Multi-state Markov Models for the Analysis of EMRs Diffusion in Healthcare




Li, Meng-Hao

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Prior studies on diffusion of innovations typically research the same units of analysis, top-down diffusion, bottom-up diffusion, spatial proximity or network analysis, paying little or no attention to the effect of bottom-up networks on the multilevel diffusion process. The adoption decision is formed at the organizational level, but the factors that formulate the decision possibly result from either inter- or intra-organizational networks, or mixed effects of inter- and intra-organizational networks. It remains unclear how individuals and organizations respectively are exposed to adoption information in their networks and collectively form an adoption decision at the organizational level. Using data (2009-2015) from the hospital’s adoption of Electronic Medical Records (EMRs), individual healthcare provider referral networks, and hospital system networks, this study applies multi-state Markov models to examine how the mixed effect of intra- and inter-hospital networks influences the process of bottom-up EMRs diffusion. The findings suggest that hospital system networks, individual provider networks within and between hospitals, and proximity of hospital locations play different roles in the transitions between EMRs states (i.e., non-adopters, basic adopters, intermediate adopters, and comprehensive adopters). The results enrich our understanding of how individuals (bottom) in an organization interact with internal and external environments to influence the organization’s decisions (up) collectively. This study further offers four network-based policy intervention strategies for facilitating the adoption of advanced EMRs and suggests devising an EMRs incentive scheme based on hospital EMRs states.



Public policy, Diffusion of Innovations, Electronic Medical Records, Policy Diffusion, Social Contagion, Social Network