Although saving money hasn’t been a priority for drug manufacturers in the past, with new healthcare reforms in action, many companies are looking at ways to stay within a budget.
Big Data is making enormous strides across several industries, including pharmaceuticals. With the collection of large amounts of data, companies can save money while increasing patient safety, managing risk, improving the efficiency of clinical trials and collaborating with other pharmaceutical companies to share innovations and data.
Although saving money hasn’t been a priority for drug manufacturers in the past, with new healthcare reforms in action, many companies are looking at ways to stay within a budget. The development of a new drug costs an average of $500 million and the collection of large amounts of data.
There are five ways that Big Data is making major changes to the industry.
Industries like agriculture use predictive analysis to forecast possible issues with heavy machinery. The pharmaceutical industry can use predictive modeling to qualify a particular drug for a patient based on the patient’s genetics, diseases or disorders and lifestyle. This type of analysis also takes into account the risk factors that could prove fatal to a patient.
Companies that specialize in Big Data analytics for drug discoveries use algorithms to analyze data in the cloud. Companies like Numerate are branching out to include programs specifically geared toward therapies for such conditions as cardiovascular and neurodegenerative diseases. With a targeted and personalized approach to care, patients will no longer take needless medications that don’t improve their condition.
Using Big Data and predictive analysis, companies can conduct effective clinical trials. The patients selected for these trials can meet certain prerequisites found through multiple databases, and researchers can monitor the participants in real-time.
Big Data also has its place in predicting side effects for specific compounds before the clinical trial begins. Currently, there is a method that predicts drug toxicity in compounds. In the past, human trials may have found the toxicity too late. With the Proctor method analyzing 48 drug features, companies can save time, money and lives.
Pharmaceutical companies can now use Big Data to work in collaboration with insurance companies, data management firms and scientists outside their company. By sharing information with insurance companies and providers in their network, a pharmaceutical company can widen its database for future clinical trials and predictive modeling.
Scientists working outside a particular pharmaceutical company can submit their findings regarding a compound to the company for analysis and testing. Data collection in the cloud makes sharing ideas and information accessible to the entire industry.
Pharmaceutical Sales and Marketing
The sales and marketing side of the pharmaceutical industry can benefit from the integration of Big Data analytics. Pharmaceutical representatives can focus on specific physicians in a geographical area with patients most likely to need the promoted medication based on predictive analysis.
Drug companies can save time and money by sending their pharmaceutical reps to only those physicians that require a visit. According to a 2013 survey, as much as 25 percent of marketing is now accomplished on a digital platform. Although drug rep visits are not obsolete yet, companies are finding that Big Data analytics can improve their return on investment.
Pharmaceutical companies can now build a relationship with consumers through social media platforms and digital apps. This electronic data flow links all aspects of the industry, from patient follow-ups and R&D to electronic physician medical records.
Digital apps, wearable monitors, and other electronic devices provide companies with real-time monitoring of patients’ health, and this information gives companies a first-hand look into patient compliance. Physicians, as well as companies, can receive instant feedback regarding patients through these apps and devices.
Why Big Data Growth Is Slow in the Pharmaceutical Industry
Cost is one of the largest factors in the slow growth and acceptance of Big Data analytics in the pharmaceutical industry. It’s expensive to overhaul an entire infrastructure, so many companies are breaking changes down into small compartments in order of priority.
Patient privacy is another barrier prohibiting explosive growth in data sharing. Unlike data sharing in other industries, the pharmaceutical industry could potentially expose patient information. Drug companies must remain HIPAA-compliant while making changes and collecting data to avoid costly lawsuits and damage to their reputation.
The pharmaceutical industry will benefit from Big Data analytics as long they adhere to the regulations and laws and continue to protect patient privacy.