Data Analytics in Healthcare: Improving Diagnosis, Treatment, and Disease Prevention
Overview
This paper examines the critical role of data analytics in modern healthcare organizations. It argues that understanding patterns and trends in healthcare data is essential for improving diagnosis, treatment decisions, medication administration, and disease prevention, with examples such as malaria elimination efforts in Zambia demonstrating practical applications.
What this essay covers
- Introduction to data analytics in healthcare and its significance
- Applications of data analytics in diagnosis, treatment, and infectious disease tracking
- Practical example of Zambia's Natural Malaria Elimination Centre
- Benefits of pattern identification including time efficiency and improved medical services
Preview
Data Analytics
Student’s Name
Institutional Affiliation
Data Analytics
Understanding recent patterns and trends in healthcare data, which is understood as data analytics, is an essential element in healthcare organizations (Geng, 2017). Data analytics is significant and valuable because it improves diagnosis, analysis of symptoms, drugs to be administered, and dosage. The ability to understand data analytics is crucial because it assists in eliminating life-threatening infectious diseases. These diseases can be reduced by keeping track of their prevalence and hence, influence the decisions of medical personnel. The decisions determine whether change medication is needed, the number of people expected to be treated, and the population with a high risk of contracting the disease. In this regard, the personnel will have pertinent information about the condition and prevent its transmission.
A good example is Zambia's Natural Malaria Elimination Centre, where the data directs the practitioners to determine where and when supplies like medicine and bed nets are urgently needed. The data also assists in determining whether there is a need for change in medication. Identifying trends and patterns save time because it allows quick to identification disease symptoms in the recent trends and patterns and provides efficient medical services to the patient. Also, medical personnel can learn of new efficient medicine from trends and patterns, thus administer it. This saves time, especially in healthcare organizations, with a large number of patients and few medical personnel.
References
Geng, H. (2017). The internet of things & data analytics handbook. Hoboken, NJ: John Wiley & Sons.