Abstract 033: Achievement in Clinical Trials: Setting a New Standard Through the Successful Implementation of a Telehealth Enabled Clinical Core Lab for Neurologic Assessments
Bibliographic record
Abstract
Background Telehealth has become a standard of care in almost every setting of clinical practice. In clinical Neurosciences/Neurovascular space it is widely used in prehospital field evaluations on the mobile stroke units, acute assessment and decision making in emergency department, ICUs and stroke unites as well as in outpatient clinical consultations and follow‐ups. Some of these applications allow patient assessment in their homes or other nonhospital based care facilities streamlining the access to clinical care. There is growing interest in applying similar telehealth concepts to clinical trials. FDA recently provided recommendation for implementing decentralized clinical trials where some of the activities occur at locations other than traditional clinical trial sites. HealthMerit/NeuroMerit is a novel platform of a Clinical Core Lab developed to complete the task of connecting with study subjects anywhere and performing standardized clinical assessments remotely. In this study, we performed a feasibility study to assess the ability of our Clinical Core lab with Remote Clinical Assessment Application. Methods We performed a prospective, single‐arm non‐randomized study of 17 healthy volunteers in 4 US sites to evaluate the effectiveness of using the NeuroMerit Clinical Core Lab interface to virtually complete the Montreal Cognitive Assessment (MoCA), NIH Stroke Scale (NIHSS), and modified Rankin Scale (mRS). The primary study endpoint was completion of all 3 neurological assessments remotely. Secondary endpoints included logistical parameters, including success of appointment scheduling, timeliness of establishing a remote connection, and success of data transfer. User satisfaction was also investigated via the Telehealth Useability Questionnaire (TUQ) and Computer System Use Ability Questionnaire (CSUQ). Results All 3 neurological assessments were successfully completed in all 17 subjects. Study sites were able to schedule a remote telehealth session within 72 hours in all subjects with availability (14/14 or 100%) and connected remotely within 10 minutes of the appointment time in most cases (82.4%). Mean assessment time was 19.8 minutes. Data transfer was successful in all cases. User satisfaction of the application was rated highly by both the subjects and study coordinators: the average response rate on the TUQ was 6.4 (scale of 1 through 7 with 7 being most favorable) and was 1.3 on the CSUQ (scale of 1 through 7 with 1 being most favorable). Conclusion Utilizing Clinical Core Lab remote clinical assessment application to conduct neurologic evaluations for clinical trials is feasible and offers high user satisfaction. Currently we are using the Clinical Core Lab telehealth platform in a pilot clinical trial in Republic of Georgia and Australia, followed by pivotal trial in the US. We plan to continue to test the platform on clinical trial participants to evaluate the real‐life impact on clinical trial workflow and patient satisfaction. We hope the implementation of Clinical Core Lab run telehealth clinical assessments holds promise to standardize neurologic evaluations and improve patient recruitment and retention in clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.323 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".