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