Medication adherence monitoring system and method

Patent metadata

Field	Value
Publication number	US-11417422-B2
Application number	US-202117166995-A
Country	US
Kind code	B2
Filing date	Feb 3, 2021
Priority date	Jun 11, 2014
Publication date	Aug 16, 2022
Grant date	Aug 16, 2022

Abstract

A medication management system is described that is operable to determine whether a user is actually following a protocol, provide additional assistance to a user, starting with instructions, video instructions, and the like, and moving up to contact from a medication administrator if it is determined that the user would need such assistance in any medical adherence situation, including clinical trial settings, home care settings, healthcare administration locations, such as nursing homes, clinics, hospitals and the like. Suspicious activity on the part of a patient or other user of the system is identified and can be noted to a healthcare provider or other service provider where appropriate.

First claim

What is claimed is:

1. A system for determining duplicate enrollment in a clinical trial, the system comprising:
an image capture device configured to capture one or more video sequences associated with administration of medication by a first user;
one or more processors configured to perform operations comprising
receiving the captured one or more video sequences, and
determining whether an image of the first user in the captured one or more video sequences is biometrically similar to an image of a second user included in another one or more captured video sequences associated with the second user; and

a presentation device configured to present an alert to an operator to manually review images of the first user and the second user to confirm whether the first user and the second user are the same user.

2. The system of claim 1 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a deep learning process.

3. The system of claim 1 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a supervised learning process.

4. The system of claim 1 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based an unsupervised learning process.

5. The system of claim 1 , wherein the presentation device is configured to present, to the operator, a dashboard adapted to assist the operator to compare the image of the first user to the image of the second user.

6. A system for determining duplicate enrollment in a clinical trial, the system comprising:
an image capture device configured to capture one or more video sequences associated with administration of medication by a first user;
one or more first computer processors associated with the image capture device, the one or more first processors configured to perform operations comprising receiving the captured one or more video sequences, and determining whether an image of the first user in the captured one or more video sequences is biometrically similar to an image of a second user included in another one or more captured video sequences associated with the second user;
one or more second computer processors associated with a dashboard, the one or more second processors configured to automatically review a determination by the one or more first computer processors that the image of the first user is biometrically similar to the image of the second user; and
a presentation device configured to present the dashboard to an operator, the dashboard including an alert on the dashboard to alert the operator that the first user and the second user are the same user.

7. The system of claim 6 , wherein the dashboard is adapted to assist the operator to compare the image of the first user to the image of the second user.

8. The system of claim 6 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a deep learning process.

9. The system of claim 6 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a supervised learning process.

10. The system of claim 6 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based an unsupervised learning process.

11. A method for determining duplicate enrollment in a clinical trial, the method comprising:
capturing, by an image capture device, one or more video sequences associated with administration of medication by a first user;
determining whether an image of the first user in the captured one or more video sequences is biometrically similar to an image of a second user included in another one or more captured video sequences associated with the second user; and
presenting, by a presentation device, an alert to an operator to manually review images of the first user and the second user to confirm whether the first user and the second user are the same user.

12. The method of claim 11 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a deep learning process.

13. The method of claim 11 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a supervised learning process.

14. The method of claim 11 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based an unsupervised learning process.

15. The method of claim 11 , further comprising:
presenting a dashboard to the operator by the presentation device, the dashboard adapted to assist the operator to compare the images of the first user and the second user; and
receiving, from the operator via the dashboard, confirmation that the first user and the second user are the same user.

16. A method for determining duplicate enrollment in a clinical trial, comprising:
capturing, via an image capture device, one or more video sequences associated with administration of medication by a first user;
determining whether an image of the first user in the captured one or more video sequences is biometrically similar to an image of a second user included in another one or more captured video sequences associated with the second user;
automatically reviewing, by a computer processor associated with a dashboard displayed on a presentation device, a determination that the image of the first user is biometrically similar to the image of the second user; and
presenting, by the presentation device, an alert to an operator on the dashboard to alert the operator that the first user and the second user are the same user.

17. The method of claim 16 , further comprising providing assistance to the operator on the dashboard to compare the image of the first user to the image of the second user.

18. The method of claim 16 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a deep learning process.

19. The method of claim 16 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based a supervised learning process.

20. The method of claim 16 , wherein determining whether the image of the first user is biometrically similar to the image of the second user is based an unsupervised learning process.

Assignees

• Aic Innovations Group Inc

Inventors

• Hanina Adam
• Kessler Gordon
• Guan Lei

Classifications

• G06V20/52

  Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) · CPC title

• A61B5/0022

  Monitoring a patient using a global network, e.g. telephone networks, internet · CPC title

• G16H20/10 Primary

  relating to drugs or medications, e.g. for ensuring correct administration to patients · CPC title

• H04N7/183

  for receiving images from a single remote source · CPC title

• G16H20/13

  delivered from dispensers · CPC title