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In very simple words, the process of predicting the probability of an event using mathematical models is defined as predictive modelling. In different fields there has been a wide-spread application of predictive modelling. This is mainly for the purpose of decision making. Recently, the technological advancements have made it possible for predictive modelling to expands its reach within the healthcare sector.

Making right decisions at the right time is the success formula in today’s competitive environment. Therefore, just like other industries, there is a great opportunity within the healthcare industry to leverage the power of big data analytics and insights.

I strongly believe that there will be significant improvement in the service level within the healthcare industry once the healthcare data is put to use and it results in better and faster decision making. This will be a win-win situation for everyone. Some of the questions that might have started pondering your head at this point might be:

  1. Why isn’t the health care industry using predictive modelling and big data analytics for decision making and other strategic planning activities?
  2. Why is health care industry still way behind in using the information that is hidden within the tons of data that it generates every day?

Well, I am 100% sure that the healthcare sector will gain tremendously by using advanced analytical technologies that will help in better understanding of future trends and needs. This is an opportunity, for optimists like us, but I do believe that data management and data mining within this industry will be a big challenge. But it’s not impossible, as the pessimists may think.

Lack of technical expertise and quantitative skills among the professionals in the healthcare industry is one of the reasons why healthcare industry has failed to leverage the power of big data analytics. Secondly, the data storage is a big issue in healthcare industry. Estimates indicate that 90% of the data that is generated within the healthcare industry is not stored properly. This is mainly because of lack of standards. In addition, the technical systems that format and store these data sets are strikingly different, both within the industry and outside the industry, creating data integration issues. Further, inconsistent data types and formats in the healthcare industry have also plagued the advancements of predictive analytics. Thus, using the existing models or models borrowed from other industries will create modelling issues.

In absence of standard practices of data analysis within the healthcare industry, decision making is not so effective. Estimates show that healthcare industry lag by approximately 20 years compared to other major industry, in terms of how to make best use of data. But we believe that time is not too far when the healthcare industry will realize the power of data in creating value, improving efficiency, and saving cost.

At this point, the healthcare sector should focus on identifying meaningful data, storing this data and using this data to draw meaningful insights to address real world challenges. In order to achieve this, industry should deeply analyze the data that it generates using advance analytical techniques and tools.

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Comment by John Mayer on May 28, 2014 at 9:41am

Hi, Nice post on HealthCare Analytics. I would also like to share below link on same.

http://www.aureusanalytics.com/healthcare-analytics/

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