The Need For Healthcare Analytics - Application of Predictive Analytics in Pharmocokinetics

Healthcare industry is in its infancy in analytics. The industry requires large data mining techniques considering the increasing need and use of data for effective and reliable medical treatment. Western countries unlike India and other third world countries have already ventured into application of data analytics in this field primarily due to the growing medical ailments that mandate strong groundwork research not only in areas of medicine but also know the various relation between drug and the patient’s exposure to it, which requires huge amount information and data about history of patient, age, time of injection and a lot more related factors.

Data analytics in healthcare research is still in the nascent stage in India and there is increasing need for analytics in healthcare and pharmaceutical as each day there is some kind of new virus that infects a person. On an average 220 million people are affected with some sort of virus around the world. Though the standard of living of the people has greatly improved over the last decade, improper lifestyle attitudes and external factors have made the human body more prone to various virus that results in a wide range of diseases. For example, in India two in ten were affected with cancer ten years ago but now there is growing number of cancer cases where eight in ten are affected with cancer. This kind of increasing ratio is also seen in many other medical conditions. Hence incorporating valid statistical method into the healthcare research would provide path breaking results and outcomes that would greatly benefit not only the stakeholders in these industries but the general public as well.

Towards this pharmacokinetics a branch of pharmacology and the related field of study, pharmacodynamics along with relevant statistical models is highly helpful in understanding about the drug effects and enhancement of effective therapeutic treatment to individually understand the reaction of drug among varied audience. Incorporating models that deal with the kinetic and dynamic nature of the drug and the body would show us many results that would greatly help us to understand many relationships which would be of great advantage to the pharmaceutical companies and clinicians to develop drugs of low cost that would efficiently target and cure diseases.

There is quite a lot of information that we can infer from such models and data. One such popular data mining application that is of great use to healthcare and pharmaceutical industry is predictive analytics. It is an area of data mining that extracts information from data and uses it to predict trends and behaviour patterns. As a result of positive impact, predictive analytics in pharmaceutical and healthcare industry is becoming more intense.  Perhaps, pharmaceutical companies can have a competitive advantage by applying data analysis as it is the industry that has great scope for new product development. Some of the notable uses of predictive analytics in pharmaceutical industry are:

• ·         Reduce cost of manufacturing
• ·         Know the right market segmentation
• ·         Increase profits
• ·         Reduce the time of bringing in new drug to the market
• ·         Understand the needs of the client better to avoid withdrawals of drugs
• ·         Improve supply chain management

Uses in healthcare industry:

• ·        Better patient care
• ·         Better hospitality management
• ·         Improved medical treatment
• ·         Better understanding about chronic medical condition

Predictive analytics also help clinical pharmacokinetics that effectively monitor and manage better therapeutic treatment by reducing the adverse effects of the drug. Basically, the core of predictive analytics is to find a relation between the explanatory variable and predicted variables of the past to identify future outcomes. Hence data analysis helps clinicians and healthcare professionals to come up with better drugs for a wide range of diseases.

The compartmental and non compartmental models are used in understanding the kinetics and dynamic process of drug in a patient’s body and provide a whole lot of useful facts and metrics such as bioavailability, bioequivalence etc. Once you get hold of the relevance of data analysis it is really fascinating to see how predictive analytics can actually transform the healthcare and pharmaceutical industries.