Urinalysis (UA) has been a cornerstone method to determine whether people in substance abuse rehabilitation centers are adhering to their program to get sober. Yet while effective, urinalysis suffers from some key challenges, including accuracy, costs, and convenience. New technologies, such as Artificial Intelligence and Machine Learning, are being used with Ocular Motor Recognition to create lie detection capability that is low cost, high accuracy, high privacy, and data rich. Taken together, UA and next-gen lie detection are a powerful combination that enhances and motivates accountability for those involved in substance abuse rehabilitation programs.
It is well known that those addicted to drugs will often lie to cover or enable their behavior. UA is used to determine whether people are using drugs when they claim they are not. While generally effective, UA suffers from several challenges including: accuracy, privacy concerns, fraud and compliance, ethical and legal considerations, and cost. Let’s look briefly at each.
UA is not infallible and can produce false-positive or false-negative results. Factors such as cross-reactivity with other substances, the presence of medications, or sample contamination can lead to inaccurate conclusions.
The process of collecting urine samples can be invasive, raising concerns about privacy. Striking a balance between the need for drug testing and an individual's right to privacy is a complex issue.
Individuals may attempt to adulterate urine samples to mask drug use. Techniques such as dilution, substitution, or the use of adulterants can interfere with the accuracy of UA. Because UA requires step by step processes to collect urine, many simply do not comply.
The use of UA in various settings raises ethical and legal questions. It's important to ensure that drug testing practices are conducted fairly, with respect for individual rights and dignity.
Finally, costs for a complete UA panel can range from $30-$250, with most tests costing between $100 and $150. For many organizations, especially those involved in rehabilitation, patients need to be tested between 1-3 times per week. Over a 12 month period, UA testing can cost north of $10,000 per patient.
Better UA may not be the only solution to these challenges. Artificial intelligence and machine learning are now being used to do lie detection at levels of accuracy that make it possible to marry these new technologies with UA to provide a series of improvements, including:
Accuracy: Lie detection technology adds another collection point for data that is not bodily fluids that can be tampered with, swapped, or otherwise adulterated. Determining compliance with rehabilitation programs is done by assessing cognitive load’s effect on OMR. This vector is tamper-proof and can validate answers from other sources. With accuracy levels now above 80%, this is a viable solution.
Flexibility and Convenience: To take a UA test requires someone to physically collect a sample, ensure its purity, mail the sample to a lab, and wait for 3-5 business days for the results. An EyeCanKnow lie detection test by comparison takes about 15 minutes to take on a mobile phone, 15 minutes to upload and score, and then results are immediately available in a secure dashboard.
Better Privacy and Less Invasiveness: Unlike a UA test, and EyeCanKnow test can be done in private, at a person’s home (or any convenient location), the data are calculated in the cloud with AI and ML, and the results are sent directly to the test maker. No people are required to intermediate with the test taker to evaluate the data or send the results.
Lower Costs: A UA test costs on average in excess of $100. An EyeCanKnow test, on the other hand, can cost as little as $10-$40. A regimen of both UA and EyeCanKnow tests can therefore significantly reduce the cost of compliance and accountability testing during the rehabilitation program.
Accountability and Motivation: In a rehabilitation program, UA is used to help providers track the success of patients. But there is little that patients get from the testing that helps them. EyeCanKnow tests are different. While the results provide similar data to the health care provider, the results also provide a mechanism for the patient and their loved ones to ensure accountability. Because the tests are so fast and cheap, it is possible to see more data points on progress or lack thereof.
Better Data: Coupling UA and AI (using EyeCanKnow testing) improves data in two ways. First, it allows the provider and patient to collect more data. A larger “n” makes it possible to understand better the relationship between treatments and outcomes. And second, it allows for different sources of data that vector on the same outcome, which improves the quality of the data.
Technology is constantly changing, which creates opportunities to do old jobs better. One of the key jobs to be done for UA is to determine when individuals are abusing substances in ways that are not consistent with their rehabilitation program. EyeCanKnow technology can be coupled with UA to improve transparency, accountability, and data collection, all of which promises to improve outcomes in general for those individuals trying to break their addictions.