US 12,087,411 B2
Method and system for codification, tracking, and use of informed consent data for human specimen research
Amelia Wall Warner, Raleigh, NC (US); and Mark Anthony Collins, Raleigh, NC (US)
Assigned to Global Specimen Solutions, Inc., Raleigh, NC (US)
Filed by Global Specimen Solutions, Inc., Raleigh, NC (US)
Filed on Jan. 4, 2021, as Appl. No. 17/140,492.
Application 17/140,492 is a continuation of application No. 15/651,357, filed on Jul. 17, 2017, granted, now 10,909,215.
Application 15/651,357 is a continuation of application No. PCT/US2016/062724, filed on Nov. 18, 2016.
Claims priority of provisional application 62/256,756, filed on Nov. 18, 2015.
Prior Publication US 2021/0151138 A1, May 20, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 10/40 (2018.01); G06N 20/00 (2019.01); G06Q 50/22 (2018.01); G16H 50/30 (2018.01); G16H 70/00 (2018.01); G16H 10/20 (2018.01)
CPC G16H 10/40 (2018.01) [G06N 20/00 (2019.01); G06Q 50/22 (2013.01); G16H 50/30 (2018.01); G16H 70/00 (2018.01); G16H 10/20 (2018.01)] 18 Claims
OG exemplary drawing
 
1. A system for secure storage and retrieval of encrypted biological specimen data comprising:
a processor; and
a non-transitory computer readable storage medium communicatively coupled to the processor, the non-transitory computer readable storage medium further comprising computer-readable instructions that, when executed by the processor, cause the processor to perform operations comprising:
codifying a pre-existing informed consent document into machine actionable rules using machine-learning, expert assessment, and natural language processing, wherein the machine actionable rules define what a patient has consented to be done with a specimen and associated data in a plurality of locations;
tracking changes to the machine actionable rules;
generating a new consent document based at least in part on global regulations data, by using the machine actionable rules with any of the tracked changes and a machine-learning regulatory intelligence knowledgebase (RIK) configured to learn regulatory data and consent approval behaviors, wherein the machine-learning RIK includes the global regulations data; and
interactively displaying, using analytics of consent approval, visual risk indicators for collection of the specimen and the associated data using the new consent document, the visual risk indicators configured to correspond to a plurality of geographic locations on a map.