Multi-Center Prospective Study Evaluating an AI-Enabled Clinical Decision Support Tool To Improve Biomarker Testing in Early-Stage NSCLC

Background
Non-adherence to guideline-concordant biomarker testing in non-small cell lung cancer (NSCLC) can limit access to targeted therapies and adversely impact survival. We evaluated an AI-enabled clinical decision support (AI-CDSS) program comprising: (1) education around baseline testing rates; (2) continuous monitoring to generate real-time alerts for eligible patients with missing biomarker testing; and (3) longitudinal feedback via dashboards. Here, we report the effectiveness of this program in identifying and closing biomarker testing gaps for patients with early-stage NSCLC.

Methods
In this descriptive study, we analyzed patients with confirmed NSCLC across 6 geographically and socioeconomically diverse US community health systems. The AI-CDSS identified early- stage patients eligible for biomarker testing (eNSCLC as AJCC 8th edition Stg IB-IIIB (T3, N2) with planned curative intent treatment). Biomarker testing included EGFR, ALK, and PD-L1. We compared testing adherence between a baseline period (BL: 24 months through 3 months prior to the health system-specific roll-out) and a post-launch period (PL: roll-out through Oct 2025). The AI-CDSS was implemented on a rolling basis across health systems (BL from Feb 2022 – Dec 2024 and the PL from Feb 2024 – Oct 2025). Testing rates were calculated as the proportion (%) of patients with complete testing within 90 days of pathologic diagnosis in each period. The improvement in test rates (absolute lift) is calculated as the difference in PL – BL testing percentages in the two periods.

Results
A total of 662 patients with eNSCLC (270 BL and 392 PL) were included in the analysis. Patients were predominately white (85%), had a history of smoking (88%), with a median age of 70 years at diagnosis. The stage distribution was as follows: Stage III (34%), Stage II (37%), Stage IB (25%), and Stage IB or IIA [indeterminate] (5%). The absolute lift in biomarker testing within 90 days of pathologic diagnosis before vs after intervention was 18% for EGFR, 24% for ALK, and 13% for PDL1 biomarkers. Among patients with molecular testing who received adjuvant treatment, 89% were on guideline-concordant adjuvant treatment.

**Conclusions
**Implementation of an AI-CDSS was associated with clinically meaningful improvements in rates of biomarker testing for eNSCLC and resulted in high concordance with guideline-directed adjuvant therapy. Appropriate and timely biomarker testing is essential for perioperative treatment planning. This study provides preliminary evidence that AI can use complex electronic health records to provide real-time interventions that can promote guideline-concordant care.

Table 1: Testing Gap Results