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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.

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

AI fixes healthcare's broken infrastructure, not just diagnosis

Hazra argues that healthcare AI's greatest value lies not in clinical insight alone, but in eliminating systemic inefficiencies—coding errors, diagnostic gaps, administrative burden—that drain resources and compromise care quality. She contends that automation and standardization powered by machine learning can simultaneously improve accuracy, reduce costs, and extend quality care to resource-limited settings where human expertise and equipment are scarce.

millions

in annual costs from inconsistent healthcare coding practices

“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

Healthcare AI's multi-layered impact across care delivery

Diagnostic accuracy and error reduction9
Closing care gaps in underserved regions8
Eliminating coder bias and cost waste9
Freeing clinicians from administrative burden8
Strengthening patient-provider relationships7

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22%Diagnostic accuracy
Diagnostic accuracy and error reduction
Closing care gaps in underserved regions
Eliminating coder bias and cost waste
Freeing clinicians from administrative burden
+1 more

3

distinct care system inefficiencies AI directly addresses in Hazra's work

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

Bridging Care Gaps: AI Solutions in Healthcare

Generative AI integration with electronic health records is poised to free clinicians from administrative burdens.

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

“Healthcare AI is reshaping diagnostic capabilities where medical expertise remains scarce.”

Themes:Automation as infrastructure repair, not just intelligence enhancementSystemic cost elimination through standardization and coding accuracyAI democratizing diagnostic capability in resource-constrained settings

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