Yale University-Mayo Clinic CERSI

Center of Excellence in Regulatory Science and Innovation

The Yale-Mayo CERSI conducts high-quality, high-impact collaborative research to support several areas of focus in the FDA strategic plan for regulatory science. Research topic areas include: adoption/de-adoption of FDA-approved medical products, postmarket surveillance, development and application of novel analytics, and patient-centered regulatory decision-making.

Current Projects

Characterizing use, safety and efficacy of brand-name and generic drugs used to treat hypothyroidism

Generic drugs are approved based on bioequivalence to the brand name agents. However, there are sometimes concerns among patients and clinicians that generic and brand name drugs are not equivalent and have differing effects. Using a large administrative claims data source that includes information on privately insured and Medicare Advantage enrollees of all ages, we will characterize patterns of use of generic and brand-name L-thyroxine products and then compare the effectiveness and safety of generic and brand-name L-thyroxine among both new users and recent switchers.


Linking data sources to elucidate non-fatal and fatal opioid-related overdose epidemiology and the role of FDA-regulated products

Overdose, or the syndrome where a drug causes loss of consciousness/coma, is the leading cause of accidental death in the U.S. It is clear that medications- not just illegal drugs- are increasingly involved in overdoses. As such, the FDA has pledged to re-examine the role of medications, particularly opioids and benzodiazepines, in the dramatic increase in overdoses. One of the main challenges in this work is that while we may know who has experienced an overdose, it has been very difficult to assess what medications they may have had access to or what they were taking. The primary reason for this difficulty is that there are many different sources of data on medication receipt and it is challenging to link these datasets. In this project, we will overcome those challenges, linking a variety of datasets that will generate a more accurate assessment of which medications at which doses put patients at risk for- or protect them from- overdose.


Characterization and analysis of high incidence of potentially unsafe prescribing of some Extended-Release (ER) opioid analgesics using Natural Language Processing (NLP) of Electronic Health Record (EHR) clinical notes

In 2012, the FDA approved a Risk Evaluation and Mitigation Strategy (REMS) to provide prescriber education to help reduce adverse outcomes resulting from misuse and abuse of extended-release (ER) opioid analgesics. Recent studies, one conducted by FDA using the Medicare database, and one conducted by FDA using the Sentinel database, focused on inappropriate prescribing of ER opioid analgesics that require prior opioid tolerance to patients who do not appear (based on prescription dispensing records) to be opioid-tolerant. Those studies suggest a high incidence of potentially unsafe prescribing behavior for ER opioid analgesics to opioid non-tolerant patients, but recommend further validation in other datasets. The objective of this new study is twofold: (1) examine ER opioid analgesic prescribing patterns in the OptumLabs claims database of commercially insured and Medicare Advantage patients using the Willy and LaRochelle approach, and (2) provide further insight to understand the context of prescribing behavior using clinical data from Electronic Health Records (cdEHR) including data extracted from raw provider notes using advanced Natural Language Processing (NLP) techniques.


Post-market surveillance with a novel mHealth platform

Medical devices play an important role in advancing patient care and reducing morbidity and mortality. All devices must receive FDA approval before they can be marketed. Once medical devices are marketed, it is necessary and important to monitor their safety and effectiveness in real-world clinical practice. Safety concerns may emerge when these devices are used in significantly more patients than were studied before marketing and when longer duration of follow-up is available. FDA therefore requires post-market surveillance of medical devices to ascertain if devices perform as intended and detect any unexpected or serious adverse effects. While the FDA currently employs multiple post-market surveillance strategies, there is opportunity to strengthen this important area given its central role in FDA regulation and, therefore, it is an FDA regulatory science priority. An important component of post-market surveillance is obtaining health data from medical records and insurance claims; these data should also include longitudinal patient-reported outcomes since the goal of these devices is to help people live better and longer.

A novel sync-for-science mobile application has been developed that unobtrusively enables patients to provide their own outcomes (through short questionnaires and through synchronizing data from mobile health trackers) to the FDA after they have received a procedure that utilizes medical devices. In addition, with user permission, this application draws data from the electronic medical record to complement patient-reported data. In this project, we will conduct a pilot study testing this mobile health application to enable the FDA to conduct post-market surveillance of two procedures that use medical devices: the multiple devices (including sutures and stapler) used to perform sleeve gastrectomy in patients seeking weight loss and an ablation catheter when used in patients with atrial fibrillation seeking a return to sinus rhythm. Patients will be enrolled before receiving each of the devices and then will be asked to report specific symptoms related to their need for the procedure and that may be expected post-procedure at baseline (enrollment, which is pre-procedure), 1, 4, and 8 weeks. Additionally, patients will be asked 2-3 short questions every 3-4 days for the first 30 days post-procedure related to post-procedure symptoms. We will also test if these patients’ electronic health record data from multiple health systems where they receive care can be synchronized into a research-ready database. Finally, we will test the feasibility of obtaining medication data from pharmacies or the current needs to create a functional system that can integrate pharmacy data into the mobile application. Integration of these multiple data sources (patient-reported outcomes, wearable/mobile device data, electronic health record data, and pharmacy data) have the potential to ultimately enable a more robust and thorough post-marketing surveillance strategy by leveraging the potential of digital health technologies.