Machine-to-machine (M2M) and mHealth will be among the technologies fueling the drive to incorporate Big Data in healthcare, but few organizations are fully prepared to handle the influx of data, according to a new survey from MeriTalk.
The report is based an online survey from January of 150 federal executives focused on healthcare and healthcare research. They said Big Data's potential to improve healthcare will be its ability to help track and manage population health more efficiently (63 percent), improve patient care within military health and VA systems (62 percent), and enhance the ability to deliver preventive care (60 percent).
Fifty-nine percent said effectively harnessing Big Data will be vital to fulfilling their agency's mission objectives in the next five years, according to an announcement.
However, less than 25 percent say their agency is very prepared to work with Big Data. Only 34 percent say their agency has invested in technology to optimize data processing, trained IT professionals to manage and analyze Big Data (29 percent), or educated senior management on Big Data issues (29 percent).
Meanwhile, just 15 percent of respondents say they have implemented M2M technologies, while 53 percent plan to do so within the next two years. The real-time data this technology can provide prompts their belief that it will have the greatest impact on improving patient care and remote patient monitoring.
Monitoring services are the "dominant and fastest-growing" segment in the mHealth market, an industry that is expected to hit projected revenue of more than $49 billion within six years, according to Grand View Research.
Reimbursement issues and murky regulations were the top mHealth challenges cited in report from the Center for Technology Innovation at the Brookings Institution, which described the industry as being in its infancy.
However, as Steven Steinhubl, a cardiologist at San Diego-based Scripps Health and director of digital medicine at the Scripps Translational Science Institute, told FierceHealthIT, the field requires more hard evidence.
"The whole medical field is full of examples of therapeutic interventions that everyone just assumes make so much sense that they have to be good for you, but a lot [of] times, those interventions turn out to be dangerous," said Steinhubl. "It's really important to have the highest-potential-level data available to show how different technologies fit in."