Category: Healthcare

Impact of Big Data Analytics in the Healthcare Industry

The healthcare industry is facing unprecedented challenges during the last years: patent expirations, the push for generics, increasing costs, lower margins, tighter Food and Drug Administration (FDA) compliance and lower Research and Development (R&D) productivity. As the R&D productivity has declined during the last years and most of the blockbusters became generic, many pharma companies could become economically unsustainable. However, several opportunities are coming with the digital revolution.

DSC05711-BDuring the last years, pharmaceutical companies have been collecting large amounts of R&D data into medical databases; payers and providers have also digitized their patients’ records. In addition, it has become easier to collect and analyze data gathered across different sources. This can be very important in the healthcare industry, as data about a single patient can be collected from payers, hospitals, clinics, laboratories, pharmacies, and many others. Now, healthcare stakeholders – the pharmaceutical industry, payers, and providers – have access to a really valuable and powerful resource of information.

But what are the benefits of big data analytics for the healthcare industry? The McKinsey Global Institute estimates that applying efficient data strategies could generate about $100 billion annually in the US healthcare system. This could be achieved by optimizing innovation, improving the efficiency of research during clinical trials in the pharma industry, as well as creating new tools for physicians, patients, and payers. Let’s look at the specific example of the pharmaceutical industry. The pharmaceutical industry has been under a lot of pressure recently because of losses related to patent expirations, cost constraints in healthcare systems, and the growing demand in regulatory requirements. During the last few years, the number of new drug approvals has not been directly proportional to the increase in R&D expenditures. Despite the increase in R&D spending, the number of innovative new medicine approvals by the FDA is decreasing.

Utilizing big data analytics well can help pharmaceutical companies to identify potential new drugs and develop them more efficiently. Currently, physicians and pharmaceutical companies still rely mainly on textbooks and on very small clinical trials, usually with healthy patients having just one disease. However, most of the real world patients have more than one health problem. A potential benefit of big data analytics is to find potential patients to enroll in clinical trials based on, for example, social media profiles, genetic information, and information collected in the Electronic Health Records (EHR) and not just on the evaluation of physicians. This could help to adjust to smaller groups, and, therefore, be less expensive.

Another potential benefit could be with the use of data from wearables (See our previous post) . Pharmaceutical companies can use smart devices to gather large quantities of real world data that was not available for R&D teams in the past. This kind of information could be used to analyze drug efficacy on an ongoing and real time basis.


Big-data in Manufacturing and Healthcare


No one doubts the advantages of using big-data. According to McKinsey, companies using data and analytics deep into their operations typically see productivity and profit increases that are five to six percent higher than competitors not using data. Big-data analytics are already making impacts in finance, retail and social networks. In order to stay competitive, manufacturing and healthcare/life sciences organizations are now adopting information systems to generate huge volumes of primary and secondary data.

But how can big-data improve manufacturing or healthcare organizations?

In recent years, manufacturers, by implementing programs as Six Sigma were able to improve quality product and reduce dramatically the waste in their production processes. Manufacturing environments in pharmaceuticals, chemicals and life sciences have complex processes that make it difficult to identify where further improvements can be made. The increase in complexity is the main reason manufacturers have a hard time diagnosing and correcting process flows, if they do not use big data as a tool. Analytics can be the answer for those problems. Managers can use advanced analytics to understand the historical process data. Taking this data and applying statistics and other mathematical tools can identify unseen patterns and relationships in the data.

Medical recording and medical devices have enabled the aggregation of R&D data in electronic databases for several years now. This data can hold important R&D information for future developments, if it can be mined properly. Pharmaceutical companies, payers and providers are now beginning to analyze big-data to obtain more insight. For example pharma companies can gather data to understand what treatments are more effective, identify patterns related to the side effects of drugs, hospital re-admissions, or any other information that can help treat patients and reduce costs. See the McKinsey Report Video here.
Many innovative companies in the private sector are creating applications and analytical tools to help patients, physicians and other healthcare stakeholders to identify value and opportunities. The wearables (See Our Blog Post) are out there collecting tons of data that need to be analysed to create insightful information.

The Big Data revolution is still in the early stages and most of its potential for value creation in any organization is still unclaimed. Big-data initiatives have the potential to transform healthcare and manufacturing as they have done already with other industries.

Is your organization prepared to embrace the Big-data era? S&A Technologies can help you. Call us for a quote.