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AI-Powered Proactive Drug Diversion Monitoring

Next-Generation Solutions Use AI

Artificial Intelligence (AI) automates tasks that would otherwise have to be manually performed. ControlCheck™’s next-generation solution uses AI to integrate data from automatic dispensing cabinets (ADCs) with data from electronic medical records (EMRs). When performed manually, this cross-analysis of information can be time-consuming, inefficient, and often does not encompass the full range of analytics needed to detect potential instances of diversion. 

ControlCheck’s proprietary Individual Risk Identification Score (IRIS) uses AI to help your team target, focus and manage investigation efforts. 

IRIS’ ease of use takes the guesswork out of investigations and provides you with quick actionable analytics. IRIS automatically identifies where you ought to start your diversion investigations without requiring you to dig through separate metrics and dashboards. Through using unsupervised machine learning, a type of AI, IRIS identifies providers whose behavior represents the biggest risk for diversion. The IRIS dashboard aggregates and ranks anomalous behavior to compare individuals to other providers in their hospital, care area, and department. 

This score identifies outliers that don’t appear when measuring averages and standard deviations, highlighting providers whose behaviors are statistically abnormal from their peer group. This provides a weighted assessment of the degree of difference across all metrics tracked by ControlCheck, leveraging industry-leading data science calculations. IRIS takes a look at behavioral abnormalities from analytic reports like Action Times, Waste Networks, Dispense Trends, Variance Trends, Shift Analysis, and more – and collates these metrics into an overarching IRIS number for each provider, allowing managers to stay focused.

Drug Diversion Monitoring Questions

  • ControlCheck uses unsupervised machine learning algorithms to analyze large quantities of data across patients, locations, time, movement, and relationships. We find unsupervised machine learning advantageous over supervised machine learning.

    With supervised machine learning, you must teach the AI what cases of diversion look like. This requires patterns of behavior from a large number of confirmed cases of diversion, which are then labeled as such to train the AI.

    As users who divert medications become smarter in their tactics, unsupervised machine learning allows our software to continuously learn unusual and ever-changing patterns of behaviors. The AI continues to “evolve” it’s behavioral learning, allowing for the detection of patterns and outliers in data without the need for a large dataset of confirmed cases of diversion.

  • Through machine learning, a type of AI, the computer determines anomalous behavior. The machine really is learning, using data to create and fit a model of normal behavior, as well as identify and quantify anomalous behavior. “Normal” and “anomalous” are different for each hospital, department, and date range, and cannot have a universal definition. As a result, we rely on machine learning to develop a model for each new dataset.

  • Studies show about one in ten healthcare providers are using or abusing drugs.

     This matches the rate in the general population but increased access to controlled substances put healthcare workers at a higher risk. 

    ControlCheck takes a proactive approach to combat this ongoing public health crisis. 

    ControlCheck uses industry-leading unsupervised machine learning to prevent drug diversion at your organization, sharpen non-compliant documentation practices, and ensure struggling team members receive the help they need.

    A single high-profile diversion event can cause significant reputational damage for a hospital, put patients at risk, and incur substantial fines from the DEA. ControlCheck’s audit coverage allows visibility into controlled substance inventory and movement across all care areas in the facility. Having access to a complete controlled substance record helps ensure patient safety, staff accountability, and compliance.