An Efficient, Clinically-Natural Electronic Medical Record System that Produces Computable Data. Issue 3 (15th December 2017)

• Record Type: Journal Article
• Authors: James, Brent C.; Edwards, David P.; James, Alan F.; Bradshaw, Richard L.; White, Keith S.; Wood, Chris; Huff, Stan
• Is Part Of: EGEMS. Volume 5:Issue 3(2017)
• Publication Date: 2017-12-15
• Subjects: natural language processing -- next generation electronic medical records -- clinical decision support; Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.5334/egems.202
• Languages: English
• ISSNs: 2327-9214

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.