A disease registry data system can enable significant advancements in our understanding of chronic and rare diseases, and in the development of therapies and cures.
However, universally accepted standards don’t yet exist in a broad landscape of open source, commercial or sponsored disease registry data systems, limiting their potential value. Furthermore, building a platform is also time-consuming, expensive and requires scarce subject matter expertise.
So, what additional value can be derived from a professionally designed disease registry solution, what challenges can affect its impact and, importantly, what core design principles are essential to consider?
Mark Robinson, Chief Technology Officer at Imosphere, sets out the foundational tenets of the construction and implementation of an effective disease registry data system.
Disease registry opportunities
Disease registries are a source of real-world data that can drive research into specific conditions forward, but they are not a new idea. Norway’s National Leprosy Registry, established back in 1856, serves as history’s first example. Yet it is only since the 1980s that they have really gained traction and, with today’s technology, we have considerable advantages over our Norwegian forebears.
Next-generation disease registries, such as Imosphere’s Atmolytics platform developed in partnership with a leading US cancer research institution, include built-in honest brokers, cohort discovery and tracking tools, and analytic capabilities which empower researchers and clinicians to generate actionable insights more efficiently than previously possible.
A robust disease registry solution can benefit research advancements, population health management, clinical decision support, quality improvement, and value-based care initiatives in the following ways:
Accelerated research: simplified, self-service access to quality, patient-centric data and analytics can speed up complex cohort identification for study feasibility and trial recruitment
Expanded scope: multi-institutional data sharing can enable larger cohort studies to improve insights and effective, equitable delivery of care and services
Temporality: tracking a patient’s disease journey can reveal the trajectory of their disease and their response to therapies to better inform choices about their care
Real-world evidence: high-quality, real-world data can help produce real-world evidence to support regulatory approval of novel therapies, accelerating their journey to market and subsequently measuring their post-approval efficacy
Efficiency and/or cost savings: rapid access to population health data enables the timely identification of actionable opportunities for care intervention and effective resource allocation
Improved care and patient outcomes: valuable comparative data can better inform clinical decision-making and enable evidence-based care practice
Nonetheless, even the current gold standard for storing and accessing clinical data still poses technical challenges for the effective collection, management, storage, and sharing of disease registry data for organizations looking to capitalize on these benefits.
Although Electronic Health Record (EHR) systems are, by federal mandate, now ubiquitous in the US, they are a complex web of incompatible databases using diverse coding systems and holding billions of inconsistent clinical data points with limited context.
There are also other disparate sources of relevant, yet incongruous clinical data that aren’t stored within EHRs, adding to the difficulty of assembling datasets primed for analysis.
Source data can include significant quantities of invalid entries and definitions used to describe identical conditions, symptoms, and therapies can vary widely. This can call query results into question and affects transparency, making it extremely challenging to identify and extract cohorts with often complex and vague disease state definitions.
Then, there is the matter of compliance. Meeting state, federal and international privacy standards is not inexpensive and can hamper trial feasibility and recruitment efforts, not to mention our ability to share data, collaborate, and therefore generate valuable insights.
The distribution of new insights can also be problematic. The report generation and sharing processes are often onerous, time-consuming endeavors that delay or even obstruct the delivery of actionable information.
Moreover, accessing and using data effectively has long required us to employ a rare combination of technical and clinical skills. The strain that places on our operations causes data request backlogs and stretched informatics resources, limiting our ability to deliver research, quality improvements and cost efficiency initiatives.
For a salient example of the challenges posed, we need look no further than COVID-19. In response to the initial wave in 2020, the Department of Health and Human Services (HHS), healthcare organizations, and others made laudable efforts to implement registries.
Yet there was an absence of uniformity. Healthcare organizations of almost all stripes collected different data elements in many and various ways, complicating the understanding of this novel and deadly virus.
Moreover, some registries ignored the patient journey altogether, resulting in a poor longitudinal view of recovery, rehabilitation, and ‘long covid’. The result was reduced data quality and latent access to data which may have impeded our response to the pandemic.
In order to surmount such obstacles, therefore, the essentials to consider when assessing your disease registry data system requirements are as follows:
Data Integrity: all data entering a registry should be validated automatically against pre-defined rules and harmonized to resolve the variety of definitions used in different locations to denote identical conditions and procedures to ensure the relevance and quality of query or analytic results.
Healthcare Aware: a registry should incorporate an extensible, healthcare specific ontology built to accommodate patient/provider relationships, clinical encounters, temporality, coding systems and custom vocabularies.
Compliant: patient privacy is sacrosanct, as is full compliance with all relevant state and national regulations, such as HIPAA and GDPR. Therefore, all data must be stored and shared securely, and only be viewable by individuals authorized by strict access controls. The ability to anonymize patient data easily is also mandatory. Best-in-class platforms also allow for data to solely reside within the source organization’s environment.
Scalable: as workflows, guidelines and clinical research scope evolve over time, altering data requirements, it is vital that registries can incrementally accommodate data from disparate novel sources without needing to rebuild the data architecture. For example, with an increased focus on patient-centric care, there is a growing need to include less commonly collected data, such as patient reported data and an ever-broadening spectrum of social determinants of health (SDoH).
Accessible: exploring clinical data has historically required specialized cross-disciplinary skills, meaning some of those well-placed to leverage it are effectively denied or held at arm’s length from access. Implementing an intuitive UI/UX that requires no programming or data assembly skills not only grants that access but encourages adoption and engagement by a wider range of stakeholders.
Searchable: a robust and flexible cohort discovery tool which includes semantic searching, rapid iterative refinement, transparency and built-in analytics is essential to revealing multiple clinical views of patients and populations to accelerate discovery of actionable insights.
Collaborative: cohorts, observations, analytics, and reports should be seamlessly sharable, while maintaining governance compliance.
The extent to which a disease registry makes a positive impact is a direct product of the quality of the insights you can draw from it and the ease and speed with which you can do so.
That, in turn, is a product of the quality of the disease registry data technology you employ. So, it is vital that organizations, clinicians, researchers, and technologists work together to understand each other’s needs and aspirations in order to make considered, strategic choices to meet them.
Only then can we begin to harness and interrogate data in ways that deliver widespread health benefits that go beyond anything our 19th century Norwegian colleagues could possibly have imagined.