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Projected Pharmaceutical Data Analytics Trends 2018

pharma analytics trends 2018Advanced analytics necessitate the use of complex statistics and mathematical systems to improve the business operations of a company. Evidence shows that companies that use advanced analytics have repeatedly reduced variability in product quality and simultaneously lowered costs and increased sales.

In 2017, the pharmaceutical industry finally saw a dramatic increase in the need for advanced analytics. While there is still work to do toward fully embracing pharmaceutical data, the analytics trends are spreading to 2018. This is evidenced by conferences such as Big Data & Analytics for Pharma Summit 2018, which will be held on June 13-14 with a focus on the advantages of using advanced analytics in pharma to deliver patient-centric outcomes.

More pharmaceutical companies are asking the question, “How can I use data analytics to my advantage?” With a spotlight on the need for advanced data analysis, here are four major viewpoints RxDataScience sees as the emerging pharmaceutical analytics trends in 2018.

1. A migration to Cloud-Based Analytics in 2018

Faster results are a must in 2018. We will see more pharma firms migrating to cloud-based systems to get faster results. To remain at the edge of their competition, pharmaceutical industry trends in 2018 will not only include an emphasis on the storage of Anonymous Patient Level Data (APLD) and Real World Evidence (RWE) that may contain billions of rows within data sets, but also the ability to process that same data rapidly.

The cloud also offers increased scalability, i.e. the capacity to adapt to changing or expanding data. Increased scalability is also a must in 2018. One of the most significant problems companies without scalability face is a delay when there is the need for a larger-scale-than-usual data processing. This latency can affect analytics performance in the long-term. To have a competitive edge, pharma firms need to analyze data immediately. Cloud-based analytics are beginning to offer companies the option to adapt to on-demand data analysis, and hence increased scalability and reduced entry points. The resulting risk/reward for trying cutting edge technologies can be very attractive.

The cloud is also useful for the analytical strategies of organizations. In a Forbes poll released in March 2017, 70.1 percent of companies believed the cloud was an essential part of their analytics strategy. Also, 60.1% of enterprises are relying on hybrid and public clouds for big data analytics. This hybrid utilization is a gateway to full cloud migration. Pharma companies that do not switch to full cloud should, at least, prepare for a hybrid migration in 2018.

Cloud-based analytics systems are generally more secure than traditional on-premise systems. The number of reported breaches of data are lower for cloud-based analytics systems than for on-premise systems. Cloud providers have massive infrastructure and manpower behind security, as the risk of breach can cost them their entire business.

Top cloud providers environments are also less costly than physical environments. In fact, most cloud providers offer a pay-per-use or streaming model, which can be particularly attractive when facing solutions like analytics, which are not "always-on". For example, Johnson and Johnson, moved to a hybrid cloud environment in 2012 as a plan to refocus their core IT strategy. This was a move to change company culture and impact how they approached data as a whole. By not locking itself into a traditional infrastructure, Johnson and Johnson was able to use the advantages the cloud offered, including transitioning from a capital-intensive IT infrastructure to a cost-friendly variable model. The company was also more prepared to deliver successful outcomes on shorter projects with agile teams.

2. The Adoption of Artificial Intelligence Technology in 2018

Artificial intelligence (AI) made waves in 2017, but we are going to see even more waves, specifically within the pharma industry, in 2018. Top pharma firms are investing heavily in recruiting senior data scientists whose resumes cite experience in analysis using machine learning. When it comes to deep learning and rare disease treatment, AI and machine learning is the next step in the pharma industry.

In previous years, AI has been leveraged in the healthcare field for various applications, including virtual health assistants, extra security layers, and improving the overall patient experience and medical procedures. Within pharma, one practical application of AI that will transform the way pharma adapts to regulatory challenges, especially with GDPR on the horizon, is the use of AI to obtain various data points that are linked to desired information that is not legally accessible. Roadblocks such as patient anonymity can be circumvented using AI technology.

Another application of AI is forecasting. With the help of machine learning, pharma can learn more about the contributing factors to forecasting, including account industry, financial markets, regulatory, and formulary inputs.

3. The Use of Real World Evidence and Anonymized Patient Level Data in 2018

Real World Evidence (RWE) is the product of analyzing Real World Data (RWD), and this information can now be used to bypass lengthy clinical trials. Thanks to the 21st Century Cures Act, RWE and RWD are changing the way the FDA approves drugs. The Cures Act was created due to a need to accelerate medical products or innovations for patients who need them speedily. Therefore, the Cures Act is a huge deregulatory giveaway for pharma firms as it could potentially help them score big in getting their products to market faster.

In 2018, we predict that more pharmaceutical organizations will be getting their hands on RWD, and RWE will be a new type of standard analysis that pharma companies implement.

4. The Use of Real-Time Analytics in 2018

There is currently a big emphasis on the use of timely and transparent data to achieve patient-centric healthcare. Therefore, there is a great pressure to utilize better techniques for optimal data collection. In many ways, the US is behind other countries in how comprehensive data is collected. A senior medical director at Johns Hopkins stated that the “provider community has done a poor job with data collection.” 

To collect high-quality, real-time data, there is an emphasis on the importance of a strong data engineering layer that can assure data quality. There is also an emphasis on performing semantic exercises as this data is being captured in real-time. Many pharma firms are investigating real-time analytics technologies that can accelerate OMOP data formatting.

In 2018, we expect to see many pharma companies partnering with healthcare providers to capture better data in real-time. Perhaps pharma companies will achieve this by providing better incentives to providers.

Conclusion

All roads lead to a patient-centric approach. Both the Big Data & Analytics for Pharma Summits in London and Philadelphia have themes that revolve around the patient. The impetus behind all of the pharmaceutical industry trends in 2018 revolve around improving patient outcomes.

Data analysis allows companies to understand their medications, existing treatments, and patient behavior to best serve the patient.

Learn more about RxDataScience’s services and how they aim to provide better patient outcomes through advanced healthcare analytics.

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