title: Translating What Works: A New Approach to Improve Diabetes Management.
abstract: BACKGROUND
  The most efficacious strategies to improve diabetes control include case management, health care team changes, patient education, and facilitated transmission of patient data to clinicians ("facilitated relay"), but these strategies have not been translated to permit general use in clinical practice.
  
  
  METHODS
  A web-based decision support program was developed to include these features, and assessed in patients who had A1c ≥7.0% despite using metformin with/without sulfonylureas or insulin. Staff entered patients' glucose data, obtained management recommendations, reviewed the plan with a clinician, and discussed the new plan with patients.
  
  
  RESULTS
  113 subjects were 96% male and 32% black, with average age 65.6 years and BMI 32.8. During prior primary care, A1c averaged 8.32 ± 0.16% (SEM). In all patients, baseline A1c was 8.18 ± 0.11%, and decreased to 7.54 ± 0.12%, 7.16 ± 0.13%, and 7.54 ± 0.16% at 3, 6, and 12 months, respectively, all P < .001. In 42 subjects who provided glucose data and made requested changes in medications, A1c was 8.12 ± 0.09% at baseline and fell to 7.29 ± 0.11%, 6.98 ± 0.10%, and 7.05 ± 0.10% at 3, 6, and 12 months, respectively, all P < .001. Chart review of 16 subjects followed for 12 months demonstrated that hypoglycemia (symptoms and/or glucose <70 mg/dl) averaged less than 1 episode/patient/month, and there was no severe hypoglycemia.
  
  
  CONCLUSIONS
  A novel decision support program improved A1c with little hypoglycemia. Use of this approach should allow primary care teams to keep patients well controlled, and reduce the need for specialist referrals.
authors.affiliation: Atlanta VA Medical Center, Decatur, GA, USA Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA medlsp@emory.edu.
authors.forename: Lawrence S
authors.initials: LS
authors.lastname: Phillips
authors.affiliation: VA Medical Center, Gainesville, FL, USA Division of Endocrinology and Metabolism, Department of Medicine, University of Florida School of Medicine, Gainesville, FL, USA.
authors.forename: Diana
authors.initials: D
authors.lastname: Barb
authors.affiliation: Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Chun
authors.initials: C
authors.lastname: Yong
authors.affiliation: Atlanta VA Medical Center, Decatur, GA, USA Division of General Medicine and Geriatrics, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
authors.forename: Anne M
authors.initials: AM
authors.lastname: Tomolo
authors.affiliation: Atlanta VA Medical Center, Decatur, GA, USA Nutrition and Health Sciences, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA.
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Jackson
authors.affiliation: Atlanta VA Medical Center, Decatur, GA, USA Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA.
authors.forename: Darin E
authors.initials: DE
authors.lastname: Olson
authors.affiliation: Atlanta VA Medical Center, Decatur, GA, USA Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA.
authors.forename: Mary K
authors.initials: MK
authors.lastname: Rhee
authors.affiliation: Atlanta VA Medical Center, Decatur, GA, USA.
authors.forename: Ingrid M
authors.initials: IM
authors.lastname: Duva
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
authors.forename: Qing
authors.initials: Q
authors.lastname: He
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Long
mesh_terms: D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D020000:Decision Support Systems, Clinical; D003920:Diabetes Mellitus / Q000628:therapy*; D004311:Double-Blind Method; D000077270:Exenatide; D005260:Female; D005845:Georgia; D006442:Glycated Hemoglobin / Q000032:analysis*; D006787:Hospitals, Veterans; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000008:administration & dosage; D020407:Internet; D008297:Male; D008687:Metformin / Q000008:administration & dosage; D008875:Middle Aged; D010045:Outpatients; D010348:Patient Care Team; D010353:Patient Education as Topic; D010455:Peptides / Q000008:administration & dosage; D011320:Primary Health Care / Q000458:organization & administration; D016730:Program Development; D012107:Research Design; D013453:Sulfonylurea Compounds / Q000008:administration & dosage; D057170:Translational Research, Biomedical / Q000379:methods*; D014481:United States; D014688:Venoms / Q000008:administration & dosage; D014728:Veterans
chemical_list: D001786:Blood Glucose; D000077270:Exenatide; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; D008687:Metformin; D010455:Peptides; D013453:Sulfonylurea Compounds; D014688:Venoms
keywords: clinical decision making; computer; decision support; hypoglycemia; management; primary care; type 2 diabetes