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Medicine and biomedical sciences are influenced by technological advances and digitalization of information in recent years. Biomedicine is concerned explicitly with building the data about diseases through the help of new technologies, medical applications. According to Highfill (2020), new medical analysis techniques provide opportunities to analyze medical statistics more thoroughly and precisely, thus improving the overall quality of health services offered. The purpose of the paper is to discuss diagnostic tests, electronic health records, information storage, and retrieval as they are strongly related to one another.
Firstly, medicine reveals the natures of diseases based on statistics; hence utilization of analysis of diagnostic tests is crucial. The main issue related to the understanding of tests is that after a person receives positive test results, the patient is qualified as sick. While studies have proven that more than 75% of the doctors misinterpret the positivity of tests, thereby the probability of precise diagnosis is decreasing (Molinaro, 2015). Misunderstanding happens due to the conditionality of test results and distinct levels of sensitivity and the possibility of disease detection. Molinaro (2015) defines the sensitivity of tests as “the probability of a positive test result given the presence of disease” (p. 2). In contrast, the positive predictive value is “the probability that a person who receives a positive test result has the disease” (Molinaro, 2015, p. 2). Thus, the new PPV technique allows medics to reveal the probability of disease occasion in case of positive results leading to more precise disease diagnosis. Consequently, in several decades, the process of testing may change, as the sensitivity of the tests will be enhanced, and cases of misinterpretation will reduce.
Secondly, the quality of medical services depends on the costs and speed of serving patients and electronic health records (EHC). Most importantly, EHC helps both doctors and patients save time and provides easy access to medical history and data of a person anytime. Moreover, studies discovered hospitals using EHC systems the displaying 7.4% lower prices and greater cost-efficiency of services (Highfill, 2020, p. 4). So EHC lowers average costs of medical services, thereby making healthcare more accessible for all regardless of financial status. Although making EHC requires additional workload and staff from medical centers, later it pays off with lower prices and faster services. Despite the relative technical difficulty of saving electronic health records, it considerably improves the speed and cost-effectiveness of medical services provided by hospitals.
Finally, as biomedical research deals with a vast amount of information, its storage and retrieval methods are critical. Biomedical research is the most rapidly advancing part of medicine due to the overwhelming prevalence of data recently. Hence, as argued by Xu et al. (2019), the presence of too much and sometimes vague data makes relevant biomedical information and storage more complicated. For this, studies propose a diversity-oriented biomedical information retrieval method based on query expansion of medical terms. Investigations revealed that “query ranking of medical terms helps to address diversity-oriented biomedical information retrieval task effectively” (Xu et al., 2019, p. 10). Consequently, technological improvement of health information systems provides an opportunity to solve related issues of medicine such as biomedical information retrieval.
To conclude, the topics discussed in the paper correlate, as they are connected to the improvement of the health care system. The better testing systems may depend on some factors, for instance, data storage and analysis. Generally, technology allows scientists to make medical data and disease diagnosis clearer and provide health services faster and cheaper. Thereby, the application of contemporary health information techniques such as predictive interpretation of diagnostic tests, electronic health records, and enhanced biomedical information storage and retrieval improve the quality of medicine and biomedical science.
References
Highfill, T. (2020). Do hospitals with electronic health records have lower costs? A systematic review and meta-analysis. International Journal of Healthcare Management, 13(1), 65–71.
Molinaro, A., M. (2015). Diagnostic tests: how to estimate the positive predictive value. Neuro-Oncology Practice, 2(4), 162–166. Web.
Xu, B., Lin, H., Yang, L., Xu, K., Zhang, Y., Zhang, D., Yang, Z., Wang, J., Lin, Y., & Yin, F. (2019). A supervised term ranking model for diversity enhanced biomedical information retrieval. BMC bioinformatics, 20(16), 590.
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