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Drug Seeking Behavior (DSB) Detection

Customer

State-wide Health Information Network (POC)

Challenges
  • Identifying patients that are seeking opiate drugs are difficult to identify quantify as their visit are spread across many providers.
  • Physicians are required to make diagnosis decisions without support from longitudinal data.
  • Statewide Health Information Networks already collect the data needed via Clinical Care Documents (CCDs) and Admit/Discharge/Transfer (ADT) data
  • Patient identifiers are both confidential (PHI) and lack standards for easy matching
Solutions
  • Patient matching systems were already in place
  • Create a Machine Learning model to identify the same patient with varying names
  • Create a Machine Learning Model to identify abnormal frequency of visits by those patient
  • Create a report for review for Emergency Department Physician Leadership of potential DSB patients that visited their facilities in the last 30 days.
  • Create an API for Emergency Department Information Systems (EDIS) to query upon patient arrival potential
Business Impact
  • M/L model provided a 2nd opinion of patient matching
  • Better standardization of CCD documents across provider systems would increase detection
  • Real-time Notification is needed to support the Emergency Department Physicians