Health Insurance Portability and Accountability Act Privacy Rule Causes Ongoing Concerns among Clinicians and Researchers
Bibliographic record
Abstract
Current Clinical Issues15 August 2006Health Insurance Portability and Accountability Act Privacy Rule Causes Ongoing Concerns among Clinicians and ResearchersJennifer Fisher WilsonJennifer Fisher WilsonAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-145-4-200608150-00019 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail When the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule took effect in 2003, physicians worried about the financial costs of implementing it, the risks of disobeying it, and the nuisance of new paperwork that it would create. The Privacy Rule, issued by the U.S. Department of Health and Human Services (HHS) to implement HIPAA, was designed to protect the privacy and security of patients' medical information and to standardize electronic health care transactions. The rule requires all "covered entities," including health plans, hospitals, clinics, and health care providers, to implement policies safeguarding all protected health information. Protected health ...References1. Armstrong D, Kline-Rogers E, Jani SM, Goldman EB, Fang J, Mukherjee D, et al. Potential impact of the HIPAA privacy rule on data collection in a registry of patients with acute coronary syndrome. Arch Intern Med. 2005;165:1125-9. [PMID: 15911725] CrossrefMedlineGoogle Scholar2. Wolf MS, Bennett CL. Local perspective of the impact of the HIPAA privacy rule on research. Cancer. 2006;106:474-9. [PMID: 16342254] CrossrefMedlineGoogle Scholar3. Shalowitz D, Wendler D. Informed consent for research and authorization under the Health Insurance Portability and Accountability Act Privacy Rule: an integrated approach. Ann Intern Med. 2006;144:685-8. [PMID: 16670138] LinkGoogle Scholar4. Lazarus D. A tough lesson on medical privacy: Pakistani transcriber threatens UCSF over back pay. San Francisco Chronicle. 22 October 2003:A1. Google Scholar5. Slutsman J, Kass N, McGready J, Wynia M. Health information, the HIPAA privacy rule, and health care: what do physicians think? Health Aff. 2005;24:832-42. [PMID: 15886179] CrossrefMedlineGoogle Scholar6. McGuire AL, Gibbs RA. Genetics. No longer de-identified. Science. 2006;312:370-1. [PMID: 16627725] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Disclosures: None disclosed.E-mail:[email protected]org PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byUltrafast homomorphic encryption models enable secure outsourcing of genotype imputationBlockchain-Based Innovations for Population-Based Registries for Rare Neurodegenerative DiseasesPersonal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation StudyRole-based efficient information extraction using rule-based decision treeThe Ethics of Sports Medicine ResearchConversion of Legal Text to a Logical Rules Set from Medical Law Using the Medical Relational Model and the World Rule Model for a Medical Decision Support SystemEvaluation of Quality of Lower Limb Arthroplasty Observational Studies Using the Assessment of Quality in Lower Limb Arthroplasty (AQUILA) ChecklistComparison of knowledge, attitudes, and trust for the use of personal health information in clinical researchA Comprehension Approach for Formalizing Privacy Rules of HIPAA for Decision SupportElectronic Merger of Large Health Care Data Sets: Cautionary Notes From a Study of Agricultural Morbidity in New York StateWeb mining and privacy concerns: Some important legal issues to be consider before applying any data and information extraction technique in web-based environmentsUnderstanding the Challenges of Adjuvant Treatment Measurement and Reporting in Breast CancerThe Impact of ConsentComparing Medical Record Ownership and Access: Australia, Canada, UK, USARecruiting Rural Participants for a Telehealth Intervention on Diabetes Self-ManagementImpact of HIPAA provisions on the stock market value of healthcare institutions, and information security and other information technology firmsIntroduction to Digital Medical Image Management: Departmental ConcernsClinical Aspects of MetabolomicsBioethics Without AnalogyRoutine data from hospital information systems can support patient recruitment for clinical studiesFamily Caregivers, Patients and Physicians: Ethical Guidance to Optimize RelationshipsETHICAL CONSIDERATIONS IN THE CARE OF PATIENTS WITH NEUROSURGICAL DISEASEWho's keeping count? The need for regulation is a relative matterA Globally Optimal k-Anonymity Method for the De-Identification of Health DataHIPAA's effects on US healthcarePatient ConfidentialitySpecifying and Analyzing Workflows for Automated Identification and Data CaptureAligning Biomedical Informatics with Clinical and Translational ScienceFoundational Ethics of the Health Care System: The Moral and Practical Superiority of Free Market ReformsHIPAA's Preconsent: Impact on Study ValidityIdentifying and Responding to Ethical and Methodological Issues in After-Death Interviews with Next-of-KinEthical Issues in Clinical ResearchConfidentiality Challenges and Good Clinical Practices in Human Subjects Research: Striking a BalanceBusiness Associates in the National Health Information Network 15 August 2006Volume 145, Issue 4Page: 313-316KeywordsDisclosureHealth careHealth care providersHealth information technologyLongitudinal studiesNursesPatient advocacyPatients ePublished: 15 August 2006 Issue Published: 15 August 2006 Copyright & PermissionsCopyright © 2006 by American College of Physicians. 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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 | 0.096 | 0.290 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.027 | 0.022 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".