Improvement Initiative to Develop and Implement a Tool for Detecting Drug-Drug Interactions During Oncology Clinical Trial Enrollment Eligibility Screening.

OBJECTIVES: Screening subjects for drug-drug interactions (DDIs) before enrollment in oncology clinical trials is integral to ensuring safety, but standard procedures or tools are not readily available to screen DDI in this setting. Our objectives were to develop a DDI screening tool for use during oncology clinical trial enrollment and to test usability in single-center and multicenter pilot studies. METHODS: A multistage approach was used for this quality improvement intervention. Semistructured interviews with individuals responsible for DDI screening were conducted to develop a prototype tool. The tool was used for screening DDI in subjects enrolling in National Clinical Trials Network trials of commercially available agents during a single-center 3-month pilot. Improvements were made, and a 3-month multicenter pilot was conducted at volunteer SWOG Cancer Research Network sites. Participants were surveyed to determine tool usability and efficiency. RESULTS: A tool was developed from semistructured interviews. A critical feature was reporting which medications had specific pharmacokinetic and pharmacodynamic characteristics including transporter and cytochrome P450 substrates, inhibitors, or inducers and QT prolongation. In the 12-site study, average (SD) DDI screening time for each patient decreased by 15.7 (10.2) minutes (range, 3-35 minutes; P < 0.001). Users reported the tool highly usable, with >90% agreeing with all positive usability characterizations and disagreeing with all negative complexity characterizations. CONCLUSIONS: A DDI screening tool for oncology clinical trial enrollment was created and its usability confirmed. Further testing with more diverse investigator sites and study drugs during eligibility screening is warranted to improve safety and data accuracy within clinical trials.

Data-Driven Diabetes Education Guided by a Personalized Report for Patients on Insulin Pump Therapy.

OBJECTIVE: It is difficult to assess self-management behaviors (SMBs) and incorporate them into a personalized self-care plan. We aimed to develop and apply SMB phenotyping algorithms from data collected by diabetes devices and a mobile health (mHealth) application to create patient-specific SMBs reports to guide individualized interventions. Follow-up interventions aimed to understand patient's reasoning behind discovered SMB choices. METHODS: This study deals with adults on continuous subcutaneous insulin infusion using a continuous glucose monitor (CGM) who self-tracked SMBs with an mHealth application for 1 month. Patient-generated data were quantified and an SMB report was designed and populated for each participant. A diabetes educator used the report to conduct personalized, data-driven educational interventions. Thematic analysis of the intervention was conducted. RESULTS: Twenty-two participants recorded 118 alcohol, 251 exercise, 2,661 meal events, and 1,900 photos. A patient-specific SMB report was created from this data and used to conduct the educational intervention. High variability of SMB was observed between patients. There was variability in the percentage of alcohol events accompanied by a blood glucose check, median 79% (38-100% range), and frequency of changing the bolus waveform, median 11 (7-95 range). Interventions confirmed variability of SMBs. Main emerging themes from thematic analysis were: challenges and barriers, motivators, current SMB techniques, and future plans to improve glycemic control. CONCLUSION: The ability to quantify SMBs and understand patients' rationale may help improve diabetes self-care and related outcomes. This study describes our first steps in piloting a patient-specific diabetes educational intervention, as opposed to the current "one size fits all" approach.

Patient Perception and Satisfaction With Insulin Pump System: Pilot User Experience Survey.

BACKGROUND: The goal of this study was to assess patient perspectives and satisfaction with the MiniMed 670G insulin pump. Those participants who used the pump as part of a hybrid closed loop were also asked to provide their views on the automatic feature (auto mode). METHODS: Adults with type 1 diabetes mellitus using the Medtronic™ 670G pump were asked about their experience with the device using a semi-structured survey developed by the research team. Responses were quantified to identify emergent themes. RESULTS: Seventeen participants used the pump as part of a hybrid closed loop system, while four participants used the pump in combination with a nonintegrated continuous glucose monitoring system. Overall, participants indicated a high level of satisfaction with the pump (14/21) mostly because of improvements in blood glucose (BG) control (15/21). Least liked features were physical design and structure (6/21), frequency of user input (5/21), alert frequency (4/21), and difficulty of use (3/21). Those using the hybrid closed loop were satisfied with the auto mode feature (11/17), mostly because of improvements in BG control (9/17). The least liked features of the auto mode technology were that blood glucose levels remained elevated (5/17) and the frequency of alerts (4/17). CONCLUSION: Participants indicated a high level of satisfaction with the pump and its auto mode featured mostly because of improvements in BG control. They also pointed out some key aspects of the device that are of potential clinical or commercial relevance. Additional research is needed to further evaluate users' perspectives on this new device.

Current State of Electronic Consent Processes in Behavioral Health: Outcomes from an Observational Study.

An integral element of value-based care is care team access to both physical and behavioral health data. Data release processes in both environments are governed by federal and state statutes. The requirements for obtaining consent are complex and often confusing. Little is known about the consent processes and practices in the behavioral health setting, specifically how patients and surrogates engage in the process and their interactions with electronic consent tools. This study analyzes the consent processes from the patient perspective at two community behavioral health clinics. Outcomes include description of the processes using electronic consent, workflows and consenter-provider interactions. Conclusions include need to streamline and standardize consent technologies and improve consenter engagement. This study supports the development of an electronic consent tool, My Data Choices (MDC), funded by the National Institute of Mental Health, that offers individuals with behavioral health conditions more control over their medical records.