Enterprise imaging and big data: A review from a medical physics perspective

Area of Science:

• Medical Physics
• Health Informatics

Background:

• Enterprise Imaging (EI) is integral to modern healthcare initiatives.
• Big data analytics presents new avenues for medical data insights.
• The convergence of EI and big data offers significant healthcare transformation potential.

Purpose of the Study:

• To review recent advancements in Enterprise Imaging and big data within medical physics.
• To outline practical aspects of EI and big data implementation.
• To examine the benefits and challenges of EI strategies in healthcare.

Main Methods:

• Literature review of recent developments in Enterprise Imaging and big data.
• Analysis of EI and big data practical applications and implementation steps.
• Examination of benefits (compliance, patient care, revenue, workflows, AI) and challenges (integration, governance, security).

Main Results:

• EI and big data integration can enhance compliance, patient care, revenue, and workflows.
• Artificial intelligence applications are key enablers for enterprise imaging.
• Key challenges include systems integration, data governance, and security/privacy.

Conclusions:

• Implementing an effective EI strategy, leveraging big data, is crucial for healthcare advancement.
• Addressing integration, governance, and security challenges is vital for successful EI adoption.
• The synergy of EI, big data, and AI promises to revolutionize medical physics and patient outcomes.