Prescription Medication Abuse: A Case Study

Prescription medication abuse has been a growing problem in the United States, and fatalities resulting from this abuse have been increasing at an alarming rate. In just five years from 1999-2004, deaths from prescription opioids have rose by 142% (Paulozzi, Kilbourne, & Desai, 2011). This growing problem has been officially labeled an epidemic by the Centers of Disease Control and Prevention (CDC) (PDMP Center of Excellence at Brandeis University, 2014). In an effort to control this growing problem, prescription drug monitoring programs (PDMPs) have been created in various states to try and reduce the abuse of these drugs. “Prompted in part by the diversion of prescription opioids and other pharmaceuticals to nonmedical use, Congress asked

This data not only includes the drug and dosage but the pharmacists are also required to record the prescribing doctor, the patient, and the dispenser. Some states also use a unique serial number that is captured in an effort to stop the duplication of prescriptions. This data is then entered into an electronic database which can then identify people who are abusing or at risk to start abusing these monitored prescriptions. These programs not only evaluate the dosage and times the drugs were dispensed, but they also can compare the chemical makeup of the prescriptions and categorize them with other similar prescriptions (Paulozzi, Kilbourne, & Desai, 2011). Therefore, they not only track the abuse of one drug but can identify a person who is receiving similar pain medications and is at risk for developing a dependence or even having an overdose. Once the programs identify at risk or abusive behavior, they can then automatically start to implement interventions which aim to deter abuse and help those who have an

In one example, the use of a specific algorithm within the system could categorize problem areas within geographic regions, and drug awareness and rehabilitation efforts could be focused towards those specific areas. Patient addresses are a required data point to be collected and inputted into the systems, and an algorithm could be easily implemented to find trends and identify the areas that are attributed with drug abuse (Islam & McRae, 2014). John Cheney-Lippold defines a ’new algorithmic identity’, as “an identity formation that works through mathematical algorithms to infer categories of identity” (2011, p. 165). While in this context he was referring to the identities of people, inferring identities about geographical communities can be defined through the same way, as it is “through algorithms, commonalities, between data can be parsed and patterns within data then identified and labeled” (Cheney-Lippold, 2011, p. 168). It is through this process that we would be able to define and label drug abuse areas. Some states programs may indeed use some special identification, but this needs to be implemented at a higher level and utilized by all, which connects back to the concept of a centralized