Infographic: How AI Elevates The Pharma Industry

In the past, Artificial Intelligence (AI) may have been a fancy buzzword that was constantly heard around various industries. Now, however, the term carries much more meaning, especially in the pharma industry.

According to Accenture, 74% of pharma executives believe AI will result in significant improvement within the next three years.

While you may have heard how beneficial AI has proven to be when assisting with drug discovery, there are many other benefits to implementing this technology into your pharma company.

Whether it’s increased productivity or automated communication, the need for AI in pharma is becoming increasingly more clear as we move forward in this technology-driven era.

Below, we have created an infographic that highlights statistics displaying the need for AI in pharma as well as how AI is elevating the pharma industry.

Pharma AI Infographic

Also, if you’d like to learn more about our Artificial Intelligence technology, contact us below for free and we would love to talk with you about how we can implement the technology to enhance your pharma company!

How Pharma AI Benefits the Time to Market for New Drugs

Artificial Intelligence is a rising star – meteoric, actually – in most industries, including pharma. But, do you really know how pharma AI can affect your business?

Pharma artificial intelligence can help you drive revenue growth and operational efficiency by powering in-depth data mining and analytics, patient engagement, compliance monitoring, and making efforts. It can impact your R&D time and costs, too!

On average, successful drug research and development takes upward of ten years, and the clinical trials success rate is less than 12 percent according to estimates. That’s far too long and costly to result in profitable R&D operations.

Thankfully, you can leverage pharma AI to boost clinical success rates, cut costs, streamline and shorten the drug design, testing, and validation cycle.

A look at how AI transforms pharma company operations will help you make an informed decision if you haven’t adopted the technology yet. Perhaps, answers to the following questions will help see why AI can be a worthy investment:

  • What pharma processes or elements can AI impact?
  • What’s the role of machine learning in healthcare outcome monitoring?
  • How does AI impact pharma speed to market?
  • What’s the effect of AI in pharma sales productivity and revenue growth?

Let’s find answers by examining the most critical ways that pharma AI can affect the time to market for new drugs…

Let’s start with the good news:

1. Integrated Information Systems and Automated Big Data Mining Speeds Up Drug R&D

One of the most significant benefits of artificial intelligence in the pharma industry is rapid drug research and development.

It involves the use of machine learning (ML) and deep learning (DL) algorithms, which “train” integrated computer systems to automatically extract and analyze massive chunks of raw genotypic and phenotypic data relevant to drug R&D.

The system streamlines and fast tracks the collection of big data from the Internet of Things (IoT) devices (such as wearable health monitoring gadgets), medical research journals, patient management software, and other public and private databases.

AI systems that gather R&D data automatically and relatively fast helps to shorten the medicine discovery time, which is longer with a human-centric effort. Pharma companies can increase their speed to market and gain a competitive edge by delivering new drugs to hospitals and patients comparatively fast, which results in turnover growth.

2. Accurate Predictive Pharma Analytics Help Boost Clinical Trials Success Rates

AI-driven pharma R&D delivers predictive analytics, enabling researchers to pinpoint with a high degree of accuracy the right compounds for manipulating diseases. It provides in-depth insights and data necessary to validate and test drug concepts as well as optimize treatment delivery methods.

It can cost up to $2B to research, iterate, test, perfect, and validate a drug concept. However, the cost can come down with the accurate prediction of drug candidates, which increases the probability of clinical trial success.

AI-powered analytics helps to eliminate trial and error in the drug discovery process, and this can reduce overall R&D costs as it allows pharma researchers to present more viable solutions for approval by the FDA most of the time.

3. Tracking Medication Adherence 

Low medication compliance rates among patients participating in a clinical trial can slow the drug R&D process. Likewise, the inability to monitor the extent to which each candidate is adhering to a prescribed drug can undermine the testing and validation phase.

Traditional health IT systems and human-centric approaches (such as requiring the patient to memorize their dosage) have not solved the non-adherence problem.

Patient taking medication in hospital.

AI can help track pharma compliance rates in several ways. For example, indigestible IoT sensors transmit adherence information to a centralized database, enabling pharma researchers to monitor and analyze drug usage against treatment results and side effects.

The technology can track critical biometrics in the patient, including blood pressure and glucose levels. It can pinpoint anomalous outcomes based on the intelligence gathered via machine learning.

Another approach is facial recognition software. It involves a patient recording themselves taking a drug, after which the AI algorithm analyzes the video to confirm that the right candidate took the pill.

4. Improvement of Treatment Outcomes by Virtual Collaboration and Coaching 

The success of any drug in the market depends on its ability to solve the intended healthcare problem. However, an otherwise appropriate medical solution may fail if the patient is not using it correctly out of ignorance, or certain lifestyle habits are hindering outcomes.

AI changes that.

A “smart” healthcare system can process natural language, and that makes it possible to “advise” patients in real-time in a language they understand, without necessarily involving a real, human doctor.

The system draws from vast amounts of healthcare data and medical records to provide highly personalized, evidence-based answers to questions that patients ask.

Businessman and businesswoman touching icon of digital screen

For example, robot-like assistants can help reinforce behavioral changes necessary for drug compliance and successful treatment. Advanced systems may also pose questions to patients, for example, to help understand why they’re skipping doses.

AI-powered IoT technology can then transmit the data to a pharma content management system for real-time monitoring. Improved treatment outcomes mean more business for pharma companies.

5. Streamlining Pharma Sales Process   

AI-powered pharma sales software can impact reps productivity and turnover in a big way. For instance, the tech can help study industry trends and customer preferences, such as the treatment options a particular practice or doctor prefers the most.

Businessman with headsets using computer in office at night

A sales team may use that intelligence in pre-calling planning to gather relevant promotional and informational material. Effective pre-meeting preparation increases the chances of converting leads to sales. It can help boost pharma revenue.

The advantages of pharma AI nutshell…

Pharma AI speeds up the drug R&D process, enabling companies to differentiate their products by delivering healthcare solutions to hospitals and patients faster than otherwise possible. It reduces R&D costs while boosting clinical trial success rates, which translates to increases profit margins for pharma businesses.

Integrating IoT and AI into patient management solutions helps to improve medication adherence and treatment outcomes, resulting in in-market success. The tech can also boost pharma sales productivity and revenue.

Now, for the bad news. There do happen to be a couple of slight detractors to using AI in the pharma industry:

1. Patients May Not Always Cooperate 

For pharma companies to monitor drug use and effects using AI-powered technology, participating patients must be willing to interact with the system consistently.

For example, a candidate may decline to ingest a biosensor or film themselves taking a pill. Without collecting vast amounts of consistent patient data, ML algorithms cannot “learn” and extract the intelligence necessary to help pharma researchers or providers to draw accurate conclusions on treatment outcomes or drug efficacy.

2. Incompatible Legacy Health IT Infrastructure 

The majority of current health IT systems do not naturally lend themselves to AI and big data mining applications.

With most of the healthcare data available in disparate, structured and unstructured sources, extracting in-depth insights for tasks like remote treatment outcome monitoring and patient engagement is usually difficult if not impossible.

As such, pharma companies have to develop (or acquire) interoperable health IT infrastructure before they can leverage AI to tap into data from diverse sources, and to extract predictive analytics/business intelligence, which enables them to make informed, real-time choices.

The fact of the matter is that presently, most healthcare systems are not AI-ready. 

Not all patients or healthcare facilities may adopt the interoperable AI infrastructure needed to supply pharma companies with large volumes of data for in-depth ML analytics.

Leverage AI to Boost Your Overall Pharma Business Efficiency (and Profits) 

Artificial intelligence in the pharmaceutical industry outperforms human-centric patient or customer data collection and analysis techniques. It’s a game-changing and product-differentiating piece of technology.

By incorporating AI into your pharma management system, you can increase business revenue and profit through enhanced speed to market, effective patient monitoring and compliance, and improved sales productivity. Contact us for free below and let’s chat!

5 Use Cases for Commercial Pharma AI Beyond Drug Discovery

Artificial Intelligence, or AI for short, has been one of those buzzwords that’s been circulating throughout the healthcare industry. Most recently, it has been widely considered to be extremely beneficial for drug discovery and in clinical trials.

However, that’s just a very small aspect of the total benefit that pharma AI can bring to the table for the pharma and biotech industries.

According to a study by Accenture, pharma AI can potentially create $150 billion in savings for the United States healthcare economy by 2026. 

Constantly hearing about the incredible benefits of AI while still not fully understanding what it can do and how it can help can be very frustrating and often deter companies from embracing it.

Artificial intelligence business man.

Before we dive into AI and its benefits, let’s briefly go over its definition:

AI consists of various computer systems and predefined algorithms with the ability to perform tasks that normally require advanced human intelligence. 

Some of these tasks include:

  • Visual Perception
  • Speech Recognition
  • Decision-Making
  • Translation Between Languages

The idea of “human versus machine” has been replaced with “human + machine” and if that just piqued your interest, it should. Pharma AI has entered the beginning of a golden age, and it is expanding into everything from manufacturing to marketing trends.

You may have wondered how patients feel about receiving AI-enabled healthcare?

54% of patients are willing to receive AI-enabled care. 

With over half of patients in favor of AI-enabled healthcare, it shows how far AI has already come in the pharma industry.

Now, will incorporating AI alone suddenly result in millions in revenue right away.

No. 

However, when AI is paired with the right tools and platforms such as Microsoft’s Cortana Intelligence Suite and pharma-specific solutions, the results will begin to show quickly.

Woman looking at a medical interface in the hospital

So, How Can Commercial Pharma AI Be Used Beyond Drug Discovery 

1. Targeted Content Delivery in Marketing

The pharma industry has come a long way since the traditional methods of marketing blockbuster drugs, thanks in large part to the integration of AI and other state-of-the-art technology.

With the addition of pharma AI, huge amounts of raw data can be collected, sorted, and interpreted in real-time. Due to this, pharma AI has the ability to improve your commercialization in some key ways:

  • Marketing Strategies
  • Product Launch
  • Product Value Proposition
  • Customer Engagement

In the past, general marketing strategies have worked because the drugs that they were selling had an impact on a large population of patients all at once, otherwise known as blockbuster drugs.

Now, with the rise of a more patient-centric approach within the industry, pharma companies need to market in a more targeted and specific way.

Unfortunately, the traditional pharma marketing systems can no longer be successful in this new pharma because they’re both sluggish and inaccurate.

With pharma AI, on the other hand, pharma companies can aggregate, process, and convert massive amounts of unstructured and difficult to manage data sources to gain rich insight into how consumers are making decisions.

By implementing the right algorithms paired with a powerful analytics solution, data can not only be accessed but can also be used to precisely target consumers who are most in need of your product or service.

“Using data intelligently to power go-to-market strategies has to be a priority if commercial teams want to stay ahead of the competition and increase both their REACH and their PRECISION.” – Dr. Dolores Baksh, GE Healthcare Life Sciences

Success in pharma marketing often requires a lot of complex decision-making, which can get overwhelming and stressful very quickly. Thankfully, pharma AI can provide your company with data analytics to reduce stress, save time, and deliver high-quality results!

“AI-powered analytics is perfect for pharma marketing departments because it can undertake large volumes of complex decisions by going through data-sets at a high degree of accuracy.” – Sudeep Pattnaik, CEO, ThoughtSpace 

The end result is a solid marketing foundation that allows you to target the right consumers or population leading to a better commercialization and pharma marketing strategy.

Doctor using phone and laptop for marketing.

2. Manufacturing Optimization 

You’re probably familiar with the old saying, “everybody makes mistakes.” Well, what if we can change that to, “Artificial intelligence DOESN’T make mistakes,” at least when speaking about pharma manufacturing.

Why is pharma manufacturing optimization so important?

The process to fully develop and test a new drug can cost up to $2.7 billion. 

Of course, not all of that money is spent in the manufacturing stage of drug development.

The various studies that need to be conducted in order to bring the drug to market constitute about $10 million to $2 billion of the total amount, but here’s the kicker…

90% of drugs that begin testing in humans don’t reach the market because they are either UNSAFE or INEFFECTIVE. 

So, errors in manufacturing could not only hit you at an unexpected time, but they can also be extremely costly. That’s where pharma AI comes into play.

Using predictive analytics, AI and Machine Learning (ML) can predict and prevent:

  • Under-demand
  • Over-demand
  • Supply chain problems.
  • Production line failures.
  • And more!!

With that being said, how exactly can AI and ML do this?

Well, instead of humans sifting through the massive amounts of raw data for hours looking for anomalies, AI and ML can do it much more quickly, efficiently, and accurately at a lower cost.

Pharma AI can be beneficial to your company’s manufacturing sector by providing:

  • Increased speed.
  • Increased precision.
  • Enhanced employee safety.
medical pills industry factory and production indoor

3. Discovering and Evaluating Pharma Market Trends 

The last thing that any pharma company wants to worry about is being the last to know about an emerging trend in the market.

Being the last to know could set your company and product development far behind your competition and you’ll end up playing catch-up.

Traditionally, the best ways to discover emerging market trends were networking, website researching, and sifting through professional studies.

However, in the current technical age, pharma AI helps your company be the first to know about an emerging trend before it happens, giving you plenty of time to plan and execute.

Applications of AI such as ML and Deep Learning (DL) have the ability to analyze large amounts of data to make sure you are the first to witness any potential market trends.

By following a programmed algorithm, these applications of AI can quickly dig through these large data-sets. This would take much longer for humans to do.

The word “analyze” doesn’t quite fully define the magnitude of automated work that will be done for your pharma company using pharma AI. Instead, the term “dynamic understanding” more effectively describes the capabilities of AI and ML-enabled processes.

ML can provide your company with a dynamic understanding of the entire market in real-time, providing the best results.

After the data is produced, your company can benefit from being fully aware of the following:

  • Market Size.
  • Patient Segmentation.
  • Targeting.
  • Provider Segmentation.
  • Payer Segmentation.
  • Messaging.
  • Health Economics and Outcomes Research (HEOR) activities.
Image of businesspeople at presentation looking at virtual project

4. Prescriber Segmentation 

As the pharma industry becomes increasingly more patient-centric, the way pharma companies create and commercialize drugs are becoming more prescriber specific.

In 2016, there were 4.45 billion prescriptions written throughout all of the United States. 

Based on that statistic alone, can you even imagine how long it would take to sort through all of the data involved for each individual prescriber? It would simply take forever!

Pharma AI helps you and your team free up time typically spent on more mundane or administrative tasks to instead focus on tasks that lend themselves to the company’s long-term goals.

For example, some types of data that pharma AI can generate efficiently are:

  • Therapy starts, changes, and add-on prescriptions.
  • Compliance and persistence.
  • Managed care access for the brand.
  • Group practice affiliation.

For instance, let’s say you want to segment prescribers into the following groups:

  • Promotion
  • Payer
  • Product
  • Patient

By leveraging pre-programmed AI algorithms to calculate large data-sets, you can create more specific groups for the four segmentations above. Let’s say two of the more specific groups are “Price-driven” and “Range of communication.”

A study that conducted similar prescriber segmentation discovered that 71% of prescribers were more specific with how they would like to be contacted, while only 29% of prescribers had a larger range of communication preferences.

Traditionally, finding this would not only take a ton of time, but it would also require significant resources to pull off within a reasonable timeframe. So, why spend all the time and resources on a task that can be done much more quickly and efficiently with AI?

Symbol of social network with people images-1

5. Chatbots

A research firm, Gartner, predicts that chatbots will handle 85% of customer service issues by 2020 and 40% of patients won’t mind talking to a chatbot as long as the information is accurate.

When people think of chatbots, they only think of the little pop-ups that come up at the bottom of the screen of websites. With the integration of AI, chatbots can be expanded in multiple ways that make your job easier and the experience much better for your consumers.

First, let’s briefly go over how AI-enabled chatbots can benefit your pharma company’s website since it is the most common understanding of the technology.

You can’t always be there to respond to every website visitor all day and every day. However, AI-enabled chatbots can help bridge the gap by providing basic information to visitors on your website and social media when they need it.

According to a State of Chatbots report in 2018, 64% of website visitors expect 24-hour chatbot customer service while 55% of website visitors expect instant responses. 

Plus, by incorporating certain algorithms into ML-enabled chatbots, insights from every conversation can instantly be learned and processed to ensure that a more human-like response is given with every encounter.

This way, you’re giving the user EXACTLY what they want. A response that is accurate, compliant, quick, and available 24/7, 365 days a year!

So, what are other ways chatbots implemented with AI can be beneficial for your pharma company?

  • Track patients after discharge.
  • Check on patients with chronic diseases.
  • Track clinical trial participants.
  • Help users find and talk to doctors.

AI-driven chatbots can also be beneficial when developed around a specific treatment option. This would allow physicians or patients to get information that is relative to their needs for either prescribing or starting treatment. This includes the ability to access:

  • Adverse Events
  • Tolerability
  • Dosing
  • Efficacy
  • Financial Resources
  • And more!

It’s clear that incorporating AI-powered chatbots into your pharma company can save time while also ensuring that website visitors, patients, and prescribers are all getting accurate information very quickly!

Business person working on computer against technology background

Pharma AI saves time and money so you can focus on tasks that drive long-term success.

By integrating pharma AI solutions into your company operations, you will reap benefits far beyond drug discovery and development.

While many of these benefits will take some time to manifest, you will see an increase in production, efficiency, and reduced cost progress over time, all with minimal involvement on your part.

1. Targeted Content Delivery Marketing

2. Manufacturing Optimization 

3. Discovering and Evaluating Pharma Market Trends

4. Prescriber Segmentation 

5. Chatbots 

Applications of AI such as ML, DL, and Predictive Analytics are being incorporated into pharma operations every single day with great, lasting results.

How will your company leverage the power of artificial intelligence to provide better products, services, and support to your audience?

Want to learn more about pharma AI? Or, curious about our artificial intelligence pharma solution built on Microsoft platforms? Get in touch with us for free today by clicking below!