Utilizing AI and ML to Understand the Current Pharma Trends

The pharma industry has a long history of relying on the latest technologies to help deliver drugs that are safe and reliable. Since Covid-19, this has proved truer more than ever as pharmaceutical companies race to bring drugs and vaccines to the world.

In the post-COVID-19 road to recovery, pharmacies and drug manufacturers have heightened their focus on optimizing operations through systems, artificial intelligence (AI) and machine learning (ML).

AI and ML are first-class trend and pattern-spotters. They quickly find patterns in vast amounts of historical data – in any industry by learning from the past, then offering accurate output that helps predict events or solve problems. Additionally, it can prescribe what the best action would be for the optimal outcome. This allows organizations to ramp up operations ahead of time in order to be prepared for the upcoming recurrence of the event, with freedom from chaos, guess-work, or crises. It is this functionality that pharma organizations rely on to stay ahead of the curve, and to bring solutions to the world’s illnesses.

AI and machine learning are successfully being used in pharma in the following applications.

  • Disease identification and diagnosis. Ranging from rare diseases, to oncology, to Covid, AI’s pattern-recognizing capabilities – both in data and in images – are speeding up disease identification and diagnosis.
  • Drug discovery and production. AI and ML are being used from the initial screening of drug compounds to forecasting the success rate based on biological factors. More than that, it supports faster drug discovery and personalized medication for individual patients.
  • Predictive forecasting. Predictive analytics, part of AI and ML, is used to predict the flow and spread of the pandemic. It is also being used to monitor and forecast future epidemic outbreaks or seasonal illnesses around the world. Knowing what is highly likely to take place ahead of time allows pharma companies to produce the right drugs at optimal quantities at the right time, eliminating bloated inventory levels or supply and demand issues.
  • Clinical trials. AI is used to identify ideal candidates for clinical trials based on medical history and disease conditions. Additional attributes can be overlaid such as infection rates, demographics, and ethnicity for an accurate view of those most impacted.

In addition to healthcare, AI and ML are being used in pharma companies as part of the overarching digital transformation strategy for areas such as their operations, supply chain, sales, and customer services.

  • Supply chain and logistics optimization and automation

The pharma industry as a whole has been forced to rapidly evolve. For one, they have adopted more digital tools as a result of the pandemic, resulting in supply chains becoming more patient-centric and app-based. AI and ML are used in planning and forecasting, and many other pivotal areas in the pharma supply chain. For example, the UK Medicines & Healthcare products Regulatory Agency (MHRA) joined forces with the UK division of Genpact to use AI to track vaccine distribution, down to location, batch and lot numbers, at scale. This is mirrored at a smaller scale with pharmacies looking to improve their own logistics and planning through technology.

  • Faster drug discovery

However, the impact of AI and machine learning during this time has been to try to identify the specific molecules that might bring an end to COVID among the masses, and to cut down the time to bring drugs to market. This could be using AI and ML to speed up drug discovery, development, clinical trials, and gain FDA approvals. Based on the speed and agility of the current vaccine – (300 days from identifying the coronavirus genome to the first vaccine study) – compared to how long it used to take (8 to 10 years), AI’s contribution has been significant. Subsequently, new market disrupting technologies are expected to emerge, such as mRNA-based vaccines.

  • Greater agility and resilience in operations

According to McKinsey, at an organizational level, pharmaceutical companies are now more focused on operational resilience and speeding up initiatives that facilitate greater agility and transparency. This is resulting in a greater demand for digital and analytics tools and automation.

For example, specialty pharmacies looking to remain competitive can utilize AI and ML to provide even greater levels of personalized, high-touch care for patients. AI quickly processes vast amounts of data to help pharmacists provide hyper-personalized care that focuses on prevention and optimal outcomes for the individual, as well as lower operational costs and improved overall efficiency.

Automated pill packaging – another example of AI-enabled technology for pharmacies – not only produces unit-dose or multi-dose packs, but also facilitates touch-free service, social distancing, and improved patient compliance, all of which are critical during the pandemic.

According to the World Economic Forum, pharmaceutical and AI companies have applied their ML expertise towards fighting the pandemic in several ways.

  • Predicting and monitoring the spread of the disease

ML acts as an early warning system by analyzing enormous data sets to help researchers and health practitioners forecast the spread of COVID-19. For example, researchers at Chan Zuckerberg Biohub have used AI and ML to predict the number of undetected COVID-19 and the potential consequences for public health worldwide.

  • Giving leaders data for informed decisions

ML is giving leaders the information they need to make significant decisions in the pandemic. For example, the open-source model that helps decision-makers understand the volume of exposure, infection and hospitalization, and enables scenario-planning for real-world issues, such as the number of hospital beds required. This initiative was started by former White House Chief Data Scientist DJ Patil who partnered with AWS and Johns Hopkins Bloomberg School of Public Health. Another great example of how AI and ML is used to give leaders information is the SEDGE herd immunity calculator that shows when herd immunity will be reached based on vaccinations, population size, and more.

  • Keeping up with rapid growth in data and insights

AI and ML are helping healthcare providers and researchers deal with the exponentially increasing volume of data on COVID-19. The surge in information can make it difficult to harvest treatment insights from. As a result AWS launched CORD-19 Search, an ML-powered search site that can extract medical information from unstructured data, and allows researchers to quickly find answers, search for research papers and access documents.

Many pharma companies feel overwhelmed at the thought of deploying artificial intelligence and machine learning in their organizations. In spite of the documented benefits of AI such as increased productivity, insights, and speed-to-market, there are many challenges to overcome, such as messy data, unskilled employees, and technological infrastructure limitations.

SEDGE – an AI and ML tool based in the cloud – provides all the benefits of AI and ML without the commitment, worry or expense of equipment or a massive internal data science team. SEDGE can quickly and accurately process your structured and unstructured data and deliver actionable insights that will give you the competitive advantage in an ever-evolving pharma marketplace.

For more complex data analytics and modeling requirements, our highly skilled team of data scientists is available to provide customized solutions that will turn your data into market-leading action!

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