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Towards a conceptual framework for addressing state-level barriers to decentralized clinical trials in the U.S.

Published online by Cambridge University Press:  03 July 2023

Stephanie J. Zawada*
Affiliation:
Mayo Clinic College of Medicine and Science, Scottsdale, AZ, USA
Kevin C. Ruff
Affiliation:
Mayo Clinic Center for Digital Health, Phoenix, AZ, USA
Tara Sklar
Affiliation:
University of Arizona James E. Rogers College of Law, Tucson, AZ, USA Arizona Telemedicine Program, Phoenix, AZ, USA
Bart M. Demaerschalk
Affiliation:
Mayo Clinic College of Medicine and Science, Scottsdale, AZ, USA Mayo Clinic Center for Digital Health, Phoenix, AZ, USA
*
Corresponding author: S. Zawada, MS; Email: [email protected]
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Abstract

Type
Perspective
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of The Association for Clinical and Translational Science

Introduction

The challenge of adapting clinical trials to decentralized care models is increasingly pressing as virtual care becomes more common. In the United States, the patchwork of state and federal policies and regulations governing virtual care, such as limited interstate physician licensure, was a major barrier to the development of hybrid care models, blending in-person and telehealth, and the implementation of decentralized clinical trials (DCTs) [1]. During the COVID-19 Public Health Emergency (PHE), many states and the federal government enacted – often on a temporary basis – regulatory flexibilities to deliver care at home, including telehealth and remote monitoring, simultaneously empowering DCT models; however, the end of the PHE threatens to reverse leaps of progress achieved in the virtual care and DCT space during the pandemic [Reference Cubanski, Kates, Tolbert, Guth, Pollitz and Freed2,Reference Andino, Zhu, Surapaneni, Dunn and Ellimoottil3]. Given the growing provider shortage and related decreased access to care, it is imperative that DCT researchers design trials with a state policy-conscious lens to prioritize diverse participant enrollment and overall retention [Reference Khushalani, Holmes and Song4Reference Goodson, Wicks, Morgan, Hashem, Callinan and Reites6]. Emerging work in this area demonstrates that engagement of patients, providers, and regulators in the design phase can identify barriers to DCTs [Reference Apostolaros, Babaian, Corneli and etal7Reference de Las Heras, Daehnke and Saini9]. Thus, strategies to facilitate interdisciplinary collaboration and the translation of stakeholder perspectives into actionable policy recommendations are needed.

Multiple existing frameworks are frequently employed to guide the process of identifying and addressing barriers to DCTs; however, none integrate perspectives from stakeholders across the translational spectrum [Reference Volkov, Ragon, Doyle and Bredella10]. As DCTs are a relatively new mode of conducting clinical trials, there is a paucity of literature examining their implementation. Moreover, the research available is preliminary, restricted by the first DCT recorded in 2011. While some DCT research evaluates pharmacological agents, others focus on remote monitoring [Reference Ali, Valk and Bjerre-Christensen11Reference Orri, Lipset, Jacobs, Costello and Cummings13]; regardless, DCTs are not limited by geographic restrictions, affording patients the option to participate from anywhere. Their scalable approach also has the potential to reduce patient burden, replacing in-person consults with telehealth, and improve the robustness of study data, capturing continuous in situ measurements using sensors [Reference Zawada, Haj Aissa, Conte, Pollock, Athreya, Erickson and Demaerschalk14]. No published research has specifically examined the role of state policies in the implementation of DCTs that enroll patients residing in-state.

We propose a novel conceptual framework to identify barriers to DCTs using stakeholder engagement that incorporates the broad perspectives of patients, their local providers, and state-based policymakers. Through the discovery of barriers experienced by these groups, this framework integrates the dynamic experiences of key stakeholders across the translational spectrum to identify and address policies that hinder the conduct of DCTs. Unlike previous frameworks, this framework addresses the hurdle of nonuniform policy landscapes that modulate the scope and scale of DCTs on a state-by-state basis in the United States. CARE-P [Reference Andino, Zhu, Surapaneni, Dunn and Ellimoottil3]’s framework builds on previous DCT frameworks and is in the pilot implementation phase at Mayo Clinic.

CARE-P3: A conceptual framework for identifying and addressing barriers to DCTs

Limited communication between scientists and policymakers has long been established as a barrier to integrating new technologies in clinical practice [Reference South, Bailey, Parmar and Vale15]. Additionally, it is critical to include the experiences of local communities, particularly those which are underserved, in identifying obstacles to virtual care [Reference George, Hamilton and Baker16]. The framework we describe below is the CARE-P3 framework, denoting: Clinical Adaptive Research Engagement – Patients, Providers, and Policymakers. CARE-P3 uses stakeholder-engaged perspectives to identify barriers to DCTs and output actionable recommendations for state policymakers. This interdisciplinary framework engages patients and providers (Fig. 1), using mixed qualitative and quantitative methods to analyze their lived experiences, while considering the scope of reforms able to be implemented by state policymakers (Table 1).

Figure 1. CARE-P3 framework.

Table 1. Stakeholders engaged and research needed to support the proposed framework

Benefits of a stakeholder-engaged approach to barrier identification and remediation in DCTs

In the U.S., state laws frequently require healthcare entities and agencies to submit reports to the legislature [Reference Gerber, Maestas and Dometrius17]; yet, no publicly available reports examine the lived experiences of patients and providers that modulate their opinions and influence the adoption of DCTs [Reference Zawada, Paulson, Paulson, Maniaci and Demaerschalk18]. Exploring barriers to DCTs beyond disciplinary silos, the CARE-P3 framework addresses a key knowledge problem faced by state policymakers programming the decentralized care ecosystem. For instance, the Federal Communications Commission, outlining strategies to expand access to remote care, advised state policymakers to remove barriers to internet services [19]; however, optimal policy reforms to increase access to the internet vary on a state-by-state basis, with underserved populations in some states also suffering from lack of electricity access [Reference Tanana and Bowman20]. Knowledge of the particular circumstances affecting DCTs in a given state at a point in time, such as sociodemographic barriers to internet access or restrictive prescribing policies linked with telehealth visits, must be gathered from dispersed groups in society, especially patients eligible for DCTs [21]. Including provider perspectives is critical, given their limited experience with emerging modes of decentralized care, a factor influencing their perceptions about patient eligibility for DCT recruitment [Reference Garavand, Nasim, Hamed, Saeideh and Shirin22]. Such a strategy provides robust evidence for state policymakers to identify and address barriers to DCT enrollment and retention.

Conclusion

To implement, evaluate, and refine the CARE-P3 framework, DCT case studies characterized by a range of interventions and representative of various socioeconomic and diagnostic conditions are critical. While the pilot implementation of this framework has been trialed in both rural and urban settings, refining this approach will require multiple rounds of analysis and feedback to identify major gaps and arrive at a robust framework that can be used by researchers before, during, and after a DCT [Reference Garavand, Nasim, Hamed, Saeideh and Shirin22Reference Zawada, Sweat, Paulson and Maniaci24]. A chief challenge facing this work is limited research funding; however, evidence from the growing body of DCT research conducted during the pandemic may suggest that the economic impact of not addressing state-specific barriers will be more costly.

Depending on which temporary flexibilities introduced during the PHE are made permanent, this work may be more urgently needed for specific states or patient populations. Another challenge associated with this work is disparate terminology and communication styles that are stakeholder specific. For instance, language used by patients is not identical to terminology employed by providers. Similarly, policymakers use different terms than health care professionals and assess research findings with domain-specific methodologies [Reference Maret25].

Understanding the challenges faced by patients of different socioeconomic groups who elect or decline to participate in DCTs is crucial to diversifying clinical trials in the digital age. To develop safe and robust DCTs, it is essential that provider concerns and hurdles are addressed. Future research should also consider how to engage federal policymakers in the development of national DCT networks. Effectively communicating these findings and identifying actionable recommendations for policymakers are integral to developing equitable DCTs that yield high-quality data to fully realize the promise of decentralized clinical research.

Funding statment

This research was partly supported by a Society for Neuroscience (SfN) Neuroscience Scholars Program Enrichment Funds Award. This publication was supported by CTSA Grant Number TL1 TR002380 from the National Center for Advancing Translational Science (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Competing interests

The authors have no conflicts of interest to declare.

References

Center for Connected Health Policy and the National Telehealth Policy Resource Center. Summary Report: State Telehealth Laws and Medicaid Program Policies Fall 2022. CCHP; 2022 (https://www.cchpca.org/2022/10/Fall2022_ExecutiveSummary8.pdf). Accessed March 22, 2023.Google Scholar
Cubanski, J, Kates, J, Tolbert, J, Guth, M, Pollitz, K, Freed, M. What Happens When COVID-19 Emergency Declarations End? Implications for Coverage, Costs, and Access. Kaiser Family Foundation; 2023 (https://www.kff.org/coronavirus-covid-19/issue-brief/what-happens-when-covid-19-emergency-declarations-end-implications-for-coverage-costs-and-access/). Accessed March 22, 2023.Google Scholar
Andino, JJ, Zhu, Z, Surapaneni, M, Dunn, RL, Ellimoottil, C. Interstate telehealth use by medicare beneficiaries and after COVID-19 licensure Waivers, 2017-20. Health Affairs. 2022;41(6):838845. doi: 10.1377/hlthaff.2021.01825.CrossRefGoogle ScholarPubMed
Khushalani, JS, Holmes, M, Song, S, et al. Impact of rural hospital closures on hospitalizations and associated outcomes for ambulatory and emergency care sensitive conditions. J Rural Health. 2023;39(1):7987. doi: 10.1111/jrh.12671.CrossRefGoogle ScholarPubMed
McNeely, J, Schatz, D, Olfson, M, Appleton, N, Williams, AR. How physician workforce shortages are hampering the response to the opioid crisis. Psychiatr Serv. 2022;73(5):547554. doi: 10.1176/appi.ps.202000565.CrossRefGoogle Scholar
Goodson, N, Wicks, P, Morgan, J, Hashem, L, Callinan, S, Reites, J. Opportunities and counterintuitive challenges for decentralized clinical trials to broaden participant inclusion. NPJ Digit Med. 2022;5(1):58. doi: 10.1038/s41746-022-00603-y.CrossRefGoogle ScholarPubMed
Apostolaros, M, Babaian, D, Corneli, A, etal, Legal. Regulatory, and practical issues to consider when adopting decentralized clinical trials: recommendations from the clinical trials transformation initiative. Ther Innov Regul Sci. 2020;54(4):779787. doi: 10.1007/s43441-019-00006-4.CrossRefGoogle ScholarPubMed
de Jong, AJ, van Rijssel, TI, Zuidgeest, MGP, et al. Opportunities and challenges for decentralized clinical trials: european regulators' perspective. Clin Pharmacol Ther. 2022;112(2):344352. doi: 10.1002/cpt.2628.CrossRefGoogle ScholarPubMed
de Las Heras, B, Daehnke, A, Saini, KS, et al. Role of decentralized clinical trials in cancer drug development: Results from a survey of oncologists and patients. Digit Health. 2022;8:20552076221099997. doi: 10.1177/20552076221099997.Google ScholarPubMed
Volkov, BB, Ragon, B, Doyle, JM, Bredella, MA. Adaptive capacity and preparedness of clinical and translational science award program hubs: Overview of an environmental scan. J Clin Transl Sci. 2022;7(1):e31. doi: 10.1017/cts.2022.400.CrossRefGoogle ScholarPubMed
Ali, Z, Valk, TJ, Bjerre-Christensen, T, et al. Exploring decentralized glucose and behaviometric monitoring of persons with type 2 diabetes in the setting of a clinical trial. J Diabetes Sci Technol. 2023;17(1):117124. doi: 10.1177/19322968211045656.CrossRefGoogle ScholarPubMed
Li, SX, Halabi, R, Selvarajan, R, et al. Recruitment and retention in remote research: Learnings from a large, decentralized real-world study. JMIR Form Res. 2022;6(11):e40765. doi: 10.2196/40765.CrossRefGoogle ScholarPubMed
Orri, M, Lipset, CH, Jacobs, BP, Costello, AJ, Cummings, SR. Web-based trial to evaluate the efficacy and safety of tolterodine ER 4 mg in participants with overactive bladder: REMOTE trial. Contemp Clin Trials. 2014;38(2):190197. doi: 10.1016/j.cct.2014.04.009.CrossRefGoogle ScholarPubMed
Zawada, SJ, Haj Aissa, N, Conte, GM, Pollock, BD, Athreya, AP, Erickson, BJ, Demaerschalk, BM. In Situ physiologic and behavioral monitoring with digital sensors for Cerebrovascular Disease: A scoping review. Mayo Clinic Proceedings: Digital Health. 2023;1(2):139160. doi: 10.1016/j.mcpdig.2023.03.007.Google Scholar
South, A, Bailey, J, Parmar, MKB, Vale, CL. Effectiveness and acceptability of methods of communicating the results of clinical research to lay and professional audiences: Protocol for a systematic review. Syst Rev. 2019;8(1):150. doi: 10.1186/s13643-019-1065-x.CrossRefGoogle ScholarPubMed
George, SM, Hamilton, A, Baker, R. Pre-experience perceptions about telemedicine among African Americans and latinos in South Central Los Angeles. Telemed J E Health. 2009;15(6):525530. doi: 10.1089/tmj.2008.0152.CrossRefGoogle ScholarPubMed
Gerber, BJ, Maestas, C, Dometrius, NC. State legislative influence over agency rulemaking: the utility of ex ante review. State Polit Policy Quart. 2005;5(1):2446. doi: 10.1177/153244000500500102.CrossRefGoogle Scholar
Zawada, S, Paulson, N, Paulson, M, Maniaci, M, Demaerschalk, B. Chapter in “Diagnosing in the Home:” A Pathway for High-Value Home Hospital Programs: Statutory, Reimbursement, and Community-Building Strategies in the Digital Age. Cambridge, Massachusetts: Petrie-Flom Center at Harvard Law; 2023.Google Scholar
Intergovernmental Advisory Committee. In the matter of state, local, tribal, and territorial regulatory and other barriers and incentives to telemedicine in light of the COVID-19 pandemic. 2022 (https://www.fcc.gov/sites/default/files/iac_telehealth_report12.16.22.pdf). Accessed June 13, 2023.Google Scholar
Tanana, H, Bowman, W. Energizing Navajo Nation: How Electrification can Secure a Sustainable Future for Indian Country. Brookings Institute; 2021 (https://www.brookings.edu/blog/how-we-rise/2021/07/14/energizing-navajo-nation-how-electrification-can-secure-a-sustainable-future-for-indian-country/). Accessed June 13, 2023.Google Scholar
State Tracker: States Allowing Telehealth Prescriptions for Opioid Use Disorder. National Academy for State Health Policy. 2021 (https://nashp.org/states-allowing-telehealth-prescriptions-for-opioid-use-disorder/). Accessed June 13, 2023.Google Scholar
Garavand, A, Nasim, A, Hamed, N, Saeideh, A, Shirin, D. Acceptance of telemedicine technology among physicians: A systematic review. Inform Med Unlocked. 2022;30:100943. doi: 10.1016/j.imu.2022.100943.CrossRefGoogle Scholar
Paulson, N, Paulson, MP, Maniaci, MJ, Rutledge, R, Inselman, S, Zawada, SJ. Why U.S. patients declined Hospital-at-Home during the COVID-19 public health emergency: An exploratory mixed methods study. J Patient Exp. 2023;10:19. doi: 10.1177/23743735231189354.Google Scholar
Zawada, SK, Sweat, J, Paulson, MR, Maniaci, MJ. Staff successes and challenges with telecommunications-facilitated patient care in Hybrid hospital-at-home during the COVID-19 pandemic. Healthcare. 2023;11(9):1223. doi: 10.3390/healthcare11091223.CrossRefGoogle ScholarPubMed
Maret, S. On Their Own Terms: A Lexicon with an Emphasis on Information-Related Terms Produced by the U.S. Federal Government (6th ed.), revised 2023 (http://www.fas.org/sgp/library/maret.pdf). Accessed June 13, 2023.Google Scholar
Figure 0

Figure 1. CARE-P3 framework.

Figure 1

Table 1. Stakeholders engaged and research needed to support the proposed framework