Misuse of prescription opioids remains a public health crisis. Appropriate short-term use of these medications in opioid-nave patients is indicated in select health care settings, but intentional short-term use is emerging as a previously under-recognized segue to unintended prolonged opioid use (UPOU). Clinical strategies aimed at preventing UPOU in health care settings are lacking due, in part, to absence of information about how this poorly-understood clinical phenomenon develops. Investigators at Mayo Clinic recently organized a group of thought leaders to develop a conceptual framework to explain UPOU. Such a framework is essential both to guide the study of this problem and to identify potential targets for interventions to reduce UPOU. The framework is comprised of three domains, including (1) patient characteristics; (2) practice environment characteristics; and (3) opioid prescriber characteristics that interact to either facilitate or impede UPOU. Within each domain, potential factors, drawn from the relevant literature, moderate or mediate the influence of each domain. However, much of the information needed to evaluate this framework does not currently exist. The widespread adoption of electronic health records (EHR) provides unique potential opportunities for translational research, including identifying subjects eligible for study participation and serving as a data sources for retrospective or prospective studies. However, interoperability between EHRs poses a considerable challenge to taking advantage of these opportunities. Researchers at the Yale School of Medicine recently launched Hugo, a secure mobile personal health (mHealth) platform that enables patients to access their information from multiple EHRs and other healthcare information sources, including commercial pharmacy records. The Hugo platform has tremendous potential to facilitate clinical research, especially research conducted across multiple centers ? as information from diverse source systems at each institution can be easily integrated into a common dataset. In this application, four CTSA hubs (Mayo Clinic, University of Minnesota, University of Michigan, and Yale) will explore the Hugo platform's potential to facilitate clinical research, with the UPOU study as a use case. We will use the Hugo platform to identify incident cases of UPOU and prospectively recruit patients and opioid prescribers for assessments, as well as to evaluate the proposed conceptual framework using structural equation models. At this study's conclusion, we will have successfully deployed a highly innovative mHealth platform across multiple centers and this platform will be immediately available for widespread dissemination across the entire CTSA consortium and other clinical research sites. The information gained about UPOU will significantly advance core knowledge about this poorly understood clinical phenomenon. This newly acquired information will be used design, test, and deploy prevention strategies aimed at mitigating the risks of UPOU.

Public Health Relevance

Misuse of prescription opioids remains a public health crisis. Appropriate short-term use of these medications in opioid-nave patients is indicated in select health care settings, but intentional short-term use of prescribed opioids is emerging as a previously under-recognized segue to unintended prolonged opioid use (UPOU). In this application, four CTSA hubs will explore the potential of a secure mobile personal health platform to identify incident cases of UPOU and to prospectively recruit patients and opioid prescribers for assessments that will be used to evaluate our proposed conceptual framework of UPOU.

Agency
National Institute of Health (NIH)
Institute
National Center for Advancing Translational Sciences (NCATS)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01TR002743-01
Application #
9738962
Study Section
Special Emphasis Panel (ZTR1)
Program Officer
Davis Nagel, Joan
Project Start
2019-03-15
Project End
2023-02-28
Budget Start
2019-03-15
Budget End
2020-02-29
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905