Privacy Pilot Project
September 22 Meeting
Link to US Surgeon General’s Advisory: Confronting Health Misinformation
September 1 Meeting Summary
We had a great first meeting to discuss
the scope of the project
the general topic of information blocking and 21st Century Cures act was excluded - and those interested - please refer to information blocking
the P3 group agrees to focus on
outlining the general topic (there is a lot of confusion about privacy)
provide a concrete overview of GDPR vs. HIPAA
attempt to outline practical hurdles to inform the community (with a clear focus on stakeholder s with influence)
de-identification (for peer-to-peer sharing) and relevant governance layers
National patient identifier NPI, including a federated identifier/token model (FITM)
The group also discussed data governance, provenance, permissible purposes, as well as medicare fraud in relation to the utility of NPI.
Current members
Rama Gullapalli
Joe Lennerz
Paul Bunting
Joe Sirintrapun
Laura Lasiter
Ula Green
Gina Giannini
Jason Crites
Project Overview
Title: Privacy Pilot Project
Authors: Joe Lennerz, Paul Bunting, Joe Sirintrapun
Addressed Parties: We are looking for collaborators supporters and stakeholders interested in privacy issues related to diagnostic pathology. This includes creating a resource that covers topics related to privacy (e.g. national patient identifier, GDPR, HIPAA, state level privacy changes etc).
Background: One current challenge is that new regulations (21st Century Cures Act, NPI Repeal Act) directly affect pathology practice; however, there is no resource that captures privacy related regulatory content. We aim to create a collaborative forum to discuss key resources and their implications for diagnostic pathology.
Approach & Objectives: We will host three online meetings to a) outline the scope, b) discuss key issues, and c) put together relevant documents and links.
Deliverable(s): The key deliverables are a set of resources (weblinks, powerpoint presentation, and regulatory documents) to serve as a resource.
Value proposition:
“How will the proposed project be valuable from each of these categories?” Address each in detail:
· Clinical: Regulations are intended to improve the privacy and data control of patients. Knowing about how this applies to pathology can be considered an important skill and/or knowledge base.
· Regulatory: Providing a discussion forum or platform to discuss the regulatory implications
· R&D: Providing outlines on practical technical solutions will outline real-world challenges
Funding sources: N/A
Benefit to patients: technical advance, increased quality, outcome, access, affordability
References/Resources (optional): N/A
Click to download the powerpoint.
References
Hedlund et al., 2020, Relevance of GDPR and privacy in AI applications in pathology
Development of world-class artificial intelligence (AI) for medical imaging requires access to massive amounts of training data from clinical sources, but effective data sharing is often hindered by uncertainty regarding data protection. We describe an initiative to reduce this uncertainty through a policy describing a national community consensus on sound data sharing practices.
2. Chauhan & Gullapalli et al., 2021, Ethics of AI in Pathology: Current Paradigms and Emerging Issues
Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM)paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Additionally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. AI, if used unethically, may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. This review explores the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI: transparency, accountability, and governance, are described in the context of pathology. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive future implementation of ethical AI in the practice of pathology.(Am J Pathol 2021
3. National patient identifiers