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At least 91 records · Page 5Linked to original sources
PubMed1994

Security requirements for electronic patients records: the Norwegian view.

Information security, including secrecy, privacy and data integrity, quality and availability, are fundamental issues when electronic patient records are introduced and used. In this paper we outline the results of a project that KITH carried out on an assignment from the Norwegian Ministry of Health. We describe the general and detailed requirements set up in the project for electronic patient records, and to the hospitals that take such into use. We believe that these requirements are the minimum necessary to give a positive answer to the question of whether electronic patient records can meet all the security-related requirements and intentions given by current regulations. In our work we have focused on secrecy, privacy and data integrity, but also on elaborating requirements that allow a user friendly and suitable implementation of electronic patient record systems.

Computer Communication Networks↗
PubMedDate not supplied

Confidentiality.

Explore the source record for details and available documents.

Computer Security↗
PubMed1994

Health information, privacy, confidentiality and ethics.

Electronic patient records are becoming tologically reified entities that play the role of epistemic patient analogues in information space. The traditional property-model of patient records is therefore no longer appropriate. A shift in paradigm is required. This paper suggests a new paradigm, examines its ethical implications and explores ways in which these could be reflected in legal and regulatory mechanisms. Special attention is paid to privacy, security and access relative to the so-called 'fair information principles'.

Computer Security↗
PubMed1994

Health information, the fair information principles and ethics.

If advanced electronic patient records are construed as epistemic patient analogues in information space, then the traditional property-model of patient records is longer appropriate. A new paradigm is required. This paper suggests a new paradigm, examines its ethical implications and explores ways in which these could be reflected in legal and regulatory mechanisms. Special attention is paid to privacy, security and access relative to the so-called "fair information principles".

Beneficence↗
PubMedDate not supplied

A generic methodology for health care data security.

The aim is to outline the framework of a generic methodology for specifying countermeasures in health care environments. The method is specifically aimed at the enhancement of security in existing health care systems, and a key element is the use of predetermined 'profiles' by which these may be classified. Example scenarios are presented to illustrate how the concept could be applied in practice. The paper is based upon work that was initially carried out as part of the Commission of European Communities SEISMED (Secure Environment for Information Systems in MEDicine) project, the aim of which is to provide security recommendations for European health care establishments (HCEs).

Computer Security↗
PubMedDate not supplied

Legal aspects of digital image management and communication.

This paper outlines the legal issues that arise in digital image management and communication systems (IMACS). At the heart of this study is the digital image which is manipulated, processed, communicated, stored, compressed and archived, and which is difficult to ascertain from a legal point of view because of its intangible nature. On the one hand, personal data protection and the patient's right to privacy need to be protected through a data protection and security policy. This is particularly important in view of the capacity of IMACS to integrate with other systems such as HIS and RIS, since the information generated by the totality of these systems offers a very complete picture of any patient. On the other hand, the evanescent nature of the digital image creates legal uncertainties as to questions of evidence, of procedural and legal admissibility and acceptability and of liability.

Belgium↗
PubMed1994

Enhancing medical database security.

A methodology for the enhancement of database security in a hospital environment is presented in this paper which is based on both the discretionary and the mandatory database security policies. In this way the advantages of both approaches are combined to enhance medical database security. An appropriate classification of the different types of users according to their different needs and roles and a User Role Definition Hierarchy has been used. The experience obtained from the experimental implementation of the proposed methodology in a major general hospital is briefly discussed. The implementation has shown that the combined discretionary and mandatory security enforcement effectively limits the unauthorized access to the medical database, without severely restricting the capabilities of the system.

Computer Security↗
PubMed1994

A methodology for evaluation of knowledge-based systems in medicine.

Evaluation is critical to the development and successful integration of knowledge-based systems into their application environment. This is of particular importance in the medical domain--not only for reasons of safety and correctness, but also to reinforce the users' confidence in these systems. In this paper we describe an iterative, four-phased development evaluation cycle covering the following areas: (i) early prototype development, (ii) validity of the system, (iii) functionality of the system, and (iv) impact of the system.

Artificial Intelligence↗
PubMed1995

Integrating legacy laboratory information systems into a client-server world: the University of Minnesota Clinical Workstation (CWS) project.

The development of an innovative clinical decision-support project such as the University of Minnesota's Clinical Workstation initiative mandates the use of modern client-server network architectures. Preexisting conventional laboratory information systems (LIS) cannot be quickly replaced with client-server equivalents because of the cost and relative unavailability of such systems. Thus, embedding strategies that effectively integrate legacy information systems are needed. Our strategy led to the adoption of a multi-layered connection architecture that provides a data feed from our existing LIS to a new network-based relational database management system. By careful design, we maximize the use of open standards in our layered connection structure to provide data, requisition, or event messaging in several formats. Each layer is optimized to provide needed services to existing hospital clients and is well positioned to support future hospital network clients.

Artificial Intelligence↗
PubMed1994

Small PACS for digital medical images--reliability and security in a clinical setting.

Recently we introduced a small Picture Archiving Communication System (PACS) for handling CT and MRI data. Our experience with this system has been useful in identifying potential problems in a clinical setting. Since the use of PACS raises both medical and social concerns, it cannot be instituted without addressing concerns related to the reliability and security issues. Although PACS is useful in the management of medical images, there are concerns about their reliability and security in a clinical setting. Reliability encompassed image quality, timeliness of the reports, and the PACS to the Radiological Information System (RIS) interface including the retrieval of the previously obtained images. The security of the patient's medical data is also essential.

Computer Security↗
PubMed1992

Automated ambulatory medical records systems. An orphan technology.

Automated ambulatory medical records systems (AAMRSs) have been operational for over 20 years but have not been adopted by more than a small fraction of their potential users. This paper presents a detailed analysis of the uses and benefits of the COSTAR-based AAMRS at the Harvard Community Health Plan and of the factors which have inhibited the dissemination of COSTAR. We conclude that AAMRSs have been an orphan technology and cite trends in health care that favor the future development of AAMRSs.

Ambulatory Care Information Systems↗
PubMed1995

Development of an interactive data base management system for capturing large volumes of data.

Accurate collection and successful management of data are problems common to all scientific studies. For studies in which large quantities of data are collected by means of questionnaires and/or forms, data base management becomes quite laborious and time consuming. Data base management comprises data collection, data entry, data editing, and data base maintenance. In this article, the authors describe the development of an interactive data base management (IDM) system for the collection of more than 1,400 variables from a targeted population of 6,000 patients undergoing heart surgery requiring cardiopulmonary bypass. The goals of the IDM system are to increase the accuracy and efficiency with which this large amount of data is collected and processed, to reduce research nurse work load through automation of certain administrative and clerical activities, and to improve the process for implementing a uniform study protocol, standardized forms, and definitions across sites.

Cardiac Surgical Procedures↗
PubMed1995

Use of medical information by computer networks raises major concerns about privacy.

The development of computer data-bases and long-distance computer networks is leading to improvements in Canada's health care system. However, these developments come at a cost and require a balancing act between access and confidentiality. Columnist Michael OReilly, who in this article explores the security of computer networks, notes that respect for patients' privacy must be given as high a priority as the ability to see their records in the first place.

Civil Rights↗
PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗