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Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
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4

Clinical Trial Networks and Collaborations

Ruth Carlos, University of Michigan, said that two prime organizations that generate large volumes of clinical trials data are those within the National Cancer Institute (NCI), National Clinical Trials Network (NCTN), and the NCI Community Oncology Research Program (NCORP). Figure 4-1 shows the structure of the NCTN. The five core NCTN research bases conduct therapeutic and cancer control and outcomes research with imaging-based screening and diagnostic trials housed within ECOG-ACRIN (formed as a merger between the Eastern Cooperative Oncology Group [ECOG] for cancer therapy and the American College of Radiology Imaging Network [ACRIN] for cancer imaging). These research bases individually host extensive multidimensional data from their clinical trials. Academic centers and community oncology practices can participate in these clinical trials only through specific base affiliation, and each practice can belong to multiple bases. Carlos noted that no data are routinely shared across research bases.

Two additional research bases conduct only cancer control and outcomes research, such as research on symptom science, patient-reported outcomes, and cancer care delivery, through the NCORP with more than 1,000 practices throughout the United States. Carlos described the NCORP as a valuable setting for cancer clinical trials, because 80 percent of cancer patients receive their treatment in community oncology practices.

Carlos said that while the ECOG-ACRIN datasets are small compared to the national and state datasets discussed, they contain a lot more clinical information, including data on therapeutics, clinical outcomes, potential adverse events, treatment tolerability, treatment adherence, and survival.

Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×

Some of the recent studies included the MATCH trial, the National Lung Screening Trial, and the Tomosynthesis and Mammographic Imaging Screening Trial. In addition to these large trials, they also conduct a variety of trials on cancer care delivery, such as an observational trial on financial toxicity (financial distress), an intervention trial on remote delivery of smoking cessation, and a trial on guideline-concordant optimization (de-implementation) of care. Carlos noted that the goal of many of the trials that collect patient-reported outcomes, such as those concerning treatment tolerability, adherence, or quality of life, is to produce information that is actionable and allows clinicians to make decisions on altering, modifying, or otherwise providing supportive care for their patients.

Health equity has also been a particular emphasis within ECOG-ACRIN, with data on ancestry and race, insurance and access, neighborhood deprivation, stress/physiologic dysregulation (allostatic load), and outcome disparities. Carlos and her colleagues conducted retrospective analyses of some of the data from prior trials and noted the absence of information that reflects contemporary thinking about the topics of health equity, structural racism, and discrimination. Addressing an earlier discussion about the collection of genomic data, she argued that it is important to capture both race and ancestry, because race data can provide information on the phenotypic risk of the experience of discrimination, while ancestry may provide information on biological risk.

Carlos noted that there are opportunities for building data capacity within all of these streams of work. In the case of cancer care delivery trials, it becomes important to understand both the clinic- and system-level characteristics and practices, and capture that information in a way that makes it possible to analyze patient outcomes within specific practice types. With patient-reported outcomes, the challenge is to develop ways to relay the information back to the clinician in a manner that ensures that it is received and is actionable. In the area of health equity, capacity could be enhanced by capturing evolving patient-specific insurance design features, capturing ZIP+4 as part of the address information and translating that into measures of structural inequity, and decreasing barriers to data extraction from electronic medical records to be able to obtain data such as allostatic load.

Carlos said that the complexity of the data types and location, as well as the need for equity, transparency, and regulatory compliance, underpinned by strong ethical principles in collection, access, and use, can rapidly seem daunting. This highlights the need to choose actionable potential targets for phased implementation that will ultimately expand data capacity for patient-centered outcomes research (PCOR).

The potential and the challenge of building data capacity for PCOR, according to Carlos, are highlighted by the “four Vs” of big data: volume, variety, velocity, and veracity. She argued that a fifth V worth adding is

Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×

value, to characterize data in the context of priorities, such as that of providing actionable information.

Laura Esserman, University of California, San Francisco, said that electronic health records (EHRs) can play a role in accomplishing the goals laid out by previous speakers. EHRs help with organizing information in one place, bill for services, and keep orders and messages collated. Esserman said that learning health systems require another layer of functionality. She explained that EHRs, as they are usually designed and used, do not facilitate the reuse of data for multiple purposes in real time; rather, their unstructured nature makes it difficult to share data, tools, and processes across institutions. Additionally, she said the prevalence of unstructured data in EHRs makes it difficult to use those data for decision support and quality improvement in clinical settings. She noted that current versions of EHRs do not support registries or trials, although they could.

Esserman argued that to realize the vision of shared data, it is necessary to reimagine the process of generating clinical data. As an example, she discussed her work on the OneSource project in collaboration with the UCSF-Stanford Center of Excellence in Regulatory Science and Innovation.

Esserman noted that one challenge associated with how data are captured in EHRs is that the notes that are produced are unstructured and can be contradictory. However, it is possible to imagine a more integrated approach, she said, one where each clinician is responsible for capturing the pieces of data that were important to them in a structured format. Such an approach could contribute high-quality information to “a single source of truth” that could be consistently used for secondary purposes.

After examining source data from EHRs, Esserman and colleagues concluded that there is a disconnect between the data needed for clinical research and what clinicians record in their notes. She explained that EHR data could be more useful to health care providers, patients, and clinical investigators if a system was developed that focused on what data are already captured by clinicians, what is needed beyond that, and a way to integrate that into the clinical workflow. These considerations led to the idea of the OneSource checklist. Esserman noted that the goals of the checklist are to focus on data that are truly essential, determine when they need to be collected, and facilitate creation of a workflow that allows teams to work together to collect high-quality data. She said that the checklist could result in structured data that could be entered once over the course of care but used for multiple purposes.

As an example, Esserman discussed the I-SPY COVID-19 trial. For this study, Esserman and her colleagues began to think about workflow requirements and streamlining data capture before they opened the trial for enrollment. They developed a daily standardized checklist, which is integrated with the EHR, and includes items that clinicians wanted to

Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×

capture routinely. The system automates the capture of demographics, medications, and laboratory results. It also supports decisions for both clinical care and research and sends daily checklist and trial reports back to the EHR system. Working groups, such as the safety working group, can easily access information on adverse events that have been reported. Researchers involved with the trial can track what is happening at every site, which makes it possible not only to troubleshoot but to keep the data clean as well. Esserman said the approach is generalizable across sites. It can be built once and then easily integrated into an existing system without an additional major investment.

Esserman noted that in the case of platform trials that can run several studies, centralized agreements can increase quality and efficiency, as well as facilitate collaboration around common approaches to data collection. A system such as OneSource can greatly simplify the workflow and processes and can generate data with the power to change practice. She argued that clinical research is just a special case of clinical care. In systems like OneSource, Esserman said, clinical care teams assemble essential data that support decisions, and by making the clinical trial summaries visible to clinicians, they make it possible to create more disciplined data collection in the clinic setting. That, in turn, improves the process for all patients.

Lesley Curtis, Duke University, began by describing two organizations that she has worked with, the National Institutes of Health (NIH) Healthcare Systems Collaboratory and the Patient-Centered Outcomes Research Institute’s Patient-Centered Outcomes Research Network (PCORnet). The NIH Healthcare Systems Collaboratory’s goal is to strengthen national capacity to implement large-scale cost-effective studies that engage health care delivery systems as research partners for clinical trials. Curtis noted that the coordinating center that she co-leads is involved with pragmatic trial demonstration projects designed to identify best practices and develop general knowledge and resources that are then made available to the research community. She said that the demonstration projects are required to make use of routinely collected EHR data. Curtis described PCORnet as a network of eight large clinical research networks that work together to answer clinical questions by using EHR data that are routinely refreshed, curated, and made accessible through a distributed research network.

Curtis next discussed some challenges and opportunities for improving the PCOR data ecosystem. She pointed out that complete outcomes data are essential for randomized trials. She also noted that the process of acquiring complete outcomes data requires negotiating several individual project-specific and site-specific data-sharing agreements and licenses. Obtaining complete data is often a cumbersome process, she said, because most potential research participants do not receive their care in a closed integrated delivery system.

Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×

Advances in privacy preserving record linkage (PPRL) solutions, Curtis said, have offered opportunities to access rich private data resources. However, in order to use those solutions researchers have to negotiate multiple network licenses, pay annual project fees, and access federal sources separately. Curtis also noted that those network license negotiations are time consuming, and she suggested that it would be beneficial to create a standard license for PPRL for federally funded projects to save time and money.

Echoing other speakers, Curtis said that Centers for Medicare & Medicaid Services (CMS) claims data are very useful for outcomes data and clinical trials. She noted that a significant challenge to using those data in clinical trials is that most clinical trials do not collect a Medicare beneficiary ID or social security number, which makes it difficult to link CMS claims data. She said that an additional challenge to using CMS claims data is that beneficiary IDs or social security numbers are usually stored in areas of EHRs that are separate from clinical data records and are difficult to access even with patient consent. She noted that a PPRL solution that could resolve these issues would be very helpful. Curtis also noted that Medicaid data are useful for PCOR and clinical trials but require researchers to negotiate with each individual state to access those data. She agreed with prior speakers that multistate coalitions of those states willing to share their Medicaid data for research would be beneficial to facilitate improved access.

Curtis argued that she would like to see an efficient and comprehensive PCOR data ecosystem that allowed participants to consent to their health data being used for research and allowed researchers to access all of those data without having to rely on the patient providing consent at multiple sites of care. She said that ideally this could be accomplished by the creation of a national identifier system. She noted that a barrier to such an idea is the current climate of misinformation and disinformation that has impacted patient trust in science and research. She underlined the need to develop strategies for disseminating information that emphasizes the value of research and science and combats misinformation.

Curtis concluded by highlighting the importance of standardized and structured data collection. She noted that increased access to raw U.S. Census data has the potential to be a source for social determinants of health (SDOH) data. However, she said use of those data for PCOR requires SDOH data expertise as well as specific skills for downloading and compiling raw Census data. She suggested that one solution could be to create a common set of important SDOH measures that are available as an extractable data package for researchers. She noted that currently researchers frequently must harmonize, clean, and integrate routinely collected data from multiple institutions to create high-quality research datasets. She

Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×

emphasized that research and clinical care would benefit from structured and standardized data capture.

DISCUSSION

A key theme that emerged from the discussion also echoed the conversations from previous sessions about the disconnect between the data collected as part of clinical care and the data needed for research. The discussion surfaced concerns about the burden associated with capturing these data and the lack of incentives. Some of the presentations (discussed above) offered ideas for simplifying and streamlining the process of collecting clinical data, which could potentially make it easier to accommodate the need to collect additional data for research, if carefully designed to consider the implications for the resulting information.

Participants discussed ways of integrating data from sources other than medical records into clinical research studies. This could greatly expand research on SDOH, among other subjects. The need to establish data-use agreements was highlighted as a major challenge for sharing and linking data, especially in the case of collaborations that involve several institutions.

CONCLUSION

The session on clinical trial networks and collaborations illustrated the need for better integration between clinical care and research in ways that align differing interests and are mutually beneficial. Better integration can improve both the data available for patient care and the data needed for research.

CONCLUSION 4-1: Infrastructure investments could enhance the utility of data routinely generated in the course of care for clinical trials.

Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×

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Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
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Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Page 44
Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Page 45
Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Page 46
Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Page 47
Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Page 48
Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Page 49
Suggested Citation:"4 Clinical Trial Networks and Collaborations." National Academies of Sciences, Engineering, and Medicine. 2022. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 3 - A Comprehensive Ecosystem for PCOR. Washington, DC: The National Academies Press. doi: 10.17226/26396.
×
Page 50
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The Office of the Assistant Secretary for Planning and Evaluation (ASPE), in partnership with other agencies and divisions of the United States Department of Health and Human Services, coordinates a portfolio of projects that build data capacity for conducting patient-centered outcomes research (PCOR). PCOR focuses on producing scientific evidence on the effectiveness of prevention and treatment options to inform the health care decisions of patients, families, and health care providers, taking into consideration the preferences, values, and questions patients face when making health care choices.

ASPE asked the National Academies to appoint a consensus study committee to identify issues critical to the continued development of the data infrastructure for PCOR. The committee's work will contribute to ASPE's development of a strategic plan that will guide their work related to PCOR data capacity over the next decade.

As part of its information gathering activities, the committee organized three workshops to collect input from stakeholders on the PCOR data infrastructure. This report, the third in a series of three interim reports, summarizes the discussion and committee conclusions from the third workshop, which focused on ways of enhancing collaborations, data linkages, and the interoperability of electronic databases to make the PCOR data infrastructure more useful in the years ahead. Participants in the workshop included researchers and policy experts working in these areas.

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