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Visual data is an essential component in the healthcare industry as it represents complexmedical stats in a digestible graphical format (Karatas, et al., 2022). Data visualization allows users to easily acquire qualitative insights, patterns, changes, and trends as they are in the process of drawing valuable conclusions. Healthcare providers can draw valid and useful conclusions even without developing mathematical skills. Data visuals entail representing data and information in various forms, such as charts, graphs, diagrams, flowcharts, pictures, tables, maps, dashboards, and infographics, among others (Lamba & Madhusudhan, 2022). Visual data demonstrates a transparent and clear correlation between particular medical matters and events and offers healthcare providers evidence that their practices result in an improved situation. Data visualizations in healthcare provide significant benefits that decision-makers require and provide improved medical practices daily. Visual data offers improved patient care for clinics and healthcare centers and enhanced access to data insights for healthcare providers.

Healthcare administrators, policymakers, and industry leaders use data visualizations to find emerging trends, patterns, and correlations sometimes overlooked in raw data. Data visualization offers healthcare providers the ability to interpret the patient directly and lucidly (Inastrilla, 2023, September). It helps analyze the histories of the patients, monitor treatment progress, and identify health patterns. Data visualizations are accessed through several digital tools, including smartphones, computers, apps, websites, interactive software dashboards, and spreadsheets. Clinical data visualization relies on information found in electronic patient record systems such as the Electronic Health Records (EHR). EHR contains several arrays of patient health data, including medical histories of individuals, diagnoses and treatment plans, lab test results, and medical examinations, among others. Healthcare data visualization requires an adequate amount of medical information and a well elaborated mathematical models that advocate for patient data visualization.

References

Inastrilla, C. R. A. (2023, September). Data visualization in the information society. In Seminars in Medical Writing and Education (Vol. 2, pp. 25-25).

Karatas, M., Eriskin, L., Deveci, M., Pamucar, D., & Garg, H. (2022). Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications, 200, 116912.

Lamba, M., & Madhusudhan, M. (2022). Information Visualization. In Text Mining for Information Professionals: An Uncharted Territory (pp. 243-293). Cham: Springer International Publishing.

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