Leveraging Cloud Technology to Address Drug Addiction Issues
Cloud Services can Reduce Deaths due to Overdose of Drugs.
As per the CDC, opioids were associated with 46,802 overdose deaths (69.5% of all drug overdose deaths) in 2018. The CDC has predicted that as a result of only prescription opioid abuse, the total yearly economic burden to the US totaled upwards of USD 78.5 billion, including the costs of healthcare, addiction treatment, lost productivity, and involvement of the criminal justice system. As the COVID-19 pandemic stretches on, deaths in the United States from Opioids and other habit-forming drugs like alcohol are likely to increase beyond 2020. Cloud could be a great help in this scenario.
The number of deaths could be reduced by offering help to addicts in better ways. The combinations of artificial intelligence (AI), cloud, and internet of things (IoT) could replace rehab clinics as the preferred way to overcome dangerous addictions. Core to this approach is the notion of habit tracking or monitoring behaviors for a long period could be another way to determine if someone has a drug-related problem. Then finding a way to resolve it on our own using technology could be beneficial.
At times, there are conflicting views within the chemical dependency treatment field as to what the term recovery means, associated with substances from which an opioid addict abstains. However, many would agree that recovery from opioid addiction is a challenging goal to not only achieve but also maintain on a long-term basis. Relapse is an unfortunate yet common part of the story along the path of an opioid addict seeking recovery. And because opioid tolerance can drastically decrease during a period of abstinence, relapse with opioids often leads to overdose and death.
Here lies an area of opportunity in which AI and cloud-related services may be useful in reducing deaths related to opioid overdose or any other addictions. For example, a virtual breathalyzer is in development to combat alcoholism. Cloud-connected sensors in devices that we carry like smartphones or fitness bands can detect someone’s level of intoxication based on walking patterns. Automobiles might be prevented from starting until the intoxication subsides.
The wearable device can gather objective data like heart rate, skin temperature, and heart rate variability in real-time. When assessed in conjunction with the monitoring of behavioral data like location via GPS monitoring, machine learning (ML) platform can assess whether the user is in a pre-relapse state of craving, a dangerous situation for any opioid addict and related addict in recovery. Certain automated processes are triggered, like not allowing driving, or more importantly, kicking off treatment to processes that could stop at least 10 to 20 percent of addition-related deaths.
These systems would not be possible without cloud computing and related services like AI and IoT. These won’t be effective unless the cost is low and the back-end processing powerful. Moreover, humankind has to carry or wear these devices, so the user experience must be compelling.
It is a matter of leveraging technologies such as cloud and IoT that turn into a commodity to solve another problem. Most people know an addict or probably are one. Humans have the potential to do a lot of good with a minimal amount of cost and technology.