title: Personalized Diabetes Management Using Electronic Medical Records.
abstract: Current clinical guidelines for managing type 2 diabetes do not differentiate based on patient-specific factors. We present a data-driven algorithm for personalized diabetes management that improves health outcomes relative to the standard of care.
  
  
  
  We modeled outcomes under 13 pharmacological therapies based on electronic medical records from 1999 to 2014 for 10,806 patients with type 2 diabetes from Boston Medical Center. For each patient visit, we analyzed the range of outcomes under alternative care using a k-nearest neighbor approach. The neighbors were chosen to maximize similarity on individual patient characteristics and medical history that were most predictive of health outcomes. The recommendation algorithm prescribes the regimen with best predicted outcome if the expected improvement from switching regimens exceeds a threshold. We evaluated the effect of recommendations on matched patient outcomes from unseen data.
  
  
  
  Among the 48,140 patient visits in the test set, the algorithm's recommendation mirrored the observed standard of care in 68.2% of visits. For patient visits in which the algorithmic recommendation differed from the standard of care, the mean posttreatment glycated hemoglobin A1c(HbA1c) under the algorithm was lower than standard of care by 0.44 ± 0.03% (4.8 ± 0.3 mmol/mol) (P < 0.001), from 8.37% under the standard of care to 7.93% under our algorithm (68.0 to 63.2 mmol/mol).
  
  
  
  A personalized approach to diabetes management yielded substantial improvements in HbA1coutcomes relative to the standard of care. Our prototyped dashboard visualizing the recommendation algorithm can be used by providers to inform diabetes care and improve outcomes.
authors.affiliation: Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA dbertsim@mit.edu.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Bertsimas
authors.affiliation: Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA.
authors.forename: Nathan
authors.initials: N
authors.lastname: Kallus
authors.affiliation: Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA.
authors.forename: Alexander M
authors.initials: AM
authors.lastname: Weinstein
authors.affiliation: Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA.
authors.forename: Ying Daisy
authors.initials: YD
authors.lastname: Zhuo
mesh_terms: D000368:Aged; D001786:Blood Glucose / Q000378:metabolism; D015992:Body Mass Index; D001900:Boston; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D019468:Disease Management; D004359:Drug Therapy, Combination; D057286:Electronic Health Records; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007328:Insulin / Q000097:blood / Q000627:therapeutic use; D008297:Male; D008687:Metformin / Q000627:therapeutic use; D008875:Middle Aged; D057285:Precision Medicine; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007328:Insulin; D008687:Metformin