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

Objectives: Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved. Methods: Literature search based on “Electronic Health Record”, “Medical Record”, and “Medical Chart” using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review. Results: By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today’s rapidly changing healthcare environment. Conclusion: The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.

/pdf/electronic-health-records-then-now-and-in-the-future-5auyjzy445.pdf

Electronic Health Records: Then, Now, and in the Future

LORIS (Longitudinal Online Research and Imaging System) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, genetics) to storage, processing and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying and administrative functions provide LORIS with a triple capability (i) continuous project coordination and monitoring of data acquisition (ii) data storage/cleaning/querying, (iii) interface with arbitrary external data processing “pipelines”. LORIS is a complete solution that has been thoroughly tested through the full life cycle of a multi-center longitudinal project# and is now supporting numerous neurodevelopment and neurodegeneration research projects internationally.

/pdf/loris-a-web-based-data-management-system-for-multi-center-5g9o121fqk.pdf

LORIS: a web-based data management system for multi-center studies.

Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data.

Validation of the modified Sgarbossa criteria for acute coronary occlusion in the setting of left bundle branch block: A retrospective case-control study

The DEDUCE Guided Query tool

Large amounts of information, as well as opportunities for informing research, education, and operations, are contained within clinical text such as radiology reports and pathology reports However, this content is less accessible and harder to leverage than structured, discrete data We report on an extension to the Duke Enterprise Data Unified Content Explorer (DEDUCE), a self-service query tool developed to provide clinicians and researchers with access to data within the Duke Medicine Enterprise Data Warehouse (EDW) The DEDUCE Clinical Text module supports ontology-based text searching, enhanced filtering capabilities based on document attributes, and integration of clinical text with structured data and cohort development The module is implemented with open-source tools extensible to other institutions, including a Java-based search engine (Apache Solr) with complementary full-text indexing library (Lucene) employed with a negation engine (NegEx) modified by clinical users to include to local domain-specific negation phrases

http://www.dukeinformatics.org/wp-content/uploads/2014/11/Rusincovitch_AMIA-CRI-2013-DEDUCE-CT-Abstract.pdf

DEDUCE Clinical Text: An Ontology-based Module to Support Self-Service Clinical Notes Exploration and Cohort Development.

The majority of UK food recalls are due to allergen mislabelling, misleading allergen claims and/or the unintentional presence of allergens – representing a significant food safety risk and cost to industry. Labelling legislation must be followed to ensure food is safe and what it says it is, and this requires good allergen management and accurate allergen information communication down the supply chain. Distilling this information accurately, to inform labelling and/or communication of allergen information, can be particularly challenging for small to medium food businesses due to the low adoption of advanced labelling technology. In November 2018, a joint FSA/EIT (European Institute of Innovation & Technology) workshop discussed potential solutions to tackling the increase in food allergen mislabelling incidents. It was concluded that the situation could be improved by developing accessible and affordable tools for food businesses, to aid in the automation of food data collection, validation and management. As a result, the FSA are funding this initial development project that aims to develop an online system targeted at small and medium-sized food businesses, to help reduce the number of product recalls due to allergen mislabelling. The tool is also predicted to support more reliable knowledge transfer and incident tracking when things do go wrong.

Prevent food allergy alerts: an incentive-based approach

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The DEDUCE Guided Query tool

DEDUCE Clinical Text: An Ontology-based Module to Support Self-Service Clinical Notes Exploration and Cohort Development.

Prevent food allergy alerts: an incentive-based approach

Modular design, application architecture, and usage of a self-service model for enterprise data delivery