Prescription Drug Monitoring Programs - PDMP Center of Excellence

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Prescription Drug Monitoring Programs: An Assessment of the Evidence for Best Practices 26PDMP COE analyses of de-‐identified data from states neighboring Georgia identified zip codes withinGeorgia where Georgia prescribers were issuing unusually large numbers of prescriptions for controlledsubstances (Carnevale & Associates and PDMP COE, 2010). This information enabled Georgia officials toidentify possible pill mills within their state borders, even before their PDMP was enacted into state law.These examples suggest that PDMPs are a rich but underutilized resource for surveillance andevaluation efforts aimed at preventing prescription drug abuse and overdose. To assess the range ofapplication of PDMP data beyond providing prescription history reports, states could be surveyed on thetypes of further analyses they produce and the end users receiving the prescriptions.Barriers to adoption: Barriers to further analysis and dissemination include lack of PDMP resources;PDMPs’ lack of familiarity with such analytical methodologies; the absence of working relationshipsbetween PDMPs and state and community organizations that could benefit from access to the analyzeddata, e.g., substance abuse prevention groups; and state restrictions on reporting to or collaboratingwith outside research organizations.SummaryRationale: Epidemiological analyses can assist in drug abuse surveillance, evaluation, and preventionefforts.Evidence of effectiveness: Unpublished data analyses.Current adoption status: Several PDMPs have provided analyses for communities and state agencies.Barriers to adoption: Insufficient program resources or expertise to carry out analyses, absence ofcooperative working relationships between PDMPs and other groups, and restrictions on providing datato researchers.E. Develop automated expert systems to expedite analyses and reportsRationale and evidence of effectiveness: Reliable and valid analysis of PDMP data to identifyquestionable activity, track prescribing trends, and conduct other research often involves multiple stepsand requires familiarity with prescription information (e.g., drug classifications, standard doses, dataambiguities) gained over years of personal hands-‐on experience. Automated expert systems that captureat least some of this expertise may increase the speed and accuracy of such analyses and theirreporting, freeing up staff time and program resources for other initiatives. Automated systems can alsogenerate unsolicited reports and alerts based on criteria of questionable activity (see B. Determine validcriteria for possible questionable activity, above). Given that those meeting such criteria sometimesnumber in the thousands, automated algorithms to reliably identify such individuals and generate alertsto their prescribers and pharmacists may be the only feasible means to conduct proactive reporting on thenecessary scale. Research is needed to document existing PDMP expert systems, evaluate theirefficiencies, and help develop software programs and standard algorithms that reliably identify probablequestionable activity and accelerate other analyses. Given the wide application of expert systems inother public health and safety contexts, it seems likely that PDMPs would gain in effectiveness byadopting automated procedures in analyzing and reporting their data.
Prescription Drug Monitoring Programs: An Assessment of the Evidence for Best Practices 27Current adoption status: Some states have explored the design of automated expert systems that canexpedite analyses. Massachusetts and Oklahoma are using off-‐the-‐shelf business intelligence softwareto track prescribing patterns and PDMP utilization. States could be surveyed on what, if any, expertsystems and software are being used and their impact on improving PDMP productivity.Barriers to adoption: These include the limited resources of PDMPs, leaving them without staff, time, orfunds to explore such issues; the absence of guidance material or information that PDMP administratorscould utilize; design and implementation costs for customized systems; and whether a state’s softwarevendor (if it has one) has the capacity and flexibility to implement such a system.SummaryRationale: Expert systems and automated analyses and reports may increase the productivity of PDMPs.Evidence of effectiveness: Accumulated field experience, key stakeholder perceptions.Current adoption status: At least a few states have explored expert systems.Barriers to adoption: Limitations of PDMP resources, absence of information or guidance documents,design and implementation costs, software vendor capacity.F. Record data on prescriber disciplinary status and patient lock-‐insRationale: PDMPs could enhance their effectiveness if they could obtain and match to prescriptionrecords data on prescribers’ deaths or disciplinary status, such as a DEA registration suspension. Uponreceipt of prescription information from pharmacies, the PDMP could flag any such prescribers,prompting referral to appropriate agencies. The Government Accountability Office (GAO) found in its2009 examination of Medicaid programs that state Medicaid agencies paid for prescriptions ofcontrolled substances that were issued by deceased prescribers or those barred from such prescribing,or that were dispensed by pharmacies not legally authorized to do so (GAO, 2009). These forms ofdiversion could be effectively monitored by PDMPs were they able to link to the relevant databases.Similarly, information from Medicaid or third-‐party payers on patients who are in a restricted recipientprogram or “lock-‐in” to a single prescriber and pharmacy could be recorded by the PDMP. If a check ofthe PDMP indicates the prescription about to be dispensed is not from the specified prescriber andpharmacy, the pharmacist could take steps to make sure that dispensing is appropriate. This would beconsistent with GAO’s 2011 recommendation to CMS for the Medicare program that a restrictedrecipient program be implemented (GAO, 2011). Even if the pharmacist does not detect this prior todispensing, the PDMP could detect that the prescription was issued and dispensed by an unauthorizedprescriber and/or pharmacy and report it to the Medicaid program. Alternatively, the PDMP could makethe data available to Medicaid or other third-‐party payer so it could analyze the data and identify theviolation of a lock-‐in, as has Washington State (see User access and report dissemination, B. Optimizereporting to fit user needs, below).Evidence of effectiveness: The potential effectiveness of giving PDMPs access to data on prescriberdisciplinary status and patient lock-‐ins is suggested by other instances of data sharing that enableidentification of problematic prescribing and dispensing, for instance Washington State’s provision ofPDMP data to its Medicaid program (see User access and report dissemination, B. Optimize reportingto fit user needs, below).
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