Toward an Interoperable and Centralized Consent Centric Access Control Model for Healthcare Resources: Model and Implementation
Bibliographic record
Abstract
Although patients have the legal right in Canada and many other countries to specify how, when and by whom their medical records can be accessed, the harsh reality is that in almost all cases using existing systems and solutions, patients are unable to ensure that their expressed consent directives are respected. Almost all health information systems deployed today lack the most basic ability to express and enforce consent at a data field level, and all are stretched when consent management must span disparate systems. \n \nThis is not an unrecognized problem in the consent management domain. Numerous consent model types have been proposed, along with a multitude of access control mechanisms. Unfortunately, most contemporary consent models used today are either paper-based, an online consent directive with a digital signature, a simple checkbox to either opt-in, opt-out, or employ simple browser cookies. The result is that most consent models can capture only the most basic of consent expressions. Despite there being many different approaches for expressing and managing consent, few models actually enable patients to express discrete consent directives at the resource or at the data attribute level. As a result, contemporary consent models are mainly used to meet the compliance obligations of healthcare organizations as opposed to empowering patients to manage their privacy and control access to their medical records. No architecture or system that we are aware of can adjudicate field-level consent directives in the multi-system, multi-jurisdiction, multi-provider, multi-patient environ¬ments that exist in healthcare today. The inability to effectively and efficiently capture and enforce patient consent directives leaves many data custodians vulnerable to inadvertent data release – mitigated only by the fact that many providers attempt to secure a carte-blanche consent directive from all patients to relieve themselves of the problem of needing to respect more restrictive consent directives. \n \nAdvances in healthcare IT systems are adding to, rather than reducing, the complexity of protecting patient privacy which exposes an important research question: How can we empower patients to have control over their health records and be able to dictate who has access to their records, where and when? This thesis addresses this question by proposing a consent-centric architecture called consent-centric attribute-based access control (C-ABAC). C-ABAC offers a new standard for authorization. It allows expression of consent at any abstraction level – from the record to the data field level – and also guarantees that patient consent directives can be enforced at the system level, ensuring that patient data is made available only to parties entitled to access it. \n \nThe C-ABAC model offers (1) a new standard for “authorization,” (2) a new profile and application of attribute-based access control, (3) support for fine-grained access control, (4) seamless interoperability, (5) automation of a complex process and (6) dynamic flexibility allowing for both rich consent expression and complex consent enforcement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".