(Yicai Global) Oct. 28 -- A first-of-its-kind report released yesterday charts the terrain of the real-world studies (RWS) sector and projects it will log 40 percent growth each year in China.
The seminal report titled ‘Landscape of AI Technology Companies in Real-World Studies Industry,’ was jointly produced by leading Biotech and Health tech investment intelligence agency Deep Pharma Intelligence (DPI), Shanghai-based medical firmEvomics Medical, which applies artificial intelligence (AI) diagnosis and clinical services, and the Yuan, an open forum on a quest to make AI also stand for ‘All-Inclusive.’
Real-world data (RWD) derives from real-world evidence (RWE), with RWS being the overarching term for this process of data-mining, compilation, and analysis. RWE comes from scientific publications, electronic health records (EHR), patient registries, health surveys, medical claims or billing databases, patient-generated data, mobile devices, and social media. These data are used in drug development, patient diagnostics, and patient treatment.
RWE is beneficial, but its lack of structure makes it hard to find, access and use, but AI helps here, according to the study.
RWS increases the success of drug discovery and is the cheapest and fastest alternative to gold-standard randomized controlled trials (RCT) for testing new medicines. RWS hones the precision of treatment selection, controls chronic diseases, and helps doctors find personal treatment for patients based on the history of billions of others. The RWS sector’s growth will outpace that of RCT in China, per the study.
Regulators have limited RWE to safety and post-marketing use cases. Pharma applies RWD and RWE in clinical development, medical engagement, value and access, and lifecycle management. This explosion has seen new and innovative RWD and RWE applications emerge, such as biomarker identification for early diagnosis, synthetic control arms for faster clinical trial approval, value-based contracting for better reimbursements, and patient prediction for earlier detection and treatment.
Pharma firms splurge on RWD because these curated data yield evidence, particularly in difficult environments and with rare diseases. This proactive approach overcomes issues of data availability, privacy, and regulatory compliance, and collecting and analyzing data can shorten the time needed to market a medicine.
AI-powered Big Data analytics also help discover drug-event associations for specific groups, improving the detection of potential events and improving risk-benefit assessments, and reducing screening costs by 80 percent and general annotation costs by 50 percent, while eliminating setup costs.
Natural language processing (NLP) algorithms swiftly analyze large datasets derived from social media, news articles, medical literature, medical records, and other text data. This method employs AI and trained analysts to seek signals indicating unexpected benefits or negative reactions to provide valuable real-world intelligence unobtainable by mining data from controlled clinical settings. Natural language generation (NLG) is used to create framework reports, freeing up human experts to provide additional analysis and polish.
AI Diagnostic Imaging is one of the most promising directions in healthcare. Computer-aided diagnostics are more accurate, sensitive, and find small anomalies unnoticeable by a doctor.
Medical imaging is perfectly fitted for AI, which is a solution for a lack of highly-skilled experts, offering quick, accurate diagnoses in emergencies. AI holds enormous potential to improve the health of millions worldwide. It improves speed and accuracy of diagnosis and screening, aids in clinical care, strengthens research and drug development, and supports diverse public health interventions, such as disease surveillance, outbreak response, and health system management.
China’s AI and RWS market achieved substantial growth due to the COVID-19 during 2020-2021 and RWE investment will maintain a growth rate of more than 40 percent from 2021 to hit CNY17.6 billion (US$2.76) in 2024. Real-world research into traditional Chinese medicines will be one of its focuses.
RWE can be submitted to the Center for Drug Evaluation (CDE) of the China National Medical Products Administration (NMPA) as relevant evidence for drug and device review and approval and can be used in the country to support drug development and regulatory decision-making and other scientific purposes.
RWE is well regulated in China EU and the US, but standardization and model risk assessment has to be improved in future by regulators. That field of the healthcare industry passes early stages of evolution, a comparison with self-driving cars arises so in both fields, AI educated on datasets can reduce the probability of mistakes but needs a supervisor to make key decisions.
Companies that use RWS operate mostly in drug development and biomarkers. Biomarker RWE can be used for patient treatment as cutting-edge technologies allow prediction and prevention of cancer development, progression of chronic diseases, sound an alarm on a patient and declare an emergency based on AI data interpretation. RWE imaging technologies support precise and detail-oriented solutions for healthcare providers.
RWE providers extract data from billions of individual patient datasets anonymously, so right answer extraction is the main challenge of modern RWS. The whole industry is in the early stages of growth, so this affords a huge opportunity for all stakeholders.
Deep Pharma Intelligence produces regular analytical reports on major areas of high-potential in the pharmaceutical and healthcare industries, maintaining ratings of companies and governments based on their innovation potential and business activity in the Biotech space, and providing strategic consulting and investment intelligence services to top-tier clients, including major investment funds and banks, family offices, insurance companies, government organizations, and big pharma companies among others. The company is a joint venture between the two highly specialized UK-based market intelligence hubs in the Pharma/Biotech space.
Evomics Medical is a platform company in the 2.0 era that generates real-world evidence as its engine with a virtual digital medical platform to conduct more accurate mining of and predictions from medical Big Data. The company developed a predictive model of gallbladder cancer benign and malignant lesions in cooperation with Shanghai Xinhua Hospital. After collecting blood test information such as blood routine, coagulation function, and tumor markers, combined with the patient's preoperative CT image feature data, Evomics used its self-developed AutoML platform to build the model, and evaluated its differential diagnosis performance using a cross-validation method.