Inefficient infrastructure and limited supporting resources impede the ability of health care organizations to incorporate research routinely into clinical practice. In turn, this reflects policy gaps that heighten the cost and limit the feasibility and interest of health care organizations to participate in an improved clinical evidence generation system. Figure 1 illustrates current barriers to transforming the evidence-generating system, including inefficient infrastructure, gaps in policy, and a lack of research prioritization. We further address these barriers and note present day solutions below.
Fig. 1
Barriers to transforming the evidence-generating system
Building a more efficient data and research infrastructure
Our current data infrastructure is inefficient, lacking sufficient reliability and accuracy of clinical data captured in routine practice for trial purposes (e.g., for participant identification or to collect outcomes) [26, 27]. This is due in part to technical issues but also substantial administrative processes and lack of data uniformity.
Limited technical interoperability across medical record systems, digital health technologies, and other real-world data sources creates a fragmented data system. Full adoption of standards and open application programming interfaces (APIs) has yet to be realized, preventing streamlined access, authentication, and auditing of data [28]. Both patient and clinical trial capabilities are thus compromised, inefficient, and uncoordinated due to duplicative or missing data.
Reforms in health care payment and progress in medical record interoperability are contributing to a more robust data infrastructure to support longitudinal clinical care. However, regulatory and payment policies for clinical research complicate its integration [29]. Questions also remain whether longitudinal data that are “good enough” for care are also fit-for-purpose for real-world clinical trials [30]. Administrative requirements create operational challenges that discourage trial activation and participation, especially at locations not accustomed to participating in research [31,32,33,34]. These requirements include complex budgeting and contracts and varied expectations from institutional review boards, even for trials that involve approved drugs where there is strong evidence on safety and clinical equipoise between arms.
Solutions to minimize administrative burdens include broad use of reusable protocols, master agreements, and central management approaches that are adaptable for future studies. Current trends in health care policies and practices offer solutions toward a data infrastructure that better captures accurate and complete data along a patient’s health care journey. The Health Information Technology for Economic and Clinical Health (HITECH) Act and ensuing actions by the Office of the National Coordinator for Health IT (ONC) and Center for Medicare and Medicaid Services (CMS) are driving efforts to increase adoption of interoperable standards in electronic health records (EHRs) [35, 36]. The U.S. Core Data for Interoperability (USCDI) and USCDI + are building on Health Level 7 (HL7) and related standards to create “use cases” that cover an array of clinical care and public health activities, and CMS is increasingly requiring EHRs to support these standards [37].
The CMS, private insurance payers, and states are shifting their payments and care models away from “fee for service” and toward accountability for improving outcomes and equity. These models aim for reducing costs with attention to key clinical and patient-reported outcome measures [38, 39]. The enhanced longitudinal primary care and specialty care integration required to succeed in these models is supporting investments in a more reliable, interoperable health data infrastructure that can power research integrated with care [40].
Policy reform to support transformation
Regulatory policies and reform guidelines should support modernizing trials for efficient evidence generation [41].
The U.S. Food and Drug Administration (FDA), MHRA, and other organizations are modernizing clinical trial guidance aligned with reforms to the International Council for Harmonisation (ICH) E6 Good Clinical Practice (GCP) [11, 42, 43]. ICHE6(R3) initial draft revisions provide a strong start, but additional efforts are needed to ensure focus on principles and purpose rather than process, with an emphasis on generating actionable information about the effects of an intervention [44]. International efforts that focus on the fundamental scientific and ethical principles underpinning randomized trials while embracing flexibility and innovation are critical to these efforts [10, 41, 45, 46].
Further clarification around areas of regulatory flexibility with case examples that support efficient risk–benefit management would also be useful. For example, there are opportunities to clarify investigator oversight requirements and essential record documentation. In the U.S., the FDA Form 1572 Statement of Investigator is commonly used to delegate authority and track information on investigators, sub-investigators, and clinical facilities used in trials [47]. Such attestation is unlikely to materially reduce risk for clinicians who are practicing in organized health systems that are implementing trials through common electronic record and practice support systems. In such cases, Form 1572 is likely more appropriate at the health system level, building from the various codes of practice already in place, such as good documentation, data privacy training, and mentoring. Regulatory clarifications could better delineate the role of providers and staff involved in trial-related work, especially trials integrated at the point of care [26], and standardize this role across whole-institution settings. In addition, clarifications around essential record documentation with an emphasis on fitness for-purpose and proportionality, could support the reduction of unnecessary documentation and reduce burden.
Addressing the lack of research prioritization
While frontline health care providers have a strong interest in assuring that their patients receive well-informed care, incentives to participate in trials are often limited and/or misaligned with clinical care activities.
The lack of participation in trials is partly due to overly complicated trial designs and the burden to conduct them [48, 49]. Additionally, this lack of participation is due to a culture that does not decidedly value high-quality clinical trials as an important component of a high-quality clinical care system and evidence development [50].
Supported by government efforts to address infrastructure and regulatory modernization, health system leadership can play a critical role in driving culture change. Organizations increasingly use electronic data, quality improvement, and safety initiatives to improve care models; therefore, contributing to a “learning health system” is a natural complement to improving patient health and avoiding unnecessary health care costs.
Health system and policy leaders should align around goals to increase access to and expand the conduct of randomized clinical trials integrated into routine clinical care. Health care insurance payers, purchasers, trial sponsors, and health systems should collectively support key clinical questions to fill evidence gaps. Sponsors should engage health care providers and patients early in trial design to ensure that the research question is important and that participation in the trial would not unduly complicate patient care. Regulatory organizations should focus on good trial principles, participant safety, and trial integrity while allowing for flexibility. There should be alignment in and facilitation of efficient, appropriate research training and education that will support research participation. Current initiatives, such as ENRICH-CT, ACT@POC, and the U.S. FDA’s C3TI, show promise to address these needs [51,52,53].
Moving from shared goals for improving evidence generation to practical actions requires recognition of the constraints facing clinical practice today. Health system staff turnover is high [54, 55], creating challenges to devote limited staff time and effort to clinical research even as learning health care concepts spread. However, if the costs of participation are low and the research questions are relevant to their patients, health system executives should strengthen the connection between evidence development and the quality of care in their health systems.
Policies, such as the Patient Protection and Affordable Care Act (ACA) and the CLINICAL TREATMENT Act [56, 57], are enabling action by requiring coverage of routine care related to clinical trial assessments. Additionally, CMS has taken important steps, such as considering participation in a COVID-19 clinical study to be a Quality Improvement activity for the Merit-based Incentive Payment System (MIPS) [58].
While policy changes are underway, more actions and collaborations are needed to enable transformation.
Enabling trial transformation
The technological capabilities, regulatory momentum, and trial design innovations exist to improve the data infrastructure and mitigate administrative, operational, and participation burdens. Yet, strategized efforts and resources will help harness these capabilities toward implementation.
Collaboration, pilot projects, and case examples can address remaining gaps and the challenges highlighted in this commentary [15, 23, 52, 59].
Government agencies can continue to advance policies and reimbursement opportunities. Quality improvement programs, such as MIPS or other Medicare payment initiatives, can support providers who participate in well-designed point-of-care trials that address key questions for Medicare beneficiaries [3]. CMS can also further clarify its support for covering the cost of innovative technologies in well-designed studies in its Coverage with Evidence Development (CED) program and Transitional Coverage for Emerging Technologies (TCET) initiatives [60, 61].
Health care systems and their practicing clinicians can help build public understanding, trust, and engagement in research to foster better evidence generation.
Sponsors should design trials with a greater focus on quality of data and processes rather than quantity [62]. Particularly for approved drugs with known side effects and interactions, trial data collection should focus on an essential set of data elements, such as major patient risk factors, meaningful endpoints, relevant and serious adverse events, and key concomitant medications [63].
An extensive, guided set of actions are suggested in Table 1. We propose priority actions at the top of each section of the table, specifically around improving trial capacity management, the value of research, data protections, integrity and interoperability, and appropriate risk-proportionate regulatory pathways. With that said, we should strive to address all of the barriers listed to improve our capacity to efficiently generate high-quality, practical evidence from trials.
Table 1 Barriers and needed actions to transform the evidence-generating system