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Detecting Diversion of Anesthetic Drugs by Providers

2007; Lippincott Williams & Wilkins; Volume: 105; Issue: 4 Linguagem: Inglês

10.1213/01.ane.0000282022.75007.f7

1526-7598

Franklin Dexter,

Hemodynamic Monitoring and Therapy

The value of the intraoperative components of anesthesia information management systems (AIMS) extends beyond clinical care to managerial applications. The AIMS can contribute to reduced anesthesia drug costs (1–3), increased capture of anesthesia-related charges (4–8), and reduced staffing costs (9,10). Two articles in this issue of Anesthesia & Analgesia describe an additional benefit: enhanced monitoring for diversion of controlled substances (11,12). The importance of using information systems to identify drug abuse is reinforced by a third paper in the current issue (13). In addition to the drugs routinely monitored (e.g., narcotics, barbiturates, benzodiazepines, and ketamine), propofol is being abused, with an incidence of approximately 10 cases per 10,000 anesthesia providers per decade (13). Wischmeyer et al.'s survey found that there was no established system to control or monitor propofol at 71% of anesthesia programs. Such programs accounted for 22 of the 25 individuals who abused propofol (P = 0.048), and all seven deaths. This provides a strong rationale for programs to have a heightened awareness of propofol diversion. The two papers (11,12) took different approaches to screening and detecting the diversion of anesthetic drugs. The results differed, as did the resulting insight into monitoring for diversion. Vigoda et al. (11) reconciled the disposition of opioids, benzodiazepines, ketamine, and thiopental by aligning use between an automated medication dispensing system and an AIMS record of medications administered. There were discrepancies in 15% of cases, caused mostly by errors in the documentation of drug dose in the AIMS or in drug wastage in the dispensing system. Epstein et al. (12) identified abuse of controlled substances based on frequent transactions (e.g., checking out a narcotic) for patients an hour or more after the end of the procedure. Another pattern associated with diversion was the checking out of drugs from dispensing machines in one anesthetizing location for procedures performed at a different location. The former could be applied to a facility with drug dispensing machines in common areas or a person checking out drugs, whereas the latter would be limited to facilities with machines in individual anesthetizing locations. As Vigoda et al. recommends, successful achievement of automated reconciliation of medication discrepancies requires an interface between the automated drug dispensing machines or the satellite pharmacy checkout and the AIMS, so that feedback is provided to the user before the record is closed or finalized. When a facility purchases its AIMS, share Vigoda et al.'s study with the project manager writing the request for proposals (i.e., make it clear that “we want this”). Nevertheless, identification of discrepancies in the timing and location of drug dispensing versus the timing and location of the anesthetic does not rely on data unique to an AIMS. The data for Epstein et al.'s method could equally be from the ubiquitous operating room information systems. Just as for analyses of anesthesia staffing and staff scheduling, the data from AIMS and operating room information systems are close to interchangeable (9,10). What matters is that the method of analysis be robust to the multiple errors and discrepancies in the data. Anesthesia departments can improve drug surveillance by having data analysts and anesthesiologists working together. The clinically informed analysis of AIMS data can identify outlying behaviors of controlled substances, and may facilitate intervention before harm comes to the practitioner or the patient.