title: Use of an automated decision support tool optimizes clinicians' ability to interpret and appropriately respond to structured self-monitoring of blood glucose data.
abstract: OBJECTIVE
  We evaluated the impact of an automated decision support tool (DST) on clinicians' ability to identify glycemic abnormalities in structured self-monitoring of blood glucose (SMBG) data and then make appropriate therapeutic changes based on the glycemic patterns observed.
  
  
  METHODS
  In this prospective, randomized, controlled, multicenter study, 288 clinicians (39.6% family practice physicians, 37.9% general internal medicine physicians, and 22.6% nurse practitioners) were randomized to structured SMBG alone (STG; n = 72); structured SMBG with DST (DST; n = 72); structured SMBG with an educational DVD (DVD; n = 72); and structured SMBG with DST and the educational DVD (DST+DVD; n = 72). Clinicians analyzed 30 patient cases (type 2 diabetes), identified the primary abnormality, and selected the most appropriate therapy.
  
  
  RESULTS
  A total of 222 clinicians completed all 30 patient cases with no major protocol deviations. Significantly more DST, DVD, and DST+DVD clinicians correctly identified the glycemic abnormality and selected the most appropriate therapeutic option compared with STG clinicians: 49, 51, and 55%, respectively, vs. 33% (all P < 0.0001) with no significant differences among DST, DVD, and DST+DVD clinicians.
  
  
  CONCLUSIONS
  Use of structured SMBG, combined with the DST, the educational DVD, or both, enhances clinicians' ability to correctly identify significant glycemic patterns and make appropriate therapeutic decisions to address those patterns. Structured testing interventions using either the educational DVD or the DST are equally effective in improving data interpretation and utilization. The DST provides a viable alternative when comprehensive education is not feasible, and it may be integrated into medical practices with minimal training.
authors.affiliation: Endocrine and Metabolic Consultants, Rockville, Maryland, USA.
authors.forename: Helena W
authors.initials: HW
authors.lastname: Rodbard
authors.forename: Oliver
authors.initials: O
authors.lastname: Schnell
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Unger
authors.forename: Christen
authors.initials: C
authors.lastname: Rees
authors.forename: Linda
authors.initials: L
authors.lastname: Amstutz
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Parkin
authors.forename: Zhihong
authors.initials: Z
authors.lastname: Jelsovsky
authors.forename: Nathan
authors.initials: N
authors.lastname: Wegmann
authors.forename: Matthias
authors.initials: M
authors.lastname: Axel-Schweitzer
authors.forename: Robin S
authors.initials: RS
authors.lastname: Wagner
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation; D001786:Blood Glucose / Q000032:analysis; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data; D002138:Calibration; D002983:Clinical Competence; D003627:Data Interpretation, Statistical; D003657:Decision Making / Q000502:physiology; D020000:Decision Support Systems, Clinical / Q000592:standards* / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000175:diagnosis / Q000628:therapy; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged
chemical_list: D001786:Blood Glucose