Top 5 Key Trends in Real-World Evidence to Watch for in 2024

Real-world data (RWD) is collected through routine patient care and is often transactional or administrative in nature. It comprises information produced from data routinely collected on patients’ health status and/or the delivery of health care from various sources other than traditional clinical trials. These sources can include electronic health records, claims, patient-generated data, including in home-use settings, data from mobile devices, as well as patient, product, and disease registries. Real-world evidence (RWE) is increasingly being used for post-marketing surveillance, risk-benefit assessments, product approvals, payer discussions, and value co-creation.

RWE tops the list1 and consistently stays in the top ten trends for health economics and outcomes research. As we continue to explore RWE, it is worth our time to predict the trends for the coming year.
1. Regulatory guidelines and frameworks
RWE research is playing an increasingly important role in health care decision-making. Multiple international initiatives, guidelines, and legal frameworks have been released by the United States Food and Drug Administration (US FDA) and the European Medicines Agency (EMA) over the years. RWE has already been included in various approval procedures by regulatory authorities, reflecting its actual acceptance and growing importance in evaluating and accelerating new therapies. The FDA and EMA play a pro-active role in exploring gaps and potentials of RWE to guide stakeholders.2,3 The ongoing implementation of RWE in early benefit assessments of newer therapies will potentially speed up approval processes for effective interventions and reduce costs of drug development, which will in turn reduce time-to-market, leading to early patient access. Newer research from non-traditional data sources and various types of digital health technologies (DHTs) will increase in the near future. Initiatives have been kicked off to fund RWE projects, and there is a conscious attempt by the regulators and the industry to involve all stakeholders in the development of future guidelines to increase the trust, credibility, reproducibility, and transparency of RWE studies.
2. Active participation by health care systems
A health care system is a group of health care organizations (e.g., physician practices, hospitals, and skilled nursing facilities) that are jointly owned or managed. Due to increasing awareness, acceptance, and demand for RWE, multiple health care systems have created eco-systems to generate and streamline the availability of good-quality RWD by establishing a data governance framework and implementing data quality policies and standards. A pro-active move to assess current data quality and implement data quality improvement initiatives by investing in data quality tools and technologies is observed. This leads to enhanced data capture and entry processes and better meta-data management. Multiple instances of collaboration between health care systems and data providers have been observed recently. RWD, thus generated, is not just limited to drug development research but also helps in providing prescriptive decision-making, personalized patient-level disease management, improving health care system efficiencies, and revenue management.
3. Digital Health Technology
DHTs include wearable devices, mobile applications, sensors for health care, personalized patient management plans, computing software, telehealth, and telemedicine.4 Health care practitioners (HCPs) and patients have shown a trend towards active participation in RWE generation. DHTs can be used to track activities of daily living, prevent predictable infectious diseases, manage chronic conditions, and aid in the early diagnosis of certain disease conditions. These can also help with the mapping of epidemiological data. DHTs can be useful for reporting patient-reported outcomes as well as caregiver feedback. Disease forums provide for active participation in the research of the disease where the patient feels included and is an active participant in the drug development and disease management processes. This completes the loop on real-time patient feedback and helps create better personalized therapies where the patient is truly at the center of decision-making.
4. Use of AI and ML
Artificial intelligence (AI) and machine learning (ML) have potential impacts across the healthcare and life sciences industries. We can leverage generative AI to engage patients better through chatbots, improve operational efficiency, and shift from reactive to predictive management.5 AI can be used to accelerate drug research and realize the value of data across all aspects of business. AI/ML can help improve the efficiency and planning of RWE studies, as well as generate evidence from DHTs. Across clinical research, AI/ML can be used for scanning relevant literature as well as patient recruitment, selection, and stratification. Adherence and persistence to treatment can be improved through DHTs using e-tracking tools. Data integration efforts can be enhanced using supervised and unsupervised learning. AI/ML can be used to process large volumes of diverse data from various sources. Asset-agnostic and asset-specific algorithms can be written for predictive and prescriptive analytics. Generative AI can also be used to create synthetic data sets for rare and orphan diseases.
5. Value co-creation
The average human lifespan is increasing due to better awareness of health conditions as well as better health care. Since economic viability, quality, and accountability are critical to health systems worldwide, equitable health care has become a challenge due to various economic, social, political, and environmental factors. With an increasing focus on quality of life while balancing health care costs, it is necessary to evaluate the impact of interventions on health systems while managing patient expectations. Value co-creation in health care (VCCH) refers to the integration of resources through activities and interactions with collaborators to realize the benefit of patients in the health care service delivery network.6 RWE can be used to evaluate and monitor aspects of VCCH by creating an eco-system of patients, caregivers, providers, and payers. Resources are invested with the intention of creating mutual benefits for all stakeholders. DHTs and AI/ML can play a big role in enrolling, managing, and utilizing RWE to effectively co-create value in health care.
Dr. Kavita Lamror | Partner, RWE and Digital Transformation, Maxis Clinical Sciences

In her current role, Dr. Lamror capitalizes on her extensive expertise in the healthcare industry to amplify Real-world evidence strategies. With a clinical background and an MPH from Johns Hopkins Bloomberg School of Public Health, she specializes in enhancing strategies for RWE, spearheading digital transformation, and expanding patient-centric solutions. Dr. Lamror has a track record of over 15 years in healthcare, where she has led substantial projects and developed innovative strategies for real-world evidence. Her work focuses on leveraging data to improve patient health outcomes and drive healthcare innovation.


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