The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Clinical information extraction applications: A literature review.

Security and privacy in electronic health records: A systematic literature review

The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411-8.997], P < 10-30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107-11.524], P < 10-30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925-8.530], P < 10-30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01-2.349], P < 10-30), with strongest effect for OUD (AOR = 4.162 [3.13-5.533], P < 10-25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.

/pdf/covid-19-risk-and-outcomes-in-patients-with-substance-use-1fiovdsdec.pdf

COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/wps.20806

Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States

Clinical research is on the threshold of a new era in which electronic health records (EHRs) are gaining an important novel supporting role. Whilst EHRs used for routine clinical care have some limitations at present, as discussed in this review, new improved systems and emerging research infrastructures are being developed to ensure that EHRs can be used for secondary purposes such as clinical research, including the design and execution of clinical trials for new medicines. EHR systems should be able to exchange information through the use of recently published international standards for their interoperability and clinically validated information structures (such as archetypes and international health terminologies), to ensure consistent and more complete recording and sharing of data for various patient groups. Such systems will counteract the obstacles of differing clinical languages and styles of documentation as well as the recognized incompleteness of routine records. Here, we discuss some of the legal and ethical concerns of clinical research data reuse and technical security measures that can enable such research while protecting privacy. In the emerging research landscape, cooperation infrastructures are being built where research projects can utilize the availability of patient data from federated EHR systems from many different sites, as well as in international multilingual settings. Amongst several initiatives described, the EHR4CR project offers a promising method for clinical research. One of the first achievements of this project was the development of a protocol feasibility prototype which is used for finding patients eligible for clinical trials from multiple sources.

/pdf/electronic-health-records-new-opportunities-for-clinical-50hxqq6o0g.pdf

Electronic health records: new opportunities for clinical research

Properly obtained consent is needed for all clinical trials, yet one size doesn't necessarily fit all. This article looks at different consent models and emphasises the need to support choice rather than just observing the formalities of “gaining consent”

/pdf/consent-for-the-use-of-personal-medical-data-in-research-t1yt3k20jm.pdf

Consent for the use of personal medical data in research

CLEF is an MRC sponsored project in the E-Science programme that aims to
establish methodologies and a technical infrastructure for the next generation of
integrated clinical and bioscience research. One of the major goals of the project is
to provide a pseudonymised repository of histories of cancer patients that can be
accessed by researchers. Robust mechanisms and policies are needed to ensure that
patient privacy and confidentiality are preserved while delivering a repository of
such medically rich information for the purposes of scientific research. This paper
summarises the overall approach adopted by CLEF to meet data protection
requirements, including the data flows, pseudonymisation measures and monitoring
policies that are currently being developed. Once evaluated, it is hoped that the
CLEF approach can serve as a model for other distributed electronic health record
repositories to be accessed for research.

Security and confidentiality approach for the Clinical E-ScienceFramework (CLEF)

CLEF aims to join up clinical care and biomedical research. It is developing methods formanaging and using pseudonymised repositories of the long-term patient histories which can belinked to genetic, genomic and image information or used to support patient care. CLEFconcentrates on removing key barriers to managing such repositories ? ethical issues, informationcapture, integration of disparate sources into coherent ?chronicles? of events, user-orientedmechanisms for querying and displaying the information, and compiling the required knowledgeresources. This paper describes the overall information flow and technical approach designed tomeet these aims within a Grid framework. Details of work on language technology and ethicalissues are discussed in separate papers at this conference.

CLEF: joining up healthcare with clinical and post-genomic research

CLEF aims to join up clinical care and biomedical research. It is developing methods for managing and using pseudonymised repositories of the long-term patient histories which can be linked to genetic, genomic and image information or used to support patient care. CLEF concentrates on removing key barriers to managing such repositories – ethical issues, information capture, integration of disparate sources into coherent “chronicles” of events, user-oriented mechanisms for querying and displaying the information, and compiling the required knowledge resources. This paper describes the overall information flow and technical approach designed to meet these aims within a Grid framework. Details of work on language technology and ethical issues are discussed in separate papers at this conference. Background: Common need for improved clinical information Information on the long-term course of patients’ illnesses and treatments is needed both to improve clinical care and to enable post genomic research. Scientists and clinicians alike must answer the fundamental questions about patients’ histories: What was done and why? What happened and why? Simple as these questions sound, they are difficult to answer using current technology without recourse to manual examination of patients’ notes – a time consuming and expensive process. Yet, without answers to these questions, it is difficult either to measure the quality of care or to investigate the factors affecting onset of disease or outcome of care. Hence CLEF aims to provide a repository of well organised clinical histories and which can be queried and summarised both for biomedical research and clinical care. Barriers to improved clinical information CLEF addresses key barriers to capturing clinical information and managing it in such repositories. • Privacy, consent, and security – at all levels: policy, organisational structure, and technical implementation. [4] • Information capture – much of the information required is available only as dictated texts from which it must be extracted. • Information integration and ‘chronicalisation’ – in their raw form, clinical records consist of hundreds of diverse documents. To be useful, a coherent ‘chronicle’ of events must be inferred from them. • Information presentation and summarisation – The CLEF repository is to be used by clinicians and scientists, not IT specialists. The questions to be asked are complex and may require information from many sources. The questions to be asked can be only partly predicted in advance. • Knowledge resources – all of the above tasks are knowledge intensive. They require recognising which information is significant in which context and recognising the implied meaning of information. While many knowledge resources exist, many do not, and coordinating those that do is a task in itself • Standards – cooperation requires standards, which are only just emerging. The basic CLEF Information Flow The basic CLEF information flow is shown in Figure 1. The top cycle represents the information capture from the clinician into the repository and its use either for clinical care of research. Starting with the “Patient care and dictated text” at the top of the diagram, the flow is: • Capture of the information. Some information comes from dictated and transcribed text .Other information comes directly from hospital information systems – e.g. laboratory results, prescriptions, etc. • Pseudonymisation of all information by removal of overt identifying items – name, date of birth, etc and by providing a CLEF Entry identifier that can only be reversed by the NHS provider or a trusted third party. • Depersonalisation of the texts to remove any residual information that might risk identification – e.g. names of relatives, nick names, place names, unusual occupations, etc. • Information extraction of key information from the texts into predefined “templates”, possibly with the help of context provided by the information already in the repository. • Integration into the health record repository of all information including laboratories, radiology, and potentially genomic analyses • Constructing the chronicle to infer a coherent view of the patient’s history. Typically the same information occurs in many different documents with different levels of granularity, clarity and sometimes conflicts that must be reconciled. From this point the information can go in two directions. • Back to the clinicians in the form of summaries for patient which can be reidentified by the hospital. Providing a concise up-to-date summary of a patients’ condition is a prime request of clinicians for improving patient care. • On to researchers in response to queries. The overall use of the repository for research is under the control of an Ethical Oversight Committee to which researchers must apply to gain access to anything except pre-computed results and metadata. Despite other precautions, it is assumed that if individual records can be read in detail, there is the risk of identification. Therefore, all information is treated with the as if it were for identifiable. [4]. Most researchers will be accredited only for aggregated results controlled through the privacy enhancing technologies. Special permission of the Ethical Oversight Committee is required to gain access to individual patient records, since there is always the risk that an individual patient can be recognised from the course of their illness. Pseudonymised Repository K n o w l e d g e Enrichment Summari se & Formulate Quer ie s Privacy E n h a n c e m e n t Technologies ( P E T ) Reidentify Pseudonymise

CLEF - Joining up Healthcare with Clinical and Post-Genomic Research

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