Rationale:

Research that requires real-time recruitment in emergency department/acute care setting is labor intensive and costly. Relying on clinicians leads to low recruitment. Clinicians in busy acute care settings often find it challenging to screen and enroll patients for research studies while maintaining clinical priorities. This is further complicated by several different ongoing studies (often led by research faculty from outside departments) and it remains challenging to  keep clinicians current on the various eligibility criteria due to variable work hours.  While most emergency departments use the traditional approach of having a research assistant who screens for eligibility based on physician/resident notification, this approach has inherent problems: the cost and/or availability of research assistant coverage 24/7, variability among physicians in their notification of research assistants about eligible subjects, and the resulting patient ineligibility due to lack of timely screening. We believe that some of these drawbacks of the traditional approach could be overcome by use of an automated electronic screening tool.

Plan: 

We propose to examine the utility of an automated electronic clinical research screening technology to assess the comparative effectiveness of this method to the traditional system of research associates. This technology is currently installed at the University of California, San Francisco Medical Center Emergency Department (UCSF). We will utilize this technology in the setting of an ongoing NINDS clinical trial.

We propose to accomplish this in a pilot study by

(1) Demonstrating the available features of the automated tool using an ongoing clinical trial, the NINDS funded POINT trial.

(2) Comparing the performance characteristics of the automated tool to a traditional patient recruitment method for screening and enrollment.

Our specific aims are to:

Specific Aim 1: To identify and document study subject characteristics that will be used in developing a screening criteria for the automated tool (Phase 1)

Specific Aim 2: To demonstrate the performance characteristics and comparative effectiveness of automated real-time screening tool using the ongoing  clinical trial in the emergency department at UCSF (Phase 2)

Hypothesis: Automated screening will be more sensitive, and less specific than the traditional method of using research assistants in identification of subjects.

Specific Aim 3: To demonstrate that the operation of real-time clinical research eligibility screening meets HIPAA privacy requirements and does not compromise PHI.

Hypothesis: The technologies and architecture that is employed in developing the Patient Locate service will be sufficient to ensure that HIPAA privacy requirements can be met.

Study Design: In the first three months of the study, we will study the baseline characteristics and presenting symptoms of the eligible study subjects as well as patients enrolled in POINT. We will use the information to develop screening criteria which will be tested in the second phase of the study. During the second phase  KDH system will be activated to identify study eligible patients and the effectiveness of the tool will be compared with the traditional method of recruitment.

Metrics: Sensitivity, specificity, positive and negative predictive value, and likelihood ratios will be calculated to determine the screening performance of the automated screening tool system for eligibility. The performance characteristics will be compared to the research assistant supported system currently in use at UCSF. 95% confidence intervals will also be calculated to assess the strength of the point estimates.

Anticipated result and impact on trial enrollment procedures:

Upon the culmination of this study, the results will help our research team and the CTSI better understand the relative merits of the automated real-time patient recruiting system.

Budget and Justification: Salary support for the investigators and a research assistant for a 12 month period ($60,000)

Collaborators: John Stein (Co-PI), Department of Emergency Medicine, Claude Hemphill, Department of Neurology and Dan Carnese, KDH systems