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Health Insurance Portability and Accountability Act Privacy Rule Causes Ongoing Concerns among Clinicians and Researchers

2006· article· en· W2042601392 on OpenAlexaboutno aff
Jennifer Fisher Wilson

Bibliographic record

VenueAnnals of Internal Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
Fundersnot available
KeywordsHealth Insurance Portability and Accountability ActProtected health informationAccountabilityMedicineHealth carePrivacy policyPrior authorizationSafeguardingInformation privacyInternet privacyPublic healthHealth policyNursingHRHISLawPolitical science

Abstract

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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. All Rights Reserved.PDF downloadLoading ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.544
GPT teacher head0.620
Teacher spread0.075 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations52
Published2006
Admission routes1
Has abstractyes

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