"We believe that more accessible clinical trials can facilitate participation by more diverse patient populations within diverse community settings where patient care is delivered, and in the process can generate information that’s more representative of the real world and may help providers and patients make more informed treatment decisions." (Dr. Scott Gottleib, FDA Commissioner speaking the Bipartisan Policy Center, January 28th, 2019)
Why is it so challenging to realize this vision? Here are a few potential reasons.
Interventional trials have long been considered the gold standard for research, with good reason. Experts conduct clinical trials that test a specific hypothesis, are rigorously controlled and are engineered to eliminate or correct for biases. However, when taking a broader view of clinical research within the context of industry-wide digital transformation, it becomes clear that reliance on this model presents challenges:
"Trial specialist model" limits the number of studies that can be conducted to the number of specialist roles in the clinical trials' ecosystem.
High costs and long timeframes are the norm.
View of the patient's overall healthcare journey is limited to the data collected for the individual studies.
Little interoperability, connectivity between studies, or reuse of the data outside the study creates 'research data silos'.
Patient recruitment and retention are difficult because patients are often required to participate in a parallel environment to their normal clinical setting.
Strict inclusion requirements are needed to accurately test a hypothesis, often are not generalizable to product use 'in the real world'.
Data generated through healthcare digitization is largely untapped for research despite the wealth of medical insights and data available.
Integrating research at the point of care helps address these challenges and makes research more accessible and relevant.
The widespread adoption of electronic health record (EHR) systems provides an opportunity to reimagine the traditional clinical research model. Many of today's challenges can be addressed by moving from a study-centric model to a 'learning healthcare system' where a patients' healthcare journey is electronically documented, organized, shared and can be reused across multiple studies and stakeholders. EHR platforms can be integrated with technology in the normal workflow to support clinical research – and with this integrated model, research becomes a natural output of the patient care process. This approach has several advantages:
Enables research to be performed through a network and platform that connects multiple stakeholders, through the use of a separate dedicated model.
Reduces research data silos, enabling interventional and observational data to be included in the same research protocol.
Provides a foundation for the patient journey by creating a longitudinal record of real-world care delivery where interventional and digital healthcare data can be linked to fill in and extend the record.
How can this vision be realized?
To fully realize the value of this approach, it is necessary to implement a complementary set of roles, processes and technology capabilities that reflect a new research operating model. One of the most significant changes will be reduced dependence on the specialized roles – such as Clinical Research Coordinator or Clinical Research Associate – engaged with clinical trials. Just as rideshare organizations like Uber and Lyft introduced technology-enabled drivers to replace the taxi driver and dispatch services, we envision a network of technology-enabled research clinicians serving as an alternative to the specialized roles associated with traditional clinical trials.
What opportunities will be provided by enhancing core EHR platforms with new technologies and capabilities?
Study Feasibility: An integrated research platform based on EHR technology inherently collects data about patient characteristics, availability, site performance and investigator availability all in one place. This can enable more effective and efficient feasibility studies. In the current study-centric model it is difficult to perform effective feasibility studies because the data is siloed and difficult to integrate.
Digitized Protocols: Digitized protocols standardize key elements into a format that is easily quarried; streamlining the review process and allowing for detailed analysis of the relationship between the trial design and conduct. Study protocols and documents are some of the most important artifacts in a clinical trial; often authored and managed as unstructured documents resulting in inefficient manual review and poor-quality control.
Protocol, patient, and physician match-making: An EHR based, integrated research platform can act as a 'match-maker', automatically identifying patients who qualify for study protocols and alerting their physicians.
Source data to populate case report forms: While one of the strengths of traditional clinical research is the high quality of the data collected – augmenting it with real-world data captured at the point-of-care may provide richer, deeper information and context on the patient status. eSource initiatives seek to use technology such as Artificial Intelligence (AI) and machine learning to curate RWE into high-quality data for use in clinical research.
Patient Engagement: Researchers can use patient engagement technology already integrated into many EHR systems (e.g. text messaging) to reach patients who qualify for a study protocol, determine their interest and secure informed consent.
Extending EHR platforms to support research Electronic Data Capture (EDC): With extended EHR systems that support data capture, researchers can link research data collected directly back to the patient data record. Studies often require the use of surveys, patient diaries, and other approaches to capture information. These systems are standalone and disconnected from EHR platforms that record routine care.
Leveraging Virtual Visits: Telemedicine capabilities available in existing EHR platforms can be extended to support virtual study visits, allowing data to be captured without requiring the patient to travel to the study site.
Creating a 'Digital Healthcare Journey': Using the EHR as the foundation of research enables the creation of an integrated ‘digital healthcare journey’ for each patient. EHR interoperability standards enable disparate digital healthcare data to be linked and connected to the appropriate places within the longitudinal patient record.
By developing learning healthcare systems and integrating research into the point-of-care, we have the potential to lower costs, increase efficiencies, and remove bottlenecks that inhibit research – all while improving the welfare of patients. By removing data silos, a fully integrated research model has the potential to allow the blending of the best interventional and observational approaches. The barriers preventing this shift have largely been removed. Now it's up to leaders, innovators, regulators and decision-makers in healthcare to commit to developing the technologies, workflows, processes and compliance frameworks to support a research model designed to enable, empower and benefit research across the industry.