Biomedical Informatics makes it Easier for Biomedical Researchers to Examine EHR Data
Received: 04-May-2022, Manuscript No. ejbi-22-64693; Editor assigned: 05-May-2022, Pre QC No. ejbi-22-64693(PQ); Reviewed: 19-May-2022 QC No. ejbi-22-64693; Revised: 23-May-2022, Manuscript No. ejbi-22-64693(R); Published: 30-May-2022, DOI: 10.24105/ejbi.2022.18.5.48-49
Citation: Ghose R (2022). Biomedical Informatics makes it Easier for Biomedical Researchers to Examine EHR Data. EJBI. 18(5):48-49.
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Abstract
Cohort identification, proteomics, pharmacogenomics, and global health surveillance all benefit from electronic health records (EHR). It is critical to enable rapid and independent EHR data examination by end users such as biomedical researchers in order to achieve the promise of EHR data for advancing clinical research. This study examines current techniques as well as critical methodological considerations. By expanding on channels for additionally called in the context of aiding end users’ interrogation of EHR data, we extended a previously released theoretical foundation for interactive information retrieval, which identifies three entities: user, channel, and source. The information science literature, on the other hand, has provided detailed theories and methodologies for user modelling and question formulation support. The two bodies of literature are complimentary, meaning that cross-disciplinary idea interchange is possible. On this foundation, we identify future informatics research directions to better our understanding of customer needs and requirements for supporting biomedical researchers’ independent interrogation of EHR data. We believe that cross-disciplinary research community between bioinformatics and information systems can help our research in the life sciences by providing efficient data access.
Introduction
First and foremost, the number of data in the healthcare informatics sectors of known human genes labeled in the Swiss-Prot database is increasing dramatically. The data volume of Proteomics DB is 5.17 TB. The HITECH Act has nearly doubled the adoption rate of health records in hospitals. Millions of patients' data has already been collected and kept in an electronic format, and this information might be used to improve health-care services and expand research prospects. Furthermore, medical imaging generates enormous amounts of data with much more sophisticated properties and dimensions. The Visible Living beings Project, for example, include an archive of female datasets. Future opportunities will be provided by this and other datasets [1].
Remote patient care allows healthcare providers to relocate treatment from the clinic to the patient's home thanks to advancements in healthcare computing and information, as well as remote health gadgets. This helps to considerably save healthcare expenditures while also lowering the danger of patients during COVID-19. An EHR is an electrical device that healthcare organisations use to collect and preserve patient medical data. EHRs are used in clinical treatment and healthcare management to keep track of a range of medical data from individual patients throughout time and manage clinical workflows [2].
The diversity of data kinds and structures is the second aspect of big data. Many various tiers of data sources make up the biomedical big data ecosystem, which provides researchers with a diverse set of data. Sequencing technologies, for example, generate "omics" data at practically all levels of cell functions, from genomes to proteomics to metabolomics to binding proteins to phenomics. Unstructured data presents various potential and a distinct challenge for developing novel investigations. The third attribute of big data is velocity, which relates to how quickly data is produced and processed. The latest generation of sequencing data allows for the low-cost synthesis of billions of DNA sequencing each day. Because DNA sequencing necessitates greater speeds, big data solutions will be customized to meet the speed at which data is generated and processed. Big data technologies will also give biological researchers with time-saving methods for detecting new patterns among demographic groups using social media data in the public health arena [3].
Informatics nurses can quickly collect data using EHRs as well as other clinical systems, allowing for innovation. Informatics nurses lead efforts that promote patient well-being because they are well-positioned to uncover healthcare shortages and find revolutionary approaches to drive practise. Greater access to health services enhanced patient safety, increased care coordination, and more empowered people are all advantages. We are now living in the "big data" era, inside which big data technology is quickly being used to biological and health-care disciplines. We provided several examples in this review of how big data technology has played a key role in the modern-day health-care revolution, transforming people's perceptions of health-care activity [4].
Big data applications facilitate three key clinical activities, according to the first three main sections of this review, while the last segment paints a comprehensive picture of how diverse clinical activities have been completed in a pipeline to manage patient populations from multiple perspectives. We summarised current developments in each field's most important topics, such as large data storage and retrieval, error detection, data security, and data analytics. While big data has great promise to improve health care, there are numerous common problems that all four disciplines face when employing the technology; the most fundamental one is database integration [5].
Conclusion
In addition, we found from this review that computational biology is the principal discipline where big data analytics are now being used, owing to the vast volume and complexity of biomedical data. The use of big data in bioinformatics is fairly advanced, with advanced platforms and tools, such as gene sequenced mapping tools, already in use to aid in the analysis of biological data. However, there is significant, unexplored potential for big data applications in other biomedical research domains like health informatics, diagnostic imaging computer science, and public health informatics.
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