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An Efficient, Clinically-Natural Electronic Medical Record System that Produces Computable Data.
James, Brent C; Edwards, David P; James, Alan F; Bradshaw, Richard L; White, Keith S; Wood, Chris; Huff, Stan.
Afiliação
  • James BC; Intermountain Healthcare, US.
  • Edwards DP; Intermountain Healthcare, US.
  • James AF; Intermountain Healthcare, US.
  • Bradshaw RL; Intermountain Healthcare, US.
  • White KS; Intermountain Healthcare, US.
  • Wood C; Intermountain Healthcare, US.
  • Huff S; Intermountain Healthcare, US.
EGEMS (Wash DC) ; 5(3): 8, 2017 Dec 15.
Article em En | MEDLINE | ID: mdl-29881757
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.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: EGEMS (Wash DC) Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: EGEMS (Wash DC) Ano de publicação: 2017 Tipo de documento: Article