According to McKinsey, machine learning and artificial intelligence in pharma and medicine are going to revolutionize the industries to help them make better decisions, optimize innovations, improve the efficiency of clinical and research trials, and provide for new tools for physicians, consumers, regulators, and even insurers. In fact, they estimate the technology could generate up to $100B in value annually.
Simply put, the more insights that data science and AI bring to the table based on biomedical data, the faster the medicine and pharma industry grows.
We’re a long way away from true artificial intelligence like the human-like robots and computers that we see in science fiction movies, but we are at least in a place where AI can outperform human beings at certain tasks.
And AI could be particularly powerful in the health care industry. One piece of research from Accenture found that key clinical health AI applications can potentially create $150 billion in annual savings for the US health care economy by 2026. Another report from Tractica found that the AI health care market will be worth $34 billion by 2025.
AI is great at performing repetitive tasks, and there’s no shortage of them in the health care industry. In the United States, physicians spend more time filling out electronic health records (EHRs) than they do interacting with patients. If artificial intelligence could take on the bulk of that record keeping, it would free up their time to spend it with patients. In fact, it would have the same impact as hiring hundreds of thousands of new doctors.
According to Gartner, AI will have eliminated 1.8 million jobs by 2020. At the same time, it will create 2.3 million new jobs, leading to an overall increase of 500,000. Other predictions are similarly optimistic.
Part of this is because of the way that AI and physicians would likely interact. AI-powered clinical decision support tools could provide physicians with suggestions based on hard data, but it would be down to physicians and their patients to take this data and to decide together on the best way to proceed.
But on top of that, there’s just something about health care that calls for a human touch. Just imagine that you’ve just been diagnosed with cancer. Would you prefer to be told the news by an emotionless robot that’s nothing more than an algorithm, or would you prefer to be told by a friendly family doctor?
Data protection in medicine and pharma
The amount of data in the healthcare industry knows no bounds. All the data accumulation by companies and hospitals are done during commercial researches, health outcomes over weeks, months and years, research and development projects, and clinical studies in pharma. But for decades, data analytics has been a customarily manual task for healthcare professionals.
In fact, the biggest challenge in the medicine and pharma industry has been data sharing and regulation. Drug manufacturers actively collaborate with tech companies, researchers, startups and more, thus sharing the data of millions of people. When Royal Free hospital partnered with DeepMind to develop an app for kidney injury detection, it shared the personal data of 1.6 million people with Google’s subsidiary. A subsequent investigation ruled that the hospital failed to comply with the then governing Data Protection Act 1998, now replaced by the GDPR. The problem is that currently no official regulatory framework exists to make the adoption of AI in pharma widespread.
However, some businesses manage to implement governance control for such collaborations, making a positive impact on the healthcare industry. The advancements in healthcare using data science have been previously covered in a dedicated article. Here we’ll look at the main applications in pharma.
Machine learning-driven innovation in medicine and pharma
1. Faster and Better Diagnosis
There are cases in which a patient goes undiagnosed for an extremely long period. They are not able to find the right treatment and continually struggle with multiple medical therapies to find a solution to an inaccurately identified problem. The biggest challenge here being the lack of ability to pull in past records and a medical trial for the patient.
David Talby, CTO Pacific AI and an Experfy Expert says, “With machine learning, hospitals and even pharmaceutical companies can draw up medical patterns of a patient based on specific criteria – like symptoms, medications, data from wearable devices, labs, etc. The information can then be used for a timely and better diagnosis, tracking progression, and recommending personalized treatments. For example, Kaiser Permanente improved hospital patient flow by combining natural language understanding from free-text medical notes with structured data to predict which patients will require hospitalization and for how long.”
Google has developed an algorithm that helps in identifying cancerous tumors on mammograms, using machine learning. Stanford is actively using the technology to identify the different types of skin cancer.
2. Pharmaceutical recommendations
Knowing a patient’s history and early detection of disease, medical professionals can recommend the right treatment and put a patient on the right path sooner. But what the data also enables, is letting pharmaceutical companies run targeted campaigns to promote medications and treatments, or make recommendations backed by data that could help build awareness amongst undiagnosed patients.
This doesn’t just help pharmaceutical companies increase their sales but can also support the identification of individuals at risk due to early detection of disease symptoms via the campaigns.
3. Health outcomes
The patient journey is what makes medical treatments more effective. It refers to the process of tracking how a patient suffering from a disease is responding to medication or different lines of therapies. This data is then used by medical professionals to predict health outcomes for a positive impact on the patient.
Machine learning helps create treatment pathways for patients with even the rarest of diseases, tracking their response to every little change in medication, to help optimize their journey, increasing their comfort on their way to desired health outcomes.
4. Physician trends
AI can also help medical organizations and pharmaceutical companies map physician trends. This could include the number of times a certain treatment path was chosen to treat a disease or medication was being recommended to patients in a specific area. The data doesn’t just help analyze medical practices, but also assist in understanding the common needs of patients based on where they were and the environment they were exposed to.
The data, in this case, has also been used to conduct extensive commercial market research in the medicine and pharma industry, using Associative Rules Mining.
5. Risk monitoring
Data science can help gather critical patient information in real-time and respond proactively to symptoms to prevent an event from occurring. Risk-based monitoring has been used in association with sensors and electronic data capturing devices, such as oximetry, ECG, and others to acquire data and detect suspicious changes in a patient’s vital signs.
Let’s take for instance a blood pressure monitor. A machine learning algorithm could be trained to recognize critical events based on perturbations of a patient’s to prevent adverse health outcomes with timely interventions.
6. Physician matching and automation
As mentioned before, the health and pharma industries have massive databases – of physicians across all departments and patients suffering from various diseases. With machine learning applied over these data sets, you could match physicians to patients quickly instead of using general categorization to choose a doctor to treat a certain disease or provide for a treatment path.
The richer the data sets, the more relevant the matching will be, leading to patients getting access to the right physicians and treatments in a timely manner.
7. Social Media Analytics & Influencer Mapping
Pharmaceutical companies have been leveraging the influence of experienced physicians and researchers to find more patients on a global scale – for adoptions of new drugs or for clinical trials. But today, artificial intelligence enables them to measure the influence these physicians have with more meaning and value by quantifying the success of a campaign.
Pharmaceutical companies can use machine learning for influencer marketing by mapping the right physician for their campaign needs. The criteria could be the topics they extensively discuss or write about, their experience, or others. This will enable companies to isolate and reach out to a relevant target audience.
As Single Grain correctly highlighted in the following infographic, – using machine learning and AI you go beyond the superficial layer of the “number of followers”, and dig deeper into the hidden layers to predict the outputs.
8. Recruitment for Clinical Trials
Nearly 80 percent of clinical research and trials either fail to finish on time or get delayed by six months or more. The reason being that 85 percent of these trials fail to retain enough patients, with an average churn of around 30 percent.
With machine learning and AI, healthcare companies can extract pertinent EMR information to sift through physician notes efficiently and effectively. The data collected can then be used to identify appropriate patients for clinical trial enrollments. Even during the span of the trial, the technology can be used to predict patient churn using real-world evidence (RWE) from their medical history, giving the companies a buffer to find replacements.
9. Manufacturing Optimization
Statistical analysis has remained at the core of ensuring greater product quality and maintaining minimal consumer risk. The data helps engineers understand why and how a manufacturing process can be optimized to yield an expected quality with a known certainty.
Simply put, statistical analysis helps to ensure that the best of practices are followed in the manufacture of pharmaceutical products and medical devices, for consumer safety. Along with machine learning, pharma companies can improve their manufacture efficiency, product yield and cost, and final product quality.
10. Digital Health, Digital Medicines, and Patient Safety
Digital Health is an emerging trend in the healthcare industry. Both pharma and tech companies have been developing mobile applications that can keep track of health-related parameters, record drug prescriptions, improve compliance, and remind patients about upcoming medical appointments. Digital medicines are pharmaceuticals which combine drugs with an ingestible sensor used to monitor whether the patient correctly followed medical advice.
These systems are not only designed to record and store data but also to share information with health-care professionals, reducing communication asymmetries between patients and physicians.
Machine Learning will play a big role in the development of more effective Digital Health applications, for example, by incorporating models that can send alerts and information at the right time to the right person. Ultimately this will result in increased patient safety.
Adoption of AI in medicine and pharma
While the application of data science and AI in the medicine and pharma industry looks promising, there are still challenges that are yet to be addressed.
Sometimes, making the right diagnosis takes more than just a set of rules. It requires experience in addressing certain symptoms and health outcomes over the years. Similarly, the choice of the best treatment for a patient requires information about the most recent developments in the field, current clinical trials, and recent scientific advances.
For these reasons, successful healthcare ML applications will require medical experience to be fully engaged.
What’s next?
Artificial intelligence goes hand-in-hand with machine learning, natural language processing and other technologies, all of which can be combined to process the huge amounts of big data that we create on a daily basis. In the health care industry, being able to process this data and to draw new conclusions isn’t just a matter of making money — it’s a matter of life and death.
It won’t be long until artificial intelligence is being used as standard practice throughout the health care industry, and that’s good news for all of us. After all, we’ll all become patients at some point in our lives, and AI has the potential to usher in a new era of health care in which we’re all treated with personalized health care plans based on data and not just the results of clinical trials.
And the good news is that we won’t even lose our doctors. AI won’t replace them — it’ll just help to make them more efficient. It’s a true case of man and machine working better together than either could in isolation, and it spells a bright future for all of us.
Simply put, the more insights that data science and AI bring to the table based on biomedical data, the faster the medicine and pharma industry grows.
We’re a long way away from true artificial intelligence like the human-like robots and computers that we see in science fiction movies, but we are at least in a place where AI can outperform human beings at certain tasks.
And AI could be particularly powerful in the health care industry. One piece of research from Accenture found that key clinical health AI applications can potentially create $150 billion in annual savings for the US health care economy by 2026. Another report from Tractica found that the AI health care market will be worth $34 billion by 2025.
AI is great at performing repetitive tasks, and there’s no shortage of them in the health care industry. In the United States, physicians spend more time filling out electronic health records (EHRs) than they do interacting with patients. If artificial intelligence could take on the bulk of that record keeping, it would free up their time to spend it with patients. In fact, it would have the same impact as hiring hundreds of thousands of new doctors.
According to Gartner, AI will have eliminated 1.8 million jobs by 2020. At the same time, it will create 2.3 million new jobs, leading to an overall increase of 500,000. Other predictions are similarly optimistic.
Part of this is because of the way that AI and physicians would likely interact. AI-powered clinical decision support tools could provide physicians with suggestions based on hard data, but it would be down to physicians and their patients to take this data and to decide together on the best way to proceed.
But on top of that, there’s just something about health care that calls for a human touch. Just imagine that you’ve just been diagnosed with cancer. Would you prefer to be told the news by an emotionless robot that’s nothing more than an algorithm, or would you prefer to be told by a friendly family doctor?
Data protection in medicine and pharma
The amount of data in the healthcare industry knows no bounds. All the data accumulation by companies and hospitals are done during commercial researches, health outcomes over weeks, months and years, research and development projects, and clinical studies in pharma. But for decades, data analytics has been a customarily manual task for healthcare professionals.
In fact, the biggest challenge in the medicine and pharma industry has been data sharing and regulation. Drug manufacturers actively collaborate with tech companies, researchers, startups and more, thus sharing the data of millions of people. When Royal Free hospital partnered with DeepMind to develop an app for kidney injury detection, it shared the personal data of 1.6 million people with Google’s subsidiary. A subsequent investigation ruled that the hospital failed to comply with the then governing Data Protection Act 1998, now replaced by the GDPR. The problem is that currently no official regulatory framework exists to make the adoption of AI in pharma widespread.
However, some businesses manage to implement governance control for such collaborations, making a positive impact on the healthcare industry. The advancements in healthcare using data science have been previously covered in a dedicated article. Here we’ll look at the main applications in pharma.
Machine learning-driven innovation in medicine and pharma
1. Faster and Better Diagnosis
There are cases in which a patient goes undiagnosed for an extremely long period. They are not able to find the right treatment and continually struggle with multiple medical therapies to find a solution to an inaccurately identified problem. The biggest challenge here being the lack of ability to pull in past records and a medical trial for the patient.
David Talby, CTO Pacific AI and an Experfy Expert says, “With machine learning, hospitals and even pharmaceutical companies can draw up medical patterns of a patient based on specific criteria – like symptoms, medications, data from wearable devices, labs, etc. The information can then be used for a timely and better diagnosis, tracking progression, and recommending personalized treatments. For example, Kaiser Permanente improved hospital patient flow by combining natural language understanding from free-text medical notes with structured data to predict which patients will require hospitalization and for how long.”
Google has developed an algorithm that helps in identifying cancerous tumors on mammograms, using machine learning. Stanford is actively using the technology to identify the different types of skin cancer.
2. Pharmaceutical recommendations
Knowing a patient’s history and early detection of disease, medical professionals can recommend the right treatment and put a patient on the right path sooner. But what the data also enables, is letting pharmaceutical companies run targeted campaigns to promote medications and treatments, or make recommendations backed by data that could help build awareness amongst undiagnosed patients.
This doesn’t just help pharmaceutical companies increase their sales but can also support the identification of individuals at risk due to early detection of disease symptoms via the campaigns.
3. Health outcomes
The patient journey is what makes medical treatments more effective. It refers to the process of tracking how a patient suffering from a disease is responding to medication or different lines of therapies. This data is then used by medical professionals to predict health outcomes for a positive impact on the patient.
Machine learning helps create treatment pathways for patients with even the rarest of diseases, tracking their response to every little change in medication, to help optimize their journey, increasing their comfort on their way to desired health outcomes.
4. Physician trends
AI can also help medical organizations and pharmaceutical companies map physician trends. This could include the number of times a certain treatment path was chosen to treat a disease or medication was being recommended to patients in a specific area. The data doesn’t just help analyze medical practices, but also assist in understanding the common needs of patients based on where they were and the environment they were exposed to.
The data, in this case, has also been used to conduct extensive commercial market research in the medicine and pharma industry, using Associative Rules Mining.
5. Risk monitoring
Data science can help gather critical patient information in real-time and respond proactively to symptoms to prevent an event from occurring. Risk-based monitoring has been used in association with sensors and electronic data capturing devices, such as oximetry, ECG, and others to acquire data and detect suspicious changes in a patient’s vital signs.
Let’s take for instance a blood pressure monitor. A machine learning algorithm could be trained to recognize critical events based on perturbations of a patient’s to prevent adverse health outcomes with timely interventions.
6. Physician matching and automation
As mentioned before, the health and pharma industries have massive databases – of physicians across all departments and patients suffering from various diseases. With machine learning applied over these data sets, you could match physicians to patients quickly instead of using general categorization to choose a doctor to treat a certain disease or provide for a treatment path.
The richer the data sets, the more relevant the matching will be, leading to patients getting access to the right physicians and treatments in a timely manner.
7. Social Media Analytics & Influencer Mapping
Pharmaceutical companies have been leveraging the influence of experienced physicians and researchers to find more patients on a global scale – for adoptions of new drugs or for clinical trials. But today, artificial intelligence enables them to measure the influence these physicians have with more meaning and value by quantifying the success of a campaign.
Pharmaceutical companies can use machine learning for influencer marketing by mapping the right physician for their campaign needs. The criteria could be the topics they extensively discuss or write about, their experience, or others. This will enable companies to isolate and reach out to a relevant target audience.
As Single Grain correctly highlighted in the following infographic, – using machine learning and AI you go beyond the superficial layer of the “number of followers”, and dig deeper into the hidden layers to predict the outputs.
8. Recruitment for Clinical Trials
Nearly 80 percent of clinical research and trials either fail to finish on time or get delayed by six months or more. The reason being that 85 percent of these trials fail to retain enough patients, with an average churn of around 30 percent.
With machine learning and AI, healthcare companies can extract pertinent EMR information to sift through physician notes efficiently and effectively. The data collected can then be used to identify appropriate patients for clinical trial enrollments. Even during the span of the trial, the technology can be used to predict patient churn using real-world evidence (RWE) from their medical history, giving the companies a buffer to find replacements.
9. Manufacturing Optimization
Statistical analysis has remained at the core of ensuring greater product quality and maintaining minimal consumer risk. The data helps engineers understand why and how a manufacturing process can be optimized to yield an expected quality with a known certainty.
Simply put, statistical analysis helps to ensure that the best of practices are followed in the manufacture of pharmaceutical products and medical devices, for consumer safety. Along with machine learning, pharma companies can improve their manufacture efficiency, product yield and cost, and final product quality.
10. Digital Health, Digital Medicines, and Patient Safety
Digital Health is an emerging trend in the healthcare industry. Both pharma and tech companies have been developing mobile applications that can keep track of health-related parameters, record drug prescriptions, improve compliance, and remind patients about upcoming medical appointments. Digital medicines are pharmaceuticals which combine drugs with an ingestible sensor used to monitor whether the patient correctly followed medical advice.
These systems are not only designed to record and store data but also to share information with health-care professionals, reducing communication asymmetries between patients and physicians.
Machine Learning will play a big role in the development of more effective Digital Health applications, for example, by incorporating models that can send alerts and information at the right time to the right person. Ultimately this will result in increased patient safety.
Adoption of AI in medicine and pharma
While the application of data science and AI in the medicine and pharma industry looks promising, there are still challenges that are yet to be addressed.
Sometimes, making the right diagnosis takes more than just a set of rules. It requires experience in addressing certain symptoms and health outcomes over the years. Similarly, the choice of the best treatment for a patient requires information about the most recent developments in the field, current clinical trials, and recent scientific advances.
For these reasons, successful healthcare ML applications will require medical experience to be fully engaged.
What’s next?
Artificial intelligence goes hand-in-hand with machine learning, natural language processing and other technologies, all of which can be combined to process the huge amounts of big data that we create on a daily basis. In the health care industry, being able to process this data and to draw new conclusions isn’t just a matter of making money — it’s a matter of life and death.
It won’t be long until artificial intelligence is being used as standard practice throughout the health care industry, and that’s good news for all of us. After all, we’ll all become patients at some point in our lives, and AI has the potential to usher in a new era of health care in which we’re all treated with personalized health care plans based on data and not just the results of clinical trials.
And the good news is that we won’t even lose our doctors. AI won’t replace them — it’ll just help to make them more efficient. It’s a true case of man and machine working better together than either could in isolation, and it spells a bright future for all of us.
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