Survey: Healthcare Technology Pros See Poor Return On Investment from EHRs

Fortunately, survey respondents also overwhelmingly signaled that analytics software—a technology designed to make the clinical data in EHRs more valuable—holds great promise for the future. 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.

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%) or “poor” (42%). Another 29 percent said the ROI from EHR investments has been “mediocre.” Only 10 percent rated the ROI from EHRs as either “positive” (9%) or “superb” (1%).

By contrast, 83 percent of HAS 17 attendees who responded to a series of online mini-surveys 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.”

Analytics progress lags its promise … but optimism is high

Despite their enthusiasm for analytics, most HAS 17 attendees 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%) 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

Survey takers 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%) 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%) about the potential of analytics, or rated themselves as “advocates” (41%) who want to “help lead the change and a make a difference.” Much smaller numbers said they were in a “wait and see” mode (9%), 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.

Survey results were generated by HAS 17 attendees using the conference’s mobile app to provide answers to questions posed by keynote presenters.

For more information on HAS 17, which is hosted by Health Catalyst, visit https://hasummit.com/.

About Health Catalyst

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