Empirical research
An Efficient, Clinically-Natural Electronic Medical Record System that Produces Computable Data
Authors:
Brent C. James ,
Intermountain Helathcare, US
About Brent C.
Executive Director, Intermountain Healthcare Institute for Healthcare Delivery Research Member, National Academy of Medicine Clinical Professor (Affiliated), Dept of Medicine, Division of General Medical Disciplines, Stanford University School of Medicine Visiting Lecturer, Dept of Health Policy & Management, Harvard School of Public Health Adjunct Professor, Depts of Medical Informatics and Family & Preventive Medicine, University of Utah School of Medicine Chair, Board of Directors, High Value Healthcare Collaborative, Hanover, NH Vice Chair, Board of Directors, Institute for Healthcare Improvement, Boston, MA
David P. Edwards,
Intermountain Healthcare, US
Alan F. James,
Intermountain Healthcare, US
Richard L. Bradshaw,
Intermountain Healthcare, US
Keith S. White,
Intermountain Healthcare, US
Chris Wood,
Intermountain Healthcare, US
Stan Huff
Intermountain Healthcare, US
Abstract
Current commercially-available electronic medical record systems produce mainly text-based information focused on financial and regulatory performance. We combined an existing method for organizing complex computer systems—which we label activity-based design—with a proven approach for integrating clinical decision support into front-line care delivery—Care Process Models. The clinical decision support approach increased the structure of textual clinical documentation, to the point where established methods for converting text into computable data (natural language processing) worked efficiently. In a simple trial involving radiology reports for examinations performed to rule out pneumonia, more than 98 percent of all documentation generated was captured as computable data. Use cases across a broad range of other physician, nursing, and physical therapy clinical applications subjectively show similar effects. The resulting system is clinically natural, puts clinicians in direct, rapid control of clinical content without information technology intermediaries, and can generate complete clinical documentation. It supports embedded secondary functions such as the generation of granular activity-based costing data, and embedded generation of clinical coding (e.g., CPT, ICD-10 or SNOMED). Most important, widely-available computable data has the potential to greatly improve care delivery management and outcomes.
How to Cite:
James BC, Edwards DP, James AF, Bradshaw RL, White KS, Wood C, et al.. An Efficient, Clinically-Natural Electronic Medical Record System that Produces Computable Data. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2017;5(3):8. DOI: http://doi.org/10.5334/egems.202
