Earticle

초록

영어

AI-based health care professional (HCP) targeting in the US enables individualized segmentation, Next-Best-Action orchestration, and closed-loop measurement by integrating three individual-physician-level data layers: prescriber inputs, engagement responses, and outcome feedback. This study asks why these capabilities have not emerged in Korea, using a comparative qualitative analysis grounded in the Technology–Organization–Environment (TOE) framework. The US permits commercial aggregation of individual-level HCP data; Korea's Personal Information Protection Act (PIPA) classifies physician identifiers as personal information and forecloses this pathway at every layer. The three data layers therefore cannot form: rules-based targeting remains viable, but self-improving AI-based targeting is structurally precluded. The paper shows that when AI depends on regulated personal data, regulation determines which class of AI capability emerges, with the binding constraint at the data layer rather than at organizational or algorithmic capacity. For Korea, the path runs through reclassifying prescriber identity and behavioral data as professional rather than personal

목차

Abstract
1. Introduction
2. Literature Review
2.1 HCP Targeting and NBA
2.2 Data Infrastructure
2.3 Privacy Regulation
3. Research Method
3.1 Research Design
3.2 TOE Framework
3.3 Case Selection
3.4 Data Sources and Analytical Procedure
4. The U.S. Case
4.1 Prescriber Data
4.2 Engagement Data
4.3 Outcome Data
5. The Korean Case
5.1 Prescriber Data
5.2 Engagement Data
5.3 Outcome Data
6. Comparative Analysis
6.1 TOE Mapping
7. Discussion
8. Conclusion
References

키워드

저자

• Tracey Choi [ 뉴메스 ]
• 어태준 [ 뉴메스/연세대학교 ]
• 원혜연 [ 뉴메스 ]
• 이희상 [ 뉴메스 ]
• 윤대명 [ 뉴메스/연세대학교 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658