Summary and Recommendations
V. Summary and Recommendations
A comprehensive range of potential PDMP best practices has been identified and discussed in this white paper. The primary objective of this review was to summarize the available scientific evidence on each potential best practice identified. The literature review drew from a number of sources, including published, peer-reviewed academic literature; unpublished evaluation reports and case studies; and written opinions and recommendations on PDMP best practices from experts in the field.
A secondary objective of the paper was to identify promising areas for future research based on the findings of this review (see Recommendations for Research and Development of PDMP Best Practices, below).
Table 1 presents a summary of the type and quality of the evidence identified for each of the 35 potential best practices identified. As described earlier, while published, peer-reviewed research on PDMP effectiveness exists, the empirical evidence is not extensive, and the research base on PDMP best practices is in an even earlier stage of development. For example, accumulated experience and key stakeholder perceptions predominantly form the basis for more than half (21 out of 35) of potential best practices. Research studies and documented expert opinion still need to be developed for these areas:
- Collect positive ID on persons picking up prescriptions
- Collect data on method of payment, including cash transactions
- Integrate electronic prescribing with PDMP data collection
- Improve data quality
- Link records to permit reliable identification of individuals
- Determine valid criteria for possible questionable activity
- Conduct periodic analyses of questionable activity
- Develop expert systems to guide analyses and reports
- Record data on disciplinary status, patient lock-ins
- Optimize reporting to fit user needs
- Integrate PDMP data with health information exchanges, electronic health records
- Publicize use and impact of PDMP
- Proactively identify and conduct outreach to potential high-impact users
- Conduct recruitment campaigns
- Streamline certification and enrollment processing
- Mandate enrollment
- Mandate utilization
- Institute financial incentives
- Delegate access
- Evaluation of PDMPs
- Funding of PDMPs
This set of promising practices was identified through anecdotal discussions with experts in the field, but no research evidence demonstrating effectiveness or formal written documentation of expert opinions was located.
Documented expert opinions or case studies served as the highest level of evidence for an additional six potential best practices:
- Adopt a uniform and latest ASAP reporting standard
- Collect data on nonscheduled drugs implicated in abuse
- Reduce data collection interval; move toward real-‐time data collection
- Enable access to data by appropriate users; encourage innovative applications
- Enact and implement interstate data sharing among PDMPs
- Collaborate with other agencies and organizations
Thus, we found research evidence (excluding case studies) for approximately one-‐quarter (eight out of 35) of the potential best practices identified in this paper:
- Collect data on all schedules of controlled substances
- Institute serialized prescription forms
- Conduct epidemiological analyses
- Provide continuous online access to automated reports
- Send unsolicited reports and alerts
- Conduct promotional campaigns
- Improve data timeliness and access
- Conduct user education
For these eight practices, the research evidence included only observational studies; to the authors’ knowledge, no RCTs or meta-‐analyses of PDMP best practices have been completed to date. Most of this research is unpublished. We found only three PDMP practices — serialized prescription forms, unsolicited reporting, and education — with published, peer-reviewed papers reporting on the effectiveness of the practice. Although a few analyses examined health outcomes, such as decreased prescription drug use or drug-related mortality, many were focused on intermediate or indirect outcomes (e.g., increased PDMP use).
Even among the eight practices with some type of unpublished or published research evidence, the quantity of research studies was minimal. Only a few had more than one source of research evidence. Results were inconsistent for the most studied practice, unsolicited reporting. In one study, unsolicited reporting was associated with lower prescription drug sales (Simeone & Holland, 2006), while case studies on Wyoming’s and Nevada’s PDMPs describe reduced doctor shopping after unsolicited reporting. However, no effect on drug overdoses or opioid-related mortality was found
after unsolicited reporting in another study (Paulozzi et al., 2011).
In summary, this analysis identified and reviewed 35 potential PDMP best practices. Overall, the findings indicate that good research evidence is not available for the vast majority of candidate PDMP best practices, as the research in this area is scarce to nonexistent. All of the studies that have been conducted have employed nonexperimental designs. No systematic reviews, meta-analyses, or RCTs were identified about any of the POMP practices in either the published, peer-reviewed literature or other sources. Thus, the reviewed practices appear promising, but major gaps exist in the
evidence base that should be addressed in future research. Confirmation of their effectiveness is needed using scientific techniques.
Recommendations for research and development of PDMP best practices
Our review of candidate best practices for PDMPs indicates that several practices, such as collecting prescription information on all schedules of controlled substances, shortening the data collection interval, using the most recent ASAP standard, and providing continuous online access to prescription data, are already widely adopted or constitute long-term program goals for many PDMPs. Having plausible rationales, they will likely become universal or nearly universal among PDMPs, even if documented evidence supporting their effectiveness has not yet been forthcoming. In contrast, many other candidate practices, some with a preliminary evidence base, have not thus far been widely adopted, despite having plausible rationales.
In this section, we recommend research and development focused on a subset of practices that in our judgment show the most promise in increasing the effectiveness and impact of PDMPs. This judgment incorporates the following considerations: 1) the need to assure the accuracy, completeness, and consistency of PDMP databases as a necessary underpinning for all aspects of PDMP data utilization; 2) the need to optimize all subsequent phases of PDMP operations, including data preparation, analysis, reporting, recruitment of users, and utilization of data; 3) the impact of a practice on
enhancing other PDMP capacities and functions, and maximizing PDMP effectiveness, were it widely adopted; 4) the feasibility of implementing the practice; and 5) the extent to which the practice serves to integrate PDMPs into the wider public health and public safety systems.
In addition, we have focused on practices with the potential for research that can produce strong evidence in support of the practices — that is, practices that can be studied by either a randomized controlled trial or an observational study with a comparison group. This is not to suggest that candidate practices surveyed above but unmentioned here are not worthy of research, development, and adoption as best practices, should findings prove positive. We offer this simply as an informed prioritization that may need revision in light of further developments in the field and the research itself.
The recommendations for research and development are:
A. Data collection and data quality
B. Linking records to identify unique individuals
C. Unsolicited reporting and alerts
D. Valid and reliable criteria for questionable activity
E. Medical provider education, enrollment, and use of PDMP data: the question of mandates
F. Extending PDMP linkages to public health and safety
A. Data collection and data quality
The accuracy, completeness, and consistency of PDMP databases are prerequisites for the reliability and effectiveness of PDMP data analysis, reporting, and utilization. All users rely on the data they receive from PDMPs. Prescribers and pharmacists depend on the data to make good clinical care decisions; drug treatment programs and office-based opioid treatment physicians depend on the data when making treatment decisions; state Medicaid agencies and workers’ compensation depend on the data to fill in missing data regarding their enrollees' obtaining of controlled substances; medical examiners depend on the data when determining causes of death; and investigators depend on the data to
determine how and what to investigate. All statistical summaries, epidemiological research and evaluation, and
geospatial analyses also depend on the data.As noted previously (see Data collection and data quality, E. Improve data quality: pharmacy compliance, error, and missing data correction), best practices need to be identified for all stages of data collection and management. Of necessity, PDMPs will have in place some such systems, but there is no accepted data management gold standard by which they can be assessed. Research is needed to survey current PDMP data management practices in order to determine their common objectives, characteristics, and parameters; develop consensus on achievable data quality goals (e.g., pharmacy reporting compliance rates, target error and completeness rates); determine which data management systems and procedures best achieve those goals; and develop a means to promulgate their adoption.
The results of a PDMP data quality research and development program could be modeled on the development and promulgation of ASAP reporting standards: a specification of systems and procedures that have been proven by research and field testing to produce high-quality PDMP data, as recommended by a recognized expert body. Such an initiative could recruit PDMP administrators and vendors to actively engage in data quality improvement and to collaborate with researchers with the relevant expertise. Convening a meeting of PDMP stakeholders to explore such an initiative would be a first step in the process of identifying best practices in improving and maintaining PDMP data quality. Once clearly defined benchmarks for data quality have been established, as well as the best practices for achieving them, PDMPs will be in a position to measure their effectiveness in this domain.
B. Linking records to identify unique individuals
The capability to link prescription records belonging to an individual, a PDMP data preparation function, is critical to providing accurate prescription information to all users and essential for analyzing the impact of PDMPs, e.g., measuring the level of questionable activity as correlated with program operations. This holds for individual PDMPs, PDMPs that share data, and PDMPs and other organizations that collect or use prescription history information such as IHS, the VA, Medicaid, and private third-party payers. As a discrete data processing capability, optimized record linking seems a feasible objective for most PDMPs.
Research is needed to identify standards for assessing linking algorithms, survey current PDMP practices in linking, and evaluate them in light of accepted standards. For instance, a PDMP's linking methods could be tested on a dummy data set and its output (e.g., number of uniquely identified individuals) compared to the output of a highly rated system. Both SAMHSA and the CDC have developed public domain software — Link Plus and The Link King, respectively — that can be applied for linking records within a PDMP database belonging to the same patient. These have been evaluated
with respect to each other and to a basic deterministic algorithm, and both were found superior to the deterministic algorithm (Campbell et al., 2008). However, we are not aware of any PDMPs actually using this software.
Typically, an IT vendor to a PDMP will have developed its own proprietary linking software or purchased such software. To date, no standards have been put forth for comparing such proprietary linking software.
Similarly, research is needed to assess methods of identifying unique individuals across data sets, whether of PDMPs or collaborating agencies. This would permit improved integration of PDMP databases with the wider health care system. Unlike many other kinds of health data, PDMP data do not include a unique numerical patient identifier, such as Social Security number. Linking algorithms needs to incorporate multiple fields such as patient name, street address, birth date, and gender, each of which is subject to various kinds of errors. For this reason, linking algorithms typically incorporates probabilistic matching based on “fuzzy” logic. Considerable research has been done in other fields on probabilistic matching, but research is needed to identify optimal linking algorithms using data fields available in PDMP data and their typical error rates.
Besides testing linking algorithms for relative efficiency, evaluations could assess the impact of better record linking on intermediate measures such as estimates of questionable activity, which themselves depend on actual numbers of uniquely identified individuals in a database. The requirements for optimal linking may suggest which data fields PDMPs should collect and which quality controls they should use to reliably identify individuals, whether patients or prescribers. When generating unsolicited reports, improved linking will increase the identification of individuals currently in a prescriber's
practice who may need help, and provide more accurate prescription histories. Better identification of individuals and more accurate prescription histories will also improve the quality of solicited reports. Obtaining end-user feedback on unsolicited and solicited reports, pre- or post- any change in record-linking practices, can help assess the extent to which improved linking on the front end improves PDMP output to end users.
C. Unsolicited reporting and alerts
Findings mentioned above suggest that proactive data analyses and reporting of PDMP data to prescribers and pharmacists serve to inform them of possible questionable activity and patients at risk, increase their awareness and utilization of PDMPs, and contribute to lower rates of questionable activity as measured by the subsequent number of individuals meeting a threshold and prescriptions obtained by suspected doctor shoppers. Proactive analyses and reporting to law enforcement and health professional licensing agencies can identify probable pill mills and doctor shopping rings, and expedite the investigation of possible criminal activity, reducing the supplies of controlled
substances for abuse and street trafficking. Some, but not all, PDMPs send unsolicited reports to prescribers and pharmacists, and a smaller number send them to law enforcement investigators, regulatory agencies, and licensing boards. This suggests that unsolicited reporting is well within the capacity of PDMPs, hence a feasible best practice. However, currently, just 40 percent of PDMPs send them to prescribers and pharmacies, and only 20 percent send them to law enforcement and professional licensing agencies.
Expansion of unsolicited reporting appears to be a prudent public health measure given the rapid escalation in prescription drug-‐related emergency department admissions, overdose deaths, and drug treatment admissions. The evidence currently available regarding unsolicited reporting, the CDC recommendations, and the requirements for NASPER promulgated by SAMHSA also support its expansion, even while additional scientific evidence is sought. Broader distribution of the existing evidence for the effectiveness of unsolicited reporting and education of state legislatures, agency heads, and other policy makers is needed.
In addition, research is needed to confirm scientifically the hypothesis that unsolicited reporting has the effects suggested by the evidence thus far. For example, published studies of unsolicited reporting have not controlled for possible confounding factors influencing prescription behavior, although there are some under way in Massachusetts (MA PDMP) and Nevada (with Abt Associates). The Massachusetts PDMP is conducting an evaluation of the prescription histories of patients about whom unsolicited reports were sent to prescribers, compared with a matched comparison group about whom reports were not sent. The Schedule II prescription histories of both groups are being tracked for the 12 months prior to the reports (and corresponding period for matching comparison group member) and the 12 months following the reports (MADPH presentation at National Rx Drug Abuse Summit, 2012). The CDC has reportedly funded Abt Associates to conduct a randomized controlled trial of the effects of unsolicited reporting in Nevada on the medical claims of Medicaid patients. Results from this latter study will likely not be available for two years. Further studies are needed to assess the systems and impact of unsolicited reports sent not just to prescribers, but to pharmacists, law enforcement agencies, licensing boards, health departments, diversion programs, collaborating health agencies
(e.g., VA, Medicaid) and other PDMP users. Such reporting, were it to become a standard practice, would help integrate PDMPs into other health care and public safety systems.
Research could examine the criteria used in selecting individuals for reports; the means by which reports or alerts are generated, validated, and delivered; the end-user response to reports, e.g., changes in prescribing and dispensing; and how data are used in investigations. Research is also needed on the effect of reports on health outcomes and diversion, such as rates of questionable activity; individual-level PDMP data on prescription purchases; data on overdoses,
drug-related deaths, and hospitalizations; and numbers and disposition of diversion investigations. Studies can be done of
states' current unsolicited reporting initiatives, examining doctor shopping rates and prescription behavior in relation to reporting. Isolating the effect of reports from confounding factors will require more sophisticated studies involving collaboration between PDMPs and partners such as government and academic research institutes.
As evidence regarding the efficacy of unsolicited reporting accumulates, further investigation will be necessary to assess the relative efficiency of systems for delivering reports and alerts. For example, automated systems with the capacity to notify prescribers for all individuals in a state meeting a threshold for questionable activity, who can number in the thousands, need to be developed and tested, especially with regard to minimizing false positives. Electronic alerts,
while considerably more cost-effective than sending out unsolicited reports via mail, need to be tested for relative efficacy compared to reports. If they are found to be effective, the minimal resources needed would make them feasible
for any PDMP. However, electronic alerts depend on providers registering with the PDMP and providing their email addresses.
D. Develop valid and reliable criteria for questionable activity
As noted above, although some published research exists, there is no science-based consensus on valid and reliable criteria for identifying questionable activity or patients at risk of prescription drug abuse. States vary in thresholds and other criteria use to generate unsolicited reports. Although some patient characteristics, diagnoses, and drug classes, especially being prescribed multiple classes (e.g., pain relievers and anti-‐anxiety medications), seem to be associated with being at risk, these findings are still preliminary. A PDMP best practice would be to use the "gold standard" for questionable activity. The development of such a standard would therefore significantly increase PDMP effectiveness given the importance of accurate identification of such activity for many PDMP functions and uses.8
8 - For example, when a medical provider downloads a PDMP report, this is usually to help ascertain whether the patient might have a drug-‐related problem. Research on thresholds and other criteria for patients potentially at risk would help inform this judgment. PDMPs could automatically flag individuals who meet validated criteria for questionable activity; this flag would show up in downloaded reports, proactively informing prescribers and pharmacists about a possible patient at risk.
However, it is possible that criteria for questionable activity vary by state or region, just as drugs of choice for abuse vary. Further research to develop valid and reliable criteria, across all states and/or by region, therefore seems indicated. For example, surveys of prescribers could help validate criteria by obtaining patient-level information: What proportion of patients meeting the criteria were judged to actually have drug-related problems in need of intervention? What proportion were "false positives" — those whose prescriptions were medically necessary? What information about the patient, had it been incorporated into the criteria, might have avoided misclassification? Is there a linear or nonlinear relationship between the extent to which individuals exceed a given threshold and the probability of being at risk? Are certain individual characteristics of doctor shoppers, e.g., gender, age, ethnicity, income, education, and urbanicity, differentially associated with different thresholds? Criteria could also be developed by retrospective analysis: What were the prescription histories, characteristics, and diagnoses of individuals judged by prescribers to have drug abuse or diversion problems in advance of consulting a PDMP database?
Research to illuminate patterns of prescription behavior leading up to meeting a threshold for questionable activity — the "natural history" of doctor shopping — could contribute to predictive models that might enable earlier identification of patients at risk. Such patterns — for instance, how long, on average, individuals stay under a given threshold before meeting it, and how long they stay at or above a threshold — may vary by patient characteristics, diagnoses, geographic area, and state policies related to prescribing and diversion, including the use of PDMPs themselves. These questions could be addressed by conducting longitudinal analyses of PDMP databases and other associated health data sets, ideally matched at the individual level but de-identified to protect patient privacy.
These are just a sampling of the questions that research on criteria for problematic prescription behavior could investigate. Consensus on a coordinated, systematic research agenda could be developed by convening a group of investigators tasked with clarifying study objectives and methods, followed by issuing a request for proposals. Since the development of criteria beyond simple thresholds will likely involve non-PDMP health data, the development process will promote relationships and data linking between PDMPs and other health care systems. A similar research agenda could be developed to identify reliable indicators within PDMP data of questionable prescribing on the part of individual providers or practices.
E. Medical provider education, enrollment, and use of PDMP: the question of mandates
As PDMP data and reports become easier to access, become integrated into health care practice, and gain acceptance as a clinical tool, the question of how to increase use of PDMPs by medical providers becomes increasingly salient, including possible actions up to and including mandating prescriber education about, enrollment in, and use of a PDMP. A handful of states now require that prescribers consult the PDMP database in specific circumstances, such as when prescribing controlled substances for the first time for a new patient and periodically thereafter, or when prescribing methadone for treating pain. Other states are considering such requirements. This suggests that instituting a mandate is
an attainable policy objective, should a state decide to pursue it via legislative and regulatory reform.
However, whether mandates should become a best practice depends on proving their feasibility and benefits. Many questions need study: How well, compared to voluntary approaches, do mandates increase the actual use of a PDMP? Is the requirement that all prescribers receive education in the prescribing of controlled substances and use the PDMP, whatever their level of prescribing, the most efficient use of a prescriber’s time and PDMP resources? Is mandatory use associated with improvements in patient outcomes, such as lower rates of addiction, overdoses, and deaths? Do states with mandates outperform other states in such measures? Do mandates have unintended consequences, such as leading some providers to discontinue or cut back on controlled substance prescribing? If there were reductions in prescribing, are they accompanied by decreased drug-related morbidity and mortality? Can mandates be successfully enforced, and by what kinds of monitoring and penalties for noncompliance? By what legislative and regulatory means were they
Investigating these and related questions will require descriptive studies of currently existing mandates and their consequences; studies comparing provider behavior with and without mandates, controlling for other factors; studies of how mandates were instituted; and studies of the feasibility and efficacy of enforcement mechanisms, such as monitoring use of the PDMP. Since lack of participation in PDMPs by prescribers is widely cited as a factor limiting their effectiveness, settling the question of whether mandates are better than voluntary approaches to increasing
participation has immediate practical significance that should figure in setting a PDMP research agenda. Moreover, obtaining answers to such questions takes on a new sense of urgency with four states enacting mandates in 2012 alone, and other states considering such legislation.
F. Extending PDMP linkages to public health and safety
A potential best practice examined above was for PDMPs to expand their scope of application to include users beyond prescribers, pharmacists, law enforcement agencies, and professional licensure boards. Case studies carried out by the PDMP COE suggest that PDMP data have additional applications that, when implemented, link PDMPs to other public health and safety systems, potentially increasing the impact and effectiveness of PDMPs in addressing prescription drug abuse. These studies indicate that in some states, PDMP data are being made available to drug courts, medical examiners, drug treatment programs, and criminal diversion programs. Findings suggest that these data are proving valuable in their respective applications.
Case studies could be developed to document other promising uses of PDMP data and the systems supporting such use. For instance, the Washington State PDMP is making its data available to the Workers’ Compensation unit in Department of Labor and Industries. Mississippi’s PDMP is contacting individuals whose prescription histories suggest questionable activity. Documenting these initiatives and their outcomes would be a first step in developing an evidence base for the utility of PDMP data in these applications. Studies should be undertaken to explore the uses to which PDMP data are applied by state Medicaid agencies and the impact of such use on the quality, safety, and costs of medical care provided to Medicaid enrollees. Another area for exploration is the feasibility of health care institutional peer review organizations using PDMP data to identify and intervene to correct prescribers’ deficiencies and problems. Field research is needed to identify other innovative applications of PDMP data being explored by states that could lend themselves to case studies.
Although findings from case studies serve as important preliminary assessments of novel PDMP data applications, more systematic research and evaluation are needed to establish their value, should it exist, in increasing PDMP effectiveness and impact. The case studies conducted thus far could be followed up by formal studies, for example, of how PDMP data are used in substance abuse prevention and treatment programs and the outcomes of such use, or how, in quantitative terms if possible, PDMP reports enhance the work of drug courts, criminal diversion programs, and drug enforcement
investigators. Studies could also be conducted comparing different approaches to how PDMP data are used in specific applications. As the evidence base grows in support of particular uses and the practices supporting their use, their adoption will grow. This, in turn, will increase PDMPs' integration with public health and safety systems, helping to maximize their effectiveness in improving the legitimate use of controlled substances, while mitigating the prescription drug abuse epidemic.