Network Abstractions of Prescription Patterns in a Medicaid Population

Radhakrishnan Nagarajan, Jeffery Talbert

Research output: Working paperPreprint

1 Downloads (Pure)

Abstract

Understanding prescription patterns have relied largely on aggregate statistical measures. Evidence of doctor-shopping, inappropriate prescribing, drug diversion and patient seeking prescription drugs across multiple prescribers demand understanding the concerted working of prescribers and prescriber communities as opposed to treating them as independent entities. We model potential associations between prescribers as prescriber-prescriber network (PPN) and subsequently investigate its properties across Schedule II, III, IV drugs in a single month in a Medicaid population. Community structure detection algorithms and geo-spatial layouts revealed characteristic patterns in PPN markedly different from their random graph surrogate counterparts rejecting them as potential generative mechanism. Outlier detection with recommended thresholds also revealed a subset of prescriber specialties to be constitutively flagged across Schedule II, III, IV drugs. Presence of prescriber communities may assist in targeted monitoring and their deviation from random graphs may serve as a metric in assessing PPN evolution temporally and pre-/post- interventions.
Original languageUndefined/Unknown
StatePublished - Nov 21 2018

Bibliographical note

9 Pages, 3 Figures, 3 Tables

Keywords

  • stat.AP
  • physics.soc-ph

Fingerprint

Dive into the research topics of 'Network Abstractions of Prescription Patterns in a Medicaid Population'. Together they form a unique fingerprint.

Cite this