Reducing Door-to-Needle Time for Acute Ischaemic Strok Thrombolysis: An Eighteen-Month Quality Improvement Programme

Author
Dr. Vikas Sharma, Dr. Shiv Kumar Gupta, Mohini Dhabhai
Keywords
Acute Ischaemic Stroke; Thrombolysis; Alteplase; Door-To-Needle Time; Quality Improvement; Stroke Alert; Functional Outcomes
Abstract
Door-to-needle time (DNT) for intravenous thrombolysis in acute ischaemic stroke is one of the most carefully measured performance indicators in acute medicine, with each 10-minute reduction associated with approximately 1% absolute increase in patients achieving an independent functional outcome. We undertook a structured quality improvement programme at a tertiary stroke centre across 18 months, comparing 124 thrombolysis activations in the 6-month pre-QI baseline, 98 during the 6-month implementation phase, and 96 during the 6-month post-QI sustained phase. Median DNT fell from 78 minutes (IQR 64-104) pre-QI to 48 minutes (IQR 38-62) post-QI. The proportion achieving DNT below 60 minutes rose from 27% pre-QI to 76% post-QI. Ninety-day functional outcomes shifted substantially: modified Rankin scale 0-2 (independent) at 90 days rose from 53% pre-QI to 75% post-QI, and 90-day mortality fell from 8% to 2%. Strongest predictors of DNT <60 minutes included the post-QI phase, pre-notification by emergency medical services, stroke alert activation at the door, CT in the emergency department, and stroke nurse coordinator availability. The findings reproduce trial-equivalent DNT improvements in a real-world tertiary setting through structured operational redesign.[/mysc_spoiler] [mysc_spoiler title="References" open="no" style="default" icon="plus" anchor="" class=""] [1] Agarwal, A., Kumar, D., & S, P. M. (2026). Optimizing clinical effectiveness of enhanced recovery after surgery (ERAS): Multidisciplinary pathways, patient-centered outcomes, and data-driven performance analytics. International Innovations & Scholarly Trends Journal, 2(2). [2] Bhatnagar, M., Kumar, N., & Shivam. (2026). Quality improvement frameworks in modern surgical practice: Evidence-based models, implementation science, and outcome-oriented performance evaluation. International Journal of Scientific Research and Engineering Development, 9(2). [3] Bhatnagar, V., Tyagi, N., & John, L. (2026). Simulation-based competency development in anesthesia training: educational theory, assessment validity, and clinical impact. International Journal of Versatile Research and Analysis, 4(2). [4] Catherine, S., Gupta, N., Gopi, E., & Swadhi, R. (2025). Enhancing patient engagement and outcomes through digital transformation: Machine learning in medical marketing. In Impact of digital transformation on business growth and performance (pp. 285–312). IGI Global. [5] Deepa, R., Swadhi, R., Udayavani, V., Lakshmi, R., & Rafiq, S. (2026). Motion-controlled wearables for physiological monitoring and predictive diagnostics. In R. Vettriselvan & N. Suresh (Eds.), Intelligent motion control for human-centered systems (pp. 1–28). IGI Global. [6] Gautam, M., Samyal, M., & Chaudhary, S. (2026). Preoperative risk stratification and surgical outcome prediction: Integrating clinical scoring systems, data-driven models, and patient-centered optimization. International Innovations & Scholarly Trends Journal, 2(3). [7] Jha, S. C., Kumar, P., & Neha. (2026). Artificial intelligence-assisted decision support in internal medicine: Enhancing clinical judgment, precision care, and health system performance. International Journal of Scientific Development and Research, 11(2). [8] Kumar, R., Sharma, K., & Gupta, S. K. (2026). Multimorbidity patterns and therapeutic complexity in adult medical practice: Implications for polypharmacy and patient-centred care. International Journal of Creative Research Thoughts, 14(2). [9] Sahu, R. L., Sharma, K., & Gupta, S. K. (2026). Biological and mechanical determinants of fracture healing: An integrated mechano-biological, systemic, and translational framework. International Journal of Recent Development in Engineering and Technology, 15(3). [10] Selvi, K., Anbarasan, P., Madhumita, G., Janaki, L., & Devi, K. K. (2026). Governance, security, and ethical considerations in AI-driven motion control systems. In Methodologies and applications of intelligent motion control systems (pp. 217–242). IGI Global. [11] Subramani, M., Chillagattu, V., Gayathri, K., Rastogi, V., & Ranganathan, S. (2026). Digital twin integration for predictive and real-time motion control in infrastructure engineering. In Methodologies and applications of intelligent motion control systems (pp. 189–216). IGI Global. [12] Swadhi, R., Gayathri, K., Suresh, N. V., Catherine, S., & Velmurugan, P. R. (2025). Leveraging machine learning for enhanced patient engagement and outcomes: Revolutionizing healthcare marketing. In Impact of digital transformation on business growth and performance (pp. 313–340). IGI Global. [13] Vettriselvan, R., Ramya, R., Selvalakshmi, V., Jyothi, P., & Velmurugan, P. R. (2026). Empowering patients through knowledge: Educational strategies in rehabilitation. In Holistic approaches to health recovery (pp. 263–290). IGI Global. [14] Vettriselvan, R., Velmurugan, P. R., Varshney, K. R., EP, J., & Deepika, R. (2025). Health impacts of smartphone and internet addictions across age groups: Physical and mental health across generations. In Impacts of digital technologies across generations (pp. 187–210). IGI Global. [15] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Velmurugan, P. R., & Hasine, J. (2025). Strategic collaborations in medical innovation and AI-driven globalization: Advancing healthcare startups. In Navigating strategic partnerships for sustainable startup growth (pp. 85–110). IGI Global. [16] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026). Transforming the future of management and medical education: AI-driven innovations in curriculum design. In AI education strategies for future-proofing curriculum design (pp. 459–476). IGI Global. [17] Yatish, Khatoon, N., & Kumar, A. (2026). Advancing preventive strategies for chronic disease management: Clinical, behavioral, and population-level perspectives. International Journal of Novel Trends and Innovation, 4(2). [/mysc_spoiler] [/mysc_accordion]

Received : 17 May 2026
Accepted : 22 July 2026
Published : 26 July 2026
DOI: 10.30726/esij/v13.i3.2026.1330056

56.-A-29-Stroke-DNT-QI.pdf