Performance of the Tobacco, Alcohol, Prescription Medication, and Other Substance Use (TAPS) Tool for Substance Use Screening in Primary Care Patients.

BACKGROUND: Substance use, a leading cause of illness and death, is underidentified in medical practice. OBJECTIVE: The Tobacco, Alcohol, Prescription medication, and other Substance use (TAPS) tool was developed to address the need for a brief screening and assessment instrument that includes all commonly used substances and fits into clinical workflows. The goal of this study was to assess the performance of the TAPS tool in primary care patients. DESIGN: Multisite study, conducted within the National Drug Abuse Treatment Clinical Trials Network, comparing the TAPS tool with a reference standard measure. (ClinicalTrials.gov: NCT02110693). SETTING: 5 adult primary care clinics. PARTICIPANTS: 2000 adult patients consecutively recruited from clinic waiting areas. MEASUREMENTS: Interviewer- and self-administered versions of the TAPS tool were compared with a reference standard, the modified World Mental Health Composite International Diagnostic Interview (CIDI), which measures problem use and substance use disorder (SUD). RESULTS: Interviewer- and self-administered versions of the TAPS tool had similar diagnostic characteristics. For identifying problem use (at a cutoff of 1+), the TAPS tool had a sensitivity of 0.93 (95% CI, 0.90 to 0.95) and specificity of 0.87 (CI, 0.85 to 0.89) for tobacco and a sensitivity of 0.74 (CI, 0.70 to 0.78) and specificity of 0.79 (CI, 0.76 to 0.81) for alcohol. For problem use of illicit and prescription drugs, sensitivity ranged from 0.82 (CI, 0.76 to 0.87) for marijuana to 0.63 (CI, 0.47 to 0.78) for sedatives; specificity was 0.93 or higher. For identifying any SUD (at a cutoff of 2+), sensitivity was lower. LIMITATIONS: The low prevalence of some drug classes led to poor precision in some estimates. Research assistants were not blinded to participants' TAPS tool responses when they administered the CIDI. CONCLUSION: In a diverse population of adult primary care patients, the TAPS tool detected clinically relevant problem substance use. Although it also may detect tobacco, alcohol, and marijuana use disorders, further refinement is needed before it can be recommended broadly for SUD screening. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

A centralized informatics infrastructure for the National Institute on Drug Abuse Clinical Trials Network.

BACKGROUND: Clinical trial networks (CTNs) were created to provide a sustaining infrastructure for the conduct of multisite clinical trials. As such, they must withstand changes in membership. Centralization of infrastructure including knowledge management, portfolio management, information management, process automation, work policies, and procedures in clinical research networks facilitates consistency and ultimately research. PURPOSE: In 2005, the National Institute on Drug Abuse (NIDA) CTN transitioned from a distributed data management model to a centralized informatics infrastructure to support the network's trial activities and administration. We describe the centralized informatics infrastructure and discuss our challenges to inform others considering such an endeavor. METHODS: During the migration of a clinical trial network from a decentralized to a centralized data center model, descriptive data were captured and are presented here to assess the impact of centralization. RESULTS: We present the framework for the informatics infrastructure and evaluative metrics. The network has decreased the time from last patient-last visit to database lock from an average of 7.6 months to 2.8 months. The average database error rate decreased from 0.8% to 0.2%, with a corresponding decrease in the interquartile range from 0.04%-1.0% before centralization to 0.01-0.27% after centralization. Centralization has provided the CTN with integrated trial status reporting and the first standards-based public data share. A preliminary cost-benefit analysis showed a 50% reduction in data management cost per study participant over the life of a trial. LIMITATIONS: A single clinical trial network comprising addiction researchers and community treatment programs was assessed. The findings may not be applicable to other research settings. CONCLUSIONS: The identified informatics components provide the information and infrastructure needed for our clinical trial network. Post centralization data management operations are more efficient and less costly, with higher data quality.