Data analytics has become prevalent in the health care industry, and it has dramatically improved the quality of patient care around the world. Analytics offers unprecedented business value, yet not all avenues for its implementation have been explored. One such area where analytics is being utilized is in substance abuse and treatment. Applying data analysis to this area is revolutionizing how treatments are approached and how patients are monitored.
Tracking Prescription Drug Misuse
Many substance abusers practice what’s known as “doctor shopping,” a practice in which patients are seen by several local doctors. They present with symptoms that are usually treated with prescription drugs, specifically valid prescriptions for opioids. Pharmacy chains have access to a high-tech system that monitors prescription drug efforts. Within this system is information related to pharmacy laws in different states. Machine learning technologies help pharmacists track these trends and identify signs of prescription drug misuse.
Maintaining a large amount of data can become overwhelming without the right solution to manage it. Master data management (MDM) ensures that all shared data, or master data, is consistent and accurate. Modern organizations operate several different systems that each contain important data about customers, business operations, and business KPIs such as CRM. This results in data silos, duplicated data, and incomplete data that present a disjointed view of the business. MDM softwares eliminate this disjointed view by offering capabilities such as flexible and multi-domain models, multi-style MDM, real-time secure data, data and workflow visualization, and a business-friendly user interface. Master data management is an ongoing team effort that enforces best practices for data quality.
Identifying Substance Abuse Patterns
Street drugs have nicknames that rapidly change depending on the local culture. Where people go to find street drugs also changes. These variations make it challenging for people trying to help addicts overcome substance abuse problems. Leveraging big data offers a solution to some of these challenges. Big data looks at factors such as phrases used on social media and common keyword searches to pinpoint substance use in certain areas. Big data also uses geospatial data to create heat maps indicating areas of high substance abuse.
Prediction of Substance Abusers Seeking Help
Making predictions about whether or not someone with a substance abuse problem will seek help isn’t straightforward. Machine learning could be used to help make more accurate predictions. By predicting whether or not individuals will seek treatment, they can receive more accurate help. The better that clinicians can predict the need for substance abuse help, the better they can target their efforts toward those who are open to treatment.
Predicting Abuse Through Medical Records Analysis
Physicians, in addition to pharmacists, can monitor for substance abuse. Big data provides real-time insights that result in faster, more accurate conclusions. Patients who are prescribed pain medications have a high risk of becoming dependent on these drugs. Researchers can use data to identify who is at higher risk of developing abuse problems, such as those with mental health disorders. Other people at a higher risk of developing abuse problems include the homeless and those with economic hardships or legal complications. This data can also identify other threats to public health efforts and treatment interventions, such as the risk of someone committing suicide while using specific prescription medication.
Quantifying the Effects of Marijuana Legalization
As marijuana becomes legalized in more states, lawmakers and enforcers are struggling to navigate this new territory. One of the biggest complications of marijuana legalization is determining what constitutes a Driving Under the Influence (DUI) charge. While there is a blood alcohol content scale for intoxication due to alcohol, there’s no standard system for marijuana yet. Big data helps legislators analyze statistics and gain clarity on how much marijuana in someone’s system makes it unsafe to operate a vehicle.
Data analytics helps track prescription drug misuse, identify substance abuse patterns, predict abusers likely to seek treatment, predict abuse through medical record analysis, and quantify the effects of marijuana legalization.