Pharmaceutical professionals believe artificial intelligence (AI)will be the most disruptive technology in the industry in 2021. As AI and machine learning (ML) become crucial tools for keeping pace in the industry, clinical development is an area that can substantially benefit, delivering significant time and cost efficiencies while providing better, faster insights to inform decision making. However, for patients, these tools provide improved safety practices that lead to better, safer, drugs. Here is how AI/ML can be used to support pharma companies in delivering safer drugs to market.

Overcoming Barriers to Using AI in Clinical Research

Today, AI and ML can be used to support clinical research in numerous ways; including the identification of molecules that hold potential for clinical treatments, finding patient populations that meet specific criteria for inclusion or exclusion, as well as analyzing scans, claims reports, and other healthcare data to identify trends in clinical research and treatments that lead to safer and faster decisions.

However, to take full advantage of the benefits of AI/ML technology, organizations performing clinical trials must first gain access to the tools, expertise, and industry-specific datasets enabling them to build algorithms to fit their specific needs. Healthcare data, unlike purely numerical data pulled from monitoring systems and tools such as IoT or SaaS platforms, is typically unstructured due to the way the data is collected (through doctor visits, and unstructured web sources) and must meet strict security protocols to ensure patient privacy.

To truly leverage AI and ML for clinical research, data must be collected, studied, combined, and protected to make effective healthcare decisions. When clinical researchers collaborate with partners that have both technical and pharmaceutical expertise, they ensure that data is being structured and analyzed in a way that simultaneously reduces risks and improves the quality of clinical research.

The Benefits of AI for Clinical Research

When it comes to research study design, site identification and patient recruitment, and clinical monitoring, AI and ML hold great potential to make clinical trials faster, more efficient, and most importantly: safer.

Study design sets the stage for a clinical research initiative. The cost, efficiency, and potential success of clinical trials rest squarely on the shoulders of the study’s design and plans. AI and ML tools, along with natural language processing (NLP), can analyze large sets of healthcare data to assess and identify primary and secondary endpoints in clinical research design. This ensures that protocols for regulators, payers, and patients are well defined before clinical trials commence. Defining parameters such as these optimize study design by helping to identify ideal research sites and enrollment models. Ultimately, better study design leads to more predictable results, reduced cycle time for protocol development, and a generally more efficient study.

Identifying trials sites and recruiting patients for clinical research is a tougher task than it seems to be at face value. Clinical researchers must identify the area that will provide enough access to patients who meet inclusion and exclusion criteria. As studies become more focused on rarer conditions or specific populations, recruiting participants for clinical trials becomes more difficult, which increases the cost, timeline, and risk of failure for the clinical study if enough patients cannot be recruited for the research. AI and ML tools can support site identification for clinical research by mapping patient populations and proactively targeting sites with the most potential patients that meet inclusion criteria. This enables fewer research sites to meet recruitment requirements and reduce the overall cost of patient recruitment.

Clinical monitoring is a tedious manual process of analyzing site risks of clinical research and determining specific actions to take towards mitigating those risks. Risks in clinical research include recruitment or performance issues, as well as risks to patient safety. AI and ML automate the assessment of risks in the clinical research environment, and provide suggestions based on predictive analytics to better monitor for and prevent risks. Automating this assessment removes the risk of manual error, and decreases the time spent on analyzing clinical research data.

Strategies for Using AI for Clinical Research

During clinical trials, there’s a limited patient population to pull from, as research subjects must meet pre-set parameters for inclusion in the study. On the other hand, as opposed to post-market research, clinical researchers are blessed with vast amounts of information surrounding their patients including what drugs they are taking, their health history, and their current environment.

In addition, because the clinical researcher is working closely with the patient and is well-educated on the drug or product being researched, the researcher is very familiar with all potential variables involved in the clinical trial. To put it simply, clinical trials have a lot of information to analyze, but few patients with whom to conduct the research. Because of this disproportionate ratio of information over patients, every case in a clinical research setting is extremely important to the future of the drug being researched.

The massive amount of patient and drug information available to clinical researchers necessitates the use of NLP tools to analyze and process documents and patient records.NLP can search documents and records for specific terms, phrases, and words that might indicate a problem or risk in the clinical trial. This eliminates the need for manual analysis of clinical trial data – reducing, and in some cases eliminating, the risk of human error while also increasing patient safety. This is especially useful in lengthy clinical trials, for which researchers will need to analyze patient histories and drug results over an extended period of time. Many clinical trials have long document trails and questionnaires that can add up to hundreds of pages of patient data that researchers must analyze.

In a clinical trial, researchers are ultimately trying to determine whether the benefits of a specific treatment outweigh the risks. AI can be especially helpful in clinical trials of high-risk drugs. If a researcher knows that a drug cures or alleviates an illness or condition, but also know that the potential side effects of that drug can have a significant negative impact on the patient, they’ll want to know how to determine if a patient is likely to present those negative side effects. NLP can be used to produce word clouds of potential signals of the negative side effects of a drug that patients would experience.

The only way to do this type of analysis manually is to identify those words using human researchers, then analyze the patient reports to find those words, and group those reports into risk profiles. NLP can automate that entire process and provide insights on risk indicators in patients much more efficiently and safely than human researchers ever could.

Integrating AI & ML with Clinical Research Creates Competitive Results

AI and ML technologies, especially NLP, hold huge promise to support and optimize clinical research. However, that assurance can only be achieved by organizations that have the necessary tools, expertise, and partners to leverage the full benefits of AI and ML. AI and ML solutions support the optimization of clinical research by more efficiently analyzing research data for risks and allowing faster trial planning and research. Those who fail to engage AI and ML for clinical research may find that their competitors are doing so, and as a result, are going to market with new drugs and products faster with higher profits due to decreased research time and safer practices.

Author

Updesh Dosanjh, Practice Leader, Pharmacovigilance Technology Solutions, IQVIA

As Practice Leader for the Technology Solutions business unit of IQVIA, Updesh Dosanjh is responsible for developing the overarching strategy regarding Artificial Intelligence and Machine Learning as it relates to safety and pharmacovigilance. He is focused on the adoption of these innovative technologies and processes that will help optimize pharmacovigilance activities for better, faster results.  Dosanjh has over 25 years of knowledge and experience in the management, development, implementation, and operation of processes and systems within the life sciences and other industries.  Most recently, Dosanjh was with Foresight and joined IQVIA as a result of an acquisition. Over the course of his career, Dosanjh also worked with WCI, Logistics Consulting Partners, Amersys Systems Limited, and FJ Systems. Dosanjh holds a Bachelor’s degree in Materials Science from Manchester University and a Master’s degree in Advanced Manufacturing Systems and Technology from Liverpool University.

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Machine learning document analysis accelerates quality contract processing, customer onboarding and payment solutions.

Longmont, CO, Feb. 09, 2021 (GLOBE NEWSWIRE) — Parascript, which provides document analysis software processing for over 100 billion documents each year, announced today the Smart-Force (SFORCE) and Parascript partnership to provide a digital workforce that augments operations by combining cognitive Robotic Process Automation (RPA) technology with customers’ current investments for high scalability, improved accuracy and an enhanced customer experience in Mexico and across Latin America.

“Partnering with Smart-Force means we get to help solve some of the greatest digital transformation challenges in Intelligent Document Processing instead of just the low-hanging fruit. Smart-Force is forward-thinking and committed to futureproofing their customers’ processes, even with hard-to-automate, unstructured documents where the application of techniques such as NLP is often required,” said Greg Council, Vice President of Marketing and Product Management at Parascript. “Smart-Force leverages bots to genuinely collaborate with staff so that the staff no longer have to spend all their time on finding information, and performing data entry and verification, even for the most complex multi-page documents that you see in lending and insurance.”

Smart-Force specializes in digital transformation by identifying processes in need of automation and implementing RPA to improve those processes so that they run faster without errors. SFORCE routinely enables increased productivity, improves customer satisfaction, and improves staff morale through leveraging the technology of Automation Anywhere, Inc., a leader in RPA, and now Parascript Intelligent Document Processing.

“As intelligent automation technology becomes more ubiquitous, it has created opportunities for organizations to ignite their staff towards new ways of working – freeing up time from the manual tasks to focus on creative, strategic projects, what humans are meant to do,” said Griffin Pickard, Director of Technology Alliance Program at Automation Anywhere. “By creating an alliance with Parascript and Smart-Force, we have enabled customers to advance their automation strategy by leveraging ML and accelerate end-to-end business processes.”

“Our focus at SFORCE is on RPA with Machine Learning to transform how customers are doing things. We don’t replace; we compliment the technology investments of our customers to improve how they are working,” said Alejandro Castrejón, Founder of SFORCE. “We make processes faster, more efficient and augment their staff capabilities. In terms of RPA processes that focus on complex document-based information, we haven’t seen anything approach what Parascript can do.”

“We found that Parascript does a lot more than other IDP providers. Our customers need a point-to-point RPA solution. Where Parascript software becomes essential is in extracting and verifying data from complex documents such as legal contracts. Manual data entry and review produces a lot of errors and takes time,” said Barbara Mair, Partner at SFORCE. “Using Parascript software, we can significantly accelerate contract execution, customer onboarding and many other processes without introducing errors.”

The ability to process simple to very complex documents such as unstructured contracts and policies within RPA leveraging FormXtra.AI represents real opportunities for digital transformation across the enterprise. FormXtra.AI and its Smart Learning allow for easy configuration, and by training the systems on client-specific data, the automation is rapidly deployed with the ability to adapt to new information introduced in dynamic production environments.

About SFORCE, S.A. de C.V.

SFORCE offers services that allow customers to adopt digital transformation at whatever pace the organization needs. SFORCE is dedicated to helping customers get the most out of their existing investments in technology. SFORCE provides point-to-point solutions that combine existing technologies with next generation technology, which allows customers to transform operations, dramatically increase efficiency as well as automate manual tasks that are rote and error-prone, so that staff can focus on high-value activities that significantly increase revenue. From exploring process automation to planning a disruptive change that ensures high levels of automation, our team of specialists helps design and implement the automation of processes for digital transformation. Visit SFORCE.

About Parascript

Parascript software, driven by data science and powered by machine learning, configures and optimizes itself to automate simple and complex document-oriented tasks such as document classification, document separation and data entry for payments, lending and AP/AR processes. Every year, over 100 billion documents involved in banking, insurance, and government are processed by Parascript software. Parascript offers its technology both as software products and as software-enabled services to our partners. Visit Parascript.

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ThoughtWorks, a global software consulting firm, has announced that the company is acquiring Fourkind, a privately-held Finnish consulting services company that combines data science and machine learning (ML) with strategy, design and engineering. 

As ThoughtWorks’ second acquisition in 2021, it will allow the company to expand its European footprint, and improve its customer service experience in Finland, Netherlands and the Nordics countries.

Founded in 2017 in Helsinki, Fourkind is a consulting services firm that helps forward-looking organisations create futureproof services and products. Fourkind has a strong team of 40 data scientists, machine learning experts, designers, engineers and strategists, and has managed to work with 80+ organisations in 10+ countries. And with this acquisition, ThoughtWorks will leverage its machine learning and data science capabilities for its business.

According to the news media, Fourkind will continue to operate from their existing premises in Helsinki and Amsterdam, along with its founders and leadership team.

When asked about this acquisition, Guo Xiao, the President and Chief Executive Officer of ThoughtWorks said that the company has been looking to partner with a company that can help them “accelerate growth in our key strategic focus areas of data, digital transformation, enterprise modernisation and customer experience.” And this acquisition will allow ThoughtWorks to take advantage of Fourkind’s competence in machine learning, data science, strategy, design and engineering to “help clients lower the perceived risk of adopting new approaches to artificial intelligence,” said Xiao.

He further added, “We were impressed with their track record of delivering significant return on investment for their clients’ AI and ML investments, and believe that our complementary strengths and culture make Fourkind and ThoughtWorks a winning combination,” said Xiao.

Even Fourkind has been quite excited with this recent partnership. Expressing views, Jonne Heikkinen, managing director and one of the founding partners, Fourkind stated that the company’s vision was to create futureproof services and products for companies. 

As a matter of fact, the company has been known for utilising ML in revolutionary ways for groundbreaking innovation. Being a part of ThoughtWorks will allow the company to flourish and expand faster, and “be part of an agile and successful global team of like-minded, passionate technologists” who are continuously striving to create a remarkable impact on the world,” said Heikkinen.

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Companies are constantly reinventing themselves, resulting in creative-disruption or creation of new ecosystems. New innovative business models are springing up, inspired by Uber, Ola and Airbnb. The ability to create waves of innovation or ride them is what marks the difference between successful companies and those that are not.

Artificial Intelligence (AI) is one technology that is both the cause as well as the effect of such transformations, and across all industries. However, AI is not just for big companies; even smaller companies create waves or ride them, eventually forming trends. Let’s take one specific industry where the potential of AI is enormous, like in retail, and examine the top five ways how companies can benefit.

Neither AI nor forecasting is new, but applying AI Deep learning for forecasting demand is powerful. According to a McKinsey study, grocery retailers who use AI systems to forecast sales of fruit and vegetables can increase their profit margin (based on total business) by 1 to 2 percentage points. The accuracy of AI-based forecasts for internet sales enabled one trader to reduce his inventory by 20 percent. Combined with the power of AI-based pricing, promotions and campaigns can be personalized resulting in increased sales by as much as 4 to 6% in grocery retail and much more in fashion retail.

It is the ability to crunch in enormous data and scalability in AI that makes it possible to realize leaner inventory and order back office management.

According to a PWC report, “…45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety with increased personalization, attractiveness and affordability over time”.

The online retail store recommended items based on personal buying patterns and others’ shopping carts, made possible thru AI. Now, AI predicts a user’s needs and scours the internet for the best bargains and deals.

Take another example of a personalized service, pioneered by Stitch Fix – an online but personal styling service company, based in California, USA – heavily dependent on AI for its business. It is all eCommerce, but the model does away with a traditional shopping cart, instead relying on a set of style choices, social data feeds and trends which will is used by AI to predict and decide the results. Flexibility in the model allows users to return, free-of-charge, items which are not liked. An important feature of this process is the results are fed back into the model, thus making it better with time. Increased customer experience leads to increased royalty, social marketing and enhanced revenues.

Loyalty towards stores fare better than loyalty towards brands, so customer experience in stores and online are getting traction. This has given rise to a concept called “experiential stores” where customer experience is given more importance as much as the products themselves, where they come and experience the shopping.

77% OF RETAILERS CONSIDER SOCIAL OR EXPERIENTIAL ENVIRONMENTS FOR CUSTOMERS AN IMPORTANT OR CRITICAL AND STRATEGIC PART OF THEIR IN-STORE APPEAL; 55% OF RETAILERS USE AUGMENTED REALITY FOR THIS PURPOSE.

Imagine the scale of such data inter-relationships at play; this is huge, even a mid-sized retail with hundreds of thousands of data points. But we do not need to look beyond AI to create the insights required for providing the rich but personal experiences at these experiential stores. AI also bridges the gap between online and physical stores, bringing in personalization at scale for delivering the much-needed customer engagement in its interactions. Amazon-Go is a case in point – conceptually it is akin to a driverless-car in retail. In effect, it combines the best of both worlds – physical shopping and online, since the biggest pain point in physical shopping is waiting in line to pay your bill.  On this concept (of Amazon-Go), according to Wharton marketing professor Peter Fader: “To the extent that it revolutionizes retail, the idea here is knowing who is buying without relying on loyalty programs. But in addition to knowing who is looking at what, who is picking an item off the shelf and in what sequence — that idea of really seeing everything could have dramatic implications.” It could change the way stores are laid out, he notes, and it could change where a concierge person comes in. “I think that the data part of it could be the big breakthrough, but at this point it’s still icing on the cake.”

One of the most common shortcomings in AI-based business applications, at least in the emergent phases, is its lack of transparency and complexity to use. However, due to advances like Natural Language Generation – NLG , it is possible to develop better interfaces.  Natural Language Generation simply turns data into plain English which translates well for end consumers. This helps build user interfaces in mobiles and desktops to deliver personalized and easy-to-use information, useful for quick and informed decision-making, and reducing costs in running them.

The rise of chatbots has also given way to enriched conversational interfaces. Imagine generating insights when all “intents” work together for a company! The data can be used to design new products, unearth customer service issues and a lot more.

Furthermore, according to a report from KPMG, “…by 2020 an estimated 80 percent of business-to-customer conversations will be conducted by machines. That will have enormous implications for all organizations both in terms of business processes and also future staffing needs”.

AI takes a lot of skill and this is one reason why it is expensive to develop and complex to build. The AI stack also looks a lot different than the traditional software stack. Large or tech-driven companies can handle this, but how do we reach the small and mid-sized outfits? The answer is AI on the cloud. Hosting AI applications as a set of services on the cloud can largely mitigate both the issues. Products and services available in the SaaS model make them modular and affordable as users pay only for what and how much they use. However in some cases there could be a small upfront fee but it is quite affordable on long range plans.To conclude, even small and mid-sized retailers can ride the AI wave and benefit from the trends that are happening in business & technology.

The article has been penned down by Kishore Rajgopal,CEO & Founder of NextOrbit

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