By leveraging Data Science, AI, and other digital technologies, the healthcare industry could build complementary health solutions that are personalized to the specific needs of patients. Here is how and why. 

The world population grows by more than 80 million per year, according to a 2017 report by the United Nations. By 2050, there will be 10 billion people on this planet and people over the age of 60 years and above are expected to double. It’s clear that a growing and aging world population needs better and more sustainable solutions for health and nutrition. Data Science has the power to fundamentally transform patient health and the healthcare industry needs to leverage digital technologies for better solutions. 

Artificial Intelligence and Machine Learning help to leverage data so patients can be diagnosed earlier and to gain a thorough and deep understanding of diseases. This is key to tailor treatments to the individual needs of a patient and to identify those patients who will benefit the most. Moreover, data-driven digital solutions help to bring new medicines to patients faster than ever before.

Earlier detection of diseases 

Integrated patient care starts with identifying relevant information. Diagnosing a disease early on can have a significant impact on the outcome. Digital solutions could help healthcare professionals to make an appropriate diagnosis as early as possible. 

For example, a software could use deep learning methodology to support radiologists in identifying signs of CTEPH (chronic thromboembolic pulmonary hypertension), a rare form of pulmonary hypertension, in CTPA (computed tomography pulmonary angiogram) scans. The software processes image findings of cardiovascular, lung perfusion and pulmonary vessel analyses in combination with the patient’s history of pulmonary embolism. 

Also, an AI algorithm could help doctors identify patients with a high risk of cardiovascular diseases (such as heart failure and recurrent stroke) earlier and with more precision than ever. Patients could be differentiated based on complex individual profiles made up of a unique combination of characteristics (demographics, and clinical risk factors such as diabetes and genotypes). This requires the application of outcome data from clinical studies, genomic and imaging data for this stratification. 

Tailoring treatments

Individualized patient treatment is a core ambition that can be delivered through data. Another line of work seeks to tailor treatments to the underlying cause of an individual patient’s disease. One such example is the development of an AI algorithm that is intended to identify patients whose cancer is likely the result of an NTRK gene fusion in their tumor cells – often resulting in an altered TRK fusion protein, leading to cancer growth. While overall rare, TRK fusion cancer affects both children and adults and occurs in varying frequencies across various tumor types, which makes testing for this alteration so important. The AI algorithm may help physicians to identify patients who are likely to have TRK fusion cancer, based on their tumor pathology. These results are then verified by the specific validated diagnostic methods that are already used. Ultimately, the AI algorithm can help to support consistent and wide-spread testing for TRK fusion cancer across the different tumor types, leading to more patients benefiting from a precision oncology treatment used to treat solid tumors specifically caused by an NTRK gene fusion. 

 Innovation in the healthcare sector goes beyond the pill. By leveraging Data Science and AI, as well as other digital technologies, our industry could build complementary health solutions that are personalized to the specific needs of patients and customers. One example of digital therapeutics is mobile gaming that could help patients proactively manage chronic health conditions such as stress, anxiety, and depression. 

Accelerating the development of new medicine

The research and development of new drugs remain an important challenge, as pharmaceutical research is highly complex and projects have a relatively low probability of success. Data from the CRM international consortium show that less than ten percent of projects make it from early research (Phase I) to market approval. And today, it takes 12-15 years from early research to marketing approval of a new drug and even in a late stage of clinical development, the majority of projects fail. 

Advances in data analytics accelerate drug discovery and improve the drug development productivity in terms of quality, cost and cycle time. In practice, there are two areas of application that see the great potential and already use the technology of AI. 

Firstly, Big Data and advanced analytics will help to identify new targets for innovative medicines in drug discovery – much faster, more accurately and more efficiently than ever before. Once a target is identified, the application of AI in disease stage modeling, lead selection and optimization through computational biology has the potential to increase productivity and speed up the process of developing the new medicine. 

Secondly, the increase in speed and efficiency of clinical trials is one of the most highly anticipated benefits of decentralized clinical trials. Instead of all study interactions taking place inside the walls of a clinical research site, decentralized clinical trials take the research to the patient. This is done via a combination of data points including e-consent, telehealth, wearables, sensors, surveys, and modified in-person visits via labs, home health or other medical providers. By building a strategy to enable the organization to decentralize and digitize an increasing number of clinical trials, you could make research more accessible to larger groups of patients. By bringing the trial to the patient via decentralized technologies, participation becomes more convenient, while gathering real-life data and processing data becomes more efficient and faster. The length of clinical trials can be reduced, and we expect to be able to predict real-life patient outcomes more precisely and with smaller trials thanks to AI-based patient stratification and risk prediction capabilities.

A wealth of data sets is a critical prerequisite for applying data-driven technologies. External collaboration and strategic partnerships – from academia, the biotech industry and the startup community – are also crucial ingredients to leveraging data science technology in the most meaningful and efficient way to enable better health outcomes for patients. 

Data Science is the lifeline of our digitalization as an industry, and is fundamental to transforming patient health today, and well into the future. 

Like the article? Find the Bayer team at Data Natives Conference 2019 and talk to them in person about transforming patient health.

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