Method and system for codification, tracking, and use of informed consent data for human specimen research
The subject matter described herein includes methods, systems, and computer program products for codification, tracking, and use of informed consent data for human specimen research. According to one method, an informed consent document is codified and consent rules are attached to a specimen. The consent rules and any changes to the consent rules are tracked. Allowed use analysis of the specimen and associated data is performed and a regulatory intelligence knowledgebase (RIK) is provided that includes global regulations data derived from proprietary and public sources. A consent document is automatically generated using the codified informed consent document and the RIK.
1. A method comprising:
codifying, by a server, a pre-existing informed consent document into a machine actionable set of rules using machine-learning, natural language processing, and expert assessment, wherein the machine actionable set of rules defines what a patient has consented to be done with a specimen and data derived from the specimen in at least some of a plurality of locations;
storing within non-transitory memory in the server, the machine actionable set of rules;
tracking, by the server, changes to the machine actionable set of rules;
automatically generating, by the server, a new consent document based at least in part on global regulations data derived from proprietary and public sources, by using the machine actionable set of rules with any changes and a machine-learning regulatory intelligence knowledgebase (RIK), 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 in association with the new consent document as visually corresponding to the plurality of locations on a map.
2. The method of claim 1 , wherein the codifying is linked to and performed based upon prevailing global, country, regional, and local regulations in force at a time of generating the new consent document in at least one of the plurality of locations.
3. The method of claim 1 , wherein tracking changes to the machine actionable set of rules includes dynamically tracking changes in restrictions regarding what can be done to the specimen and/or whether the patient withdraws consent.
4. The method of claim 3 , wherein dynamically tracking changes in restrictions includes providing rule-based querying for specific consent profiles.
5. The method of claim 3 , wherein dynamically tracking changes in restrictions includes providing a risk assessment from a consent perspective.
6. The method of claim 1 , wherein automatically generating, by the server, the new consent document includes generating the new consent document based on an outline of desired consent, categories of consent needed, and regulations in at least one of the plurality of locations.
7. The method of claim 1 , wherein the map includes filters operable to provide interactive visualization of different risk categories.
8. A system comprising:
a machine-learning regulatory intelligence knowledgebase (RIK), wherein the machine-learning RIK includes global regulations data derived from proprietary and public sources; and
a server with a processor and a non-transitory memory configured to:
codify a pre-existing informed consent document into a machine actionable set of rules using machine-learning, natural language processing, and expert assessment, wherein the machine actionable set of rules defines what a patient has consented to be done with a specimen and data derived from the specimen in at least some of a plurality of locations;
store within the non-transitory memory, the machine actionable set of rules with the specimen;
track changes to the machine actionable set of rules;
automatically generate a new consent document based at least in part on global regulations data derived from proprietary and public sources, by using the machine actionable set of rules with any changes and the machine-learning RIK, wherein the machine-learning RIK includes the global regulations data; and
interactively display visual risk indicators for collection of the specimen as visually corresponding to the plurality of locations on a map.
9. The system of claim 8 , wherein the codifying is linked to and performed based upon prevailing global, country, regional, and local regulations in force at a time of generating the new consent document in at least one of the plurality of locations.
10. The system of claim 8 , wherein automatically generating the new consent document includes generating the consent document based on an outline of desired consent, categories of consent needed, and regulations in at least one of the plurality of locations.
11. The system of claim 8 , wherein tracking changes to the machine actionable set of rules includes dynamically tracking changes in restrictions regarding what can be done to the specimen and/or whether the patient withdraws consent.
12. The system of claim 8 , wherein the map includes filters operable to provide interactive visualization of different risk categories.