Preference-Sensitive Management of Post-Mammography Decisions in Breast Cancer Diagnosis

• 成果类型: Article
• 署名作者: Ayvaci, Mehmet Ulvi Saygi; Alagoz, Oguzhan; Ahsen, Mehmet Eren; Burnside, Elizabeth S.
• 署名单位: University of Texas System; University of Texas Dallas; University of Wisconsin System; University of Wisconsin Madison; Icahn School of Medicine at Mount Sinai; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12897
• 发表日期: 2018
• 页码: 2313-2338
• 关键词: risk-sensitive Markov decision processes; dynamic programming; breast cancer; preferences; medical decision-making; UTILITY THEORY; healthcare analytics

摘要

Decision models representing the clinical situations where treatment options entail a significant risk of morbidity or mortality should consider the variations in risk preferences of individuals. In this study, we develop a stochastic modeling framework that optimizes risk-sensitive diagnostic decisions after a mammography exam. For a given patient, our objective is to find the utility maximizing diagnostic decisions where we define the utility over quality-adjusted survival duration. We use real data from a private mammography database to numerically solve our model for various utility functions. Our choice of utility functions for the numerical analysis is driven by actual patient behavior encountered in clinical practice. We find that invasive diagnostic procedures such as biopsies are more aggressively used than what the optimal risk-neutral policy would suggest, implying a far-sighted (or equivalently risk-seeking) behavior. When risk preferences are incorporated into the clinical practice, policy makers should bear in mind that a welfare loss in terms of survival duration is inevitable as evidenced by our structural and empirical results.