The Social Implications of Technology Diffusion: Uncovering the Unintended Consequences of People's Health-Related Mobile Phone Use in Rural India and China

Abstract

After three decades of mobile phone diffusion, thousands of mobile-phone-based health projects worldwide (“mHealth”), and hundreds of thousands of smartphone health applications, fundamental questions about the effect of phone diffusion on people's healthcare behavior continue to remain unanswered. This study investigated whether, in the absence of specific mHealth interventions, people make different healthcare decisions if they use mobile phones during an illness. Following mainstream narratives, we hypothesized that phone use during an illness (a) increases and (b) accelerates healthcare access. Our study was based on original survey data from 800 respondents in rural Rajasthan (India) and Gansu (China), sampled from the general adult population in 2014 in a three-stage stratified cluster random sampling design. We analyzed single- and multi-level logistic, Poisson, and negative binomial regression models with cluster-robust standard errors. Contrary to other research at the intersection of mobile phones and healthcare, we captured actual health-related mobile phone use during people's illnesses irrespective of whether they own a phone. Our analysis produced the first quantitative micro-evidence that patients’ personal mobile phone use is correlated with their healthcare decisions. Despite a positive association between phone use and healthcare access, health-related phone use was also linked to delayed access to public doctors and nurses. We considered theoretical explanations for the observed patterns by augmenting transaction cost and information deficit arguments with the prevailing health system configuration and with notions of heuristic decision-making during the healthcare-seeking process. Our study was a first step toward understanding the implications of mobile technology diffusion on health behavior in low- and middle-income countries in the absence of specific mHealth interventions. Future research will have to explore the causal relationships underlying these statistical associations. Such a link could potentially mean that development interventions aimed at improving access to healthcare continue to require conventional solutions to sustain healthcare equity.