Privacy in Context: The Costs and Benefits of a New Encryption Method

ISBN-13: 9783659928017 / Angielski / Miękka / 2016 / 228 str.

Stan Trepetin

Opis

The American public continues to be concerned about medical privacy. Health organizations need personally identifiable data to make care decisions; yet identifiable data are often the basis of information abuse. This book shows how de-identified data may be used for important healthcare operations. A technology adoption model is constructed to explore if a for-profit health insurer could use de-identified data. A close data analysis finds support for adding privacy protections to the insurer's quality-of-care applications. A cost-benefit model is constructed describing the Predictive Modeling application (PMA), used to identify the insurer's chronically-ill policy-holders. The model quantifies the decline in policy-holder care and rise in the insurer's claim costs as the PMA must work with suboptimal data due to policy-holders' quality-of-care privacy concerns. A new encryption approach to link records despite linkage variable errors is constructed. It's tested as part of a general data de-identification methodology--and an actual PMA's performance is found to be the same as if executing on identifiable data. That is, key medical applications can be run on de-identified data.

O autorze

Dr. Trepetin is the former Chief Information Security Officer of the NYC Health Department and has held leadership positions at several financial services organizations, including HSBC Bank. He holds two information security patents. He has a PhD in Health Informatics from MIT, a Master's degree from Duke, and a Bachelor's degree from Cornell.