One of the reasons that “usability” of EHRs has been so elusive is that it has had no clear definition. We’ve heard that it is a barrier to full EHR adoption, but without a sense of what it is and how it could be measured, it’s difficult to improve. We have defined its attributes –efficiency, efficacy, and satisfaction as one carries out one’s tasks – and identified some early basic ways to assess usability as it pertains to safety. But we have yet to come to consensus on how to achieve an environment where focused, organized, comprehensive clinical information is immediately available in the time and format that is needed and how to assure that data can be quickly and easily incorporated into the electronic record of choice.
Discussions are now in progress at both federal and state levels on the topic, and the National Institute of Standards and Technology (NIST) has been working on a Usability Framework that will be shared in early June. National Coordinator for Health IT, Dr. Farzard Mostashari, has signaled the need to progress in this arena, and to do so with a goal of improvement…not a goal of measurement for certification or other qualifying purposes. The Certification/Adoption Work Group of the federal Health IT Policy Committee has heard a full day’s worth of testimony on usability in preparation for making recommendations to the full Committee. As a result of this activity and the work done to date, we now have a foundation on which to build:
- A usable system has access to comprehensive data and integrates all elements of data input, storage, and use. This requires interoperability and assurance that all components (or modules) of the system are integrated, as is the case for CCHIT Certified® 2011 products.
- Usability refers to how an EHR supports clinical workflow — not a unique measure, objective, or criteria. Including practicing clinicians in the process of determining and assessing usability is therefore critical to its success.
- Tools will be required that can quickly find and filter accurate data that is specific to the user and presented as information that can be organized to support real time patient care in different settings – one size does not fit all situations or exam rooms.
- Usability testing will change as hardware and operating systems evolve – what may be applicable in a Windows desk top environment may not work in an iPad or other mobile smart device environment.
- Usability must address the access needs of persons with disabilities.
- Attention to patient communications (both efferent and afferent) will need to be incorporated into the usability framework. Use of EHRs affects the patient-clinician interaction.
- Vendors understand that usability will provide a marketing edge. Many of the larger more stable vendors have already invested in usability programs in house with clear principles and practices. These programs are not, however, transparent, and many products are reconfigured to meet customized needs at installation, necessitating assessment in the working environment.
- User centric design and human factors research are robust scientific fields that can guide the development of usable systems, as well as add to the current measurement base.
- Usability testing should result in demonstrated improvement in specific outcomes, such as measures of patient safety, provider productivity, and clinician satisfaction with workflows.
- Usability will likely be improved as we migrate to a “plug and play” EHR environment, where one individual workflow application can be exchanged for another which will integrate with those already in place.
So…what can we expect in the coming months? Clearly, we will be seeing greater access to interoperable health data as one of the key components of usability. We can also expect NIST to continue to refine and develop a framework based on human factors research and user centric design that will guide the development of more robust usability testing. I hope that we will see ONC Certification Criteria move toward integration testing and criteria that are oriented toward clinical workflow. Ultimately, new applications and approaches will evolve and usability testing will evolve along with them. In the meantime, CCHIT will continue to conduct basic patient safety focused usability testing that has been validated and demonstrated to have inter-rater reliability on all CCHIT Certified® 2011 ambulatory EHR products.
Karen M. Bell, MD, MMS
Chair, Certification Commission
Karen Bell, MD, MMS, is Chair of the Certification Commission for Health Information Technology (CCHIT®. Dr. Bell has wide and varied expertise in health information technology (HIT), quality assurance and clinical practice, in both the private and public sectors. Previously, she served as Senior Vice President, HIT Services, Masspro, the federally-contracted Quality Improvement Organization within Massachusetts, where she oversaw the development, implementation and distribution of products and services to support adoption of electronic health records (EHRs) within the health care system. Between 2005 and 2008, Dr. Bell was Director, Office of Health Information Technology Adoption, Office of the National Coordinator (ONC), U.S. Department of Health and Human Services (HHS), and, in 2006, served as Acting Deputy of ONC. She was ONC’s representative on CCHIT’s Board of Commissioners from 2006 to 2008.
Prior appointments held by Dr. Bell include Division Director, Quality Improvement Group/Office of Standards and Quality for the Centers for Medicare and Medicaid (CMS), and Medical Director of Blue Cross Blue Shield (BCBS) of Rhode Island and of Anthem BCBS of Maine.
She received her medical degree from Tufts University School of Medicine, Boston, and her master of medical science degree from Brown University, Providence, R.I. Dr. Bell has clinical experience as a board certified physician in internal medicine and also was an Associate Professor at the University of Rochester, and Clinical Instructor at Harvard University School of Medicine.

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EHRs are good at displaying medical information, but have poor data capture mechanisms. Although the method by which the physician captures data in the EHR largely determines the EHR’s efficacy, efficiency, and clinician satisfaction, efforts to improve usability over-emphasize improvements to the medium of the record (the EHR) and under-emphasize improvements to the data capture method. To illuminate the point by analogy, if our medium were paper, this incongruity would be akin to over-emphasizing improvements to how the paper records were organized in folders, the speed at which ink dried on the paper, or the paper’s color and under-emphasizing improvements to the data capture method for paper (e.g. handwriting at 20 wpm versus typing at 60 wpm or transcription from dictation at 140 wpm).
Natural Language Processing (NLP) technology has shown great promise as part of a new breed of tools that enhance EHR usability by improving the EHR data capture method. These tools use NLP to extract structured, EHR-populatable data from records that are dictated and transcribed. To the benefit of efficacy and patient safety, they allow the physician to retain the health story while capturing more complete and granular data in structured formats required for interoperability. Dictation is fast; using it for data capture can improve efficiency and productivity. With regard to clinician satisfaction, dictation is a natural skill for physicians, and unlike standard EHR data capture methods, does not require mastering a new, non-intuitive interface.
To truly evaluate prospective EHR usability improvements, certain standard metrics must be selected. To measure efficacy – specifically record completeness and granularity – the most useful metrics may be the number and accuracy of the codes and modifiers generated in standard terminologies required for interoperability (i.e. SNOMED-CT, ICD-9/10, CPT-4, LOINC, and RxNORM). To measure efficiency, a suitable metric may be the number of codes and modifiers generated for records divided by the amount of clinician time spent on those records. These metrics would enable comparison of the above-mentioned tools that use dictation and NLP for EHR data capture against standard EHR data capture methods.
Extremely enlightening.
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