Healthcare Analytics, Population Health Management, Healthcare Big Data

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EMRAM Forecast Puts Hospitals Decades Away from Analytics Maturity

Only around half of hospitals and health systems are projected to reach full EMRAM analytics maturity by 2035.

EMRAM hospital analytics maturity

Source: Thinkstock

By Jennifer Bresnick

- Healthcare organizations may have decades to go before the majority of hospitals and health systems reach widespread data analytics maturity, according to a new study from the Journal of Medical Internet Research.

Based on current trends, only about half of hospitals will reach Stage 7 on the HIMSS electronic medical record adoption model (EMRAM) by 2035, says corresponding author Hadi Kharrazi, MHI, MD, PhD, from the Johns Hopkins Bloomberg School of Public Health.

The slow health IT maturity curve, which assumes no new incentive programs or revolutionary technology breakthroughs, may become a significant challenge for regulators, payers, and other stakeholders looking to shepherd the provider community towards value-based reimbursement.

“Not reaching Stage 7 of EHR maturity by 2035 is concerning as the continued lack of interoperability may adversely affect patient safety, clinical outcomes, and population health management efforts,” observed the team of researchers from The Ohio State University, the University of Sydney Business School, the University of Washington, and the University of Alabama Birmingham.

The EMRAM score has become an industry standard for gauging an organization’s sophistication with electronic health records and associated data analytics technologies.  Introduced by HIMSS in 2006, the model divides organizations into eight categories. 

READ MORE: Using Big Data Analytics for Patient Safety, Hospital Acquired Conditions

Stage 0 organizations may have an underlying EMR strata available but have no ancillary capabilities (laboratory, radiology, and pharmacy) installed.  They lack support for clinical documentation, clinical decision support protocols, CPOE, and health information exchange.

Organizations that reach the highest designation, Stage 7, have a complete EMR and data analytics functions that actively contribute to improving quality care. 

EMRAM inpatient adoption scores from 2006 to 2014
EMRAM inpatient adoption scores from 2006 to 2014

Source: JMIR

Stage 7 hospitals leverage an EMR-derived data warehouse to support clinical analytics, uses digital documentation and CPOE in at least 90 percent of situations, and can enable interoperability functions to share data with other entities. 

The organization will be able to create and sustain data continuity with emergency departments, ambulatory settings, and outpatient locations while maintaining high standards of privacy and security.

As of the end 2017, only 6.4 percent of more than 5200 hospitals had achieved the Stage 7 recognition.

READ MORE: Providers Embrace Predictive Analytics for Clinical, Financial Benefits

About two-thirds of organizations were at Stage 5 or Stage 6, meaning they have accomplished the majority of the EMR adoption tasks, but have not yet consistently integrated advanced analytics into their care models.

The team predicts that Stage 5 will reach its peak by 2019, and Stage 6 by the year 2026.  By 2025, the majority of organizations will be Stage 5 or higher.

EMRAM adoption forecast until 2035
EMRAM adoption forecast until 2035

Source: JMIR

However, “a considerable number of hospitals (800+) will stall their EHR adoption at Stage 5, while a higher number of hospitals (2200+) will remain in Stage 6 over an expanded period of time until 2035,” the article stated. 

Breaking through to the highest category of maturity will be difficult for organizations that may not see strong incentives for doing so.

The team noted that the EHR Incentive Programs played a significant role in accelerating adoption and maturity during the first half of the decade, but that pace has slackened – and is likely to remain relatively slow – now that providers no longer receive federal financial aid for implementing health IT tools.

READ MORE: 5 Steps for Planning a Healthcare Artificial Intelligence Project

“Academic medical centers and integrated or value-based delivery systems have realized the need for advanced analytics to push forward with their academic research agenda and quality improvement efforts, hence, accepting or planning for the development of centralized EHR-derived data warehouses,” they said.

Organizations that have reached the higher stages without support from meaningful use are likely to be “mission driven” and internally motivated to improve quality or succeed in a highly competitive business environment, the study added.

“However, on the other end of this spectrum, with fewer internal incentives, smaller critical access and rural or community hospitals may not see the added value of investment in developing complex and often expensive EHR-derived data warehouses, unless the EHR vendors offer it as part of their basic or routine updates without additional charges (eg, EHR vendors attempting to keep their market share).”

Vendors, however, are certainly working to equip their customers with out-of-the-box analytics tools, and may be the key to bending the curve much more sharply in the right direction.

The study specifically mentions that the trajectory does not take into account any game-changing advances in artificial intelligence, machine learning, or natural language processing - yet in just a matter of months, these tools have started to dramatically influence the ability of healthcare organizations to leverage previously untapped data assets.

An increasing number of vendors are incorporating AI algorithms into their core offerings to provide clinical decision support, risk stratification, and performance analytics to consumers who may not be able to develop these capabilities on their own.

Such tools may help organizations leapfrog up the EMRAM scale and support more of the critical tasks that enable actionable insights and quality improvements in the clinical setting.

Organizations will need to develop intrinsic motivations for integrating these functionalities into their workflows – and many of those motivations are likely to be financial.

The concurrent growth of risk-based contracting is a strong incentive for developing predictive insights and proactive population health management strategies, and may be a primary catalyst for smaller, less technically mature hospitals to push their way up the EMRAM ladder.

How organizations will combat the inherent catch-22 of financial motivation and analytics knowhow remains to be seen, however. 

While more and more organizations are recognizing that financial success now depends on the ability to access and apply data-driven insights, the challenges of implementing advanced health IT tools will remain a pressing concern for the foreseeable future.


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