Abstract: This application describes, among other things, systems and methods for deploying a task-specific machine-learning model. An example method includes receiving a prompt associated with one or more commands and a plurality of tokens. A first task-specific machine-learning model is identified based on a first command of the one or more commands. Some or all of the tokens of the plurality of tokens are applied to a node associated with the first task-specific machine-learning model, and a plurality of restricted data is received. Based on evaluating the plurality of restricted data, a correlation between the first node and a second node is determined. If the correlation satisfies a threshold condition, a plurality of text data different than the prompt is generated, otherwise the some or all of the tokens are again provided to the first node. Some or all of the tokens are then traversed and applied to the second node.

Abstract: This application describes, among other things, machine-learning models for performing specific clinical tasks. An example method includes receiving a prompt at a first computing system in communication with a machine-learning model trained to assist in performing a clinic task that includes generating a report of a patient's medical records, guiding a patient through a care plan, creating patient care guidelines based on a patient's health profile, identifying patients requiring follow-up at a hospital, identifying changes in a standard of care for a disease setting, or evaluating unstructured data associated with a patient to identify a cohort of similar patients. Based on the prompt, a natural language response is generated that is responsive to the prompt and is based on an analysis by the machine-learning model of a repository of data that is determined to be relevant to the prompt. And the natural language response is provided to second computing system.

Abstract: This application describes, among other things, methods of providing third-party interactions to a set of task-specific components. An example method includes receiving, at a user interface of a computing device, a user identifier and a prompt related to an identified clinical task. The method includes determining a set of task-specific components and a set of databases to which the user identifier has access. The method includes 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. The method includes communicatively coupling the task-specific component to a database from the set of databases based on the prompt. The method includes providing the prompt to the task-specific component. The method includes receiving a response to the prompt generated by the task-specific component using information from the database and providing the response to a user.

Abstract: This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

Abstract: This application describes, among other things, methods of enabling data transforms across multiple task-specific orchestrations. An example method includes, based on determining that a prompt requests assistance with a clinical task, selecting, by a machine-learning model trained to select from among a plurality of task-specific components, a set of task-specific components based on the prompt. The method includes obtaining orchestration data about the set of task-specific components, where each task-specific component is configured to assist with a respective clinical task. The method includes determining, from the orchestration data, a data-compatibility criterion for clinical task data relating to the one or more clinical tasks. The method includes receiving the clinical task data.

Abstract: This application describes, among other things, systems and methods for configuring a task-specific machine-learning model. An example method includes, based on a request to modify a machine-learning model for performing a clinical task, retrieving a corresponding node architecture defining conditional logic for performing the clinical task. The conditional logic is executed based on a first order, including an input node, an output node, and an intermediate node, of a first set of interconnected nodes including a data source node, a machine-learning model node, and a conditional logic node. A representation is generated including a first feature for configuring conditional logic of the node architecture and a second feature for configuring a parameter of a node in the first set of interconnected nodes. A selection of the first or second feature defines a second order of a second set of interconnected nodes, causing the node architecture's conditional logic to be updated.

Abstract: This application describes, among other things, systems and methods for interacting with a task-specific orchestration. An example method includes obtaining medical data from one or more data collections. A set of user interface elements is presented at a user interface, where each respective user interface element of the set of user interface elements represents a respective task-specific orchestration that includes one or more respective machine-learning models fine-tuned for a specific task or domain based on the medical data. When a user interface element representing a respective task-specific orchestration is selected, at least some of the medical data is provided to the respective task-specific orchestration, and a different user interface is presented for communicating with the respective task-specific orchestration. Based on receiving a prompt at the different user interface, a response object is generated by the task-specific orchestration based on the prompt and at least some of the medical data.

Abstract: This application describes, among other things, methods of selecting a task-specific machine-learning model for addressing a clinical task. An example method includes receiving a prompt from a user. Based on determining that the prompt requests assistance with a clinical task, a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks selects a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt. The prompt is provided to the selected task-specific machine-learning model. And a response received from the selected task-specific machine-learning model is provided to the user.

Abstract: A method and system comprising storing a set of user application programs each requiring an application specific subset of data to perform application processes and generate a respective genomic variant characterization for each of a plurality of patients with cancerous cells and receiving cancer treatment. The method including, obtaining clinical records data including cancer related information, generating genomic sequencing data for the patient's cancerous cells and normal cells, shaping at least a subset of the genomic sequencing data to generate system structured data. Storing the system structured data in a first database, selecting the application specific data from the first database, storing the application specific data in a second database for application program interfacing, receiving the respective genomic variant characterization from the user application program, and storing the genomic variant characterization from the user application program in a third database.

Abstract: A method for identifying actionable care events includes receiving data sources relating to a subject; storing data from them in a first database; generating a database comprising structured data fields and metadata fields from the sources; generating output data related to fields within the data or metadata fields; populating the database with the output data; generating criteria sets corresponding to respective actionable care events; evaluating the generated database using the criteria sets; identifying whether any of the criteria sets are not sufficiently satisfied by the database, wherein an underlying error or an indication of missing or incomplete information within the database with respect to a criteria set indicates a corresponding actionable care event; determining that other data sources within the collection do not sufficiently satisfy any of the identified criteria sets; and generating, based on the identifying and determining, a notification that at least one actionable care event applies.

Abstract: A method includes displaying a cohort report; receiving a request to determine a therapy identification; causing information to be transmitted to a remote cloud server; receiving clinical trial data; and updating the cohort report. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: display a cohort report; receive a request to determine a therapy identification; cause information to be transmitted to a remote cloud server; receive clinical trial data; and update the cohort report. A computer-readable medium having stored thereon a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: display a cohort report; receive a request to determine a therapy identification; cause information to be transmitted to a remote cloud server; receive clinical trial data; and update the cohort report.

Abstract: A method for data intake and consumption includes the steps of: storing a plurality of micro-service programs, operational user application programs, and analytical user application programs in at least one computer system, storing system data received from a plurality of different sources in a database, the system data includes clinical records data in original forms, the clinical records data including cancer state information, treatment types, and treatment efficacy information, consuming, by each of the micro-service programs, defined subsets of the system data to generate a new data product, storing the new data product in a second database, and consuming the new data product by others of the micro-service programs or the operational or analytical user application programs.

Abstract: A method and system for storing user application programs and micro-service programs, for each of multiple patients that have cancerous cells and receive treatment, includes obtaining clinical records data in original forms, storing it in a semi-structured first database, generating sequencing data for the patient's cancerous and normal cells using a next generation genomic sequencer, storing the sequencing data in the first database, shaping at least some of the first database data to generate system structured data optimized for searching and including clinical record data, storing the structured data in a second database, for each user application program, selecting an application-specific subset of data from the second database and storing it in a structure optimized for application program interfacing in a third database, wherein an orchestration manager operatively connected to one or more micro-service programs receives status messages and initiates a respective micro-service program when program prere

Abstract: A method and system comprising storing a set of user application programs each requiring an application specific subset of data to perform application processes and generate a respective genomic variant characterization for each of a plurality of patients with cancerous cells and receiving cancer treatment. The method including, obtaining clinical records data including cancer related information, generating genomic sequencing data for the patient's cancerous cells and normal cells, shaping at least a subset of the genomic sequencing data to generate system structured data. Storing the system structured data in a first database, selecting the application specific data from the first database, storing the application specific data in a second database for application program interfacing, receiving the respective genomic variant characterization from the user application program, and storing the genomic variant characterization from the user application program in a third database.

Abstract: A system, method, and mobile device application are configured to capture, with a mobile device, a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient. The method includes receiving, from a mobile device, an image of a medical document comprising NGS medical information of the patient, extracting a first region from the image, extracting NGS medical information of the patient from the first region into a structured dataset, the extracted NGS medical information including at least one RNA expression, correlating a portion of the extracted NGS medical information that includes the at least one RNA expression with summarized medical information from a cohort of patients similar to the patient, and generating, for display on the mobile device, a clinical decision support report comprising the summarized medical information.

Abstract: A method and system for conducting genomic sequencing, the method comprising storing a set of user application programs wherein each of the programs requires an application specific subset of data, for each of a plurality of patients that have cancerous cells and that receive cancer treatment, obtaining clinical records data in original forms including cancer state information, treatment types and treatment efficacy information, storing the clinical records data in a semi-structured first database, for each patient, using a genomic sequencer to generate genomic sequencing data for the patient's cancerous cells and normal cells, storing the sequencing data in the first database, shaping at least a subset of the first database data to generate system structured data including clinical record data and sequencing data wherein the system structured data is optimized for searching, storing the system structured data in a second database, for each user application program, selecting the application specific subset o

Abstract: A system, method, and mobile device application are configured to capture, with a mobile device, a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient. The method includes receiving, from a mobile device, an image of a medical document comprising NGS medical information of the patient, extracting a first region from the image, extracting NGS medical information of the patient from the first region into a structured dataset, the extracted NGS medical information including at least one RNA expression, correlating a portion of the extracted NGS medical information that includes the at least one RNA expression with summarized medical information from a cohort of patients similar to the patient, and generating, for display on the mobile device, a clinical decision support report comprising the summarized medical information.

Abstract: A method and system for conducting genomic sequencing, the method comprising storing a set of user application programs wherein each of the programs requires an application specific subset of data, for each of a plurality of patients that have cancerous cells and that receive cancer treatment, obtaining clinical records data in original forms including cancer state information, treatment types and treatment efficacy information, storing the clinical records data in a semi-structured first database, for each patient, using a genomic sequencer to generate genomic sequencing data for the patient's cancerous cells and normal cells, storing the sequencing data in the first database, shaping at least a subset of the first database data to generate system structured data including clinical record data and sequencing data wherein the system structured data is optimized for searching, storing the system structured data in a second database, for each user application program, selecting the application specific subset o

Abstract: A method and system for storing user application programs and micro-service programs, for each of multiple patients that have cancerous cells and receive treatment, includes obtaining clinical records data in original forms, storing it in a semi-structured first database, generating sequencing data for the patient's cancerous and normal cells using a next generation genomic sequencer, storing the sequencing data in the first database, shaping at least some of the first database data to generate system structured data optimized for searching and including clinical record data, storing the structured data in a second database, for each user application program, selecting an application-specific subset of data from the second database and storing it in a structure optimized for application program interfacing in a third database, wherein an orchestration manager operatively connected to one or more micro-service programs receives status messages and initiates a respective micro-service program when program prere

Abstract: An automated system is disclosed for managing accounts receivable for outstanding healthcare accounts. In particular, the system in accordance with the present invention is an automated system which preemptively assesses the risk of denials of outstanding healthcare accounts by way of a self-learning engine driven by current data and prioritizes those accounts for follow up according to the risk profile of the account. As such, the system is able to minimize denials by the respective payers thereby improving optimizing the efforts of healthcare accounts receivable personnel while improving the revenue yield and minimizing the revenue cycle.