Despite being so much advancements in the field of information technology with different works of life exploiting them to fullest, still healthcare appears to be devoid of these technical assets. When a research is hypothesised in any computational domain the main aim after generating maximum efficiency is to make that component versatile so that it could be applied in different spheres with uniform scalability and reduced redundancy. Reusability of code with dynamic approaches has become a major driver in this process but when other sectors like aviation, petrochemical, forestry etc. have used these resources healthcare still seems to be largely dependent on pen-paper mode. This chapter will deal with every aspect of healthcare data and its potential sources and present limitations either computational or architectural this field is facing with plausible solutions. Then there is discussion about some modern day tools that are of prime importance in this shifting paradigm. Large chunks of valuable data gets wasted due to reduced emphasis on gathering data for future reference and usage. The fuel for data is data more the data better the throughputs leading to more precise implementations. Further certain explanations are also made to reduce the menace of high unstructuredness in the data available from several sources. Since we are facing the worst healthcare emergencies that the world has ever witnessed i.e COVID-19 pandemic, this chapter will propose certain measures to figure out how our aim to convert healthcare into data could be an efficient way out. Bioinformatics involves complex data generated in huge amounts which has to be tapped in a serialised manner immediately that could be retrieved whenever it is required. This GUI interface deals to simply the same problem. It can be used for chromosomal mapping and store/retrieve/delete data related to a particular or a set of chromosomes.
Particularly one can specify the name of organism and its chromosome number then following information can be stored:
1.Total base pairs
2.Number of genes
3.Gene Sequence
4.Array Probe used
5.Experimental information
6.mRNA expression
7.Measurement unit
8.Clinical Sample