The use of Natural Language Processing techniques to support Health Literacy: an evidence-based review

Background and objectives: To conduct a literature search and analysis of the existing research using natural language processing for improving or helping health literacy, as well as to discuss the importance and potentials of addressing both fields in a joint manner. This review targets researchers who are unfamiliar with natural language processing in the field of health literacy, and in general, any researcher, regardless of his or her background, interested in multi-disciplinary research involving technology and health care. Methods: We introduce the concepts of health literacy and natural language processing. Then, a thorough search is performed using relevant databases and well-defined criteria. We review the existing literature addressing these topics, both in an independent and joint manner, and provide an overview of the state of the art using natural language processing in health literacy. We additionally discuss how the different issues in health literacy that are related to the comprehension of specialised health texts can be improved using natural language processing techniques, and the challenges involved in these processes. Results: The search process yielded 235 potential relevant references, 49 of which fully fulfilled the established search criteria, and therefore they were later analysed in more detail. These articles were clustered into groups with respect to their purpose, and most of them were focused on the development of specific natural language processing modules, such as question answering, information retrieval, text simplification or natural language generation in order to facilitate the understanding of health information.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the projects, "Tratamiento inteligente de la informacion para la ayuda a la toma de decisiones" (GRE12-44), "Explotacion y tratamiento de la informacion disponible en Internet para la anotacion y generacion de textos adaptados al usuario" (GRE13-15), DIIM2.0 (PROMETEOII/2014/001), ATTOS (TIN2012-38536-C03-03), LEGOLANG-UAGE (TIN2012-31224), SAM (FP7-611312), and FIRST (FP7-287607)

Electronic health record data quality assessment and tools: A systematic review

OBJECTIVE: We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies. MATERIALS AND METHODS: We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process. RESULTS: We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology. DISCUSSION: There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality. CONCLUSION: Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process

Identifying Health Facilities outside the Enterprise: Challenges and Strategies for Supporting Health Reform and Meaningful Use

Objective: To support collation of data for disability determination, we sought to accurately identify facilities where care was delivered across multiple, independent hospitals and clinics. Methods: Data from various institutions' electronic health records were merged and delivered as continuity of care documents to the United States Social Security Administration (SSA). Results: Electronic records for nearly 8000 disability claimants were exchanged with SSA. Due to the lack of standard nomenclature for identifying the facilities in which patients received the care documented in the electronic records, SSA could not match the information received with information provided by disability claimants. Facility identifiers were generated arbitrarily by health care systems and therefore could not be mapped to the existing international standards. Discussion: We propose strategies for improving facility identification in electronic health records to support improved tracking of a patient's care between providers to better serve clinical care delivery, disability determination, health reform and meaningful use. Conclusion: Accurately identifying the facilities where health care is delivered to patients is important to a number of major health reform and improvement efforts underway in many nations. A standardized nomenclature for identifying health care facilities is needed to improve tracking of care and linking of electronic health records

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment