Healthcare has undergone dramatic revolutions over the last few decades with the introduction of technology.
This is especially so with the collection of medical data using everyday objects such as smartphones, the drastic drop in the price of hitherto esoteric tests, and the application of big data analytics to medical data.
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Biomedical databases are organized collections of biomedical data. They are important resources, representing where such data can be stored, collected, managed, and shared, yet remaining private except for authorized users and accredited investigators.
Biomedical informatics belongs to the family of health informatics and comprises multiple medical and biological disciplines intersecting with computational science and data science, medical engineering, and information technology.
The aim of biomedical information science is to search, organize, arrange, and process biomedical data to obtain useful information that can be exploited to design and implement optimal healthcare solutions.
Apart from clinical information, such as medical history, examination findings, prescriptions, laboratory reports, procedures, and admission/discharge forms, genomic research, and imaging technology advances have contributed immensely to this field, especially when combined with high-speed processing technological platforms.
Both written and electronic medical records are available for storage, processing, and analysis. Furthermore, data on epidemiological surveys, surveillance reports, healthcare expenditures, and unnecessary, redundant, or harmful services are all present in the mix.
The available data exists in structured and unstructured formats, including medical records, omics data, and data stored in various device types. Much of this data is in natural language and free text, which requires text mining or natural language processing to convert it into structured, standardized data.
Data privacy is also a concern, and the need to include this aspect in every attempt to decipher and analyze data slows down technological innovation and hinders the live-streaming of patient data.
Biomedical databases are used for biomedical informatics, which involves finding solutions to problems in patient diagnosis and/or treatment methods. Various medical concepts may be questioned, broken down, and modeled for better understanding and to gain more insight into health processes. Similarly, a host of putative biomedical solutions may be conceived, tested, and reiterated to arrive at a final solution based on suitable manipulation of the information in the databases.
Biomedical researchers use biomedical databases to increase knowledge about human health. Still, properly organizing and collating such data is an ongoing challenge.
To reap the benefits of biomedical databases, it is essential to develop and implement information management systems to promote collaborative research and improve research productivity. While such systems are often unfamiliar to laboratory research personnel, this step may be considered mandatory for the optimal use of such databases.
Historically, there has been a constant attempt to find a panacea for any given symptom. This ignores the enormous diversity of symptoms and signs associated with any disease, despite the same underlying disease process.
By building biomedical databases, medical scientists and informatics specialists hope to fill in a richer picture of a disease phenotype, allowing for better recognition and personalized interventions.
Biomedical informatics uses traditional methods and computational science to extract patterns and signals from biology, medicine, pharmacology, omics, and other allied disciplines.
Important attributes of biomedical databases thus include inputting data properly to ensure the efficient performance of these processes, accurate and consistent use of terminology to ensure comparability, free and rapid exchange of information across databases, and efficient storage as well as access by authorized personnel.
The heart of biomedical informatics is assigning meaning to manipulated and output data. This inevitably restricts the breadth of the outcome of the analysis and makes it more difficult to find solutions to informatics problems.
The aim of this discipline is to exploit the wealth of data contained in these databases to determine the best approach to treating individuals, using data on genetic, environmental, and lifestyle factors.
What is Biomedical Informatics?
Several major areas involving biomedical database applications include bioinformatics, clinical informatics, imaging informatics, and public health informatics.
Bioinformatics deals with cellular and molecular processes, including genetic sequencing of different types.
Biomedical databases allow quality control assessments of healthcare systems. For instance, clinical informatics is important to streamline the processes of data collection, management, storage, and sharing processes, including electronic medical records (EMR) and computerized hospital or clinic data.
This provides big data that clinical investigators can use to analyze multiple parameters of healthcare system quality in terms of access, availability, affordability, innovation, and barriers to adequate healthcare. Statistical power makes tracking and monitoring healthcare operations possible, thus helping improve or reform protocols.
The analysis of biomedical databases also helps discover trends in drug development, treatment regimens, and clinical trials using anonymized datasets, including huge numbers of patients.
Imaging informatics uses advanced imaging tools such as magnetic resonance imaging and computerized tomography scanners to explore the internal anatomy and function of organs and tissues. These images can be manipulated to yield the maximum information, thus guiding the optimal use of such tools.
Public health informatics is, conversely, focused on society as a whole, shaping epidemiological and other general studies. Big data resources are also found in linked biomedical databases like population-based study cohorts from multiple sources.
These could include electronic medical records, laboratory results, drug prescriptions, clinical records, and administrative records.
These yield a complete picture of the clinical course of an individual, multiplied millions of times, allowing health researchers to search out comorbidities and parse a spectrum of chronic disease care and prevention strategies.
Overall, human health can be immensely improved by applying advanced analytics to biomedical databases while ensuring data privacy and cost control. It is possible to derive detailed and specific pictures of an individual in health and disease. These profiles can be used, in turn, to elucidate the possible and probable causes, identify risk factors, and offer customized solutions.
The human aspect of medical care must be retained while using biomedical databases, considering the patient’s age, lifestyle, or emotional and psychological status.
With a wealth of data being available, there is much promise that biomedical databases can be appropriately manipulated to extract knowledge that will enhance clinical care as well as promote more effective research in the near future.