PharmChem Laboratories Inc (PCHM)

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305558/

The remaining 5 (5/8, 63%) studies [18,37,41,42,44] used wearable biosensors for real-time detection of opioid use [37,41] to detect physiological changes associated with opioid use [18], evaluate physiological changes associated with the wearing off of naloxone [42], and automatically detect opioid intake [44]. Studies using Q sensors, worn on the participants’ wrists, have found that an increase in electrodermal activity (EDA) is associated with opioid use [41], accurately detected substance use events within 30 min [41] and significant within-subjects increase in skin temperature and decrease in locomotion immediately after opioid administration [18]. Carreiro et al [18] found that physiological changes varied among subjects with the levels of opioid use—heavy opioid users showed a greater decrease in fidgeting movements than nonheavy opioid users. Chintha et al [42] used an E4 device (Empatica) worn on the participants’ wrists and found that heart rate and skin temperature differed significantly between before and after naloxone administration. Finally, Linas et al [38] combined EMA and wearable sweat patches, PharmChek Drugs of Abuse Patches (PharmChem Inc), to concurrently collect momentary data and sweat in the field from 109 adults with recent opioid use and found moderate-to-good agreement of EMA to sweat patches and self-report methods in capturing drug use events.

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Machine Learning
Overall, 4 studies (4/18, 22%) used machine learning techniques to analyze and predict opioid use. Furthermore, 3 (3/18, 17%) of these studies [40,41,44] predicted opioid intake. The remaining study [39] developed a model to provide personalized interventions. ect ect ya yda

it's the Future of the Patch Tek Joe was saying it might cost a few bucks to make it happen well sudden surge in revenue makes it happen...?