Authors :
Ezichi Adanna Anokwuru; Tony Isioma Azonuche
Volume/Issue :
Volume 11 - 2026, Issue 1 - January
Google Scholar :
https://tinyurl.com/2zyburcw
Scribd :
https://tinyurl.com/bdf4dwas
DOI :
https://doi.org/10.38124/ijisrt/26jan979
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This study examined the measurable impact of Agile product development combined with iterative data science
integration on efficiency, decision quality, and regulatory readiness within healthcare innovation pipelines. Using a
quantitative-dominant mixed-methods design, the research evaluated longitudinal performance data from Agile healthcare
product teams before and after the embedding of analytics-enabled decision pipelines. Key performance dimensions included
development cycle time, decision velocity, product-market fit, and regulatory readiness, operationalized through outcome-
oriented metrics such as sprint cycle duration, predictive insight utilization, feature validation rates, and compliance artifact
completeness. The findings demonstrated that analytics-enabled Agile execution produced substantial reductions in cycle
time, improved process stability, and accelerated decision-making without increasing reversal rates, indicating more
confident and durable choices. Product-market fit improved significantly as user adoption, stakeholder acceptance, and
validated feature delivery increased earlier in the development lifecycle. Importantly, regulatory readiness was enhanced
rather than compromised, with continuous documentation generation, improved traceability, and faster compliance issue
resolution embedded within sprint workflows. These results suggest that Agile methodologies, when augmented by
structured data science pipelines, can function as learning systems that align innovation speed with regulatory rigor. The
study contributes empirical evidence to healthtech product management literature by demonstrating that analytics-
integratedAgile frameworks enable healthcare organizations to scale innovation, improve market alignment, and strengthen
compliance confidence simultaneously, offering a viable model for sustainable and responsible healthcare product
development.
Keywords :
Agile Product Development; Healthcare Innovation Pipelines; Iterative Data Science Integration; Cycle Time Reduction; Regulatory Readiness.
References :
- Aluso, L. (2021). Forecasting marketing ROI through cross-platform data integration between HubSpot CRM and Power BI. International Journal of Scientific Research in Science, Engineering and Technology, 8(6), 356–378. https://doi.org/10.32628/IJSRSET214420
- Ilesanmi, M. O., Anim-Sampong, S. D., & Enyejo, J. O. (2023). Cross-sector asset management: Applying real estate portfolio optimization models to renewable energy infrastructure. International Journal of Scientific Research and Modern Technology, 2(10). https://doi.org/10.38124/ijsrmt.v2i10.1077
- Elxo, (2023) The Case for Agile Software Development in Healthcare https://blog.elxoinc.com/the-case-for-agile-software-development-in-healthcare
- Anim-Sampong, S. D., Ilesanmi, M. O., & Adetutu, Y. O. O. (2022). Bridging the gap between technical asset management and executive strategy in renewable energy: A framework for portfolio managers as policy and investment influencers. International Journal of Scientific Research in Mechanical and Materials Engineering, 6(5). https://doi.org/10.32628/IJSRMME18211
- Anokwuru, E. A., Mends Karen, Y. O., & Okoh, O. F. (2023). AI-integrated market access strategies in oncology: Using predictive analytics to navigate pricing, reimbursement and competitive landscapes. International Journal of Scientific Research and Modern Technology, 2(12), 49–63. https://doi.org/10.38124/ijsrmt.v2i12.1037
- Oladoye, S. O., Bamigwojo, O. V., James, A. O., & Ijiga, O. M. (2021). AI-driven predictive maintenance modeling for high-voltage distribution assets using sensor fusion and time-series degradation analysis. International Journal of Scientific Research in Science, Engineering and Technology, 11(2), 387–411. https://doi.org/10.32628/IJSRSET2291524
- Ocharo, D. O., Avevor, J., & Aikins, S. A. (2025). Design and performance evaluation of solar-assisted absorption cooling systems for institutional campuses in the northeastern United States. Acta Mechanica Malaysia, 8(1), 38–49. https://doi.org/10.26480/amm.01.2025.38.49
- Ocharo, D. O. (2024). Integration of photovoltaic-thermal systems with HVAC infrastructure for energy-positive buildings in Pennsylvania. International Journal of Scientific Research and Modern Technology, 3(5), 65–80. https://doi.org/10.38124/ijsrmt.v3i5.993
- Ocharo, D. O., Onyia, V. O., Bamigwojo, V. O., Adaudu, I. I., & Avevor, J. (2023). Structural and thermal behavior of building-integrated photovoltaic facades in high-rise urban buildings in Philadelphia. International Journal of Scientific Research in Civil Engineering, 7(5), 161–192. https://doi.org/10.32628/IJSRCE237418
- Ajayi, J. O., Omidiora, M. T., Addo, G., & Peter-Anyebe, A. C. (2019). Prosecutability of the crime of aggression: Another declaration in a treaty or an achievable norm? International Journal of Applied Research in Social Sciences, 1(6), 237–252.
- Anokwuru, E. A., Omachi, A., & Enyejo, J. O. (2024). Automation-enabled RFI/RFP market intelligence platforms. International Journal of Scientific Research in Science and Technology.
- Ocharo, D. O., Omachi, A., Aikins, S. A., & Adaudu, I. I. (2024). SCADA-enabled predictive maintenance framework for cogeneration systems in American manufacturing facilities. International Journal of Scientific Research and Modern Technology, 3(7), 30–44. https://doi.org/10.38124/ijsrmt.v3i7.947
- Ocharo, D. O., Omachi, A., & Omachi, A. (2022). Optimization of microgrid-controlled chiller plants for data center cooling in the northeastern United States. International Journal of Scientific Research in Science and Technology, 9(3), 865–880. https://doi.org/10.32628/IJSRST229345
- Ilesanmi, M. O., Anim-Sampong, S. D., & Enyejo, J. O. (2023). Cross-sector asset management: Applying real estate portfolio optimization models to renewable energy infrastructure. International Journal of Scientific Research and Modern Technology, 2(10). https://doi.org/10.38124/ijsrmt.v2i10.1077
- Ijiga, O. M., Anim-Sampong, S. D., & Ilesanmi, M. O. (2022). Land use optimization for utility-scale solar and wind projects: Integrating estate management and technology-driven site analytics. International Journal of Scientific Research in Science, Engineering and Technology, 9(6), 505–510. https://doi.org/10.32628/IJSRSET25122274
- Aluso, L., & Enyejo, J. O. (2023). Integrating ETL workflows with LLM-augmented data mapping for automated business intelligence systems. International Journal of Scientific Research and Modern Technology, 2(11), 76–89. https://doi.org/10.38124/ijsrmt.v2i11.1078
- Anokwuru, E. A., & Enyejo, J. O. (2025). Predictive modeling for portfolio risk assessment in multi-therapeutic pharmaceutical enterprises. International Journal of Innovative Science and Research Technology, 10(11), 2354–2370. https://doi.org/10.38124/ijisrt/25nov1475
- Nwokocha, C. R., Peter-Anyebe, A. C., & Ijiga, O. M. (2021). Evaluating FHIR-driven interoperability frameworks for secure system migration and data exchange in U.S. health information networks. International Journal of Scientific Research in Science and Technology. https://doi.org/10.32628/IJSRST523105135
- Ijiga, O. M., Ifenatuora, G. P., & Olateju, M. (2021). Bridging STEM and cross-cultural education: Designing inclusive pedagogies for multilingual classrooms in sub-Saharan Africa. IRE Journals, 5(1).
- Anokwuru, E. A., Omachi, A., & Enyejo, L. A. (2022). Human-AI collaboration in pharmaceutical strategy formulation: Evaluating the role of cognitive augmentation in commercial decision systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(2), 661–678. https://doi.org/10.32628/CSEIT2541333
- Anokwuru, E. A., Omachi, A., & Enyejo, J. O. (2024). Automation-enabled RFI/RFP market intelligence platforms: Redefining data-driven business development in global pharmaceutical markets. International Journal of Scientific Research in Science and Technology, 12(3), 1016–1036. https://doi.org/10.32628/IJSRST54310301
- Adedunjoye, A. S., & Enyejo, J. O. (2023). Artificial intelligence in supply chain management: A systematic review of emerging trends and evidence in healthcare operations. International Journal of Scientific Research and Modern Technology, 3(12), 257–272. https://doi.org/10.38124/ijsrmt.v3i12.1055
- Anokwuru, E. A., & Igba, E. (2025). AI-driven field enablement systems for oncology commercial strategy: A framework for enhancing decision-making and market execution. International Journal of Scientific Research and Modern Technology, 4(2), 118–135. https://doi.org/10.38124/ijsrmt.v4i2.1011
- Rigby, D. K., Elk, S., & Berez, S. (2020). Doing agile right: Transformation without chaos. Harvard Business Review, 98(2), 42–52.
- Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318.
- Russo, D. (2021). The agile success model: a mixed-methods study of a large-scale agile transformation. ACM Transactions on Software Engineering and Methodology (TOSEM), 30(4), 1-46.
- Food, U. S. (2017). Digital Health Innovation Action Plan. FDA.
- Leo, E. (2020). Toward a contingent model of mirroring between product and organization: a knowledge management perspective. Journal of product innovation management, 37(1), 97-117.
- Hofmann, P., Samp, C., & Urbach, N. (2020). Robotic process automation. Electronic markets, 30(1), 99-106.
- Krittanawong, C., Rogers, A. J., Johnson, K. W., et al. (2020). Integration of artificial intelligence in cardiovascular medicine. Nature Medicine, 26(12), 1836–1848. https://doi.org/10.1038/s41591-020-1019-7
- Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 8(1), 1–10. https://doi.org/10.1007/s13755-020-00104-5
- Calvo, R. A., Deterding, S., & Ryan, R. M. (2020). Health surveillance during COVID-19 pandemic. Health Informatics Journal, 26(4), 2664–2677. https://doi.org/10.1177/1460458220930975
- Ben-Assuli, O. (2015). Electronic health records, adoption, quality of care, legal and privacy issues. Health Policy and Technology, 10(1), 100–105. https://doi.org/10.1016/j.hlpt.2020.100505
This study examined the measurable impact of Agile product development combined with iterative data science
integration on efficiency, decision quality, and regulatory readiness within healthcare innovation pipelines. Using a
quantitative-dominant mixed-methods design, the research evaluated longitudinal performance data from Agile healthcare
product teams before and after the embedding of analytics-enabled decision pipelines. Key performance dimensions included
development cycle time, decision velocity, product-market fit, and regulatory readiness, operationalized through outcome-
oriented metrics such as sprint cycle duration, predictive insight utilization, feature validation rates, and compliance artifact
completeness. The findings demonstrated that analytics-enabled Agile execution produced substantial reductions in cycle
time, improved process stability, and accelerated decision-making without increasing reversal rates, indicating more
confident and durable choices. Product-market fit improved significantly as user adoption, stakeholder acceptance, and
validated feature delivery increased earlier in the development lifecycle. Importantly, regulatory readiness was enhanced
rather than compromised, with continuous documentation generation, improved traceability, and faster compliance issue
resolution embedded within sprint workflows. These results suggest that Agile methodologies, when augmented by
structured data science pipelines, can function as learning systems that align innovation speed with regulatory rigor. The
study contributes empirical evidence to healthtech product management literature by demonstrating that analytics-
integratedAgile frameworks enable healthcare organizations to scale innovation, improve market alignment, and strengthen
compliance confidence simultaneously, offering a viable model for sustainable and responsible healthcare product
development.
Keywords :
Agile Product Development; Healthcare Innovation Pipelines; Iterative Data Science Integration; Cycle Time Reduction; Regulatory Readiness.