Systems and methods for automated speech-to-transaction in healthcare operations

1 . A computer-implemented method for real-time healthcare transaction processing, comprising:

receiving, via one or more microphones of a computing device, ambient audio, without directed speech or manual activation, from a healthcare provider-patient conversation;

transcribing the ambient audio-to-text using a speech recognition engine;

analyzing transcribed text using a natural language processing engine to identify one or more clinical intents and candidate clinical transaction intents corresponding to healthcare transactions;

validating a given candidate clinical transaction intent using a context monitoring module that, prior to query generation, requires detection of confirming keywords indicative of decisional language, satisfaction of a temporal proximity threshold within a rolling conversation window, and suppression of duplicate utterances referring to the same clinical action; responsive to the validation;

suppressing one or more clinical intents unrelated to actionable medical decisions;

standardizing the identified clinical intent into a structured query format;

transmitting the structured query over an authenticated, encrypted network session to an external system over a network to request data corresponding to the clinical intent; and

receiving, via the computing device, a response from the external system, wherein the response comprises at least one of insurance coverage status, patient cost, treatment alternatives, treatment availability, diagnostic alternatives, procedure alternatives, financial assistance program data, optimal referral options, or prior authorization requirement for the clinical intent, and displaying the response in real-time to at least one of the physician or patient during the clinical encounter.

2 . The method of claim 1 , wherein identifying the clinical intent comprises detecting at least one of a recommendation for medication, imaging, a procedure, or a referral.

3 . The method of claim 1 , wherein standardizing the identified clinical intent includes mapping recognized keywords to structured formats in accordance with external data schemas.

4 . The method of claim 1 , wherein the step of receiving ambient audio is performed using a smartphone, tablet, or a web-enabled device positioned in a patient examination room.

5 . The method of claim 1 , wherein the response is received within less than thirty seconds of a transmission of the structured query.

6 . The method of claim 1 , further comprising highlighting at least one of alternative treatment, diagnostic, or referral options in a user interface if the external system returns a non-covered status.

7 . The method of claim 1 , wherein the structured query includes metadata comprising provider information, patient demographics, diagnosis codes, or medication identifiers.

8 . The method of claim 1 , further comprising storing the clinical intent and external system response in a secure patient record repository.

9 . The method of claim 1 , further comprising at least one of automatically checking patient eligibility for or automatic enrolling a patient into financial assistance programs based on the response from the external system.

10 . A system for automated healthcare transaction processing comprising:

a computing device comprising at least one processor, a memory, and a microphone;

a speech recognition module configured to convert ambient healthcare provider-patient dialogue into a text transcript, without directed speech or manual activation;

a natural language processing module configured to analyze the text transcript and detect one or more candidate clinical transaction intents associated with clinical actions, the module comprising a machine-learning natural-language understanding model;

a query generator module configured to convert the detected transaction intents into structured data queries;

a query generator module configured, responsive to the validation, to standardize the validated clinical transaction intent into a structured data query that conforms to healthcare technology standards comprising CoverageEligibilityRequest, MedicationRequest, and ServiceRequest and that includes code-mapped fields derived from ICD-10, CPT, and NDC terminology mappings;

a context monitoring module configured to validate a candidate clinical transaction intent by requiring detection of confirming keywords indicative of decisional language, satisfaction of a temporal proximity threshold within a rolling conversation window, and suppression of duplicate utterances referring to a same clinical action;

a user interface module configured to display the transaction results in real-time to the healthcare provider or patient;

wherein the transaction results comprise at least one of a medication coverage, a procedure eligibility, a cost estimate, a prior authorization requirement, or a next-best action for complying with the recommendation comprising automatically scheduling or evaluating calendars for scheduling appointments.

11 . The system of claim 10 , wherein the natural language processing module utilizes a machine learning model trained on clinical transaction data.

12 . The system of claim 10 , wherein the communications module is further configured to request and receive prior claims history from the external system.

13 . The system of claim 10 , wherein the user interface module presents a comparison of covered and non-covered options for a given clinical intent.

14 . A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause a computing system to perform a method comprising:

receiving, without directed speech or manual activation, real-time ambient audio input during a clinical encounter via a microphone;

transcribing the ambient audio into natural language text using a speech recognition engine;

detecting one or more candidate clinical transaction intents from the natural language text using machine learning-based natural language understanding;

validating a given candidate clinical transaction intent using a context monitoring module that, prior to query generation, requires detection of confirming keywords indicative of decisional language, satisfaction of a temporal proximity threshold within a rolling conversation window, and suppression of duplicate utterances referring to the same clinical action; responsive to the validation

generating one or more structured queries that conform healthcare technology standards comprising CoverageEligibilityRequest, MedicationRequest, and ServiceRequest and that include code-mapped fields derived from ICD-10, CPT, and NDC terminology mappings based on the clinical transaction intents;

suppressing queries from the one or more structured queries unrelated to actionable medical decisions;

communicating the structured queries over an authenticated, encrypted network session to an external healthcare administration system;

receiving transaction data in response to the structured queries; and

causing a graphical user interface to present the external transaction data to at least one clinical participant during the clinical encounter.

15 . The storage medium of claim 14 , wherein the machine learning-based natural language understanding is based on transformer-based models fine-tuned on electronic health record data.

16 . The storage medium of claim 14 , wherein the structured queries conform to a HL7 FHIR protocol standard.

17 . The storage medium of claim 14 , further comprising logic to determine an urgency of the transaction and prioritize queries accordingly.

18 . The storage medium of claim 14 , wherein the instructions further cause the system to log rejected transactions for audit and reporting purposes.

19 . The storage medium of claim 14 , wherein the graphical user interface presents the received transaction data using color-coded indicators based on coverage status.