title: Automating Clinical Score Calculation within the Electronic Health Record. A Feasibility Assessment.
abstract: Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records.
  
  
  
  We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings.
  
  
  
  We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable.
  
  
  
  Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments - partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study.
authors.affiliation: Christopher A Aakre, M.D., Division of General Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, Fax: 507-284-5370, Telephone: 507-538-0621, Email: aakre.christopher@mayo.edu.
authors.forename: Christopher
authors.initials: C
authors.lastname: Aakre
authors.forename: Mikhail
authors.initials: M
authors.lastname: Dziadzko
authors.forename: Mark T
authors.initials: MT
authors.lastname: Keegan
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
mesh_terms: D001331:Automation; D057286:Electronic Health Records; D005240:Feasibility Studies; D006801:Humans
keywords: Automation; clinical practice guideline; clinical score; decision support algorithm; knowledge translation; workflow