate training data based on a plurality of patient complaints and training input data corresponding to the plurality of patient complaints; and

train the at least one model based on the training data.

28. The system of claim 27 , wherein the at least one processor is further programmed or configured to:

communicate the plurality of patient complaints and the plurality of patient queries to a third computing device; and

receive, from the third computing device, a selected subset of patient queries from the plurality of patient queries, wherein the training input data comprises the selected subset of patient queries.

29. A computer program product for automated patient interaction, comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:

parse a patient complaint comprising a plurality of words relating to a health of a patient;

determine, with at least one machine-learning algorithm, a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, each patient query of the subset of patient queries configured to prompt the patient or another user on behalf of the patient to input information;

communicate the subset of patient queries to a first computing device operated by the patient or another user on behalf of the patient;

receive, from the first computing device, user input from the patient or another user on behalf of the patient, the user input comprising separate responses to each patient query of at least a portion of the subset of patient queries;

generate output data based on the subset of patient queries and the separate responses;

communicate the output data to a second computing device;

receive, from the second computing device, an input corresponding to at least one patient query of the subset of patient queries; and

train the at least one machine-learning algorithm based on the input, the at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.