EMR adoption could be contagious
Persuading influential medical centers to adopt EMRs helps speed adoption by their neighboring hospitals, according to the August issue of Management Science, a journal of the Institute for Operations Research and the Management Sciences (INFORMS).
Corey M. Angst, PhD, of the Mendoza College of Business at the University of Notre Dame in South Bend, Ind., and colleagues, sought to understand what mechanisms influence the swift and successful diffusion of technological innovations at hospitals.
The researchers used a social contagion model, which acknowledges the mutual influence among organizations within an institutional field and implicates information transmission through direct contact and observation as the mechanisms that underly the transfer of influence. They obtained results by fitting a heterogeneous diffusion model to data spanning the years 1975 to 2005 from 3,989 U.S. hospitals from the Healthcare Information and Management Systems Society (HIMSS) Analytics Database.
The authors posited that diffusion is accelerated if specific attention is given to increasing adoption among well-known, larger, older hospitals in densely populated geographic regions. Management should therefore target specific influential institutions for a new technology to increase its rate of adoption.
According to the study, the contagion model explicitly acknowledges the mutual influence that organizations exert on each other within an institutional field.
“Greater hospital size and age are positively related to the likelihood of adoption for nonadopters, whereas younger hospitals are associated with greater infectiousness for adopters,” Angst and colleagues wrote. “A hospital’s ‘celebrity’ status also contributes to its infectiousness. We further find strong effects for social proximity and significant regional effects for spatial proximity and hospital size, suggesting that geographical covariates should be included in [future] diffusion studies.”
Regional analyses indicated that hospitals in areas with relatively lower population density are significantly influenced by proximate adopters, whereas those in more densely populated areas are not, the researchers found. “This is consistent with [the argument that] lower population density increases the likelihood of competition for knowledge workers and associated transfer across hospitals.”
The study did note that although the research speculates that population density and variability in hospital size are potential drivers of regional variations, further research is needed to investigate the question in more detail.
“Our research … demonstrates that diffusion [of EMRs] could be accelerated if specific attention is given to increasing social contagion effects,” concluded the authors. “The social contagion perspective is important in this context in particular because the technology is complex and managers are seeking evidence from others that the rewards of adoption outweigh the risks.”
Corey M. Angst, PhD, of the Mendoza College of Business at the University of Notre Dame in South Bend, Ind., and colleagues, sought to understand what mechanisms influence the swift and successful diffusion of technological innovations at hospitals.
The researchers used a social contagion model, which acknowledges the mutual influence among organizations within an institutional field and implicates information transmission through direct contact and observation as the mechanisms that underly the transfer of influence. They obtained results by fitting a heterogeneous diffusion model to data spanning the years 1975 to 2005 from 3,989 U.S. hospitals from the Healthcare Information and Management Systems Society (HIMSS) Analytics Database.
The authors posited that diffusion is accelerated if specific attention is given to increasing adoption among well-known, larger, older hospitals in densely populated geographic regions. Management should therefore target specific influential institutions for a new technology to increase its rate of adoption.
According to the study, the contagion model explicitly acknowledges the mutual influence that organizations exert on each other within an institutional field.
“Greater hospital size and age are positively related to the likelihood of adoption for nonadopters, whereas younger hospitals are associated with greater infectiousness for adopters,” Angst and colleagues wrote. “A hospital’s ‘celebrity’ status also contributes to its infectiousness. We further find strong effects for social proximity and significant regional effects for spatial proximity and hospital size, suggesting that geographical covariates should be included in [future] diffusion studies.”
Regional analyses indicated that hospitals in areas with relatively lower population density are significantly influenced by proximate adopters, whereas those in more densely populated areas are not, the researchers found. “This is consistent with [the argument that] lower population density increases the likelihood of competition for knowledge workers and associated transfer across hospitals.”
The study did note that although the research speculates that population density and variability in hospital size are potential drivers of regional variations, further research is needed to investigate the question in more detail.
“Our research … demonstrates that diffusion [of EMRs] could be accelerated if specific attention is given to increasing social contagion effects,” concluded the authors. “The social contagion perspective is important in this context in particular because the technology is complex and managers are seeking evidence from others that the rewards of adoption outweigh the risks.”