A group of researchers from Johns Hopkins, the Ohio State University, and the National Cancer Institute argue that, based on projections from NCI survey data, 75 percent of US adults will use personal health records (PHRs) by 2020, even without additional interventions.
In a paper , academics extrapolated from the Health Information National Trends Survey (HINTS) from 2008, 2011, and 2013 and applied a product adoption prediction technique called Bass modeling to project PHR uptake into the future.
The writers present three different models, depending on whether they considered the innovation start date for PHR technology to be 2001 (the development date for the oldest systems) or 2007 (the first year, according to previous research, that clinically-based systems were widely available). The third model was a compromise, putting the start date in 2004.
The best of these models put adoption at 75 percent in 2020, but all of them outperformed the adoption targets of the Meaningful Use Stage 2 and 3 guidelines.
"Consumers’ PHR use is growing in both the numbers of people engaged and the degree of technological functionality they can manage," researchers wrote. "As organizations identify ways to make these tools more widely available, sophisticated PHR technologies would move from the domain of early adopters to the widespread use among a majority of consumers in the market. As this occurs, the primary factor limiting PHR functionalities’ diffusion may well be health care vendors’ and providers’ reticence to deploy these tools in a manner that resonates with the patient. It is not the consumer who is unwilling to use these tools, but the deployment and barriers they face that limits their adoption."
Based on their analysis, writers argued that Meaningful Use targets could actually slow down PHR adoption from the rates it would naturally tend towards.
"MU goals on this issue may have the same problems in PHR adoption that they experienced in EHR adoption: the standards of engagement are low enough to allow for incremental approaches to adoption as opposed to incentivizing transformative targets," they wrote. "Policy discussions in a 'post-meaningful use' world would benefit from insights provided through these types of data-based diffusion analyses, especially as the emphasis shifts away from applying [internally motivated] incentives for adoption, to driving innovation to curry the interest of engaged consumers."