This is a guest post by Jim Kelly
Owing to the development of technology, there has been growth for ‘Big Data’. The healthcare sector enjoys a fair share of Big Data, which has to be assimilated in a number of forms. Alongside this, there’s the progress of axial neural networks (ANNs). These are data networks that are used for prediction services for a number of tasks.
A simple example of the axial neural network use is in the pharmaceutical industry where prediction networks are checked to see how the drug would interact with the body components like proteins in the cells.
So in simple terms, the data scientist indicates an evolution from the business or data analyst role. Anjul Bhambhri who is the VP of big data products at IBM describes the role of a data scientist as ‘part analyst, part artist’. The idea is to look at the data and spot measurable trends.
The data scientist job is coming as a wonderful non-clinical job option for many based upon the increased demand. The demand can also be equated with the development in fields like bioinformatics and genomics.
The sequence of DNA that’s present inside the living organisms is the ‘Big Data’ for healthcare data scientists. This data is huge and can be assimilated in a number of ways. As far as the technical proficiency is concerned, there is no exclusivity. Any knowledge pertaining to various aspects of healthcare would suffice. Courses in database development and bioinformatics are an added bonus.
Science aside, data scientists are also involved in identifying others trends, such as advances in medical billing and managing patient accounts. There are several resources available for these tasks. Click here to obtain information about the latest in medical billing, medicaid enrollment, and self pay billing.
Another aspect of the data scientist’s job is that they can work in different phases of the data processing cycle. The initial task begins with data collection. This refers to the building up of data, and arranging it in the form of a database. The database is in raw form and needs several treatments. For instance, if a database is regarding hepatitis, patients are organized according to the genotype they have, which is the first step.
The second step is to fragment the data in a way that it can be used in an applicable manner. Taking the same example as above, data scientists would dissect the data by aligning different values to different genotypes (types of virus), indicating the prevalence of it in the sample population.
The final phase of the data cycle involves processing the data in the final form, and making it publicly available. The data about the HCV genotypes is then finalized and is geographically published on the global database.
By giving the phase by phase example, the scope of the data scientist is pretty clear. It’s an ideal career as a non-clinical profession. The salary is dependent on the kind of data that has to be analyzed as well as the phase. The data scientist would look at data from multiple sources, which means that they have a competitive advantage in the market.
The domain of IT health professional is also applicable here. The National eHealth collaborative is also a great initiative in this regard. It houses a resource with reference to careers in healthcare related to data scientist related jobs.