The Collaboratory has made available all the presentations from their recent Steering Committee meeting held in Bethesda May 1-2, 2019.
Highlights of Day 1 included updates on the progress and sustainability of the NIH Collaboratory, perspectives on the landscape of embedded PCTs (ePCTs) and the need for real-world evidence, challenges and lessons learned from the UH3 Demonstration Projects, updates on progress and transition plans from the UG3 Demonstration Projects, and discussions on data sharing policy and planning. Day 2 featured an intensive workshop hosted by the NIH with the goal of starting discussions on statistical issues with ePCTs.
Keith Marsolo, PhD
Department of Population Health Sciences
Duke Clinical Research Institute
Duke University School of Medicine
Approaches to Patient Follow-Up for Clinical Trials: What’s the Right Choice for Your Study?
Pragmatic clinical trial; Real-world data; Distributed research network; Electronic health records; EHR; Health data sources; Data standardization; Common data model; Fast Healthcare Interoperability Resources (FHIR); Application programming interface (API)
Different sites have different capabilities and levels of sophistication around data. Clinical trial investigators should think from the beginning about the questions they want to answer and how much data is needed.
From different sources, such as the EHR, claims, or participant, data can be procured and provided in different ways, either by the patient, staff or clinician, or through IT and data experts.
PCTs with many sites may require a “patchwork quilt” of approaches for patient follow-up depending on the needs of the trial. Clinician-generated reports, direct from patients, and solutions involving application programming interfaces (APIs) are all good options for data exchange.
How do we think through the options for getting patient data where some sites may not be in the distributed research network or use a common data model?
Fast Healthcare Interoperability Resources (FHIR) is a draft standard describing data formats and elements and an application programming interface (API) for exchanging electronic health records. The FHIR interface requests data as an object, and for each defined domain it specifies allowable values and variables and predefines the information that you get out of the system.
Until data are collected/generated using the same standards/formats as the API, there will still be a need to understand the EHR-to-interface mapping.
For more information on using health data in embedded pragmatic clinical trials, visit the NIH Collaboratory’s EHR Core webpage.
A new pragmatic trial planning grant supported by the National Institute on Aging will use the NIH Collaboratory’s Distributed Research Network (DRN) to characterize eligible patients and develop an intervention that tests the value of engaging health plan members and their caregivers, in addition to prescribers, to reduce potentially harmful medication use in patients with Alzheimer disease and related dementias (ADRD).
Patients with Alzheimer disease are at high risk for “prescribing cascades,” in which patients receive multiple, potentially unnecessary drug prescriptions to address side effects of their other medications. The Controlling and Stopping Cascades Leading to Adverse Drug Effects Study in Alzheimer’s Disease (CASCADES-AD) will be an embedded pragmatic clinical trial in 2 large healthcare systems. It would be the first evaluation of a large-scale, health plan–based educational intervention to improve medication safety and reduce the occurrence of preventable medication-related complications among patients with ADRD.
The study will characterize more than 22,500 patients with ADRD who will be identified through the DRN’s distributed data resources. The NIH Collaboratory DRN enables researchers to send queries to data partners and receive aggregate data without confidential information. Through its multiple data partners, the DRN has access to data for more than 90 million lives.
In a new article in the Journal of General Internal Medicine, over 100 million person-years of curated claims data were evaluated to assess new rates and follow-up procedures for colorectal, breast, and cervical cancer. These observational data were collected from national and regional insurers participating in the NIH Collaboratory distributed research network. The proportion of abnormal screening results was consistent with rates reported from a cancer-specific screening consortium (1.8–7.7 for colorectal cancer, 23.8–26.0 for breast cancer, and 9.5–18.2 for cervical cancer).
“A strength of this analysis is its employment of a reusable analysis program executing against standardized and curated, routinely collected electronic data from various institutions to enable rapid, privacy-protecting, cost-efficient assessment of practice.” —Raman et al. JGIM 2018