Three Ways Data Analytics Improve Personalised Medicine
Many aspects of medicine and the healthcare sector benefit from data analytics. Data analytics is very important for personalised medicine. Before going through more details about how data analytics is important for personalised medicine practice, let’s define what personalised medicine is.
Personalised medicine is a method of modern healthcare that focuses on individual differences in patient genes and other external factors in order to give medical care to the specific characteristics of each patient.
In this article, we will discuss three ways data analytics is important for personalised medicine implementation in healthcare.
Clinical Data Management
Setting up a data architecture for healthcare centres facilitates introducing Electronic Health Records (EHR). EHR includes every source where patient health records can be stored, for example daignostic test results, treatment outcomes, and medical history. This data is important for making medical decisions. On the other hand, clinical data management is important for real-time monitoring of the patient’s condition because it gives healthcare professionals a stream of data generated directly for the patient. This process is important to determine any shift in the patient from the normal condition.
Predictive Analytics
In order to get information that can help us in checking up on and preventing diseases that might show up in the future, it is important to get large datasets about the genetics, lifestyle, and environment of individuals. By analysing and using predictive models on those data sets, it is easy to predict the individual risk of developing a specific disease. This process can also help us know how individuals are likely to respond to specific treatments in order to prevent ineffective treatment and side effects.
Genomic Data Analysis
Data analytics is important for making insights from large amounts of genetic data to point out the specific genes that are more likely to be related to the diseases. This process is done by scanning the DNA of the patients to search variations in it. To evaluate the genetic variance, it is important to compare the individual genomes to reference genomes. The analytics tools are helping you understand the difference in the genomes and determine whether this deviation is benign or something else.
Finally, introducing data analytics to healthcare centres is inevitable. Without data, it is impossible to track the real-time condition of the patients and get insight for decisions related to the modern approach. Data analytics is also important for predicting the likelihood of disease infection in individuals.
