Devices, systems, and methods for predictive analytics for chronic health condition management

Inventors

Miller, David • Miller, Devin • Jones, Michael • Derrick, David

Assignees

JRE STAR INVESTMENT HOLDINGS, LLC • Tula Health Inc

Abstract

Systems, methods, and devices of a health device network may include: a non-invasive glucometer that non-invasively measures analyte levels; an invasive glucometer communicatively coupled directly to the non-invasive glucometer; a cloud-based server communicatively coupled to the non-invasive glucometer or the invasive glucometer; a user device communicatively coupled to the cloud-based server; and/or a user interface that displays the invasive glucose measurement, the non-invasive glucose measurement, a data batch, and/or processed data to the user. The non-invasive glucometer and/or the invasive glucometer may aggregate an invasive glucose measurement and a non-invasive glucose measurement into the data batch. A data analytics application on the cloud-based server may be configured to: integrate the invasive glucose measurement and the non-invasive glucose measurement; identify a correlation between the invasive glucose measurement and the non-invasive glucose measurement; and/or generate a predictive model based on the invasive glucose measurement and the non-invasive glucose measurement.

Core Innovation

The invention collects a set of data elements for a subject during a first time interval, including invasively-measured analyte levels and non-invasively measured analyte levels. The non-invasive analyte levels are measured by a plurality of sensors comprising a filter, a collimator, and an optical sensor, where the collimator comprises a wall forming one or more microtubes aligned normal to a surface of the filter or the surface of the optical sensor.

Based on the collected set of data elements, the invention predicts a future data element of a second time interval. The future data element comprises either a difference between a second non-invasive analyte measurement reading and an actual analyte level during the second time interval, or a correlation among the second non-invasive analyte measurement reading, the actual analyte level, a second invasive analyte measurement reading measured during the second time interval, an indicator of subject activity during the second time interval, and a second measurement reading of a physiological characteristic during the second time interval.

The predicted future data element is displayed to a user on a user device. The invention further integrates subject activity and physiological characteristic measurement into the prediction and correlation framework, and it supports displaying and transmitting instructions to user or measurement devices.

Claims Coverage

This patent contains three independent claims, each centered on collecting invasive and non-invasive analyte-related data over time intervals and predicting a future data element for display. The inventive features include the non-invasive sensor positioning using the specific filter/collimator/optical sensor microtube geometry, prediction using differences and/or correlations between invasive and non-invasive readings and actual analyte level, and system-level processing that collects, predicts, and displays the difference/correlation-based output, with transmission of instructions in one independent claim.

Across the independent claims, the core coverage is the same: collect invasive and non-invasive analyte-related data during a first time interval using a specified non-invasive sensor arrangement, predict a future data element for a second time interval using differences and/or correlations involving invasive and non-invasive readings and actual analyte level, optionally incorporating activity and physiological characteristics, and display that predicted future data element. One independent claim also includes transmitting instructions to one or more devices.

Stated Advantages

Documented Applications

No documented applications found