Developing new and better treatments for a disease starts with understanding the disease itself. What are the symptoms, who does it affect, and how does it progress over time? Answering these questions is the purpose of the natural history of disease study.

A natural history study is an observational, non-interventional study that tracks the development and progression of a medical condition in humans over time. The study compiles demographic, genetic, environmental, and other variables correlated with outcomes. This information is essential to pursuing the development of any new treatment.

Now, with the advent of accessible real-world data (RWD) from a variety of sources, natural history studies are benefiting from a view into large numbers of patient health journeys captured over time from the same subjects.

Increasingly, RWD can bolster natural history studies with insight to longitudinal, real-time disease progression in diverse, representative populations, offering clinical researchers a deeper understanding of the disease.

A Cornerstone Role

Comprehensive knowledge of a disease is required to design and conduct clinical trials of adequate duration and with clinically meaningful end points.

Natural history studies are crucial for identifying patient populations, clinical outcome measures,and biomarkers, as well as generating data for external controls. Patients included in natural history studies may sometimes be used as historical controls for studies that lack an internal control, thus allowing the effectiveness of the study treatment to be determined.

To achieve its goal of understanding how a disease progresses without intervention, the natural history study requires longitudinal data. RWD is an ideal source for this as it includes data collected from patients over time, capturing key milestones like diagnosis, symptoms, test results, physical signs, imaging, age, genetics, quality of life, and more.

RWD: A Growing Resource

Real-world data is all the information regarding patient health status and health care delivery outside of traditional clinical trials. This vast realm includes electronic health records (EHRs), claims, wearables, and patient registries.

A significant portion of RWD is “unstructured,” i.e. not in a format amenable to automated search or querying, for example, the clinician notes in EHRs. Historically, RWD wasn’t accessible in any practical way because the unstructured data required extensive manual processing. But advances in artificial intelligence (AI) and natural language processing (NPL) technology have made it possible to rapidly extract meaningful data from these sources.

Today, RWD readily offers a broad view of the patient experience and several advantages over information typically gathered in clinical trials, including the opportunity to gain longitudinal insights as well as a comprehensive view of diverse patient populations.

With curated, fit-for-purpose data modules drawn from RWD, clinical researchers can see a full picture of disease progression, informing treatment development and clinical trial design, and allowing a better understanding of the patient journey

A Natural Fit

It’s no surprise that RWD is well suited to inform natural history studies. Not constrained by controlled trial environments, the richness of RWD can bring a range of benefits and enhance study efficiency.

A key advantage of using RWD in a natural history study is gaining diverse and representative data. RWD covers broad patient populations, including those with comorbidities, various age groups, and different ethnicities. These qualities improve the generalizability of the study.

RWD also offers longitudinal insights. The nature of RWD easily accommodates capturing disease progression, symptom evolution, and treatment patterns over a long, continuous period, which is essential for understanding the progression of diseases, particularly chronic diseases.

In the case of rare diseases with small patient populations, RWD can play a particularly important role. RWD can bridge gaps in knowledge to identify biomarkers, define patient populations, and establish external control arms for evaluating treatment effects.

RWD also helps compare the effectiveness of different treatment approaches in real-world scenarios and carries high external validity. Because it is gathered from routine clinical practice, it reflects real-world clinical outcomes more accurately, bridging the gap between controlled research and real-world application.

Furthermore, RWD offers efficiency and cost-effectiveness to the natural history study process. By utilizing existing, routinely collected data (e.g., electronic health records, clinician notes) research teams can accelerate timelines and reduce the costs associated with prospective studies.

Data Quality Matters

One familiar note of caution bears repeating: Any study is only as good as the data it’s built on, a truism that certainly applies to natural history studies and the use of RWD.

Despite the rapid advances in AI technology and machine learning tools, the process of curating meaningful datasets from unstructured RWD requires careful oversight and guidance by clinical and subject matter experts. It’s important for research organizations to ask questions and thoroughly vet sources and suppliers of RWD to ensure that datasets are high-quality and research ready.

Today, properly processed RWD is proving invaluable in all facets of drug development, from natural history studies, to trial design, post-market monitoring, and commercialization.

Accelerating History

It’s clear that the future of natural history studies increasingly involves RWD and the application of AI-tools to analyze complex datasets.

The good news is we’re already seeing the benefits. RWD is enabling deeper insights, improved disease understanding, and more accurate pictures of patient populations and disease progression than traditional prospective natural history studies alone.

By helping to define patient populations, create external control arms, and provide timely insights into rare diseases, RWD is effectively accelerating research, and supporting the development of important new patient treatments.