Current CTN HSN-led studies
For information on CTN studies led by other nodes,
Funded in 2018:
Developing a Prescription Opioid Registry across Diverse Health Systems (CTN-0084), Cynthia Campbell, PI
Cynthia Campbell, PI
This study builds on CTN0061, which developed a prescription opioid registry at Kaiser Permanente Northern California and identified important predictors of opioid misuse and overdose across the years 2011-2014.
The goal of the proposed study is to expand the prescription opioid registry across diverse health systems with harmonized EHR data, and leverage it to answer several key ‘next step’ research questions in response to the opioid crisis. We propose to adapt and expand the registry to 8 other health systems across the nation, initially within the Health System Node (HSN) to maximize efficiency in the short study timeframe, but with potential to be expanded to other nodes in the future. To our knowledge, no study has established an EHR-based prescription opioid registry across several diverse health systems with common data algorithms with the flexibility to address multiple questions.
Funded in 2017:
Medical Cannabis Use Among Primary Care Patients: Using Electronic Health Records to Study Large Populations (CTN-0077)
Gwen Lapham, PI
The United States is seeing a rapid rise in medical and non-medical cannabis use as states expand legal access, particularly in those states with legal recreational use. Yet, remarkably little is systematically known about the prevalence and long-term benefits and risks of medical cannabis use, as well as the levels of patient exposure to different cannabis products for medical use. Research is needed to identify the conditions and symptoms for which medical cannabis is most commonly used by patients who are engaged in primary care. Patient electronic health records (EHRs) have the potential to be a rich source of data about medical cannabis use among primary care patients. This study describes medical use of cannabis, including symptoms, conditions and use patterns common to medical use, among patients in a single large health system that routinely documents primary care patients’ self-reported frequency of past-year cannabis use in a system-wide EHR. This study relies on structured EHR data for cannabis screen results and the use of natural language processing (NLP) methods whereby a computer “reads” narrative clinical text in EHR notes to distinguish medical cannabis use from other cannabis use.
Funded in 2016:
PROUD Trial (Primary Care Opioid Use Disorder CTN-0074)
Katharine Bradley, PI
The US is facing an epidemic of opioid use disorders (OUDs). Treatment with medications markedly improves outcomes for OUDs, but OUDs remain under-diagnosed and under-treated. In Massachusetts, a nurse-based collaborative care model of OUD primary care treatment was used to increase 1) the number of patients who initiated OUD treatment with medications and 2) persistence of OUD treatment, known to improve health outcomes for patients with OUDs. In the PROUD trial, we are testing whether the Massachusetts model of OUD treatment in primary care, compared to usual primary care increases the number of patients who receive medication treatment for OUDs, and/or increases the persistence of OUD treatment in those who initiate treatment, in medical settings in 6 other states. All data is obtained from electronic health records, claims, and other secondary data sources. Phase 1 of the PROUD trial evaluated OUD diagnosis and treatment across 11 health care systems, and in Phase 2 we are fielding the cluster randomized trial to compare the Massachusetts model to usual care.
Examine Patient and System-Level Factors Associated with HEDIS AOD-IET Measure Performance across Health Systems (CTN-0072)
Constance Weisner, PI
Access to care for alcohol and drug disorders (AOD) is problematic in the U.S.—only 10% of patients receive recommended care, and most of it is in specialty programs. Little is known about the HEDIS AOD Initiation and Engagement in Treatment (IET) measures and factors related to higher performance, but understanding their scope in identifying problems, referral to treatment, and engagement in AOD treatment (whether within primary care or in specialty AOD care) could be a first step for improving AOD care nationwide. In seven health plans, this study examined how: 1) performance on the AOD initiation and engagement measures varies by characteristics of the eligible population; 2) performance on the AOD initiation and engagement measures varies by system characteristics; and 3) structural differences and policies within each region and for specific facilities may explain variations in performance on the HEDIS AOD Initiation and Engagement measures.
Funded in 2015:
Evaluation of Drug Screening Implementation in Primary Care (CTN-0065)
Katharine Bradley, PI
Recent trials of screening and brief intervention for drug use have no demonstrated benefit compared to screening alone. However, the value of screening for drug use, compared to no screening, has not been evaluated. Screening for drug use may identify patients with drug use disorders who might need more intensive treatments than brief intervention. In states where marijuana use is now legal for recreation, as well as medical uses, clinicians want to be aware of and assess risks of marijuana use. Further, little is known about the predictive validity of screens for marijuana and/or drug use/misuse for subsequent adverse health outcomes. To that end, this study evaluated implementation of drug screening, among all Group Health patients who sought care in the Group Health primary care clinics to: 1) describe rates of drug and marijuana screening, and positive screens, as well as barriers and facilitators to population-based screening; 2) assess changes in rates of assessment and identification; and 3) assess whether drug and marijuana use is associated with increased subsequent urgent care, ED, and hospitalization.
Kaiser Virtual Data Warehouse - Prescription Opioid Users (CTN-0061)
Cynthia Campbell, PI
Analyzing data stored in Kaiser Permanente Northern California's Virtual Data Warehouse, this study assessed adult new and long-term prescription opioid users in a large integrated health care delivery system to identify treatment patterns, risk factors, and associated outcomes over time, and compared them to matched controls not using an opioid in 2011. This allowed us to describe the differences between opioid users to non-users at a given point in time. We were able to compare the two samples with more rigor than is usual, and learned how opioid users are different from non-users.