title: The Impact of Provider Networks on the Co-Prescriptions of Interacting Drugs: A Claims-Based Analysis.
abstract: Multiple provider prescribing of interacting drugs is a preventable cause of morbidity and mortality, and fragmented care is a major contributing factor. We applied social network analysis to examine the impact of provider patient-sharing networks on the risk of multiple provider prescribing of interacting drugs.
  
  
  
  We performed a retrospective analysis of commercial healthcare claims (years 2008-2011), including all non-elderly adult beneficiaries (n = 88,494) and their constellation of care providers. Patient-sharing networks were derived based on shared patients, and care constellation cohesion was quantified using care density, defined as the ratio between the total number of patients shared by provider pairs and the total number of provider pairs within the care constellation around each patient.
  
  
  
  In our study, 2% (n = 1796) of patients were co-prescribed interacting drugs by multiple providers. Multiple provider prescribing of interacting drugs was associated with care density (odds ratio per unit increase in the natural logarithm of the value for care density 0.78; 95% confidence interval 0.74-0.83; p < 0.0001). The effect of care density was more pronounced with increasing constellation size: when constellation size exceeded ten providers, the risk of multiple provider prescribing of interacting drugs decreased by nearly 37% with each unit increase in the natural logarithm of care density (p < 0.0001). Other predictors included increasing age of patients, increasing number of providers, and greater morbidity.
  
  
  
  Improved care cohesion may mitigate unsafe prescribing practices, especially in larger care constellations. There is further potential to leverage network analytics to implement large-scale surveillance applications for monitoring prescribing safety.
authors.affiliation: Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA. Mei-Sing.Ong@childrens.harvard.edu.|Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, 2109, Australia. Mei-Sing.Ong@childrens.harvard.edu.|Center for Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. Mei-Sing.Ong@childrens.harvard.edu.
authors.forename: Mei-Sing
authors.initials: MS
authors.lastname: Ong
authors.affiliation: Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.|Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
authors.forename: Karen L
authors.initials: KL
authors.lastname: Olson
authors.affiliation: Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.|Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Chadwick
authors.affiliation: HealthCore Inc., Alexandria, VA, USA.
authors.forename: Chunfu
authors.initials: C
authors.lastname: Liu
authors.affiliation: Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.|Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.|Center for Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Mandl
mesh_terms: D000367:Age Factors; D003299:Cooperative Behavior; D016208:Databases, Factual / Q000706:statistics & numerical data*; D004347:Drug Interactions; D005260:Female; D006801:Humans; D057970:Inappropriate Prescribing / Q000517:prevention & control*; D007400:Interprofessional Relations; D008297:Male; D008875:Middle Aged; D010818:Practice Patterns, Physicians' / Q000592:standards / Q000706:statistics & numerical data*; D012189:Retrospective Studies