title: Towards automated calculation of evidence-based clinical scores.
abstract: OBJECTIVE
  To determine clinical scores important for automated calculation in the inpatient setting.
  
  
  METHODS
  A modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories - "not important", "nice to have", or "very important". Surveys were solicitedviaspecialty-group listserv over a 3-mo interval. Respondents must have been practicing physicians with more than 20% clinical time spent in the inpatient setting. Within each specialty, consensus was established for any clinical score with greater than 70% of responses in a single category and a minimum of 10 responses. Logistic regression was performed to determine predictors of automation importance.
  
  
  RESULTS
  Seventy-nine divided by one hundred and forty-four (54.9%) surveys were completed and 72/144 (50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold (14 respondents each). For internists, 2/110 (1.8%) of scores were "very important" and 73/110 (66.4%) were "nice to have". For intensivists, no scores were "very important" and 26/76 (34.2%) were "nice to have". Only the number of medical history (OR = 2.34; 95%CI: 1.26-4.67;P< 0.05) and vital sign (OR = 1.88; 95%CI: 1.03-3.68;P< 0.05) variables for clinical scores used by internists was predictive of desire for automation.
  
  
  CONCLUSIONS
  Few clinical scores were deemed "very important" for automated calculation. Future efforts towards score calculator automation should focus on technically feasible "nice to have" scores.
authors.affiliation: Christopher A Aakre, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, United States.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Aakre
authors.affiliation: Christopher A Aakre, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, United States.
authors.forename: Mikhail A
authors.initials: MA
authors.lastname: Dziadzko
authors.affiliation: Christopher A Aakre, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, United States.
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
keywords: Automation; Clinical decision support; Clinical prediction rule; Decision support techniques