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Data Analytics transforming the Biopharma industry

Introduction

In today’s dynamic and quickly changing competitive battleground, pharma companies are scrambling to emerge on top and boost their performance without adding to their total cost of operations. The rise in innovative technologies such as artificial intelligence, robotic process automation, and big data analytics in the pharmaceutical industry requires them to innovate rapidly in order to gain a competitive edge and to harness the opportunities in the market landscape [2]. Medical schools are increasingly taking preventive measures to keep up with technological advances. Now that the possibilities of these advanced tools are endless, it is fascinating to see how digital, data, and analytics have revolutionized healthcare treatment through science and medicine. Healthcare and technology are areas that is going to grow massively, and the need to fully understand the potential of combining the two is going to be vital for the future.

One of the critical aspects of the Biopharma industry today is the need to minimize the Time to Market. In the past, it used to take a decade to go from a promising drug to the end consumer. However, today due to the advancement of technology and especially data analytics, we are expecting to see a paradigm shift in the way the Biopharma industry will be working in the future. Data analytics will provide actionable insights about the patient population and help drive the R&D decisions. Whether your organization has already begun the necessary process of incorporating data analytics and artificial intelligence into pharmacy operations or is undergoing due diligence to decide if it’s time to implement a pharmacy technology upgrade initiative, one thing is for sure: Data analytics and artificial intelligence will have a tremendous impact on how pharmacies operate, both today and well into the future. Pharmacies that are early adopters of such technology will have strategic advantages over those pharmacies that choose to delay [7]. The global pharmaceutical industry is projected to have a market worth $1170 bn by 2021, growing yearly at 5.8% from 2017. The Asia Pacific is showing strong growth trajectories with pharma sales in the Asia Pacific will grow at 8.4% in 2021 with India & China expected to grow at 10% .

Data Analytical Evolution in the Pharmaceutical Industry

In recent times, the global healthcare industry valued at US$9.59 trillion (PwC Report, 2015) has undergone sweeping changes in every aspect of its business. These include massive adoption of electronic health records (EHR) by governments, hospitals, and physicians to full digitization of research databases and billions of patient records belonging to pharmaceutical companies. The onset of mobile health apps, wearable medical devices, telemedicine, and automated medicine dispensers that work like ATMs portend a smart, digital-driven future. If there is one single factor that unifies all these healthcare trends, it is Big Data – the huge gaps faced by the industry in converting unstructured information bytes into meaningful business intelligence. “Big Data,” is a term for large volumes and varieties of data that cannot truly be made sense of by any single person or machine. Whether from accelerating drug discovery or better understanding patient trends and behavior, Big Data holds great promise for those companies looking to tap its potential. Consultancy McKinsey estimates that effective big data strategies could generate up to $100 billion in value annually in the US healthcare system alone. The advent of big data in the pharmaceutical industry will enhance R&D, improve clinical trials, and upgrade drug discovery [1,11].

Analyzing data is a rather simple affair when all data sources collect information based on unified file formats. However, the biggest challenge facing enterprises is the undefined and unpredictable nature of data emerging in multiple formats. Unstructured data can fall into any one of these categories – textual, non-textual, audio, video, presentations, pictures, and .rar files. Going hand in hand with the problem of data in motion, it’s nearly impossible to keep track of information formats emerging from multiple sources. Hence all the data collected should be compiled on one single platform in order to get at most results and make analysis easy.

Advantages

The McKinsey Global Institute estimates that applying big-data strategies to better inform decision making could generate up to $100 billion in value annually across the US healthcare system, by optimizing innovation, improving the efficiency of research and clinical trials, and building new tools for physicians, consumers, insurers, and regulators to meet the promise of more individualized approaches.

  1. Accelerate drug discovery and development

There are huge benefits when it comes to accelerating the process of drug discovery and development. Being able to intelligently search vast data sets of patents, scientific publications, and clinical trials data should, in theory, help accelerate the discovery of new drugs by enabling researchers to examine previous results of tests. Applying predictive analytics to the search parameters should help them hone in on the relevant information and also get insight into which avenues are likely to yield the best results.

2. Optimize and improve the efficacy of clinical trials

Clinical trials are costly and time-consuming to run and pharmaceutical companies want to ensure that they have the right mix of patients for a given trial. Data Analysis can assist in identifying the appropriate patients to participate in a trial (through analysis of demographic and historical data), remote patient monitoring, reviewing previous clinical trial events, and even helping to identify potential side effects before they become a reality.

3. Target specific patient populations more effectively

With information from genomic sequencing, medical sensor data, and electronic medical records more readily available than ever before, pharmaceutical companies are able to dig into the root causes of specific pathologies and realizing that one size truly does not fit all. Within any disease or condition, different patients will respond differently to treatments – for a host of reasons. Combing through the data from these different sources can allow drug companies to spot trends and patterns that will allow them to come up with more targeted medications for patients that share common features.

4. Better insight into patient behavior to improve drug delivery and effectiveness and healthcare outcomes

Greater amounts of data that companies can tap – including information from remote sensor devices – coupled with advanced analytic models, mean that pharmaceutical manufacturers can gain much greater insight into existing patient behavior. The company can then use that information to design services targeted to different demographics or at-risk patient groups in order to improve the efficacy of treatment.

5. Improve safety and risk management

Signals coming from a range of sources including social media, Google searches, etc. can act as an early warning signal for pharmaceutical companies about product safety issues and pharmaceutical companies have been thinking about how this type of unstructured data can be used more effectively.

“You can do what’s called Internet “scrapes” of information, where you draw down lots of various hits or posts from the Internet, and then analyze that data. You can listen to the chat essentially, the public sentiment, in the virtual environment. With these approaches you can capture patient data of interest; but you can also capture additional data that may include safety-related information, which will need to be transmitted to the safety group,” explains Dr. Ed Tucker, VP Janssen Research & Development, a Johnson & Johnson company in an interview with MIT Sloan Management Review.

6. Gain improved insight into marketing and sales performance

With increasing competition from generics, Big Pharma is getting smarter about analyzing and driving effectiveness in its sales and marketing operations. New, niche and underserved markets may be spotted by analyzing information from social media, demographics, electronic medical records, and other sources of data. Equally, analyzing the effectiveness of sales efforts and capturing the feedback received by the sales force during client visits, and using it effectively can help pharmaceutical companies get an edge on their competition.

7. Big Data in Business

The introduction of big data in the pharmaceutical industry will help business leaders gain insights into various drugs and their usage. With the help of these insights, businesses can make informed decisions during research and development. Hence, pharmaceutical companies can develop more effective medicines and reduce their side effects using big data. Pharmaceutical companies can collect large volumes of data generated at the different stages of the value chain from drug discovery to real-world usage.

8. Drug reactions

In some scenarios, medications may lead to harmful effects on a patient’s health known as adverse drug reactions (ADRs). ADRs can be a result of the inability to precisely replicate real-world scenarios during clinical trials. Pharmaceutical companies can mine social media platforms and medical forums for ADRs and patient reviews. For this purpose, pharmaceutical businesses can use sentiment analysis and natural language processing. The collected data can be analyzed with the help of big data analytics. With this approach, pharmaceutical companies can gather insights into adverse drug reactions. Hence, the process of reviewing drug reactions can be simplified by leveraging big data in the pharmaceutical industry.

9. Collaboration

By leveraging laboratory data, pharmaceutical representatives can identify medicines that would be appropriate for certain patients. With this approach, they can advise physicians about certain medications and explain why those medications should be a part of a patient’s treatment. Physicians can also collect patient data in real-time with the help of IoT in healthcare.

10. Volume Of Data

Many pharmacies likely have a decade of data that is accessible to them, including patient, insurance, and supply chain data. For many pharmacies, this data hasn’t been thoroughly analyzed to see what insights can be gathered. For instance, analyzing prescription purchase data can provide insight into your customer’s shopping habits. Maybe patients who fill certain prescriptions tend to also buy certain other related products. Understanding how your pharmacy’s patients shop and what they buy can help your organization stock the right items, perhaps at a scale that reduces their acquisition costs.

Today and in the future, pharmacy technology can make the difference between a pharmacy that is popular and profitable and one that isn’t. It can improve efficiencies, lower costs and enable pharmacists to deliver a better standard of care that’s more personalized. In order to remain competitive and meet industry goals, implementing pharmacy technology, such as artificial intelligence and data analytics, is inevitable. It makes a lot of sense to implement this pharmacy technology early before patients come to expect this level of care.

Conclusion

The rapid onset of large data volumes – log files, EHR data, patient readings, social media sentiments, click stream information means datasets are no longer expected to reside within a central server. In fact, the very nature of big data refers to business information that is streaming in across hundreds of thousands of disconnected sources, rapidly evolving by the minute making it nearly impossible for healthcare organizations to keep track of spontaneous information bytes. Traditional methods to analyze these information patterns are not at all adequate to handle the copious amounts of data, which in turn demands the rise of advanced analytical tools which can process and store billions of bytes of real-time data, with hundreds of thousands of transactions per second.

A highly available, compliant infrastructure, robust data integration and analytics, and focused healthcare industry expertise are the core building blocks necessary to gain critical business insights. Healthcare institutions and pharmaceutical and life sciences companies that adopt these fundamental elements as well-orchestrated, customized solutions will not only survive but also thrive in today’s rapidly changing healthcare landscape. Gains in efficiency and cost savings are nice for every business, but for pharma and life sciences industries, they can dramatically improve the R&D process and allow us to bring more innovative and effective solutions to market faster. It is not hyperbole to say that better clinical data saves lives and improves public health .

References

  1. 6 Ways Pharmaceutical Companies are Using Data Analytics to Drive Innovation & Value. Available at: https://www.iqpc.com/media/1001534/35903.pdf/. Accessed on: 01/12/2021.
  2. 8 Ways Pharmaceutical Companies Ensure Success With Analytics. Available at: https://www.polestarllp.com/analytics-in-pharmaceutical-companies. Accessed on: 01/12/2021.
  3. A history of the pharmaceutical industry. Available at: https://pharmaphorum.com/r-d/a_history_of_the_pharmaceutical_industry/. Accessed on: 01/12/2021.
  4. AI and big data will continue to disrupt pharmaceutical sector, according to healthcare industry professionals surveyed by GlobalData. Available at: https://www.globaldata.com/ai-big-data-will-continue-disrupt-pharmaceutical-sector-according-healthcare-industry-professionals-surveyed-globaldata/. Accessed on: 01/12/2021.
  5. Big Data in Pharma World: What & How!! We Want? Available at: https://www.lexjansen.com/phuse/2017/rw/RW02.pdf. Accessed on: 01/12/2021.
  6. Gottinger, H.W. and Umali, C.L., 2008. The evolution of the pharmaceutical-biotechnology industry. Business History, 50(5), pp.583-601.
  7. How Data Analytics And Artificial Intelligence Are Changing The Pharmaceutical Industry. Available at: https://www.forbes.com/sites/forbestechcouncil/2018/05/10/how-data-analytics-and-artificial-intelligence-are-changing-the-pharmaceutical-industry/?sh=3e1ad4f83644. Accessed on: 01/12/2021.
  8. How pharma can accelerate business impact from advanced analytics. Available at: https://www.mckinsey.com/industries/life-sciences/our-insights/how-pharma-can-accelerate-business-impact-from-advanced-analytics. Accessed on: 01/12/2021.
  9. How Data Analytics and Reporting is Driving Value for Pharma Industry. Available at: https://blog.gramener.com/pharma-analytics-business-value/. Accessed on: 01/12/2021.
  10. Krishnankutty B, Bellary S, Kumar NB, Moodahadu LS. Data management in clinical research: An overview. Indian J Pharmacol. 2012;44(2):168-172. doi:10.4103/0253-7613.93842
  11. The pharmaceutical industry needs big data. Here’s why. Available at: https://www.allerin.com/blog/the-pharmaceutical-industry-needs-big-data-heres-why. Accessed on: 01/12/2021.
  12. The top 3 ways big data analytics is transforming the pharma industry. Available at: https://www.cloudmoyo.com/blog/data-architecture/healthcare-pharma-lifesciences-analytics/. Accessed on: 01/12/2021.
  13. The future of data in pharma: Data privacy, data ownership and going beyond the pill. Available at: https://www.zs.com/insights/the-future-of-data-in-pharma-data-privacy-data-ownership-and-going-beyond-the-pill. Accessed on: 01/12/2021.

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