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Clinical Patient Safety Data Specialist

Arpita Hazra

Arpita Hazra, a dedicated physician, combines her medical expertise with a passion for building AI and machine learning models aimed at enhancing patient outcomes. Her boundless energy and unwavering motivation are evident in her multifaceted career. With a profound understanding of clinical data management, health education, public health, and program planning, Arpita has excelled in various domains including project management, patient safety, and risk analysis. Her versatility extends to healthcare consulting and clinical risk consulting, where she brings a wealth of qualitative and quantitative research experience to the table. Arpita is a force in healthcare business development, equipped with technical skills in Power BI, Azure Databricks, SQL, and SAS programming. Her expertise also encompasses healthcare data model architecture development and user acceptance testing (UAT), as well as medical writing. In essence, Arpita Hazra is a well-rounded professional with a mission to bridge medicine and technology for the betterment of patient care and outcomes.

5 articlesLinkedIn ↗
Contributor Brief·Arpita Hazra · 5 articles
Updated Mar 11, 2024

AI solves healthcare's invisible cost crisis: coding bias and care gaps

Hazra argues that healthcare's biggest inefficiencies stem not from clinical knowledge gaps but from systemic data and process failures—inconsistent coding practices, fragmented decision-making, and resource scarcity—that AI automation can solve at scale. She contends that technology-driven standardization, particularly in administrative workflows and diagnostic support, will unlock both immediate cost savings and equitable access in underserved settings.

millions annually

cost of inconsistent healthcare coding practices to systems

“Inconsistent coding practices cost healthcare systems millions while automation and standardization offer a clear path forward.”

Coder Bias is a Hidden Threat to Healthcare Accuracy

AI's impact across healthcare's critical pain points

Diagnostic error reduction and accuracy9
Administrative burden relief for clinicians8
Diagnostic gap identification and prevention8
Coding standardization and compliance9
Capability expansion in resource-limited settings7

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22%Diagnostic error
Diagnostic error reduction and accuracy
Administrative burden relief for clinicians
Diagnostic gap identification and prevention
Coding standardization and compliance
+1 more

5 healthcare domains

AI solutions are reshaping simultaneously across articles

Generative AI integration with electronic health records is poised to free clinicians from administrative burdens while strengthening patient-provider relations.

EHR Solutions, Backed by Oracle's AI-Enhanced Clinical Digital Assistant

Artificial intelligence is reshaping diagnostic capabilities in underserved regions where medical expertise and equipment remain scarce.

The Latest Healthcare AI Tools Should Prove Valued Assets for Resource-Limited Settings

“Automated clinical decision support tools are reshaping how providers identify and prevent diagnostic gaps.”

Themes:Hidden system failures drive healthcare waste more than clinical gapsAI standardization unlocks efficiency through data consistency, not just speedResource-limited settings are prime markets for AI's diagnostic leverage

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