Opioid Prescribing After Implementation of Single Click Access to a State Prescription Drug Monitoring Program Database in a Health System's Electronic Health Record

State-level factors associated with implementation of prescription drug monitoring program integration and mandatory use policies, United States, 2009-2020

BACKGROUND

Prescription drug monitoring programs (PDMPs) have been widely adopted as a tool to address the prescription opioid epidemic in the United States. PDMP integration and mandatory use policies are 2 approaches states have implemented to increase use of PDMPs by prescribers. While the effectiveness of these approaches is mixed, it is unclear what factors motivated states to implement them. This study examines whether opioid dispensing, adverse health outcomes, or other non-health-related factors motivated implementation of these PDMP approaches.

METHODS

Time-to-event analysis was performed using lagged state-year covariates to reflect values from the year prior. Extended Cox regression estimated the association of states' rates of opioid dispensing, prescription opioid overdose deaths, and neonatal opioid withdrawal syndrome with implementation of PDMP integration and mandatory use policies from 2009 to 2020, controlling for demographic and economic factors, state government and political factors, and prior opioid policies.

RESULTS

In our main model, prior opioid dispensing (HR 2.31, 95% CI 1.17, 4.57), neonatal opioid withdrawal syndrome hospitalizations (HR 1.55, 95% CI 1.09, 2.19), and number of prior opioid policies (HR 2.13, 95% CI 1.13, 4.00) were associated with mandatory use policies. Prior prescription opioid overdose deaths (HR 1.21, 95% CI 1.08, 1.35) were also associated with mandatory use policies in a model that did not include opioid dispensing or neonatal opioid withdrawal syndrome. No study variables were associated with implementation of PDMP integration.

CONCLUSION

Understanding state-level factors associated with implementing PDMP approaches can provide insights into factors that motivate the adoption of future public health interventions.

Changes in opioid prescribing and prescription drug monitoring program utilization following electronic health record integration-Massachusetts, 2018

OBJECTIVE

In this study, we explored key prescription drug monitoring program-related outcomes among clinicians from a broad cohort of Massachusetts healthcare facilities following prescription drug monitoring program (PDMP) and electronic health record (EHR) data integration.

METHODS

Outcomes included seven-day rolling averages of opioids prescribed, morphine milligram equivalents (MMEs) prescribed, and PDMP queries. We employed a longitudinal study design to analyze PDMP data over a 15-month study period which allowed for six and a half months of pre- and post-integration observations surrounding a two-month integration period. We used longitudinal mixed effects models to examine the effect of EHR integration on each of the key outcomes.

RESULTS

Following EHR integration, PDMP queries increased both through the web-based portal and in total (0.037, [95% CI = 0.017, 0.057] and 0.056, [95% CI = 0.035, 0.077]). Both measures of clinician opioid prescribing declined throughout the study period; however, no significant effect following EHR integration was observed. These results were consistent when our analysis was applied to a subset consisting only of continuous PDMP users.

CONCLUSIONS

Our results support EHR integration contributing to PDMP utilization by clinicians but do not support changes in opioid prescribing behavior.

Effect of integration of prescription drug monitoring program data in the electronic health record on queries by primary care providers

Objective: To evaluate the impact of PDMP integration in the EHR on provider query rates within twelve primary care clinics in one academic medical center. Methods: Using linked data from the EHR and state PDMP program, we evaluated changes in PDMP query rates using a stepped-wedge observational design where integration was implemented in three waves (four clinics per wave) over a five-month period (May, July, September 2019). Multivariable negative binomial general estimating equations (GEE) models assessed changes in PDMP query rates, overall and across several provider and clinic-level subgroups. Results: Among 206 providers in PDMP integrated clinics, the average number of queries per provider per month increased significantly from 1.43 (95% CI 1.07 - 1.91) pre-integration to 3.94 (95% CI 2.96 - 5.24) post-integration, a 2.74-fold increase (95% CI 2.11 to 3.59; p < .0001). Those in the lowest quartile of PDMP use pre-integration increased 36.8-fold (95% CI 16.91 - 79.95) after integration, significantly more than other pre-integration PDMP use quartiles. Conclusions: Integration of the PDMP in the EHR significantly increased the use of the PDMP overall and across all studied subgroups. PDMP use increased to a greater degree among providers with lower PDMP use pre-integration.

Implementation of Electronic Health Record Integration and Clinical Decision Support to Improve Emergency Department Prescription Drug Monitoring Program Use

STUDY OBJECTIVE(S)

To evaluate the implementation of 3 electronic health record (EHR)-based interventions to increase prescription drug monitoring program (PDMP) use in the emergency department (ED): EHR-PDMP integration, addition of a PDMP risk score, and addition of EHR-based clinical decision support alert to review the PDMP when prescribing an opioid.

METHODS

Three intervention stages were implemented using a prospective stepped-wedge design at 5 university-affiliated EDs split into 3 practice groups. The PDMP use and prescribing rates during the 3 stages were compared with baseline before EHR integration and a sustainability stage where the clinical decision support alert was removed, but EHR integration and risk score remained. Generalized linear mixed model with logit link function and a random intercept for clinicians was analyzed.

RESULTS

The ED provider PDMP review before opioid prescribing was low in all stages. The highest review rate occurred during interruptive clinical decision support alerts, 23.8% (interquartile range 10.6 to 37.5). Overall, opioid prescribing declined, and PDMP review was not associated with a decrease in opioid prescribing. PDMP review was associated with a reduction in the probability of prescribing an opioid as the number of prior opioid prescriptions increased (odds ratio: 0.92 [95% confidence interval: 0.91 to 0.94] for every additional prescription).

CONCLUSION

The EHR-PDMP integration did not increase PDMP use in the ED, but a PDMP risk score and a clinical decision support alert were associated with modest increases in the probability of PDMP review. When the PDMP is reviewed, ED clinicians are less likely to prescribe opioids to patients with a high number of prior opioid prescriptions.

Physician perspective on the implementation of risk mitigation strategies when prescribing opioid medications: a qualitative analysis

OBJECTIVE

To understand the physician perspective on the barriers and facilitators of implementing nine different opioid risk mitigation strategies (RMS) when prescribing opioid medications.

METHODS

We created and dispersed a cross-sectional online survey through the Qualtrics© data collection platform among a nationwide sample of physicians licensed to practice medicine in the United States who have prescribed an opioid medication within the past year. The responses were analyzed using a deductive thematic analysis approach based on the Consolidated Framework for Implementation Research (CFIR) to ensure a holistic approach to identifying the barriers and facilitators for each RMS assessed. In concordance with this method, the themes and codes for the thematic analysis were defined prior to the analysis. The five domains within the CFIR were used as themes and the 39 nested constructs were treated as the codes. Two members of the research team independently coded the transcripts and discussed points of disagreement until consensus was reached. All analyses were conducted in ATLAS.ti© V7.

RESULTS

The completion rate for this survey was 85.1% with 273 participant responses eligible for analysis. Intercoder reliability was calculated to be 82%. Deductive thematic analysis yielded 2,077 descriptions of factors affecting implementation of the nine RMS. The most salient code across all RMS was Knowledge and Beliefs about the Intervention, which refers to individuals' attitudes towards and value placed on the intervention. Patient Needs and Resources, a code referring to the extent to which patient needs are known and prioritized by the organization, also emerged as a salient code. The physicians agreed that the patient perspective on the issue is vital to the uptake of each of the RMS.

CONCLUSIONS

This deductive thematic analysis identified key points for actionable intervention across the nine RMS assessed and established the importance of patient concordance with physicians when deciding on a course of treatment.

Barriers to Increasing Prescription Drug Monitoring Program Use: A Multidisciplinary Perspective

Prescription drug monitoring programs are implemented through individual state policies and are one solution to curb the opioid crisis. The objectives of this study are to: (1) describe the multidisciplinary experiences using this program in practice; (2) identify limitations of the program and the desired features for improvement; and (3) characterize expectations for improved access when prescription drug monitoring programs are embedded in the electronic health record. A qualitative descriptive study design used semistructured interviews of 15 multidisciplinary healthcare providers. Textual data were analyzed using content analysis. Results showed the prescription drug monitoring program was helpful to decision-making processes related to opioid prescribing and referral to treatment; there were barriers limiting healthcare providers' use of the prescription drug monitoring program; preferences were delineated for integrating prescription drug monitoring program into electronic health record; and recommendations were provided to improve the program and increase use. In conclusion, the prescription drug monitoring program was viewed as useful in making strides to reduce the impact of inappropriate opioid prescribing in our country. By engaging a multidisciplinary group of healthcare providers, solutions were offered to improve the interface and function of the prescription drug monitoring program to assist in increasing use.

Evidence-Based Interventions to Improve Opioid Prescribing in Primary Care: a Qualitative Assessment of Implementation in Two Studies

BACKGROUND

The TOPCARE and TEACH randomized controlled trials demonstrated the efficacy of a multi-faceted intervention to promote guideline-adherent long-term opioid therapy (LTOT) in primary care settings. Intervention components included a full-time Nurse Care Manager (NCM), an electronic registry, and academic detailing sessions.

OBJECTIVE

This study sought to identify barriers, facilitators, and other issues germane to the wider implementation of this intervention.

DESIGN

We conducted a nested, qualitative study at 4 primary care clinics (TOPCARE) and 2 HIV primary care clinics (TEACH), where the trials had been conducted.

APPROACH

We purposively sampled primary care physicians and advanced practice providers (hereafter: PCPs) who had received the intervention. Semi-structured interviews explored perceptions of the intervention to identify unanticipated barriers to and facilitators of implementation. Interview transcripts were analyzed through iterative deductive and inductive coding exercises.

KEY RESULTS

We interviewed 32 intervention participants, 30 physicians and 2 advanced practice providers, who were majority White (66%) and female (63%). Acceptability of the intervention was high, with most PCPs valuing didactic and team-based intervention elements, especially co-management of LTOT patients with the NCM. Adoption of new prescribing practices was facilitated by proximity to expertise, available behavioral health care, and the NCM's support. Most participants were enthusiastic about the intervention, though a minority voiced concerns about the appropriateness in their particular clinical environments, threats to the patient-provider relationship, or long-term sustainability.

CONCLUSION

TOPCARE/TEACH participants found the intervention generally acceptable, appropriate, and easy to adopt in a variety of primary care environments, though some challenges were identified. Careful attention to the practical challenges of implementation and the professional relationships affected by the intervention may facilitate implementation and sustainability.

Are there differences in opioid prescriptions to urban and rural patients by oral-maxillofacial surgeons in Massachusetts from 2011-2021?

PURPOSE

Oral-maxillofacial surgeons (OMSs) are frequent prescribers of opioid analgesics. It remains unclear if prescription patterns differ for urban versus rural patients, given potential differences in access to and delivery of care. This study aimed to characterize urban-rural differences in opioid analgesic prescriptions to patients in Massachusetts by OMSs from 2011 to 2021.

METHODS

This retrospective cohort study used the Massachusetts Prescription Monitoring Program database to identify Schedule II and III opioid prescriptions by providers with specialty of "oral and maxillofacial surgery" from 2011 to 2021. The primary predictor variable was patient geography (urban/rural) and secondary predictor was year (2011-2021). The primary outcome variable was milligram morphine equivalent (MME) per prescription. Secondary outcome variables were days' supply per prescription and number of prescriptions received per patient. Descriptive and linear regression statistics were performed to analyze differences in prescriptions to urban and rural patients each year and throughout the study period.

RESULTS

The study data, which includes OMS opioid prescriptions (n = 1,057,412) in Massachusetts from 2011 to 2021, ranged annually between 63,678 and 116,000 prescriptions to between 58,000 and 100,000 unique patients. The cohorts each year ranged between 48 and 56% female with mean ages between 37 and 44 years. There were no differences in the mean number of patients per provider in urban and rural populations in any year. The study sample had a large majority of urban patients (>98%). MME per prescription, days' supply per prescription, and prescriptions received per patient were all generally similar between urban and rural patients each year, with the largest MME per prescription difference in 2019 (87.3 for rural to 73.9 for urban patients, P < .01). From 2011 to 2021, all patients had a steady decrease in MME per prescription (β = -6.64, 95% confidence interval: -6.81, -6.48; R² = 0.39) and day's supply per prescription (β = -0.1, 95% confidence interval: -0.1, -0.09; R² = 0.37).

CONCLUSION

In Massachusetts, there were similar opioid prescribing patterns by OMSs to urban and rural patients from 2011 to 2021. There has also been a steady decrease in the duration and total dosage of opioid prescriptions to all patients. These results are consistent with multiple statewide policies over the last several years aimed at curbing opioid overprescribing.

"1,000 conversations I'd rather have than that one:" A qualitative study of prescriber experiences with opioids and the impact of a prescription drug monitoring program

BACKGROUND

Prescription Drug Monitoring Programs (PDMPs) have shown impacts on a number of opioid-related outcomes but their role in clinician emotional experience of opioid prescribing has not been studied.

OBJECTIVES

This study explores the impact of PDMPs on clinician attitudes toward and comfort with opioid prescribing, their satisfaction with patient interactions involving discussion of opioid prescriptions, and their recognition of opioid use disorder (OUD) and ability to refer patients to treatment.

METHODS

Researchers conducted semi-structured interviews with five physicians and two nurse practitioners from a variety of specialties and practice environments.

RESULTS

Many participants reported negative emotions surrounding opioid-related patient encounters, with decreased anxiety related to PDMP availability. These effects were less pronounced with clinicians who had greater opioid prescribing experience (either longer careers or higher-volume pain practices). Many participants felt uncomfortable around opioid prescribing. Data from the PDMP often changed prescribing practices, sometimes leading to greater comfort writing a prescription that might have felt riskier without PDMP data. Clinicians easily recognized patient behaviors, symptoms, and prescription requests suggesting that opioid-related adverse events were accumulating, but did not usually apply a label of OUD to these situations. PDMP findings occasionally contributed to a diagnosis and treatment referral for OUD.

CONCLUSIONS

PDMP data is part of a nuanced approach to prescribing opioids. The objectivity of the data may be helpful in mitigating clinician negative emotions that are common around opioid therapy.

Provider perspectives and experiences following the integration of the prescription drug monitoring program into the electronic health record

Prescription drug monitoring programs (PDMPs) are a public health tool for prescribers and pharmacists to monitor controlled substance use at the patient level. The objectives of this study were to (1) assess attitudes about the PDMP and perceived changes in experience and decision-making following integration into the electronic health record (EHR), and (2) identify barriers to optimal PDMP use and user recommendations for improvement. This descriptive study used an electronic survey to obtain feedback from prescribers and pharmacists at a single academic medical center. Descriptive statistics were calculated, and textual data were analyzed. Of 1480 PDMP users 208 responded (14.1%). Responses demonstrated the integration of the PDMP into the EHR simplified log-in and access to PDMP information resulting in more frequent access and perceived improvement of care quality. Barriers included technical issues accessing the PDMP within the EHR and the lack of integration of other state PDMPs. Overall, the benefit of integrating the PDMP into the EHR was positive but largely limited to streamlining log-in and patient selection. Recommendations for improvement include addressing technological issues and education on PDMP interpretation and integrating new features that may modify prescribing, referral, and co-prescribing behaviors.

Flipping the script: The effects of opioid prescription monitoring on specialty-specific provider behavior

Mandatory access Prescription Drug Monitoring Programs (MA-PDMPs) aim to curb the epidemic at a common point of initiation of use, the prescription. However, there is recent concern about whether opioid policies have been too restrictive and reduced appropriate access to patients with the most need for opioid pharmaceuticals. We assess MA-PDMP's effect on specialty-specific opioid prescribing behavior of Medicare providers. Our findings suggest that requiring providers to query a PDMP differentially affects opioid prescribing across provider specialties. We find a three to four percent decrease in prescribing for Primary Care and Internal Medicine providers. This result is driven by healthcare providers at the lower end of the prescribing distribution. There is also suggestive evidence of an increase in opioid use disorder treatment drugs prescribed by these same providers. We also find no evidence for the hypothesis that MA-PDMPs restrict prescribing by providers who treat patients with potentially high levels of pain, few drug substitutes, or urgency for pain treatment (e.g., Oncology/Palliative care). This result is not dependent on whether a state provides exemptions for these providers. Our results indicate that MA-PDMPs may help close provider-patient informational gaps while retaining a provider's ability to supply these drugs to patients with a need for opioids.