Suchergebnisse

Background: Even though many safeguards and policies for electronic health record (EHR) security have been implemented, barriers to the privacy and security protection of EHR systems persist. Objective: This article presents the results of a systematic literature review regarding frequently adopted security and privacy technical features of EHR systems. Method: Our inclusion criteria were full articles that dealt with the security and privacy of technical implementations of EHR systems published in English in peer-reviewed journals and conference proceedings between 1998 and 2013; 55 selected studies were reviewed in detail. We analysed the review results using two International Organization for Standardization (ISO) standards (29100 and 27002) in order to consolidate the study findings. Results: Using this process, we identified 13 features that are essential to security and privacy in EHRs. These included system and application access control, compliance with security requirements, interoperability, integration and sharing, consent and choice mechanism, policies and regulation, applicability and scalability and cryptography techniques. Conclusion: This review highlights the importance of technical features, including mandated access control policies and consent mechanisms, to provide patients' consent, scalability through proper architecture and frameworks, and interoperability of health information systems, to EHR security and privacy requirements.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. We review the ethical, legal and social implications of these developments. We consider the values encoded in algorithms, the need to evaluate outcomes, and issues of bias and transferability, data ownership, confidentiality and consent, and legal, moral and professional responsibility. We consider potential effects for patients, including on trust in healthcare, and provide some social science explanations for the apparent rush to implement AI solutions. We conclude by anticipating future directions for AI in breast cancer care. Stakeholders in healthcare AI should acknowledge that their enterprise is an ethical, legal and social challenge, not just a technical challenge. Taking these challenges seriously will require broad engagement, imposition of conditions on implementation, and pre-emptive systems of oversight to ensure that development does not run ahead of evaluation and deliberation. Once artificial intelligence becomes institutionalised, it may be difficult to reverse: a proactive role for government, regulators and professional groups will help ensure introduction in robust research contexts, and the development of a sound evidence base regarding real-world effectiveness. Detailed public discussion is required to consider what kind of AI is acceptable rather than simply accepting what is offered, thus optimising outcomes for health systems, professionals, society and those receiving care.

Background: Electronic medical records are increasingly used for research with limited external validation of their data. Objective: This study investigates the validity of electronic medical data (EMD) for estimating diabetes prevalence in general practitioner (GP) patients by comparing EMD with national Bettering the Evaluation and Care of Health (BEACH) data. Method: A "decision tree" was created using inclusion/exclusion of pre-agreed variables to determine the probability of diabetes in absence of diagnostic label, including diagnoses (coded/free-text diabetes, polycystic ovarian syndrome, impaired glucose tolerance, impaired fasting glucose), diabetic annual cycle of care (DACC), glycated haemoglobin (HbA1c) > 6.5%, and prescription (metformin, other diabetes medications). Via SQL query, cases were identified in EMD of five Illawarra and Southern Practice Network practices (30,007 active patients; from 2 years to January 2015). Patient-based Supplementary Analysis of Nominated Data (SAND) sub-studies from BEACH investigating diabetes prevalence (1172 GPs; 35,162 patients; November 2012 to February 2015) were comparison data. SAND results were adjusted for number of GP encounters per year, per patient, and then age–sex standardised to match age–sex distribution of EMD patients. Cluster-adjusted 95% confidence intervals (CIs) were calculated for both datasets. Results: EMD diabetes prevalence (T1 and/or T2) was 6.5% (95% CI: 4.1–8.9). Following age–sex standardisation, SAND prevalence, not significantly different, was 6.7% (95% CI: 6.3–7.1). Extracting only coded diagnosis missed 13.0% of probable cases, subsequently identified through the presence of metformin/other diabetes medications (*without other indicator variables) (6.1%), free-text diabetes label (3.8%), HbA1c result* (1.6%), DACC* (1.3%), and diabetes medications* (0.2%). Discussion: While complex, proxy variables can improve usefulness of EMD for research. Without their consideration, EMD results should be interpreted with caution. Conclusion: Enforceable, transparent data linkages in EMRs would resolve many problems with identification of diagnoses. Ongoing data quality improvement remains essential.