title: Using machine learning to examine medication adherence thresholds and risk of hospitalization.
abstract: BACKGROUND
  Quality improvement efforts are frequently tied to patients achieving ≥80% medication adherence. However, there is little empirical evidence that this threshold optimally predicts important health outcomes.
  
  
  OBJECTIVE
  To apply machine learning to examine how adherence to oral hypoglycemic medications is associated with avoidance of hospitalizations, and to identify adherence thresholds for optimal discrimination of hospitalization risk.
  
  
  METHODS
  A retrospective cohort study of 33,130 non-dual-eligible Medicaid enrollees with type 2 diabetes. We randomly selected 90% of the cohort (training sample) to develop the prediction algorithm and used the remaining (testing sample) for validation. We applied random survival forests to identify predictors for hospitalization and fit survival trees to empirically derive adherence thresholds that best discriminate hospitalization risk, using the proportion of days covered (PDC).
  
  
  RESULTS
  Time to first all-cause and diabetes-related hospitalization.
  
  
  RESULTS
  The training and testing samples had similar characteristics (mean age, 48 y; 67% female; mean PDC=0.65). We identified 8 important predictors of all-cause hospitalizations (rank in order): prior hospitalizations/emergency department visit, number of prescriptions, diabetes complications, insulin use, PDC, number of prescribers, Elixhauser index, and eligibility category. The adherence thresholds most discriminating for risk of all-cause hospitalization varied from 46% to 94% according to patient health and medication complexity. PDC was not predictive of hospitalizations in the healthiest or most complex patient subgroups.
  
  
  CONCLUSIONS
  Adherence thresholds most discriminating of hospitalization risk were not uniformly 80%. Machine-learning approaches may be valuable to identify appropriate patient-specific adherence thresholds for measuring quality of care and targeting nonadherent patients for intervention.
authors.affiliation: *Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA †Department of Pharmacy, Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ Departments of ‡Health Policy and Management, Graduate School of Public Health §Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh ∥Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System Departments of ¶Biostatistics, Graduate School of Public Health #Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA **Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA.
authors.forename: Wei-Hsuan
authors.initials: WH
authors.lastname: Lo-Ciganic
authors.forename: Julie M
authors.initials: JM
authors.lastname: Donohue
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Thorpe
authors.forename: Subashan
authors.initials: S
authors.lastname: Perera
authors.forename: Carolyn T
authors.initials: CT
authors.lastname: Thorpe
authors.forename: Zachary A
authors.initials: ZA
authors.lastname: Marcum
authors.forename: Walid F
authors.initials: WF
authors.lastname: Gellad
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D015331:Cohort Studies; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy* / Q000453:epidemiology; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage; D008297:Male; D008484:Medicaid; D008875:Middle Aged; D015999:Multivariate Analysis; D010349:Patient Compliance / Q000706:statistics & numerical data*; D058996:Quality Improvement / Q000458:organization & administration; D012189:Retrospective Studies; D018570:Risk Assessment; D014481:United States / Q000453:epidemiology; D016903:Drug Monitoring
chemical_list: D007004:Hypoglycemic Agents