title: Cost-effectiveness of an electronic medical record based clinical decision support system.
abstract: OBJECTIVE
  Medical groups have invested billions of dollars in electronic medical records (EMRs), but few studies have examined the cost-effectiveness of EMR-based clinical decision support (CDS). This study examined the cost-effectiveness of EMR-based CDS for adults with diabetes from the perspective of the health care system.
  
  
  METHODS
  Clinical outcome and cost data from a randomized clinical trial of EMR-based CDS were used as inputs into a diabetes simulation model. The simulation cohort included 1,092 patients with diabetes with A1c above goal at baseline.
  
  
  METHODS
  The United Kingdom Prospective Diabetes Study Outcomes Model, a validated simulation model of diabetes, was used to evaluate remaining life years, quality-adjusted life years (QALYs), and health care costs over patient lifetimes (40-year time horizon) from the health system perspective.
  
  
  RESULTS
  Patients in the intervention group had significantly lowered A1c (0.26 percent, p = .014) relative to patients in the control arm. Intervention costs were $120 (SE = 45) per patient in the first year and $76 (SE = 45) per patient in the following years. In the base case analysis, EMR-based CDS increased lifetime QALYs by 0.04 (SE = 0.01) and increased lifetime costs by $112 (SE = 660), resulting in an incremental cost-effectiveness ratio of $3,017 per QALY. The cost-effectiveness of EMR-based CDS persisted in one-way, two-way, and probabilistic sensitivity analyses.
  
  
  CONCLUSIONS
  Widespread adoption of sophisticated EMR-based CDS has the potential to modestly improve the quality of care for patients with chronic conditions without substantially increasing costs to the health care system.
authors.affiliation: Department of Family and Preventive Medicine,University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0622, USA. tgilmer@ucsd.edu
authors.forename: Todd P
authors.initials: TP
authors.lastname: Gilmer
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: O'Connor
authors.forename: JoAnn M
authors.initials: JM
authors.lastname: Sperl-Hillen
authors.forename: William A
authors.initials: WA
authors.lastname: Rush
authors.forename: Paul E
authors.initials: PE
authors.lastname: Johnson
authors.forename: Gerald H
authors.initials: GH
authors.lastname: Amundson
authors.forename: Stephen E
authors.initials: SE
authors.lastname: Asche
authors.forename: Heidi L
authors.initials: HL
authors.lastname: Ekstrom
mesh_terms: D000368:Aged; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D020000:Decision Support Systems, Clinical / Q000191:economics* / Q000458:organization & administration*; D048909:Diabetes Complications / Q000191:economics / Q000517:prevention & control; D003920:Diabetes Mellitus / Q000191:economics* / Q000628:therapy*; D057286:Electronic Health Records / Q000191:economics* / Q000458:organization & administration*; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis; D005102:Health Expenditures; D006801:Humans; D008297:Male; D008875:Middle Aged; D015337:Multicenter Studies as Topic; D011787:Quality of Health Care / Q000458:organization & administration; D019057:Quality-Adjusted Life Years; D016032:Randomized Controlled Trials as Topic
chemical_list: D006442:Glycated Hemoglobin