Abstract: Method, systems, and apparatus for generating a plurality of candidate patient procedures based at least on one or more procedural codes; determining, for each of the plurality of candidate patient procedures, a risk score for the candidate patient procedure using a machine-learning model trained on data comprising one or more procedure types, patient information, and frequency scores of procedural incidents for each procedure type, and severity scores of procedural incidents for each procedure type; determining, for each of the plurality of candidate patient procedures, a recommendation score for the candidate patient procedure based at least on insurance provider data for the patient in the candidate patient procedure, a mapping of the candidate patient procedure to revenue per procedure, and the risk score for the candidate patient procedure; and generating one or more appointment creation messages for distribution to a plurality of candidate patients based on the recommendation scores.

Abstract: Method, systems, and apparatus for receiving appointment data about one or more appointments, wherein the appointment data comprises one or more procedural codes for each of the one or more appointments; generating an estimated amount of inventory used for each of the one or more appointments based at least on the one or more procedural codes; calculating a remaining amount of available inventory from the estimated amount of inventory used; generating a threshold for each supply in the remaining amount of available inventory based at least on historical purchase data for the supply, wherein the historical purchase data comprises a duration from order to arrival; determining, for a given supply, that the remaining amount of available inventory for the given supply is under the generated threshold; and in response to the determining, generating a notification for a user to purchase the given supply.

Abstract: Method, systems, and apparatus for receiving a request for appointment availability, wherein the request for appointment availability comprises one or more procedural codes; determining an aggregate appointment duration and provider type data based at least on the one or more procedural codes from a first mapping of procedural codes to procedural durations and a second mapping of procedural codes to provider type data; identifying a plurality of appointment constraints based at least on the request for appointment availability, the aggregate appointment duration, and the provider type data; and applying the plurality of appointment constraints to calendar availability data of a plurality of providers to generate a plurality of candidate appointment slots based at least on the plurality of appointment constraints.

Abstract: Method, systems, and apparatus for training a machine-learning model on data comprising a plurality of originally filed procedural codes, a plurality of revised procedural codes, one or more insurance providers, descriptions from procedures associated with the procedural codes, procedural approval statuses, and a plurality of fee amounts; receiving one or more candidate procedural codes with respective fee amounts; and using the machine-learning model to generate one or more recommended procedural codes that are different from the one or more candidate procedural codes.

Abstract: Trip is analyzed from a population of drivers in order to determine one or more indicators of one or more driving styles. The trip data may include sensor information obtained from one or more sensor devices which are present in a vehicle of each driver of the population. A driving style is determined for the driver during a monitored trip by analyzing sensor information obtained from one or more sensor devices of the driver during the trip for at least one of the indicators of the one or more driving styles.

Abstract: Trip is analyzed from a population of drivers in order to determine one or more indicators of one or more driving styles. The trip data may include sensor information obtained from one or more sensor devices which are present in a vehicle of each driver of the population. A driving style is determined for the driver during a monitored trip by analyzing sensor information obtained from one or more sensor devices of the driver during the trip for at least one of the indicators of the one or more driving styles.

Abstract: Trip is analyzed from a population of drivers in order to determine one or more indicators of one or more driving styles. The trip data may include sensor information obtained from one or more sensor devices which are present in a vehicle of each driver of the population. A driving style is determined for the driver during a monitored trip by analyzing sensor information obtained from one or more sensor devices of the driver during the trip for at least one of the indicators of the one or more driving styles.

Abstract: A driver verification system and method is provided in which a driver profile is determined for a driver, and the driver profile is used as a basis of comparison with monitored actions of the driver during a shift. A preliminary verification determination can be made for the driver based at least in part on the comparison. From the comparison, a verification action can be required from the driver, and the verification action can be evaluated to determine whether the driver is true and authorized as to his or her identity.

Abstract: A system can determine that a service has been initiated for a user by a service provider. During performance or progress of the service, the system can receive location data points from a computing device operated by the user or the service provider and detect an occurrence of an event in connection with the service based on one or more of a set of received location data points, or an elapsed amount of time since the initiation of the service. In response to detecting the occurrence of the event, the system can determine a score that enables the transport to be monitored for unauthorized overuse.