A Decision-Analytic Event-Study Framework for Evaluating Digital Health Technology Investments

Abstract

Digital health investments are strategic technology-management decisions for healthcare firms, yet their short-run external evaluation is difficult to observe. This paper develops a decision-analytic event-study framework for assessing market reactions to twenty-five public digital health technology investment announcements by listed healthcare and life-sciences firms from 2018 to 2024. The events cover acquisitions, AI collaborations, remote monitoring, connected care, real-world evidence platforms, and digital health analytics initiatives. Announcement dates are traced to public sources, and market data are drawn from Yahoo Finance adjusted prices, SPY returns, and Fama-French daily factors. The main Fama-French three-factor specification reports statistically weak cumulative abnormal returns: -0.32% over the minus-one to plus-one window and 1.37% over the minus-five to plus-five window, while the event-day to plus-two window is negative at -0.78%. Sign tests, Wilcoxon checks, randomization inference, and bootstrap confidence intervals indicate that the expanded public sample still does not support a uniform positive premium. Mechanism regressions based on resource-based and continuous-innovation signals are treated as exploratory screens. The framework supports technology-management decision makers by converting public digital-health investment announcements into auditable external evaluation signals.