NCT04852601

ToolBox Detect: Low Cost Detection of Cognitive Decline in Primary Care Settings

• Status: Active Not Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 41,500 (estimated)
• Sponsor: Northwestern University · Academic / Other
• Sex: All
• Age: 65 Years
• Healthy volunteers: Not accepted

Summary

Our study objective is to widely implement and evaluate a user-centered, scalable, electronic health record (EHR)-linked strategy for the routine detection of cognitive decline among diverse primary care settings. This strategy, called ToolboxDetect, will provide an efficient and sensitive cognitive screen that can be easily implemented in everyday clinical settings, and is responsive to patient, family, and caregiver concerns for potential symptoms of cognitive decline (CD) and cognitive impairment (CI).

Detailed description

Our study objective is to widely implement and evaluate a user-centered, scalable, electronic health record (EHR)-linked strategy for the routine detection of cognitive decline among diverse primary care settings. We will conduct a large-scale, primary care practice-randomized trial to implement and comprehensively evaluate ToolboxDetect as a standard of care with AWVs, linked to an EHR (Epic). Diverse, academic and community settings are included to optimize future dissemination efforts. ToolboxDetect is an iPad-based, self-administered assessment that leverages two well validated measures from the NIH Toolbox Cognition Battery: Dimensional Change Card Sorting (for executive function) and the Picture Sequence Memory (for episodic memory). It takes approximately 7-8 minutes to administer, and for practices randomized to the ToolboxDetect arm, this will be used as the practice standard to fulfill the requirement for cognitive testing as part of the Medicare Annual Wellness Visit (AWV). The aims of our investigation are to: 1. Evaluate the effectiveness of ToolboxDetect, compared to enhanced usual care, to promote timely detection of cognitive decline and its care management. 2. Disseminate and implement ToolboxDetect among a large Federally Qualified Health Center Network and assess its feasibility and acceptability for use; 3. Investigate the fidelity of ToolboxDetect, and identify any patient, caregiver, healthcare provider and/or system barriers to its optimal, sustained implementation; 4. Determine costs associated with implementing ToolboxDetect from a primary care perspective.

Conditions

• Cognitive Dysfunction
• Dementia

Interventions

Type	Name	Description
OTHER	ToolboxDetect	The clinician or staff member will identify the patient by either scanning a barcode or entering his/her name, age and medical record number on an administrative screen (allowing for proper routing of test results). After completing the test, a 'submit' button will automatically generate a secure HL7 message, sharing 1) a binary classification of the results ('impaired cognition' or 'normal function'), 2) the quantitative ToolboxDetect score, and 3) brief clinical decision support to rule out any reversible causes. These results will be linked to a discrete, queriable, Epic SmartData element. As patients undergo multiple AWVs over time, ToolboxDetect quantitative scores will be displayed in Epic Synopsis Activity, a graphical display that can visualize trend data (e.g. patient vitals) and calculate a percentage change from the prior year. This will allow a clinician to establish a patient's own baseline (instead of using normative data only) for reference.

Timeline

• Start date: 2022-08-25
• Primary completion: 2026-02-28
• Completion: 2026-05-01
• First posted: 2021-04-21
• Last updated: 2026-03-06

Locations

1 site across 1 country: United States