Clinical trial · Observational
Assessing the Performance of Artificial Intelligence (AI)-Augmented Electronic Health Record (EHR) Data Abstraction for Clinical Trial Patient Screening
NCT06561217CI-TRIAL-00092610completedResults postedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Identifying eligible patients is a key process in the clinical trial enterprise. Currently, this process relies on time-intensive manual chart review, creating a rate-limiting step for trial participation. The integration of AI technology into the trial screening process has potential to improve participation rates. This study aims to assess the performance (accuracy, efficiency) of AI-augmented patient identification and inform optimal integration into clinical research screening processes.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Chart review | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (3)
- label
- AI-alone
- interventionNames
- Other: Chart review
- label
- Human-alone
- interventionNames
- Other: Chart review
- label
- Human + AI
- interventionNames
- Other: Chart review
Primary outcomes (1)
- measure
- Abstracted Chart-level Accuracy
- timeFrame
- 1 year
- description
- The primary outcome measured was mean chart-level accuracy, defined as the percentage of elements identified by clinical research coordinators among all elements in the gold-standard set, measured for each chart, and averaged across all charts. Research coordinator-abstracted responses were identified as being accurate when they exactly matched with the gold-standard set. The gold-standard set was determined by 2-3 clinicians blinded to experimental arms.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Diagnosis of colorectal or non-small cell lung cancer. * A minimum of 5 patient documents in the Mendel database. * Most recent document was within 5 years from the time of data extraction. Exclusion Criteria: * None.
References
Publications (1)
- DERIVEDParikh RB, Kolla L, Beothy EA, Ferrell WJ, Laventure B, Guido M, Girard A, Li Y, Dosoky KEM, Tarabishy K, Patel PS, Andalcio A, Maloney K, Mena JU, Salloum W, Chen J, Emanuel EJ. Human-AI teaming to improve accuracy and efficiency of eligibility criteria prescreening for oncology trials: a randomized evaluation trial using retrospective electronic health records. Nat Commun. 2026 Feb 3;17(1):2306. doi: 10.1038/s41467-026-68873-8. PMID 41634037