A method for classifying an identification tag on a sample tube containing a sample to be processed in an automated laboratory system having a plurality of laboratory devices, the method comprising providing a sample tube having an identification tag and containing a sample to be analyzed . Each of the plurality of laboratory devices is assigned a tag reader device configured to read the identification tag for recognizing identification tag information. Further, an automated laboratory system for processing a sample tube containing a sample for at least one of pre-analytics and sample analysis is provided.

Disclosed are a reader device, system, and method for communicating with a wireless sensor. The reader device may be configured to analyze the strength of a response signal transmitted from the wireless sensor in response to an excitation pulse generated by the reader device. In one embodiment, the reader device may be configured to engage be placed in a plurality of modes to allow the reader to transmit a signal, such as a short pulse of energy or a short burst of radio frequency energy to cause the wireless sensor to output a resonant signal. The reader device may receive the resonant signal from the wireless sensor and evaluate it against predetermined values. The evaluated signals may be used to assess the strength and the proximity of the reader device relative to the wireless sensor as it is implanted in a patient.

A system includes a learning filter for deidentified healthcare data. The system provides self-improving data filtering of data for the detection of disorders for engaging patients in observational research to gather additional data. A processor performs operations including obtaining patient information about a patient and current order data. The current order data indicates an order for a laboratory test for the patient and obtaining, from a database, historical data indicating a previous laboratory test for the patient and a result of the previous laboratory test. The operations further include determining that the patient is a subject of interest for a disorder by applying the filter to historical data to produce filtered data and comparing the filtered data to a profile associated with the disorder. Additional laboratory tests can be performed, after which a hash-to-patient identifier (PID) mapping database and the filter can be updated.

A system includes a learning filter for deidentified healthcare data. The system provides self-improving data filtering of data for the detection of disorders for engaging patients in observational research to gather additional data. A processor performs operations including obtaining patient information about a patient and current order data. The current order data indicates an order for a laboratory test for the patient and obtaining, from a database, historical data indicating a previous laboratory test for the patient and a result of the previous laboratory test. The operations further include determining that the patient is a subject of interest for a disorder by applying the filter to historical data to produce filtered data and comparing the filtered data to a profile associated with the disorder. Additional laboratory tests can be performed, after which a hash-to-patient identifier (PID) mapping database and the filter can be updated.

A card-type storage device includes a processing chip and a memory module. The processing chip is selectively operated in a data accessing mode or a data uploading mode. The memory module is electrically connected with the processing chip. A first data set generated by the medical device at a first time point and a second data set generated by the medical device at a second time point are stored in the memory module. In the data accessing mode, a first storage list is established by the processing chip according to the first data set, and the first storage list is updated as a second storage list according to the second data set. If the processing chip judges that the second data set satisfies a predetermined condition, the processing chip enters the data uploading mode.

A card-type storage device includes a processing chip and a memory module. The processing chip is selectively operated in a data accessing mode or a data uploading mode. The memory module is electrically connected with the processing chip. A first data set generated by the medical device at a first time point and a second data set generated by the medical device at a second time point are stored in the memory module. In the data accessing mode, a first storage list is established by the processing chip according to the first data set, and the first storage list is updated as a second storage list according to the second data set. If the processing chip judges that the second data set satisfies a predetermined condition, the processing chip enters the data uploading mode.

A vaccination data presentation method applied to an electronic device for presenting a vaccination data is disclosed. The vaccination data presentation method includes: receiving a vaccination data and an identification data; storing the vaccination data and the identification data; generating a first data presentation page according to the vaccination data and the identification data, and displaying the first data presentation page on the electronic device, wherein the first data presentation page displays the vaccination data and the identification data and includes a data privacy button; if the data privacy button is triggered, generating a second data presentation page to replace the first data presentation page, wherein the second data presentation page displays the vaccination data and does not display the identification data.

A method of evaluating a subject using a wearable sensor includes collecting raw data at the sensor indicating movement and/or characteristics, determining a physical location of the sensor on the body of the subject, and determining whether the physical location matches a primary location on the subject corresponding to a location at which training data are collected for training pre-trained models. When the physical location matches the primary location, the physical activity and posture of the subject are determined using the raw data collected at the sensor, in accordance with a selected model. When the physical location does not match the primary location, the raw data is mapped from the physical location to the primary location using a machine-learning based algorithm to provide mapped data, and the physical activity and posture of the subject are determined using the mapped data, in accordance with the selected model.

A method of evaluating a subject using a wearable sensor includes collecting raw data at the sensor indicating movement and/or characteristics, determining a physical location of the sensor on the body of the subject, and determining whether the physical location matches a primary location on the subject corresponding to a location at which training data are collected for training pre-trained models. When the physical location matches the primary location, the physical activity and posture of the subject are determined using the raw data collected at the sensor, in accordance with a selected model. When the physical location does not match the primary location, the raw data is mapped from the physical location to the primary location using a machine-learning based algorithm to provide mapped data, and the physical activity and posture of the subject are determined using the mapped data, in accordance with the selected model.

Aspects of the subject disclosure may include, for example, transmitting, by a processing system including a processor, a request for attention, wherein the processing system stores first data pertaining to a first user, receiving, by the processing system and based on the transmitting of the request for attention, a key from a second processing system including a second processor, based on the receiving of the key, identifying, by the processing system, a first portion of the first data to be transmitting to the second processing system, wherein the first portion of the first data is less than an entirety of the first data, and based on the identifying of the first portion of the first data, transmitting, by the processing system, the first portion of the first data to the second processing system. Other embodiments are disclosed.