The current state of summarization and visualization in Electronic Health Record (EHR) based on EHR interoperability

Abstract

Recently, several health-care organizations store heterogeneous health information about patients aiming to improve the quality of health care. The Electronic Health Record (EHR) contains a huge amount of patients' information making it difficult and time-saving to find the most pertinent information. Accurate, concise, and automated summarization and visualization have the potential to save time by increasing patient safety, improving efficiency, helping clinical decision-making, and reducing medical error as well as costs. Although interoperability and standardization are considered keys to improve the quality of care services and to coordinate care and practice effective summarization, several studies have shown the difficulty of improving the quality of health care using the current summarization- and visualization-based systems since they lack interoperability and do not allow to easily express clinician needs. We found that there is no study that discusses the impact of semantic and syntactic interoperability on the EHR summarization approach, which motivated us to provide and discuss studies on the above topics. In this study, we will review health-care summarization and visualization approaches and systems and analyze the proposed studies according to interoperability and clinicians' needs and challenges. To construct our review, we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and examined papers between 1980 and 2021. Selected studies focus on health-care sub-areas, EHR visualization, EHR summarization, interoperability, and standards. Based on the above papers, we provide a systematic view of development in this field and possible future directions. We conclude that most research studies in summarizing systems lack semantic interoperability and do not rely on clinicians' needs. Besides, EHR visualization systems lack the ability to analyze efficiently health data and integrate expert knowledge domains in the decision-making process. This will promote new research to solve these issues.

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