The Role & Power of Big Data in Pharma Sector

The pharmaceutical industry always depended on utilizing massive empirical data to identify patterns, examine the theories, and understand the quality of treatments. Big data analytics would be a natural choice to access and process enormous volumes of pharmaceutical data. Taking a cue from our first article of this series, this article enumerates an extensive study/review of Big data applications in the pharmaceutical sector. Big data holds great promise in improving all aspects of the pharmaceuticals, including but not limited to research, drug development, clinical trials, patient behaviour, customer services, marketing, planning of finances, etc. Therefore the capability now exists to process and make sense of that data through analytic technology represents a great opportunity for scientists and pharmaceutical companies.

But to really garner the benefits requires a different way of looking at data. Here we discuss 8 ways (besides several others) that pharmaceutical companies can use Big data Analytics to generate business value and drive innovation.

Big Data in Pharmaceuticals - tech enablers, impacts and dynamics

The traditional and visible utilization of big data has been realised very well in industries such as telecommunications, media, healthcare, and financial services (including insurance). Similarly, the pharmaceutical industry is also evolving with the increasing utilization of big data. The emergence of big data in the pharmaceutical industry is helping in streamlining multiple complicated business procedures and improving efficiency across the board. Hence, investors from healthcare and pharmaceutical industries have invested hugely in big data in the recent years. With continued investments, pharmaceutical businesses aim?to develop several innovative applications.

Big data can enable businesses to gain insights from historical and real-time data sources such as social media, IoT sensors, log files, and patient data. Big data analytics can help in finding hidden patterns in such data that can be used to generate informative analytics. Leveraging Big data, pharmaceutical companies can take a data-driven approach to several business procedures. Therefore, business leaders must stay informed about big data and its applications to benefit from the technology.

Big data, ML and Al are well interconnected in Pharma & Healthcare

The pharmaceutical industry is a highly data-intensive business that regularly utilizes and generates a variety of data. The volume of data has been increasing exponentially every day and night. The sources of pharma data are also rising continuously. In the pharmaceutical industries, raw data are generated from various internal and external sources, such as research work, R&D processes, clinical trial processes, doctors, academicians, medical devices, and patients. Big data, ML and Al are linked in healthcare that will continue to grow rapidly- especially when considering the amount of data that can now be mined from patient records/registries, real-world?evidence, sales & marketing, connected?devices etc. It?can also be used to design treatment plans, develop drugs, or improve clinical trial outcomes.

In the drug discovery and development process, the effective analysis of Big data can enhance research and development (R&D) productivity and effectiveness through early and more targeted problem solving and decision-making mechanisms. By supporting the analysis of big data, AI has the potential to rapidly accelerate R&D timelines, making drug development cheaper and faster.

The COVID-19 pandemic gave a significant boost to data-generating areas of healthcare such as wearables, electronic healthcare records, remote patient monitoring and mobile apps. The increasing use of social and digital media tools among the physicians and patients have also contributed to increasing volumes and variety of information that the companies can access, collect, and analyze. This pandemic has also given businesses an unprecedented opportunity to implement technology-fuelled changes to the way they operate. One of these change enabler techs is "Linguamatics" - an NLP based algorithm that relies on an interactive text mining algorithm (I2E). I2E can extract and analyze a wide array of information. Results obtained using this technique are tenfold faster than other tools and does not require expert knowledge for data interpretation. This approach can provide information on genetic relationships and facts from unstructured data. Also, both AI and Big data/analytics have been identified by healthcare industry professionals as the top technologies that will transform pharmaceutical drug discovery and development processes, as well as marketing and sales.

By obtaining data from diverse sources and leveraging the power of data analytics, Pharma companies can get better insights into end-users’ behavior patterns, response to marketing campaigns, product performance, and upcoming industry trends which if comprehensively analyzed and interpreted can result in improved sales & revenue performance of the Pharma/Healthcare companies.

While Big data and AI are often touted as the innovations that can improve nearly every element of the pharma value chain, integration and data quality remains core focuses. AI?requires high-quality data, and the more data AI receives, the more accurate and efficient it can become. However, if companies do not have full visibility into its data quality, they may not be able to trust the results that their AI models generate. Besides this, processing and analysing huge unstructured data also may pose computational bottlenecks.

Quantum Mechanics and Big data Analysis

Big data sets can be staggering in size. Therefore, its analysis remains daunting even with the most powerful modern computers. For most of the analysis, the bottleneck lies in the computer’s ability to access its memory and not in the processor. The capacity, bandwidth or latency requirements of memory hierarchy outweigh the computational requirements so much that supercomputers are increasingly used for big data analysis. An additional solution is the application of Quantum approach for Big data analysis. Quantum algorithms can speed-up the big data analysis exponentially. Some complex problems, believed to be unsolvable using conventional computing, can be solved by Quantum approaches.?In addition, Quantum approaches require a relatively small dataset to obtain a maximally sensitive data analysis compared to the conventional (machine-learning) techniques. Therefore, Quantum approaches can drastically reduce the amount of computational power required to analyze Big data.

Big data can improve a whole lot of business critical functional areas in the pharma industry in a big way. And here is how?

Planning and implementing Big data analytics in the pharma industry is not easy because of so much of complexities in processes, systems, functions, regions and operations etc. To reap the benefits of Big data in the pharmaceutical industry, business leaders need to develop an effective adoption and implementation strategy. In this strategy, one must plan for necessary infrastructure, its integration with enterprise systems, the ever-increasing volume of collected data, and data security. Business leaders are expected to address these challenges beforehand and develop a comprehensive approach to tackle them.

In the following section, we show what benefits the pharma industry can leverage from Big data analytics. The figure below enlists the improvement possibilities.

Let us elaborate on the applications of Big data in the pharmaceutical industry, as below.

1. Accelerate Drug Research and Development: Many patents on drugs are expired or near expiration. According to a survey, the cost of launching a new?drug may cost billions of dollars. Big data analytics can help carry out an intelligent search of large data sets of patents, scientific publications, and clinical trials data. This knowledge about patents, publications, and clinical trial data can help researchers to accelerate the discovery and development of new drugs. A part of the pharma industry has already leveraged big data analytics to optimize the internet search of large datasets of old, new, and expired patents and relevant research publications.?
2. Improve Clinical Trials of Medicines: The pharma industry is often allured to reduce the time for the run of clinical trials. However, clinical trials are costly and time consuming. The trials need large tests with the correct mix of patients. Big data analytics can assist the pharma industry in identifying an appropriate mix of patients who participated from varied demography, facilitating remote monitoring and reviewing past clinical trial data, and reporting potential side effects before commercially launching the medicine in the market.
3. Develop Personalization and Targeted Medications: The genomic makeup indeed varies from individual to individual. Every human has got a unique genomic structure. Ideally, the medicinal solutions should be personalized to every human. It is challenging to handle complex genomic data using current biology and technology and make effective?medical diagnoses. To some extent, Big data analytics can solve this problem of the pharma industry. Big data can combine genomic data, the monitored medical data collected by the device that can be worn to track physical changes in the patient during treatment, and the records of electronic medical data. By effectively?utilizing?Big data technologies, the?unstructured?genomic?data can be analyzed to spot patterns, thereby helping to create a more effective and personalized medication for their patients.
4. Improve Safety and Risk Management: The Internet sources including social media provide data signals that can act as early warning signals about the safety of a newly launched medicine by the pharma industry. The data of side effects are the warning signals and often are unstructured data of large size. The pharma industry can utilize big data analytics to collect, process, and analyze unstructured warning data.
5. Reduce Cost and Drug Utilization: The cost of operation and efficiency of the pharma depends on the efficacy of granular analysis of key metrics, such as available ingredient cost per prescription, rebate as a percentage of total spending on drug per person per year, etc. The details of pharmaceutical analytics can help pharmaceutical businesses make smarter decisions and increase revenue and reduce cost.?
6. Drive Marketing, Sales, and Supply Chain: Big data can make consumer surveys and help predict the future sale of a particular medicine and associated demography. The analytics can be utilized to predict possible customer behavior and decide on advertisement plans. In the longer run, these results would help reach out to the relevant consumer market. The use of big data in supply chain data flows, data capture and data analysis can improve supply chain visibility which would help align the goals and set the correct expectations.
7. Improve Customer Care & Services: Pharma companies can get data from online conversations among the stakeholders. These rich Internet data can be utilized to decide on a new?product launch and remain smarter in business than competitors. Big data analytics can help them analyze the impact of newly launched products and forward the information to the safety department for necessary action. Big data analytics can make it easier for the pharma industry to collect and process data from their customers and yield results helpful in anticipating customer needs.
8. ?Optimize the Finances & Economics: Economics and finance play an essential role in every business, including the highly valued pharma industry. Big data can help analyze the giant social media and public sector databases, plus the proprietary datasets that supermarkets can collect on customers. In finance, Big data seems to fit?naturally with trade and quotes data?and can be integrated?with the requirements of the pharma industry.

Bringing it all together in a collaborative way

Pharmaceutical companies can make treatments more effective with the help of Big data. 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 loT in healthcare. With loT powered wearables, physicians can understand whether their therapy is working or not. In case a therapy fails, physicians can take suggestions from pharmaceutical companies based on patient data and available medications. In this manner, healthcare can become a collaborative and data-driven effort. ?With the help of Big data Analytics, the Pharmaceutical & Healthcare industry can identify hidden data patterns to make data-driven decisions for improved business.

Big data can make the pharmaceutical dig deeper into their data repository/history and gain insights by processing and analysing the unstructured pharmaceutical data. These data can be from old records or new/raw real-time data collected from various sources, such as social media, medical diagnostic sensors/IoT, different log files, and patient enrolment etc.

?References?

1.???Alfaro, E., Bressan, M., Girardin, F., Murillo, J., Someh, I., and??Wixom, B. H. (2019). BBVA's Data Monetization Journey. MIS Q. Exec. 78 (2), 4, 2019. 5.

2.?Berg, A., Oahlbo, C. (2014). The Capish information model-simplify access to your data. Pharmaceutical Users Software Exchange {PhUSE} Annual Conference; 72-75 October 2014; London.

3.?Goul, M. (2018). Poised Between 'a Wild West of Predictive Analytics' and 'an Analytics of Things Westworld Frontier', MIS Q. Exec., 77, 4, 333-347. 76

4.??Gubbi J, et al. (2013). Internet of Things {loT}: a vision, architectural elements, and future directions. Future Gener Computer Syst. 2013;29(7):7645-60.

5.??Hopkins, MS (2011). Big??Data, analytics and the path from insights to value. MIT Sloan Management Review. 2011; 52:27-2.

6.??Johannes, M. (2019). Big Data for Big Pharma: An Accelerator for The Research and Development Engine (Schriftenreihe Masterstudiengang Consumer Health Care Book 19).

7.? Johnson & Johnson (2014). Johnson & Johnson announces?clinical?trial data sharing agreement with Yale School of Medicine [press release]. Johnson-and-johnson-announces-clinical-triaI-data-sharing-agreement-with Yale School of medicine.

8.?Laney, D. (2001). 30 data management: controlling data volume, velocity, and variety, Application delivery strategies. Stamford: META Croup Inc; 2001.

9.?Pesqueira, A., Maria J.S. and Rocha, A. (2020). Big Data Skills Sustainable Development in Healthcare and Pharmaceuticals. Journal of Medical Systems, Vol 44. 797

10.??Real World Evidence (RWE, 2020): An ideal tool to perform a proof of concept that a pharmaceutical company deems necessary to?demonstrate?the?real scenario of healthcare in practice.

11.??Strom, BL., Buyse, M., Hughes, J., and??Knoppers, BM. (2014). Data sharing, year 7-access to data from industry-sponsored clinical trials. N Engl J Med. 2014;377(22):2052-4. doi:70.7056/ NEJMp7477794.