Remote Health Data Processing with Expected Profile Validation
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Solution Overview
Problem
In remote healthcare monitoring, patients collect measurement data at home, but errors in data collection are difficult to detect, leading to delayed identification of health condition changes, which can worsen patient conditions and increase hospital stays due to lack of real-time notification mechanisms.
Innovation Solution
A computer-implemented method compares measurement data against an Expected Data Profile (EDP) derived from historical data, marking errors and abnormalities, and assessing data to identify health condition changes, with configuration and expected value information, enabling real-time alerts and calibration of remote devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patients collect measurement data remotely at home, then healthcare expenditure is reduced and patient experience is improved, but error detection in data collection becomes difficult and health condition changes may be delayed
Solution Approach 1:
The system implements automated feedback mechanisms where measurement data is continuously compared against the Expected Data Profile, and error tags are generated and communicated back to patients when anomalies are detected, enabling real-time correction of collection errors
Solution Approach 2:
The patent introduces an intermediary processing layer (the server/system) that acts as a mediator between the patient's remote device and the healthcare provider, automatically comparing data against the EDP and generating error tags without requiring direct patient expertise
2Measurement precision
If manual calibration by healthcare professionals is performed, then measurement accuracy is improved, but real-time detection of health condition changes is delayed
Solution Approach 1:
The Expected Data Profile is pre-calibrated using historical measurement data and expert input before deployment, enabling automated real-time comparison without requiring ongoing manual calibration by healthcare professionals
Solution Approach 2:
The system performs self-calibration by automatically comparing incoming measurement data against the pre-established EDP, generating error tags autonomously without requiring manual intervention from healthcare professionals for each measurement
3Ease of operation
If remote monitoring is implemented, then patient responsibility for health management increases, but patients may not understand the implication of health data leading to incorrect collection
Solution Approach 1:
The system provides automated feedback through error tags that clearly indicate when measurement data does not match the Expected Data Profile, guiding patients to correct their collection process without requiring them to understand the underlying medical implications
Data Source
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AI summary
A method and apparatus for processing measurement data received from a remote device monitoring a subject, particularly in the field of healthcare. In order to improve the reliability of data collected by a subject using a remote device, there is a need to identify and mark potentially erroneous or abnormal readings in order to give healthcare professionals more confidence in the accuracy of the data collected. Such a method and apparatus would typically be used by hospital out-patients or patients requiring long-term monitoring.