Legal Governance of Brain Data Derived from Artificial Intelligence

You have accessThe ASHA LeaderBottom Line1 Sep 2011Patient Information Privacy Basics Kate RomanowJD Kate Romanow Google Scholar More articles by this author , JD https://doi.org/10.1044/leader.BML1.16092011.3 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In ASHA recently hosted an online private-practice institute for audiologists and speech-language pathologists that focused on how to establish, manage, and grow a profitable private practice. Recorded lectures covered topics such as strategic business planning, fees and pricing, employment law basics, managing a fee-for-service practice, increasing referrals, using web-based and social media marketing, coding updates, Medicare billing, and claims and denials. Many of the participants in one of the sessions, “Data Privacy, Security, and Enforcement: HIPAA and More,” raised several questions during and after the institute on the Health Insurance Portability and Accountability Act (HIPAA). This article clarifies points discussed during the institute to help other clinicians understand HIPAA policies. The HIPAA privacy rule protects personal health information and outlines patients’ rights with respect to that information, while allowing disclosure of information needed for patient care. The HIPAA security rule specifies a series of administrative, physical, and technical safeguards that ensure the confidentiality, integrity, and availability of electronic protected health information. Electronic protected health information includes, for example, patient data stored on a computer hard drive, or data transmitted via a computer for billing purposes. The following questions and answers outline basic HIPAA privacy and security regulations. Q: Do the HIPAA regulations apply to me? HIPAA compliance is required by all “covered entities,” defined as health plans, health care clearinghouses, and health care providers that transmit any health information in electronic form in connection with “transactions” covered under HIPAA. If you are a provider who, for example, sends patient information electronically to a billing company, then you are a “covered entity” and must comply with HIPAA regulations. Q: What transactions are covered under HIPAA? HIPAA regulations define “transaction” as the transmission of information between two parties to carry out financial or administrative activities related to health care. It includes the following types of information transmissions: Health care claims or equivalent encounter information. Health care payment and remittance advice. Coordination of benefits. Health care claim status. Enrollment and disenrollment in a health plan. Eligibility for a health plan. Health plan premium payments. Referral certification and authorization. First report of injury. Health claims attachments. Other transactions that the secretary of health and human services may prescribe by regulation (45 C.F.R. Section 160.103). Q: What does “electronic form” mean? HIPAA does not define “electronic form.” It does, however, define “electronic media” as the following: Electronic storage media including memory devices in computers (hard drives) and any removable/transportable digital memory medium, such as magnetic tape or disk, optical disk, or digital memory card. Transmission media used to exchange information already in electronic storage media. Transmission media include, for example, the Internet, extranet (using Internet technology to link a business with information accessible only to collaborating parties), dial-up lines, leased lines, private networks, and the physical movement of removable/transportable electronic storage media. Certain transmissions, including those by paper (facsimile) and by voice (telephone), are not considered e-transmissions via electronic media because the information did not exist in electronic form before the transmission (45 C.F.R. Section 160.103). Q: If I am a covered entity, what do I need to do to comply with HIPAA? You must protect the privacy of patient information. You must safeguard patient information sent electronically. For example, you must inform patients about the HIPAA privacy practices you observe and train employees about HIPAA requirements. Q: Are there sample notices of privacy practices? More information about HIPAA privacy notices is available at the Department of Health and Human Services website. ASHA includes a sample notice of privacy practices in its Practice Management Tools for SLPs available in the ASHA online store. Q: Where I can get more information? ASHA’s reimbursement web page includes an extensive section on HIPAA. An article in The ASHA Leader also outlines some basics. The Office of Civil Rights, which enforces the privacy and security rule, has information on its website. The Workgroup for Electronic Data Interchange [PDF] (of which ASHA is a member) has information to help small practices comply with HIPAA. The full HIPAA regulations are available at the Department of Health and Human Services website. (Be aware, however, that the most current version may not be posted.) Author Notes Kate Romanow, JD, director of health care regulatory advocacy, can be reached at [email protected]. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetails Volume 16Issue 9September 2011 Get Permissions Add to your Mendeley library History Published in print: Sep 1, 2011 Metrics Current downloads: 271 Topicsasha-topicsleader_do_tagasha-article-typesleader-topicsCopyright & Permissions© 2011 American Speech-Language-Hearing AssociationLoading ...

What Ethical Responsibilities Do I Have With Regard to Radiographs for My Patients?

Enforcement of the Health Insurance Portability and Accountability Act (HIPAA) began in April, 2003. Designed as a law mandating health insurance availability when coverage was lost, HIPAA imposed sweeping and broad-reaching protections of patient privacy. These changes dramatically altered clinical research by placing sizeable regulatory burdens upon investigators with threat of severe and costly federal and civil penalties. This report describes development of an algorithmic approach to clinical research database design based upon a central key-shared data (CK-SD) model allowing researchers to easily analyze, distribute, and publish clinical research without disclosure of HIPAA Protected Health Information (PHI). Three clinical database formats (small clinical trial, operating room performance, and genetic microchip array datasets) were modeled using standard structured query language (SQL)-compliant databases. The CK database was created to contain PHI data, whereas a shareable SD database was generated in real-time containing relevant clinical outcome information while protecting PHI items. Small (< 100 records), medium (< 50,000 records), and large (> 10(8) records) model databases were created, and the resultant data models were evaluated in consultation with an HIPAA compliance officer. The SD database models complied fully with HIPAA regulations, and resulting "shared" data could be distributed freely. Unique patient identifiers were not required for treatment or outcome analysis. Age data were resolved to single-integer years, grouping patients aged > 89 years. Admission, discharge, treatment, and follow-up dates were replaced with enrollment year, and follow-up/outcome intervals calculated eliminating original data. Two additional data fields identified as PHI (treating physician and facility) were replaced with integer values, and the original data corresponding to these values were stored in the CK database. Use of the algorithm at the time of database design did not increase cost or design effort. The CK-SD model for clinical database design provides an algorithm for investigators to create, maintain, and share clinical research data compliant with HIPAA regulations. This model is applicable to new projects and large institutional datasets, and should decrease regulatory efforts required for conduct of clinical research. Application of the design algorithm early in the clinical research enterprise does not increase cost or the effort of data collection.

he Privacy Rule of the Health Insurance Portability and Accountability Act (HIPAA) restricts the ways in which (health care providers that transmit health electronically in connection with certain covered transactions, health plans, and healthcare clearinghouses) can use and disclose protected health information (PHI) for purposes. Covered entities may not use or disclose PHI for purposes except as follows: (1) with the authorization of each subject; (z) after an IRB or privacy board approves a waiver of authorization; (3) for reviews preparatory to (e.g., to assess the feasibility of a study); (4) for on the PHI of decedents; (5) as part of a limited data set (stripped of the direct identifiers); or (6) where the data are de-identified under HIPAA's standards. As of HIPAA's compliance date, April 14, zoo3, covered entities conducting research (which is given the same meaning under HIPAA as under the Common Rule) must be in compliance with HIPAA's requirements in addition to existing federal and state laws and regulations. HIPAA's transition provisions address the applicability of the regulations to studies that have enrolled subjects both before and after HIPAA's compliance date (45 CFR 164.532). A covered entity may continue to use or disclose PHI from subjects enrolled in a study prior to HIPAA's compliance date assuming that, prior to April 14, 2oo3, the covered entity obtained (1) informed consent from the subjects to participate in the study, (2) IRB waiver of informed consent, or (3) other express legal permission from the subjects to use or disclose the PHI for the study. No additional HIPAA authorization or IRB waiver of the authorization requirement is required in order for the covered entity to continue to use or disclose PHI from these subjects who were enrolled prior to April 14, zoo3, unless the subjects are re-consented after the compliance date. Subjects who are enrolled after April 14, zoo3 will be required to give an authorization that meets HIPAA's requirements in addition to informed consent to participate in the study, unless the IRB granted waiver of informed consent prior to April 14, zoo3 (in which case the entire study is grandfathered after April 14, zoo3, unless circumstances change and the investigator is subsequently required to obtain informed consent from subjects enrolled after the compliance date). Under the Common certain types of are from the Common Rule's requirements. For example, is exempt from the regulatory requirements if it involves the study of existing data and the is recorded by the investigator in such a manner that subjects cannot be identified, either directly or through identifiers linked to the subjects. Yet ongoing that is exempt from the Common Rule is not necessarily exempt from the requirements of HIPAA, because of a glitch in the transition rules. Research that is determined to be exempt from the Common Rule's requirements does not require subject informed consent or IRB waiver of informed consent because it is, by definition, outside of the Common Rule's requirements. As a result, for exempt studies that straddle HIPAA's compliance date, there is generally no basis to invoke HIPAA's transition provisions and grandfather-in subjects who were enrolled prior to April 14, zoo3. It is therefore critical that covered entities like hospitals and academic medical centers identify all on-going exempt as soon as possible and obtain subject authorization, or IRB waiver of authorization, for subjects enrolled prior to HIPAA's compliance date. Otherwise, the use or disclosure of such subjects' PHI for purposes of the study should cease immediately. This diligence has been accomplished in many hospitals and institutions by sending a memorandum to all investigators asking them to identify any ongoing that is exempt from IRB review, the date Mark Barnes and Katherine E. Gallin, 'Exempt' Research after the Privacy Rule, IRB: Ethics & Human Research 25, no. 4 (200oo3): 5-6.

The rapid development of big data, cloud computing, and artificial intelligence has brought new opportunities to the development of the medical field. The emergence of electronic medical records and medical information systems has provided great convenience for people's medical treatment. However, the arrival of the big data era also poses new challenges to the protection of human medical privacy. The development and application of medical big data have led to explosive growth of data in the medical field, putting pressure on the management of medical institutions. The sharing of medical data between medical institutions and between medical institutions and third parties also poses significant security risks. Moreover, medical data leakage incidents have been continuously exposed in recent years, seriously infringing on the identity and privacy rights of patients. This article aims to explore the research on personal medical information protection based on big data. Firstly, the importance and necessity of protecting personal medical information were analyzed, as well as the current application status and existing problems of big data in medical information management. Secondly, a comparative analysis of domestic and international research on medical privacy protection was elaborated, including the analysis of medical information privacy protection standards in the United States, Europe, and Asia, and the differences and commonalities of international medical information privacy protection standards were compared and analyzed. Then, the theoretical basis of medical information privacy protection was summarized. Then, the application methods and technologies of big data in personal medical information protection were discussed. Finally, relevant countermeasures and suggestions for the protection of personal medical information in big data were discussed, including privacy protection technologies and policy recommendations, as well as ethical issues and norms for the protection of medical information privacy. Through this study, it is hoped that it can provide reference and inspiration for the protection of personal medical information in big data, and provide ideas and suggestions for future development directions.

In the ever-evolving landscape of Artificial Intelligence in Healthcare (AIH), understanding the entities and legal frameworks governing its research and development is crucial. This report delves into the intricacies of AIH in Malaysia, undertaking a comprehensive literature search on scientific databases, government portals, and news sources. Additionally, bibliometric analysis has been concurrently conducted to discern trends and developments in AIH over the years. Notably, the interest in AIH has seen a consistent rise since 2017, marked by a growing number of use cases (25 reported here) developed by both local and foreign innovators and applicators. Despite this surge in research and adoption, Malaysia lacks direct legislation specifically addressing AIH technologies, leaving them subject to 11 existing laws. This lack of clear oversight is compounded by the insufficient expertise within local regulatory and ethical bodies to effectively assess AIH research and deployment. The resultant challenges include bureaucratic hurdles for AIH innovators and applicators, raising ethical concerns related to patient autonomy, privacy, data management, AI robustness, and liability. To address these issues, this paper recommends: (1) adopting international ethical guidelines for AIH, (2) enhancing public awareness and education on AI technologies, and (3) promoting AIH research through clinical or silent trials to improve oversight and foster innovation.

Privacy was defined as a fundamental human right in the Universal Declaration of Human Rights at the 1948 United Nations General Assembly. However, there is still no consensus on what constitutes privacy. In this review, we look at the evolution of privacy as a concept from the era of Hippocrates to the era of social media and big data. To appreciate the modern measures of patient privacy protection and correctly interpret the current regulatory framework in the United States, we need to analyze and understand the concepts of individually identifiable information, individually identifiable health information, protected health information, and de-identification. The Privacy Rule of the Health Insurance Portability and Accountability Act defines the regulatory framework and casts a balance between protective measures and access to health information for secondary (scientific) use. The rule defines the conditions when health information is protected by law and how protected health information can be de-identified for secondary use. With the advents of artificial intelligence and computational linguistics, computational text de-identification algorithms produce de-identified results nearly as well as those produced by human experts, but much faster, more consistently and basically for free. Modern clinical text de-identification systems now pave the road to big data and enable scientists to access de-identified clinical information while firmly protecting patient privacy. However, clinical text de-identification is not a perfect process. In order to maximize the protection of patient privacy and to free clinical and scientific information from the confines of electronic healthcare systems, all stakeholders, including patients, health institutions and institutional review boards, scientists and the scientific communities, as well as regulatory and law enforcement agencies must collaborate closely. On the one hand, public health laws and privacy regulations define rules and responsibilities such as requesting and granting only the amount of health information that is necessary for the scientific study. On the other hand, developers of de-identification systems provide guidelines to use different modes of operations to maximize the effectiveness of their tools and the success of de-identification. Institutions with clinical repositories need to follow these rules and guidelines closely to successfully protect patient privacy. To open the gates of big data to scientific communities, healthcare institutions need to be supported in their de-identification and data sharing efforts by the public, scientific communities, and local, state, and federal legislators and government agencies.

Privacy Act Basics for Private Practitioners

The integration of Big Data Analytics and Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, treatment, and disease prevention. This paper explores how these advanced technologies enhance clinical decision-making, improve patient outcomes, and optimize healthcare processes. By leveraging vast datasets, AI-driven algorithms facilitate early disease detection, predictive analytics, and personalized medicine, significantly reducing diagnostic errors and enabling timely interventions. Furthermore, machine learning models assist in tailoring treatment plans based on patient-specific data, leading to more effective and efficient therapeutic strategies. In disease prevention, big data analytics enable epidemiological surveillance, tracking disease patterns, and identifying at-risk populations. AI-powered predictive models support proactive interventions, reducing the burden of chronic illnesses and infectious diseases. The paper highlights key advancements in AI applications, including deep learning in medical imaging, natural language processing in electronic health records, and real-time analytics in wearable health devices. Despite these transformative benefits, challenges such as data privacy, ethical concerns, and integration complexities remain barriers to widespread adoption. The study concludes that while AI and big data analytics hold immense potential to reshape healthcare, addressing regulatory, infrastructural, and ethical considerations is crucial for sustainable implementation. By fostering interdisciplinary collaboration and robust policy frameworks, healthcare systems can harness these technologies to drive innovation, enhance efficiency, and improve global health outcomes.

What is HIPAA and what effect may it have on our journal?

Electronically protected health information is held in computerized healthcare records that contain complete healthcare information and are easily shareable or retrieved by various health care providers via the Internet. The two most important concerns regarding their use involve the security of the Internet and the privacy of patients. To protect the privacy of patients, various regions of the world maintain privacy standards. These are set, for example, by the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe. Most recently developed authenticated key agreement schemes for HIPAA and GDPR privacy/security involve modular exponential computations or scalar multiplications on elliptic curves to provide higher security, but they are computationally heavy and therefore costly to implement. Recent studies have shown that cryptosystems that use modular exponential computation and scalar multiplication on elliptic curves are less efficient than those based on Chebyshev chaotic maps. Therefore, this investigation develops a secure and efficient non-certificate-based authenticated key agreement scheme that uses lightweight operations, including Chebyshev chaotic maps and hash operations. The proposed scheme overcomes the limitations of alternative schemes, is computationally more efficient, and provides more functionality. The proposed scheme complies with the privacy principles of HIPAA and GDPR.

Teleorthodontics.

Background: Telemedicine, facilitating medical services remotely, introduces ethical concerns. Safeguarding patient data, ensuring informed consent, addressing access disparities, and managing biases in artificial intelligence-driven diagnoses are critical. Navigating these challenges ethically is essential for harnessing telemedicine benefits while upholding patient trust and welfare. Objective: To ascertain the primary obstacles affecting global telemedicine practice, including issues concerning patient confidentiality, privacy, autonomy, informed consent, and data security. Methodology: Conducted a systematic literature review, for which we searched two databases (Pubmed and Google Scholar) between January 2018 to December 2022. Broad terms such as ethical issues, legal issues, health care providers were used as keyword searches. The authors used a narrative approach according to PRISMA guidelines 2020 (Checklist). The authors did a qualitative synthesis of selected studies according to inclusion criteria. The inclusion criteria required articles that reported ethical and legal concerns associated with the use of telemedicine; the full texts articles were electronically available and published in English. Systematic reviews and papers in other languages are not a part of the study. Results: Results showed that most reported ethical and legal concerns were related to privacy and confidentiality, followed by informed consent, patient autonomy and data security. From the 16 papers reviewed, authors identified or discussed the following ethical concerns faced by healthcare professionals during the use of telemedicine: patient privacy was addressed in 75% (n = 12/16) studies, informed consent 56.25% (n = 9/16), patient autonomy 31.25% (n = 5/16), data security 25% (n = 4/16). Conclusions: In the dynamic landscape of telemedicine, healthcare providers and policy makers play pivotal roles in addressing its ethical and legal concerns. Collaborative efforts are essential to establish clear guidelines, secure patient data, ensure informed consent, and create adaptable regulations, fostering a responsible and effective telemedicine ecosystem.

In 1996, Congress passed the Health Insurance Portability and Accountability Act (HIPAA), which required the enactment of new regulations to protect confidential patient health information. In December 2000, the U.S. Department of Health and Human Services (DHHS) published the agency's final rule on patient privacy and the proper use of privileged health information. The HIPAA privacy regulations cover all health-care providers that handle medical records or other identifiable patient health information. Most health-care organizations have until April 14,2003, to comply with HIPPA.

Congress enacted Health Insurance Portability and Accountability Act (HIPAA) in 1996 to limit the ability of an employer to deny health insurance coverage to employees with preexisting medical conditions. The law also directed the U.S. Department of Health and Human Services to develop privacy rules, including, but not limited to, the use of electronic medical records. This law has increased patient privacy, but in doing so has added to the financial burden, including personnel costs in health care. Nurses stand at the forefront in the resolution of the dilemma of patient privacy versus health care expediency. The purpose of this article is to assist nurses and other health care professionals to better understand their responsibilities regarding HIPAA regulations. First, responses to HIPAA regulations by covered entities to date, along with responses which are still needed, will be described. It will be noted that HIPAA is a work in progress and not a specific act. Next, future initiatives having HIPAA implications will be presented. In conclusion, the need for all covered entities and their personnel to look broadly at HIPAA as initiating a new way of work in health care will be emphasized.