INTEGRATING BIOMEDICAL AND INFORMATION TECHNOLOGY THROUGH PROJECT MANAGEMENT: A QUANTITATIVE SYNTHESIS OF HEALTHCARE INNOVATION

Abstract

Background: The convergence of biomedical innovation and information technology (IT) has redefined modern healthcare. Artificial intelligence (AI), telemedicine, wearable/Internet of Medical Things (IoMT) devices, and bioinformatics pipelines are enabling precision medicine and remote care at an unprecedented scale. Yet, project failure rates remain high—often exceeding 35–45 %—due to inadequate governance, weak data integration, and regulatory misalignment. Effective project management is increasingly recognised as the key enabler of safe, scalable, and sustainable biomedical–IT innovation. Objectives: This paper investigates how project management methodologies influence biomedical–IT integration outcomes between 2020 and 2025. It quantitatively synthesises peer-reviewed data on technological adoption, performance metrics, and implementation success across four domains—AI in healthcare, telemedicine, wearables/IoMT, and bioinformatics/genomics. It further develops an evidence-based hybrid project-management framework integrating agile, regulatory, and clinical governance practices. Methods: A structured literature synthesis was conducted across PubMed, Scopus, Web of Science, and official regulatory repositories for studies published between January 2020 and June 2025. After screening 120+ records, 68 studies met inclusion criteria, and 47 were fully analysed. Quantitative data were extracted for system accuracy, adoption rates, outcome improvements, and cost- effectiveness. Data were normalised and summarised using descriptive statistics (mean, percentage change, confidence ranges) to reveal sectoral performance and project-success metrics. Qualitative insights on project-governance mechanisms and regulatory compliance were mapped to the PMBOK, Agile, and PRINCE2 frameworks. Results: The analysis revealed significant quantitative gains in biomedical–IT performance, balanced by persistent project-governance challenges. AI in healthcare: Diagnostic AI models achieved a pooled mean accuracy of 89.6 % (95 % CI = 86.9–92.2) in image-based tasks; however, only 27 % reached real-world clinical deployment, mainly due to regulatory and workflow barriers. Telemedicine: Post-COVID adoption reached 87 % of U.S. office-based physicians in 2021, stabilising to 62 % by 2024, with reported patient-satisfaction rates above 78 % and cost savings averaging 18 % per encounter. Wearables/IoMT: Device accuracy averaged 91 % for heart-rate monitoring and 84 % for step count; yet long-term adherence dropped to 57 % after 6 months, correlating with project sustainability challenges. Bioinformatics/genomics: Pipeline throughput increased by 220 % (2020–2024) due to cloud-compute integration, but replication errors were reported in 12 % of studies lacking version-control protocols. Project-management integration significantly improved outcomes: projects employing hybrid governance (agile + regulatory stage-gates) had a 34 % higher probability of meeting performance and compliance goals compared with ad-hoc or purely technical management models. Conclusion: Quantitative synthesis confirms that biomedical–IT integration is technically successful yet organisationally fragile. Projects that combined agile iteration with formal quality assurance, clinical validation, and data-governance oversight achieved measurable improvements in scalability, compliance, and clinician adoption. The findings support a shift toward hybrid project- management models that embed data ethics, regulatory readiness, and stakeholder communication as core deliverables. These results have strong implications for health-system digital transformation, policy design, and cross-disciplinary training in the coming decade.