Speakers

Lauren Milner, PhD
Office of Clinical Policy
U.S. Food and Drug Administration (FDA)

Jonathan Casey, M.D., MSCI
Assistant Professor, Division of Pulmonary and Critical Care Medicine
Vanderbilt University Medical Center
Director, Coordinating Center Pragmatic Critical Care Research Group

Matthew Semler, M.D., MSCI
Associate Professor, Division of Pulmonary and Critical Care Medicine
Vanderbilt University Medical Center
Director, Steering Committee Pragmatic Critical Care
Research Group
Co-Director, Vanderbilt Center for Learning Healthcare

Slides

Keywords

Food and Drug Administration, FDA, Waiver of Consent, Alteration of Informed Consent, IRB, Ethics, Regulatory

Key Points

• Section 3024 of the FDA 21^st Century Cures Act (2016) amends the Food, Drug and Cosmetics Act and provides the U.S. Food and Drug Administration (FDA) with the authority to permit an exception from informed consent requirements when the proposed clinical testing poses no more than minimal risk to the human subject and includes appropriate safeguards to protect the rights, safety, and welfare of participants.

• In 2018, FDA issued the Proposed Rule that would allow an IRB to waive or alter certain informed consent elements, or to waive the requirement to obtain informed consent, under limited conditions, for certain minimal risk clinical investigators.

• The Final Rule (2023) finalizes 4 criteria for waiver or alteration of consent with minor edits and adopts a 5^th criterion for waiver or alteration of consent for identifiable information and biospecimens.

• The Final Rule is not new for IRBs and investigators familiar with the minimal risk waiver/alteration provision in the Common Rule. It has the same criteria, same process. It is relatively new to FDA’s regulated community. Previously, FDA regulations allowed waiver only for certain types of emergency research.

• The goal of the Final Rule is to advance medical product development without compromising the rights, safety and welfare of people participating in clinical research. FDA will develop a draft guidance to accompany the final rule and communicate with researchers, IRBs, patient communities and other interested parties about the rule.

• To understand how the regulations are being applied currently, a team at Vanderbilt systematically reviewed all studies meeting the NIH definition of clinical trial, published in the last year (May 2023 to April 2024) in JAMA and the New England Journal of Medicine (NEJM). Each trial was reviewed to determine whether informed consent prior to enrollment was required.

• During that time period, 33 trials were published in JAMA or NEJM that did not require informed consent. The reasons they did not require informed consent fell into 3 buckets: 1. Emergency and critical care studies (19 RCTs enrolling more than 15,000 patients where informed consent before enrollment was considered impracticable because of the urgency of the intervention and the condition of the patient); 2. Cluster-level: Infection Prevention (11 RCTs enrolling almost 3 million patients where consent was considered impracticable because the intervention was delivered to a group of patients different than the patients who would experience the outcomes (i.e. infection control) and due to the scale); 3. Interventions to promote communication and facilitate care (3 RCTs).

• In all, the systematic review found that 13% of trials did not require informed consent before enrollment, and 89% of patients (more than 3 million) in trials that did not require informed consent before enrollment.

• Upcoming FDA guidance on waiver and alteration of consent could provide the first regulatory guidance for minimal risk interventional research. While U.S. federal regulators do not currently provide guidance on minimal risk for interventional trial, trials are occurring with waiver and alteration.

• Upcoming FDA regulations present an important opportunity for the NIH Collaboratory’s goal of facilitating Learning Healthcare Systems capable of using embedded pragmatic trials to improve patient outcomes

 

Discussion Themes

-What is next from FDA in terms of the release of any further guidance? The timeline is challenging. It is under development and the FDA is working through the process.

–How does one judge the minimal risk criteria? For example, there are a lot of people who treat patients with diabetes with various medications. They may vary in terms of side effects, some of which might be rare. How do you identify if it is minimal risk? You have to meet 2 criteria to be considered for waiver of informed consent, both minimal risk and impracticability. Regardless of how you assess risk for a modestly sized study of a non-urgent intervention, then it would be conceivable to do it under consent and you would not need a waiver. If you are talking about health care system level studies, and for example, a study where these medications are already being used commonly and they are considered to be equivalent, that might be a case where people could consider choices like that. The framework of thinking is always “compared to what” and who is making the decisions. In clinical care is this something where the data is already good enough that physicians feel confident most of the time about one choice or would the patient have a strong preference about or a situation where the patient and clinician are not making the decision and there’s a spectrum in between. The question what does it look like now in clinical care can give a qualitative sense about the risk.

 

Tags

#pctGR, @Collaboratory1

Speaker

Joseph S. Ross, MD, MHS
Professor of Medicine and Public Health
Yale University

Slides

Keywords

YODA, Open Data Access, Data Sharing

Key Points

• Data sharing and open science are important because underreporting research through selective publication and reporting are common. Fifty percent of clinical trials are never published. Even when published, many trial publications are delayed more than 2 years, many are underreported, statistically significant findings are most likely to be reported, and nearly two-thirds of studies had a primary outcome that was changed, introduced, or omitted. Yet patients and physicians frequently make treatment decisions based on only a portion of the potentially available clinical data.

• The 1997 FDA Modernization Act (section 113) provided public access to information about ongoing clinical trials, which led to the creation of ClinicalTrials.gov. The International Committee of Medical Journal Editors (ICMJE) realized that ClinicalTrials.gov had slow growth, and it decided not to accept papers that had not been registered.

• In 2007 the FDA Amendments Act (FDAAA) broadened the scope, requiring expanded registry, trial results uploaded within 12 months of study completion, sharing of basic results and adverse events. Other funders started requiring similarly.

• Since 2007 there has been a sea change in thinking about data sharing. ICMJE said effective July 2018 manuscripts must contain a data sharing statement, and trials that begin enrolling participants on or after January 2019 must include a data sharing plan in the trial’s registration. Effective January 2023, all research funded or conducted by NIH must include in the proposal plans for management and sharing of all data necessary to validate and replicate research findings.

• NIH is now implementing these large-scale efforts for data sharing. Researchers have to submit a data management and sharing plan including data types and amount as well as metadata; related tools, software, and/or code that will be generated; standards (formats, documentation, dictionaries); data preservation, access, associated timelines; access, distribution or reuse considerations; oversight of data management and sharing; and budgets for allowable costs.

• The YODA Project platform launched to find ways to make clinical trial data more available for investigators to use. YODA started with core principles to answer what would the ideal clinical trial data sharing platform would look like.

• YODA was launched in partnership with Johnson & Johnson in 2014 after a proof-of-concept effort with Medtronic. J&J started sharing trial data from all pharmaceutical products (including legacy trials), device and diagnostic products as of 2015, and consumer products as of 2017. YODA established data access policy and procedures with input from a Steering Committee, experts, stakeholders and public comment.

• YODA has 459 trials currently available with about 90% that have been thus far requested. Of 385 requests submitted, 368 have been approved, 4 remain under review, 11 were withdrawn/closed, 2 were rejected. Nearly all of the requests that come in require some administrative revision, but one-quarter required scientific revisions after review for clarity. 157 manuscripts and 93 abstracts have been submitted and 119 and 89 of which have been published or presented, respectively.

• There is valuable strengthening of science that happens in these data sharing efforts. Numerous studies that might not otherwise been feasible to pursue. Data sharing has facilitated direct collaborations and developed efficiencies. Replication studies have supported the original study. There have been no instances of patient privacy breaches, no publications of spurious safety findings that received unwarranted attention or disrupted patient care, and no data have been used for commercial or litigious purposes.

Learn More

Visit the YODA Project website.

Discussion Themes

-What were some surprises compared to expectations over the 10 years of the YODA project? It is firmly in our mind that we should be living in an open science data world, and part of that is sharing data and trying to help improve patient care. How do we get the resources on the academic side to help investigators who want to do this work? On the industry side, partners have stepped up and made their data available. Across the field, how do we get more prior data available?

 

Tags

#pctGR, @Collaboratory1

Speaker

Erik van Zwet, PhD
Department of Biomedical Data Sciences
Leiden University Medical Center, the Netherlands

Slides

Keywords

P Values, Randomized Clinical Trials, Biostatistics

Key Points

• The “essence” of a clinical trial is a set of 3 numbers:  β, b, and s. β is the unobserved, “true” effect of the treatment. B is a normally distributed, unbiased estimator of β with standard error s. It is helpful to think of the estimate b as the true effect β plus a normally distributed “error” (b= β + N (0,s).

• There are 2 more quantities to consider. The z-stat, where z= b/s and the signal-to-noise ration (SNR= β/s). Usually in a clinical trial we want to test the null hypothesis that the treatment has zero effect. If the z statistic is greater than 1.96 then the p-value is less than 0.05. The power depends on the SNR. For example, if SNR = 2.8 then the power is 80%.

• Researchers have been studying data from the Cochrane Database of Systematic Reviews (CDSR) that include about 23,000 z-stats for the primary efficacy outcome of RCTs in the database.

• From the CDSR, we can estimate the distribution of the z-stat and, also, surprisingly, of the signal-to-noise ration. This is possible because there is such a simple relationship between the two. First, estimate the distribution of z directly from the observed z-stats, then derive the distribution of SNR by “removing” the standard normal error component.

• Researchers can use the estimated distributions of the z-stats and the SNRs to build a “synthetic” version of the CDSR with the same statistical properties of the real CDSR. With the synthetic database researchers can get insights to the real CDSR.

• The first thing to look at is power. RCTs are designed to have 80% or 90% power for testing that the true effect is actually 0 against an alternative that is considered to be of clinical interest, or plausible, or both. But the SNR is larger than 2.8 in only 12% of the CDSR trials.

• Many trials have low power against true effect. This has 2 consequences: if p is greater than 0.05 you might be discarding a useful treatment because you didn’t collect enough information to show that it works. If p is less than 0.05 you got very lucky. The effect estimate is likely overestimated and replication attempts will likely fail. A potential solution is to calculate the shrinkage estimation.

Learn More

Read more in NEJM Evidence.

Discussion Themes

-Overestimation was found across medical disciplines. There are a lot of small low power trials that should be published and part of math analyses. There is a lot of financial pressure to design a trial that is not too large, not to small and will have a good chance of success.

–Do you have any thoughts on how this applies to non-inferiority trials? Did you exclude those from these analyses? We did not exclude those. There is a small minority where we know they were non-inferiority trials. Maybe they would be differently sized.

Tags

#pctGR, @Collaboratory1

Speaker

Warren Jones, PhD
Director of Research, Marcus Autism Center
Children’s Healthcare of Atlanta
Norman Nien Distinguished Chair in Autism
Associate Professor, Dept. of Pediatrics
Emory University School of Medicine

Slides

Keywords

Autism Spectrum Disorder, Biomarkers, Social Visual Engagement

Key Points

• Autism affects 1 in every 36, impacting more than 9.1 million individuals and their families in the U.S. When we think about conditions that affect young children and their families, autism is one of the most common.

• Parents in the U.S. spend an average of 2-3 years between the time when they first begin to worry and the time when they finally receive a diagnosis. There are not enough expert clinicians or expert centers to meet public need. Disadvantaged families wait even longer.

• Clinicians need more measures that are objective, quantitative, dimensional and fine-grained, performance-based, standardized, efficient and community-viable, able to capture core features of social disability, and mechanistically relevant.

• Social visual engagement measures how children look at and learn from their surrounding social environment. Children look at and acquire information from what they are looking at. Researchers use eye tracking data to measure social visual engagement, which reflects early-emerging differences in autism spectrum disorder (ASD).

• In 3 studies, researchers tested the performance of eye-tracking-based assays of social visual engagement in 16-30-month-old children to accurately assess presence of ASD and accurately assess severity of ASD.

• The Discovery study included 719 participants, and the Replication study included 370 participants aged 16-30 months old. The initial Discovery study and first Replication study showed high sensitivity and specificity when comparing eye-tracking-based measures of social visual engagement with expert clinician diagnosis in children approximately 16-30 months old.

• With the results from the initial studies, the trial team embarked on a multi-site clinical trial at 6 sites across the U.S. 475 participants completed eye-tracking measurement of social visual engagement, expert clinical diagnosis, and a rating of expert diagnostic certainty.

• The study resulted in 335 participants with a reference standard certain diagnosis for ASD and 140 with a reference standard uncertain diagnosis for ASD. The children seen in the study who did not have autism did have other developmental diagnoses, which highlights the challenge of diagnosing children with developmental delays.

• Study results show a high sensitivity and specificity when comparing eye-tracking-based measures of social visual engagement with expert clinician diagnosis in children as young as 16-30 months old. Results also show a strong correlation with standardized assessments given by experienced clinicians

Learn More

Read more in JAMA.

Discussion Themes

-Is there an eventual hope that a tool like this could be used without a referral to a specialty center? Absolutely. We started at a point to try to develop a tool with gold standard outcomes, and we are going on to test screening studies in other age groups. Our hope is to extend and develop clinical tools that could be easier to use.

–What did you learn that informed the development of tools that would be more generalizable? This work has been conducted in conversation with FDA for many years, successfully moving something that was lab-based to increasingly more real-world. We are working toward making this more usable by general clinicians, asking what they need and want to make it more useful. For the screening studies, we looked at the SMART study framework for clinical trials to get to who is most likely to need a diagnostic evaluation.

 

Tags

#pctGR, @Collaboratory1

Speaker

Edward Qian, MD, MSc
Assistant Professor of Medicine
Vanderbilt University Medical Center
Division of Pulmonary and Critical Care Medicine

Slides

Keywords

ACORN, Antibiotics, Acute Kidney Injury, Critical Care Medicine, Randomized Clinical Trial, EHR

Key Points

• Sepsis is a common cause of critical illness and death. A common treatment is treating with vancomycin plus either piperacillin-tazobactam or cefepime. Piperacillin-tazobactam and cefepime have unique risk/benefit profiles but comparative data are lacking.

• There have been associations with piperacillin and acute kidney injury (AKI). Acute kidney injury is associated with a 6-to-8-fold increase in mortality in critically ill patients. There was concern that there is an association between concurrent vancomycin and piperacillin-tazobactam with creatinine elevations, but a retrospective, observational analysis was inconclusive.

• The ACORN Trial was randomized clinical trial to understand the effect of empiric antibiotic choice for patients in the ED and ICU. The trial enrolled 2,511 patients, who were adults who were in the hospital ED or ICU for less than 12 hours and received at least one dose of empiric cefepime or piperacillin-tazobactam. The intervention was a choice of empiric gram-negative antibiotic (cefepime or piperacillin-tazobactam).

• The screening process was done via an interruptive alert in the electronic health record (EHR) that was activated based on a provider placing an order for drugs. The alert would remind for exclusion criteria, and ask if the patient is eligible for ACORN trial.

• The trial operated under a waiver of informed consent. The research involved no more than minimal risk to subjects, and the research could not be carried out practicably without the waiver or alteration.

• The participant randomization happened within the EHR. The delivery of the intervention happened in the EHR, starting with an alert to enroll. Providers were sent an order set depending on the arm. The study team monitored for compliance within the EHR.

• The primary outcome was AKI ordinate scale between enrollment and Day 14. Secondary outcomes were major adverse kidney events within 14 days and days alive and free of delirium and coma to day 14.

• The trial found that, compared to cefepime, piperacillin-tazobactam does not increase the incidence of AKI. Compared to piperacillin-tazobactam, cefepime may decrease the number of days alive and free of delirium and coma.

Learn More

Read more in JAMA.

Discussion Themes

-How much pilot testing did you do? I was trained as an EPIC physician builder. In preparation for this trial, I underwent this training so all the pieces that you saw, I built and tested in a relatively short time period.

–How did you engage clinicians and make sure the question you were answering was one they wanted the answer to? There was a huge amount of leg work to engage stakeholders. We were talking to the leadership of ICU, nephrology, ID, and others for buy-in, to answer questions, and to make modifications based on suggestions.

–How portable are your EHR modifications? In particular, how big a lift would it have been to implement the same protocol in multiple EPIC installations? This whole trial was done while I was getting a master’s in applied informatics. The answer is no. The screening and randomization could be portable. Each installed EPIC has slight differences and quirks. There will be small differences in the application of the intervention.

 

Tags

#pctGR, @Collaboratory1

Speakers

Michael Pencina, PhD
Chief Data Scientist, Duke Health
Duke University School of Medicine

Brian Anderson, MD
CEO, Coalition for Health AI

Slides

Keywords

Artificial Intelligence, AI, Coalition for Health AI (CHAI), Duke ABCDS, Governance, Regulation

Key Points

• The regulatory landscape for AI is changing rapidly. There are a number of government agencies trying to establish their position on AI, including FDA and The U.S. Department of Health and Human Services Office of Civil Rights. The Office of the National Coordinator for Health Information Technology (ONC) published its final rule on AI in December.

• We are living in a “wild west” of algorithms. There’s so much focus on development and technological progress and not enough attention to its value, quality, ethical principles, or health equity implications.

• There are principles we can apply for the responsible development and use of AI, such as ensuring that AI technology serves humans; defining the task we want the AI tool to accomplish; describing what the successful use of the AI tool looks like; and creating transparent systems for continuously testing and monitoring AI tools.

• Three years ago, Duke created an algorithm-based clinical support (ABCDS) function which monitors application of algorithms, AI, in our health system. The governance is now changing as AI moves from predictive models to large-language models.

• This is complex environment where not everyone speaks the same language, clinicians, developers, patients, informaticists, statisticians, and more. It is very important that development teams are connected with what is happening at the health system level and the clinical teams on everything they build.

• Model development follows the FDA process starting with model development, silent evaluation, effectiveness evaluation, and general deployment, with evaluations at regular intervals.

• The Coalition for Health AI (CHAI) is a nonprofit that brings together more than 1,400 private sector organizations, including health care systems, payors, device manufacturers, technology companies, and patient advocates, plus U.S. government partners, to provide a framework for the landscape of health AI tools to ensure high quality care, increase trust among users, and meet health care needs.

• In April 2023, CHAI published a blueprint for trustworthy AI implementation guidance and assurance for healthcare, which outlines challenges for adhering to the core principles of trustworthy AI that the CHAI working groups are tackling.

• Generative AI will become more impactful and ubiquitous. We have an urgent need to define what is safe and effective in this space and rethink how we regulate and align generative AI

 

Discussion Themes

-The field is really exploding. How are you forecasting how the federation will come together to address the potential volume of algorithms? Part of the challenge is that it is really important that we build consensus quickly and include as many voices as we can. We are identifying as many use cases as we can, building consensus around a set of shared definitions and frameworks, and then fleshing those out in the nuances of use cases. The other challenge is, once we have that defined, we need to share freely what we learn for wide adoption. We can’t do this in silos.

–How are you approaching ethics in AI? How do you envision including an ethical component in algorithm development? For our ABCDS team for Duke, we have an ethicist on the team to make sure our principles are applied. This is not enough; we will need more as the field develops. 

Tags

#pctGR, @Collaboratory1

Speaker

Joseph Doyle, PhD
Erwin H. Schell Professor of Management and Applied Economics
MIT Sloan School of Management

Slides

Keywords

Food-as-Medicine, Randomized Clinical Trial, Diabetes

Key Points

• Diabetes is common and costly. 9% of the U.S. has the condition; it is responsible for 300,000 premature deaths/year and $330 billion in annual healthcare spending. Payers should have some incentive to improve outcomes because sustained reduction in HbA1c from poor to fair can result in cost savings. Food insecurity is associated with diabetes.

• Food-as-Medicine observational studies have had a large correlation with improved health.

• For the intervention in this Food-as-Medicine trial, participants with diabetes were prescribed 10 meals/week for participants and their families that they could fill at the program’s clinic. Clinic staff included a dietitian, a nurse and a community health worker. The dietician met with the patient to share education about nutrition, portion size, recipes to make food taste good, and information via an optional diabetes self-management program. They also screened for complications and close care gaps. The average duration of the intervention was 1 year, and the cost was about $2,000 per participant.

• The trial was for adults with HbA1c greater than 8.0, who were food insecure and lived within a residential zip code within 25 miles of a clinic. Recruitment was by phone calls and physician referrals, and consent was over the phone. Randomization was stratified by HbA1c greater than 9.5 and site. The intervention group started the program now. The control group started in 6 months and received a brochure that lists addresses of area food banks.

• The primary outcome was HbA1c after 6 months. Lab results for HbA1c, cholesterol, triglycerides, blood pressure, weight were taken at 0, 6 and 12 months. There were also surveys to assess program education, diet, a self-efficacy questionnaire, and a self-assessed physical and mental health questionnaire at 0, 6, and 12 months. Participants received a $50 gift card for completing the labs and surveys.

• Additional data sources came from EHR data, health plan claims, and program participation data including food visits and education.

• 500 patients were randomized to intervention or control groups, with a utilization of 465 and 349 participants completing the 6-month HbA1c lab sample. The treatment group started with a mean 10.3 HbA1c it dropped to a mean of 8.8 at 6 months, which plateaued in 12 months. The control group followed similarly. There were no significant drops in cholesterol, triglycerides, or fasting glucose and weight did not lower. There was not a statistically significant difference between the 2 groups at 12 months.

• The study found a null effect on HbA1c. The study did find substantial effects on diet and healthcare engagement.

Learn more

Read more in JAMA.

Discussion Themes

-Why do you think it was a negative study? The idea was that they would have to go get the food. Medically tailored meals is the alternative. We don’t have the diaries of what they do with the food. We see people visiting the fresh food pharmacies for up to 12 months. When they get home do they give the food away, eat it themselves, throw it away, we don’t know. In terms of the types of food they had, they were not especially culturally diverse.

–Can you say something about the dose of the meal, which was 10 meals a week? Were people eating less healthy meals for the non-program meals? How much food to give is a parameter that needs more research. The dietitians in the program thought that 10 meals a week was the right number and that providing all meals would have been too much and resulted in waste. From a researcher standpoint, we need more information. 

Tags

#pctGR, @Collaboratory1

Speakers

Adrian Hernandez, MD
Executive Director, Duke Clinical Research Institute
Vice Dean, Duke University School of Medicine

Christopher J. Lindsell, PhD
Professor and co-Chief of Biostatistics, Department of Biostatistics & Bioinformatics
Director, Data Science and Biostatistics, Duke Clinical Research Institute
Duke University School of Medicine
Editor in Chief, Journal of Clinical and translational Science

Slides

Keywords

Decentralized Clinical Trials, Virtual Vigilance, Data and Safety Monitoring

Key Points

• There has been global growth of decentralized clinical trials (DCTs) and with that a growing need to develop best practices and to ensure quality results are generated from trials. The global decentralized clinical trial market is expected to grow at a compound annual growth rate of 30.1% from 2021 to 2026.

• But there are concerns to developing DCTs including lack of standardization and validation, regulatory and ethical uncertainties, engagement vs. coercion, data security and privacy issues, technological literacy and access, resistance to change and adoption, and lack of “safe” sharing.

• There is agreement that trials need to meet the people, at home and covering clinical trial deserts.

• There are 5 guiding principles for defining quality that should inform DCTs: Have we enrolled the right participants according to the protocol with adequate consent? Did participants receive the assigned treatment and did they stay on the treatment? Was there complete ascertainment of primary and secondary efficacy data? Was there complete ascertainment of primary and secondary safety data? Were there any major GCP-related issues?

• Regardless of the trial inclusion and exclusion is routine. What we often do not think about is verifying the identity of the participant. In a remote study, it would be possible for duplicate enrollment, falsified or fabricated eligibility source documents, or data completed by surrogates. Consider secure digital identification, two-fact authentication or virtual/video visits. Balance with not adding barriers for participation.

• Getting study drug and other study materials into the hands of a participant requires distribution via mail or courier, breaking the traditional chain of custody. Under RBM, the process by which study materials get to participants should be considered high risk and monitored accordingly.

• As roles for sites change, it remains critical that participants can be actively managed and that data about patient status can be acted upon, including mechanisms for participants to ask questions and get timely responses, participants to report worrisome events, participants to report healthcare encounters and other events, tracking adherence to study intervention, and tracking adherence to data collection procedures. Solutions include a bi-directional EDC (electronic data capture system), MyChart for research, and active notifications to study personnel based on entered data.

• Baseline state, treatment, outcome, and safety data are critical to understanding treatment benefits and risks. Outcomes including patient reported outcomes, functional assessments including via digital technology, healthcare events or mortality may require identify verification. Supporting documentation may include recordings of functional assessments, EHR data, or other information that can be uploaded for remote review. Note that the release of medical records may be needed for health systems unrelated to sites.

• New issues to consider for DCTs: Geographic distribution of participants; enrollment of two or more participants who share the same digital resource; enrollment of participants who do not have sufficient digital resources; and rogue digital and social media recruitment practices

 

Discussion Themes

-Do you anticipate that a new set of clinical practices will emerge from this work? I think we must. One of the things we are in the process of sharing in terms of what we are observing is putting new procedures for what we monitor in a study. It is based on good study design and focusing on the key principles we are trying to adhere to. There has to be a range of approaches that could be fit for use.

–Can you comment on how this is perceived at NIH and some of the institutes? I am sure NIH is trying to accelerate our understanding of DCTs. The knowledge base isn’t there yet in terms of what you need to look for that would be missing in terms of monitoring efforts. From my perspective, if one key goal for NIH is to reach people in underserved communities having sound practices for decentralized methods will be important.

–How can we prepare research teams that do not always have the training and compensation to do all of these things? We need to train the workforce for what we need them to do. We are building out what are the core competencies, the technological, legal, ethical, clinical research administration competencies that are needed in addition to data management for a CRC. I hope we see further development of education programs that support the workforce with the skills needed. The skills may also vary between trials and getting participants the care they need.

Tags

#pctGR, @Collaboratory1