Currently, analytics has evolved as an inevitable department in each and every organization. It was in the last 10 years that technology has been revolutionized and we now produce about 2.5 quintillion bytes of data every day. This is more data than that was collected in two years, previously. All these changes have major implications for organizations today. In organizations, analytics enables professionals to convert extensive data, statistical and quantitative analysis into powerful insights that can drive efficient decisions.
Does an apple a day really keep a doctor away? Or is that better to eating a bowl of blueberries? Does the revolutionary new drug for diabetes increase the chances of getting a heart attack when had in combination with a popular medication for the common cold? If the world is affected by contagious diseases how quickly would it spread across continents- which are the likely hot spots and which groups of people would be most susceptible? How quickly can we get vaccinations to the most susceptible groups? These are just some of the unanswered questions that the healthcare, pharmaceutical and medical fields grappled with on a day-to-day basis before the beginning of the analytics era.
Now Analytics has become a universal phenomenon and is intensively used in various healthcare domains. Clinical research is one among. Clinical research is a branch of medical science that determines the safety and effectiveness of medications, devices, diagnostic products and treatment regimens intended for human use. Controlled statistical tests ensure the passage of a drug from the lab to animal testing to tests on human volunteers before they are launched in the market. Testing in this field should be errorless or negligible error in this field. Especially, Type 2 errors should be avoided as they lead to major issues. Thus in order to avoid errors, it is necessary to use strong analytical tool like SAS and R for testing.
One of the first medical health professionals to recognize the value of data and analysis was Florence Nightingale. To effect change in Military medicinal practices and policy, Florence spent many months after returning from the Crimean War in compiling data on war mortality- the reasons, location and underlying drivers of death and disease during the war. She also invented a new kind of graph, a sort of inverted pie chart called the coxcomb to display the results of her analysis. During the course of this analysis she discovered that the single largest driver for human mortality during war was sanitation- even more than the presence of basic supplies. Her analysis led to many changes in medical practices and policies and by the end of the century, military mortality was lower than civilian mortality in hospitals. This was a significant revolution in the healthcare industry. In the current times, analytics are expected to be the next disruptive technology in the healthcare segment. Further the challenges faced by the healthcare sector (pressure to reduce costs, become patient centric and improve outcomes) make it even more compelling to expand the role of analytics in the industry.
As more and more healthcare facilities switch to electronic medical records (EMRs), analytics is gradually making inroads into the healthcare sector. The global healthcare industry is gradually transforming from being a volume based market to a value based market. However, OEMs are facing difficulties in managing large volumes of healthcare data with high resolution. Also, healthcare vendors are finding it difficult to manage and interpret large amounts of patient data efficiently. The Global Healthcare Data Analytics Market is therefore filling up this space of managing healthcare data. Companies have positioned their services in the market for applications in user-friendly predictive modelling. IBM, OptumHealth, Oracle, Verisk Analytics, MEDai’s health, McKesson, Truven Health Analytics, Allscripts Healthcare Solutions, Cerner, GE and IMS health are prominent in the list. With industry majors pitching in efforts to help the healthcare industry to transform raw data into meaningful insights, the latter will soon to be able to realize their desired analytic dreams.
This Blog is written by Pooja Hegde, Senior Analyst with DART Consulting