Health Insurance Portability and Accountability Act Privacy Rule Causes Ongoing Concerns among Clinicians and Researchers
Notice bibliographique
Résumé
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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Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,005 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».