The use of computers, mobile devices, wearables, and other biosensors to gather and store huge amounts of health-related data has been rapidly accelerating. This data holds the potential to enable better designs and conduct clinical trials and studies in the health care setting to answer questions previously thought infeasible.
Real-world evidence, molecular information generated from next-generation sequencing, data from wearable devices and mobile apps, and novel clinical trials are transforming the future of healthcare. The role of wearable devices is also emerging in the collection of real-world data. Wearable devices are clinically useful for better monitoring real-time, long-term, and dynamic & pathological processes. These devices conveniently manage chronic illnesses, such as cardiovascular diseases, sleep disorders, and cognitive impairment. In recent years, there has been a growing adoption of health and wellness devices to track fitness activities, sleeping habits, body temperature, and heart rate.
In the last few years, wearable device adoption has increased along with the growing adoption of healthcare apps. The wearables industry is constantly changing and continuously evolving.
However, the wearables market is still growing and currently dominated by health, wellness, and activity tracking devices. For instance:
- In March 2020, Fitbit Inc. (U.S.) announced the launch of its Fitbit Charge 4 with the most advanced health & fitness tracker with built-in GPS, active zone minutes, and sleep tools.
- In 2019, Omron Corporation (Japan) launched HeartGuide, a wearable blood pressure smartwatch.
Wearable devices collect vast amounts of relevant data, enabling healthcare professionals to discover correlations between medical conditions and manage them more effectively. A vast amount of patient-generated data through wearable devices can be used for the production of real-world data.
Artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, has been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. AI is driving ground-breaking leaps in protein structure identification, and the advancements in regulations are providing healthcare research organizations with access to real-world data to accelerate clinical trial processes. For instance, in 2021, Savana, a leader in Deep Real World Evidence clinical research, and BREATHE—The Health Data Research Hub for Respiratory Health—partnered to further the use of EHR data for vital respiratory research.
Savana developed EHRead1, a powerful technology that applies AI in the form of Natural Language Processing (NLP) and deep learning techniques to analyze structured and unstructured information in EHRs while safeguarding the privacy and security of patient data. The partnership is aimed at accelerating the use of data from de-identified EHRs to monitor disease progression and outcomes in UK patients hospitalized with COVID-19 as part of the international BigCOVIData study.
The innovative technology advancements of machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) offer pharmaceutical, biotechnology, and medical device companies the power to generate enhanced RWE output, decrease time to insights, and make the most out of the vast real-world data sources available. Companies operating in the real-world evidence solutions market have also adopted various strategies to align themselves with the changing dynamics of the real-world solutions space. For example, in September 2020, Parexel International Corporation (U.S.) acquired Roam Analytics, Inc. (U.S.), a healthcare software company, to leverage its Artificial Intelligence (AI) and Machine Learning (ML) capabilities to drive innovations across drug development and life sciences sectors. This acquisition was also aimed at enhancing Parexel’s Pharmacovigilance and other Real-World Data capabilities. Furthermore, in May 2020, Parexel International Corporation (U.S.) launched the #KeepingPatientsFirst integrated real-world evidence (RWE) research platform focused on aggregating, analyzing, and predicting real-world COVID-19-related disease progression and outcomes using state-of-the-art machine learning, artificial intelligence, and analytics.
In addition, the integration of artificial intelligence also improves the visibility of treatment. Life science companies are adopting advanced tools, such as AI and big data, to better prepare organizations to adapt to this RWE market. Combining these technologies with RWE can make life science organizations more effective in developing better products in a shorter timeline. Similarly, Natural language processing (NLP), an AI tool, can be useful in processing and evaluating large amounts of unstructured RWE data. Thus, the global RWE solutions market is expected to grow at a CAGR of 11.8% to reach $4.9 billion by 2029, according to the Meticulous Research®.
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