By Ian Bonzani, Head of RWE Specialty Solutioning Group, IQVIA and Joydeep Sarkar, Director, Patient & Site Mediated Incubation, IQVIA
Patient registries continue to evolve into data sources that can simultaneously support research, care management tools and policy development. As technology and standards reduce barriers to data access, collection, integration and analysis, registries continue to become valuable and strategic platforms for generating ongoing evidence to meet a range of healthcare needs.
Aligning strategic and scientific objectives with operational enhancements that encourage patient participation while reducing data collection burdens supports an increased depth of real-world evidence (RWE), which can improve healthcare decision making and overall patient outcomes.
Wearable devices can collect data passively without burdening patients or sites. Mobile technology and telehealth services streamline patient data and sample collection by meeting patients where they are. And AI can optimize the entire data journey, from automating data collection to unearthing insights that otherwise wouldn’t be discerned.
This transformation isn’t being driven by technological advances alone. It’s happening in response to shifts in healthcare, including:
- Demands for more robust and representative patient data.
- Growing regulatory demand for RWE.
- Sites and provider capacity constraints.
- Rising expectations to capture the patient voice and deliver patient-centred outcomes.
More technology-enabled registries can help organizations address these demands by improving how they generate, access and use RWE.
Expanding the value of patient registries
Patient registries, which collect and aggregate real-world patient data, have been limited by the scope and quality of registry data.
For example, registries have historically relied on manual entry of patient record data. This process can be time-consuming, error prone and difficult to scale. Patient data has also become fragmented across systems and providers, so some information may be incomplete. In addition, barriers such as the need for site visits sometimes prevent patients from participating in studies.
Today, the convergence of AI, digital health tools and interoperability standards is changing that reality, enabling registries to collect more high-quality data with a lower stakeholder burden. This is transforming registries into strategic assets that can improve analysis and decision making across healthcare stakeholders.
A good starting point for organizations to enhance their patient registries is to consider their strategic and scientific needs. By identifying knowledge gaps and areas where they can reduce stakeholder burdens, they can evaluate how technology can best support their goals.
Some opportunities for improvement that organizations should consider include:
Reducing data collection burdens: A variety of technological advances are transforming how patient registries can collect data with less burden on patients, providers and site staff. This enables organizations to generate more complete data sets and expand RWE creation.
Traditionally, physicians or site personnel have been required to manually transcribe data from patient records into patient registry forms. This process can be time and resource intensive, especially as studies scale, putting a strain on healthcare teams.
Now, technology can be integrated into existing workflows to streamline data collection. For example, AI-powered natural language processing can extract registry fields directly from electronic health records (EHRs) and clinical documentation, convert unstructured clinical notes into data and populate form fields. This can reduce or eliminate data entry tasks for physicians or site personnel.
For patients, decentralized data collection makes the process less cumbersome. Patients can use mobile apps to complete clinical outcome assessments, surveys and questionnaires when and where it’s convenient for them. Passive data collection via wearables captures important health data while minimizing participant effort.
By reducing patient burdens, these innovations can remove barriers to participating in studies and enhance retention, which helps position long-term studies for success and improve the representation of diverse patient populations in studies.
Creating a greater depth of data: Even with quality checks in place, patient registries today are limited by the availability of good, structured data. Modern technologies can help provide this data while still using quality checks to create richer, more multidimensional patient records for more robust RWE.
Wearables and medical devices can provide a deeper layer of granularity to patient records. Their ability to track a wide range of patient variables — such as their sleep, activity and glucose levels — can create continuous data streams on patients’ health between their visits to a provider. This can provide new insights into outcomes, the patient experience and potential barriers to care.
Meanwhile, patient-mediated access to electronic health record data is becoming available in more parts of the world, while interoperability standard advancements are enabling registries to access patient medical records across health systems. This allows registries to compile a more complete picture of patient records, while tokenization anonymously links disparate data sets at the patient level to help protect patient privacy.
Looking to the near future, AI has the potential to revolutionize the processing of data for registries. For example, it can be used to standardize and map data from different sources into unified formats, flag anomalies and reconcile differences in coding and terminology. It can also capture insights from clinical notes and dialogue between patients and clinicians, potentially preserving details that may otherwise be missed.
Expanding insights from data: Streamlined data collection and deeper data sets inherently enhance the insights and value that can be gathered from patient registries. Technology can play a crucial role in helping healthcare stakeholders make sense of this richer registry data and turn it into RWE that can enhance decision making across healthcare stakeholders.
AI is central to this shift. AI-powered data analysis can evaluate the entire patient journey and the treatment or clinical management of diseases, enabling predictive analytics, real-time benchmarking and pattern recognition that would be missed manually.
For example, analysis of registry data has been used to forecast asthma exacerbations, enabling pre-emptive care interventions that can prevent asthma cases from getting worse or requiring hospitalization. Similar opportunities may also exist to enhance cardiovascular, diabetic and cardio-metabolic care.
While optimizing care for every patient is priority No. 1, robust analysis of deep datasets also enables much more in healthcare. It can provide new insights into population-level disease trends, help inform policy decisions and enable studies to reflect more heterogeneous patient experiences.
A foundation for better care
Patient registries are evolving from data repositories to dynamic, adaptable platforms that, enabled by technology, can reduce data collection burdens while expanding registry data and insights. The resulting RWE can enhance decision making across the healthcare spectrum and ultimately improve patients’ lives.
By making sure this evolution aligns with their strategic and scientific needs, organizations can make registries indispensable RWE engines that support the needs and interests of all healthcare stakeholders.
Ian Bonzani is the Global Head of Specialty Solutioning within the Real World Evidence business at IQVIA. In this role, Ian leverages his scientific background and RWD consulting experience to design and implement direct-to-patient and decentralized real-world research and commercial support platforms across life sciences, government and not-for-profit organizations globally. Ian has been with IQVIA for 16 years, managing large, global teams and innovative data programs for pharmaceutical companies, providers and other healthcare organizations. Prior roles include data and evidence strategy and implementation roles in Real World Analytics Solutions, Pricing & Market Access & Thought Leadership functions. He is a Regenerative Medicine PhD graduate of Imperial College London and has a BSc in Biomedical Engineering from Worcester Polytechnic Institute in Massachusetts. Ian is based in London, UK.
In his role, Joydeep leverages his extensive scientific research and deep technical, data analytics, and AI expertise to drive the modernization of clinical trials, real-world research, registries and public health programs using site and patient mediated EMR data globally. Leveraging the power of Agentic AI, he is helping enable IQVIA accelerate to becoming the AI-native CRO of the Future. Joydeep brings nearly 20 years of experience leading global teams and building advanced healthcare data and technology products, and a long history of clinical and scientific research with peer-reviewed publications in scientific journals. Prior to joining IQVIA, he was the chief analytics officer at Holmusk, a mental health-focused real-world data company. Joydeep has a PhD in Biomedical Engineering from Case Western Reserve University and a B-tech in Chemical Engineering from the Indian Institute of Technology, Kharagpur, India.
