The present disclosure is directed to managing device authorization through the use of digital signature thresholds. Individual components of a device, or individual devices in a network environment, are associated with separate secret shares from which a digital signature can be derived. The digital signature may be used to authorize performance of a function. A threshold number of such secret shares are used in order to derive the digital signature. Therefore, an authorization process that relies on digital signature verification to determine that a function is authorized will do so if a threshold number of secret shares are available at authorization time.

Systems, devices, and methods are disclosed for wireless communication of analyte data. In embodiments, a method of using a diabetes management partner interface to configure an analyte sensor system for wireless communication with a plurality of partner devices is provided. The method includes the analyte sensor system receiving authorization to provide one of the partner devices with access to a set of configuration parameters via the diabetes management partner interface. The set of configuration parameters is stored in a memory of the analyte sensor system. The method also includes, responsive to input received from the one partner device via the diabetes management partner interface, the analyte sensor system setting or causing a modification to the set of configuration parameters, according to a system requirement of the one partner device.

Systems, apparatus, methods and computer-readable storage media facilitating management of operation of an implantable medical device (IMD) using a number of communication modes are provided. An IMD is configured to operate in a disabled mode wherein radio frequency (RF) telemetry communication is disabled, or operate in a first advertising mode using the RF telemetry communication. The IMD receives a clinician session request from a clinician device via an induction telemetry protocol while operating in the disabled mode or the first advertising mode, and transitions to operating from the disabled mode or the first advertising mode to operating in a second advertising mode based on receiving the clinician session request. From the second advertising mode, the IMD can establish a clinician telemetry session with the clinician device using the RF telemetry communication and a unique security mechanism facilitated by an identifier for the clinician device included in the clinician session request.

A computer implemented method and system are provided for verifying authenticity of a medical component endpoint. The method is under control of one or more computer systems configured with specific executable instructions. The method receives, at a local medical equipment (LME) node, a cipher message combination that includes a challenge and a corresponding valid response, the LME node is unable to independently calculate the valid response. The method conveys the challenge, from the LME node, to a medical component endpoint that includes an authentication circuit, receives a candidate response from the component endpoint, where the candidate response is generated by the authentication circuit based on the challenge and determines whether the candidate response matches the valid response from the corresponding cipher message combination. The method further authenticates the component endpoint based on whether the candidate and valid responses match.

A system and method that facilitates the automated replication of electronic medical record information between a patient and a health-care provider (HCP), such as a doctor, pharmacy, drug manufacturer, biologic manufacturer, or medical device manufacturer. The system uses: a cloud-based infrastructure that includes databases, mathematical models, and configuration information; a patient's electronic health record system providing personal data around a patient's individual personal electronic medical record (PEMR); and a server used to coordinate and authenticate the replication of data between the cloud-based infrastructure, the PEMR, and the EMR/EHR system of the HCP. The system provides support and security, such as by geographically distributed data fragmentation, for mobile platforms and web-based platforms and sophisticated mechanisms for the transmission of data between these systems, as well as patient interaction modules to promote patient care.

The present invention provides systems and methods for supporting encrypted communications with a medical device, such as an implantable device, through a relay device to a remote server, and may employ cloud computing technologies. An implantable medical device is generally constrained to employ a low power transceiver, which supports short distance digital communications. A relay device, such as a smartphone or WiFi access point, acts as a conduit for the communications to the internet or other network, which need not be private or secure. The medical device supports encrypted secure communications, such as a virtual private network technology. The medical device negotiates a secure channel through a smartphone or router, for example, which provides application support for the communication, but may be isolated from the content.

An Electronic Health Record (EHR) data blockchain system configured to allow multiple entities (e.g., pharmacy industry entities and healthcare providers that can act as data, service, product and service providers, and consumers) to connect to an EHR patient transaction blockchain (e.g., EHR-DATA-BC) and an EHR Data Patient Portal (e.g., EHR-Data-PP) to provide a centralized location for messages and subsequent edits to ensure uniform message data is presented. The EHR data blockchain system can include an EHR Data API, an EHR patient transaction blockchain API, and an EHR patient transaction blockchain. The EHR data blockchain system can provide workflow on the blockchain that can utilize smart contracts to define workflow processes, expected outcomes, and financial costs. When a prescription transaction is complete, it will result in the settlement of each of the smart contracts that were added to the prescription workflow.

A medical implant can comprise a physical structure configured to secure the medical implant within a body of a patient, a proximity communication component physically coupled to the physical structure, and a memory operatively coupled to the proximity communication component. The memory can store a private key, wherein the memory is configured to provide the private key through the proximity communication component to an external device for enabling the external device to access one or more electronic medical records associated with a patient that is implanted with the implant from a distributed blockchain ledger of electronic medical records.

An electrosurgical system includes a generator and an electrosurgical device and is configured to establish two-way communication using a resistance level communication protocol. The electrosurgical device includes an electrical connector configured to receive a treatment signal and a continuous signal from the generator, an AC-DC converter configured to convert the continuous signal, a processor configured to receive the converted continuous signal, and a resistor connected to the processor and configured to vary the current of power used by the processor over time. The generator is configured to interpret variances in the current as data and to provide an acknowledgment to the electrosurgical device.

A mechanism is provided in a data processing system to implement a multi-sensor health monitoring platform. The mechanism applies a machine learning model to predict patient needs and patient activity trends based on physiological features and activity features of the patient. The mechanism applies the machine learning model to predict energy requirements for a plurality of medical sensors based on the predicted patient needs and patient activity trends. The mechanism schedules recharging of the plurality of medical sensors based on the predicted energy requirements and identifying one or more sensors to set to an activate state based on the predicted patient needs and patient activity trends. The mechanism collecting sensor data from the one or more sensors and applies the machine learning model to generate a point-of-care recommendation based on the collected sensor data.