EHR Adoption > Negative effects of EHR systems on adoption and informatics

Negative effects of EHR systems on adoption and informatics

Author | Date September 24, 2012

With its heavy focus on clinical documentation and workflows, the current state of electronic health record (EHR) systems has created a closed environment that limits the ability of digital health information to improve patient outcomes within and beyond a health system, according to the author of a recent article in the Journal of the American Medical Informatics Association (JAMIA). And the consequence of this design could be a significant impediment to future adoptions of EHR systems.  “In their current form, EHRs function as a walled garden and prevent the integration of outside tools and services. This impedes the widespread adoption and diffusion of research interventions into the clinic,” writes Dr. Keith Marsolo of the Cincinnati Children’s Hospital Medical Center.

The ultimate aim of the Centers for Medicare and Medicaid Services (CMS) EHR Incentive Programs and the Office of the National Coordinator for Health Information Technology (ONC) Certification programs is to improve healthcare for all patients and the efficiency of its delivery through EHR and health IT systems. Progress in the EHR Incentive Programs is measured by how effectively electronic information can be leveraged to support providers to make well-informed clinical decisions — that is, though the use of tools such as clinical decision support (CDS).

While the architects of federal programs such as meaningful use have pegged EHR and health IT systems as the vehicles for driving changes in healthcare, Marsolo contends that their efforts to increase the adoption of these systems appear to have more significantly benefitted vendors rather than providers. While users of EHR systems must adjust to working electronically and avoid reproducing paper-based approaches to using this technology, they are subject to the vendor’s vision of how these systems work with limit power to effect change. “Without outside pressure from the marketplace, from patients, and from clinicians, however, vendors have no incentive or motivation to change,” argues Marsolo.

Unless researchers can more easily extract meaningful information from EHR systems, they will be unable to demonstrate the value of capturing health information electronically. According to Marsolo, the inability of current EHR systems to export valuable information has forced researchers to turn to unattractive solutions for data capture such as free text, which is wrought with inefficiencies:

With EHRs, the easiest method of data collection is to typically create a text-based template where a clinician can fill in certain keywords. This leads most clinicians to believe they are capturing data discretely, but unless a significant amount of effort is undertaken to tag those keywords with a unique identifier, the result is stored as a blob of text. Other data collection methods may store data discretely, but do not integrate into the clinical workflow. The design of EHRs should be such that making the ‘right’ choice in terms of designing data collection forms is the easy choice.

As it stands, researchers are forced in the uncomfortable situation of creating workarounds like parsing unstructured data. Unfortunately, having recourse to even problematic forms of data is itself a luxury. The difficulty of conducting research is compounded by priorities that are competing for resources. “Since few institutions have EHR staff dedicated to handling research requests, this project must compete for priority against initiatives like the ICD-10 conversion and Phase 1 of Meaningful Use or the implementation of the EHR itself,” observes the author.

According to the author, the first step toward remedying the situation begins with the relationship between clinicians and researchers. “For years, the relationship between clinical operations and research at most institutions ranged anywhere from non-existent to hostile, to, in the best cases, something close to benign neglect,” continues Marsolo, “The implementation of enterprise-wide EHRs has now caused these worlds to come crashing together.” The benefit of this collaboration will then be realized in the configuration of a useful EHR system capable of meeting the demands of both sides. “If we are to ever achieve the vision of a learning health system, where learning occurs with every patient encounter, we need to ensure that the clinical information systems are configured to allow this learning,” the author concludes.

Stage 1 Meaningful Use deals with data capture. Stage 2 Meaningful Use targets health information exchange (HIE). And Stage 3 Meaningful Use ultimately aims to care for populations. But these efforts are meaningless if researchers aren’t able to make sense of patient and provider data.

Related Articles:

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• AHA questions AHRQ quality measure timing
• Optum Institute finds patient engagement lacking
• EHR, population health, and the clinic community
• Meaningful use: Stage 1, Stage 2 comparison

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