The billions in taxpayer dollars spent on electronic health records (EHRs) since 2009 have unfortunately generated a poor return for the nation’s healthcare system, according to a survey of more than 1,100 healthcare professionals conducted by Salt Lake City-based data analytics vendor Health Catalyst.
Health Catalyst polled healthcare professionals attending the fourth annual Healthcare Analytics Summit September 12-14 in Salt Lake City.
When asked to assess the “return on digital investment” produced by the billions of dollars invested in implementing EHRs since the 2009 federal stimulus program, 61 percent of respondents to the online survey answered either “terrible” (19 percent) or “poor” (42 percent). Another 29 percent said the ROI from EHR investments has been “mediocre.” Only 10 percent rated the ROI from EHRs as either “positive” (9 percent) or “superb” (1 percent).
By contrast, 83 percent of respondents rated analytics as “extremely important” to “the future of healthcare and population health.” Fourteen percent of respondents said analytics is “very important,” while 3 percent rated it “moderately important.” No respondents rated the technology as either “somewhat important” or “not important.”
The divergent views of the two technologies likely reflects the industry’s abrupt shift away from data collection and toward data analysis as healthcare transitions from fee-for-service to value-based reimbursement.
The survey results also indicate that, despite their enthusiasm for analytics, most survey respondents work for organizations that have yet to make full use of the technology’s capabilities. When asked to compare their organizations’ use of analytics with a 4-level scale of analytics sophistication, half (50 percent) of respondents ranked their organization’s use of analytics as “artisanal,” at the bottom of the scale.
According to the model, developed by author and HAS17 keynote Tom Davenport, the four levels or analytics adoption are: Artisanal Analytics – the most basic level, consisting mainly of data integration and curation; Big Data – analytics 2.0, enabling experimentation, open source coding and visual analytics; The Data Economy – analytics incorporating machine learning, agile methods and change management; Cognitive Analytics – enabling natural language process, event stream processing, work design and neural networks or deep learning.
The survey found that respondents generally aligned with levels 1, 2 or 3 on the scale, with 26 percent assigning “Big Data” standing to their organizations, and 17 percent selecting “data economy” analytics. Only a handful of survey takers (5 percent) rated their organizations as having achieved the most advanced form of analytics, “cognitive analytics.”
While most healthcare organizations may be early in their adoption of analytics, survey takers overall were optimistic about the technology. Seventy-six percent of respondents said they were either “optimistic” (35 percent) about the potential of analytics, or rated themselves as “advocates” (41 percent) who want to “help lead the change and a make a difference.”
Fewer respondents said they were in a “wait and see” mode (9 percent), or “worried” about other priorities getting in the way of analytics success. Just four percent of survey takers said they were “skeptical” about making analytics work as promised.
