Prescribing Of Controlled Substances Research Articles

An Emergency Preparedness Response to Opioid-Prescribing Enforcement Actions in Maryland, 2018-2019.

Federal and state enforcement authorities have increasingly intervened on the criminal overprescribing of opioids. However, little is known about the health effects these enforcement actions have on patients experiencing disrupted access to prescription opioids or medication-assisted treatment/medication for opioid use disorder. Simultaneously, opioid death rates have increased. In response, the Maryland Department of Health (MDH) has worked to coordinate mitigation strategies with enforcement partners (defined as any federal, state, or local enforcement authority or other governmental investigative authority). One strategy is a standardized protocol to implement emergency response functions, including rapidly identifying health hazards with real-time data access, deploying resources locally, and providing credible messages to partners and the public. From January 2018 through October 2019, MDH used the protocol in response to 12 enforcement actions targeting 34 medical professionals. A total of 9624 patients received Schedule II-V controlled substance prescriptions from affected prescribers under investigation in the 6 months before the respective enforcement action; 9270 (96%) patients were residents of Maryland. Preliminary data indicate fatal overdose events and potential loss of follow-up care among the patient population experiencing disrupted health care as a result of an enforcement action. The success of the strategy hinged on endorsement by leadership; the establishment of federal, state, and local roles and responsibilities; and data sharing. MDH's approach, data sources, and lessons learned may support health departments across the country that are interested in conducting similar activities on the front lines of the opioid crisis.

Does An ERAS Protocol Reduce Postoperative Opiate Prescribing in Plastic Surgery?

SummaryBackgroundEnhanced recovery after surgery (ERAS) protocols are effective at reducing inpatient opiate use. There is a paucity of studies on the effects of an ERAS protocol on outpatient opiate prescriptions. The aim of this study was to determine whether an ERAS protocol for plastic and reconstructive surgery would reduce opiate use in the outpatient postoperative setting.MethodsA statewide (Massachusetts, USA) controlled substance prescription monitoring database was retrospectively reviewed to assess the prescribing patterns of a single academic plastic surgeon performing common plastic surgical outpatient operations. The time period prior to implementation of the ERAS protocol was then compared with the time period following protocol implementation. An additional three months of post-implementation data were then compared with those of each of the previous time periods to investigate whether the results were sustained.ResultsA comparison of opiate prescriptions in pre-ERAS, immediate post-ERAS procedures, and follow-up ERAS implementation procedures revealed a statistically significant decrease in opiate prescriptions after ERAS protocol implementation. This decrease in the quantity of opiates prescribed was sustained over time.ConclusionsERAS protocols are effective at reducing outpatient opiate prescriptions after a variety of plastic surgery operations. Appropriate patient and physician education is paramount for success.

Use of Blockchain Technology for Electronic Prescriptions.

Distributed ledger technology can be used as a transparent, shareable ledger, that can record transactions between two parties efficiently and in a more secure, verifiable, and permanent way than the current electronic prescribing systems. We studied the use of a distributed ledger electronic prescribing programme, Prescription Abuse Greatly Reduced (PAGR) Prescriptions, to examine the effect of blockchain on provider prescribing efficiency at three family medicine clinics. The PAGR was installed side-by-side to the electronic health record at three family medicine practice clinics in middle Tennessee. A prospective, convenience sample of patients at all three clinics was used for analysis. Trained observers were used in each clinic to document the side-by-side use of current prescribing practice versus the use of the PAGR electronic prescribing system by the individual providers.The primary outcome was total time to write the prescription. Secondary metrics included compliance with checking the state's Physician Drug Monitoring Program (PDMP.) , accuracy of medicine reconciliation, use of patient's eligibility on insurance, prescription benefits, and change in prescription caused by benefits analysis or drug-interactions. Provider satisfaction was measure on a 4-point Likert scale.Data were analysed using two-tailed, paired Student T-tests with alpha set at 0.05. A sample size of 107 patients was calculated to have a power of 80% to detect a 50% change in the prescription writing time. The primary outcome of total prescription writing time was 171 ± 41 sec for current prescribing practice versus 63 ± 15 sec for the PAGR system (p = 0.0006). All providers were extremely satisfied with the use of the PAGR programme. Use of the PAGR electronic prescription programme significantly saved a mean of 1 min 48 sec per written prescription at the three Family Medicine Clinics. The PAGR also provided accurate medicine reconciliation and complete PDMP checks for controlled substance prescriptions. The patient real-time benefits check and drug-drug and allergy-drug reviews resulted in the provider changing the prescription 28% of the time, enhancing safety and out-of-pocket patient expenses. Future enhancements include expanding the insurance benefits analysis and developing provider notifications when patients are non-compliant with filling their prescriptions.

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee.

ObjectiveTo develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.Materials and MethodsStudy data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric “weak learner” models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.ResultsWithin 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79–0.83; Spiegelhalter P value: 0–.12). Risk concentration captured 47–52% of cases in the top quantiles of predicted probabilities.DiscussionPartitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.ConclusionPredicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

Secondary effects of an opioid-focused academic detailing program on non-opioid controlled substance prescribing in primary care

Background: Educational outreach programs that focus on safe opioid prescribing and awareness of state prescription monitoring programs may modify clinicians’ prescribing behavior. The objective of this study was to evaluate the secondary effects of an opioid-focused academic detailing (AD) program on non-opioid controlled substance prescribing in primary care. Methods: A quasi-experimental pre-post study of primary care clinicians exposed and unexposed to the AD program was conducted using data from the Illinois Prescription Monitoring Program from December 2017 to February 2019. Outcomes were mean monthly prescriptions for benzodiazepines (BZD), non-BZD sedative-hypnotics, and carisoprodol, per clinician. A difference-in-differences (DID) approach utilizing repeated-measures mixed-effects linear regression models was used to compare changes in outcomes six-months before and after the program. Results: Mean monthly BZD prescriptions declined in both groups of clinicians (AD-exposed n = 151; controls n = 399) after implementation of the AD program. Although the mean monthly number of BZD prescriptions decreased in both groups after the AD program, BZD prescribing in the AD-exposed group declined at a slower rate following the AD program (DID = 0.73; 95% CI: 0.14, 1.31). The AD-exposed group had a 0.06 (95% CI: −0.11, −0.01) lower rate of mean monthly carisoprodol prescriptions compared to the control group following the AD program. There was no change in the rate of mean monthly non-BZD sedative-hypnotic prescriptions between the two groups. Conclusions: The higher relative rate of BZD prescribing in the AD-exposed group compared to the control group following the AD program may be reflective of an unintended consequence of opioid-focused AD programs as clinicians learn to be cautious about opioid prescribing. Our findings may suggest the need for incorporation of targeted education on appropriate BZD prescribing into opioid-focused AD programs as a featured component. These findings warrant further consideration and investigation before large-scale implementation of opioid-focused educational outreach programs.

Toxicological and pharmacologic sex differences in unintentional or undetermined opioid overdose death

IntroUnderstanding sex differences in toxicological etiologies of opioid-related drug overdose death could inform future sex- and gender-specific approaches to prevention and treatment. MethodsA retrospective review of accidental or undetermined opioid-involved overdose deaths in Rhode Island 2016–2019 was performed using the Rhode Island Department of Health State Unintentional Drug Overdose Reporting System (SUDORS) database. Decedent toxicology data was linked with state Prescription Drug Monitoring Program (PDMP) records. ResultsOf 766 cases in the analytical sample, 568 cases were in men (74.2%) and 198 cases were in women (25.6%). Median age was 40.0 years for males and 42.0 years for females. Statistically significant sex-differences in drug exposures were found. Compared to men, women were more likely have exposure to benzodiazepine, antipsychotic, and antidepressant drug classes and less likely to have fentanyl and alcohol co-exposure. No sex differences were found in cocaine and amphetamine exposure. Female decedents were more likely than male decedents to have a prescription for benzodiazepines or opioids in the 30 days before death (40% vs 21%). The proportion of decedents with a benzodiazepine on post-mortem toxicology testing in combination with a benzodiazepine prescription (p < 0.001) or an opioid prescription (p = 0.005) was over two times higher in women than men. ConclusionHigher rates of controlled substance prescription prior to death and prescription drug co-exposures suggest that female opioid-involved drug overdose decedents are often in contact with the health care system immediately preceding their death, presenting the opportunity to create patient-centric approaches for prevention, harm reduction, and substance use treatment.

Nonsurgical Providers Provide the Majority of Postoperative Opioid Prescriptions After Hand Surgery.

IntroductionThe increased use of Prescription Drug Monitoring Program (PDMP) websites has helped physicians to limit overlapping controlled substance prescriptions and help prevent opioid abuse. Many studies have investigated risk factors for prolonged opioid use after orthopedic surgery, but few studies have investigated who is prescribing opioids to postoperative patients. The purpose of this study is to investigate the types of medical providers prescribing opioids to hand surgery patients postoperatively.MethodsInstitutional Review Board approval was obtained prior to initiation of this study. An institutional database search was performed to identify all patients ≥18 years old that underwent a single hand surgery at our institution during a specified time period. Patients with more than one surgical procedure during this time were excluded to prevent potential crossover with opioid prescriptions for different surgical procedures. A search of the state PDMP website was performed to identify opioid prescriptions filled by hand surgery patients from six months preoperatively to 12 months postoperatively. Opioid prescribers were classified into several groups: 1) the patient’s operating surgeon, 2) other orthopedic surgery providers, 3) general medicine providers (internal medicine, primary care, family medicine, and adult health providers), and 4) all other medical providers.ResultsThree hundred twenty-seven patients could be identified in the PDMP database who received an opioid prescription on the day of surgery. Of these, 108 (33.0%) filled a total of 341 additional opioid prescriptions postoperatively. Non-orthopedic providers prescribed 81.5% of all opioid prescriptions within 12 months postoperatively, with the patient’s operating surgeon prescribing only 10% of all prescriptions. General medicine providers were the highest prescriber group at 28.7% of total postoperative opioid prescriptions. From six to 12 months postoperatively, the patient’s operating surgeon prescribed only 4.9% of total opioid prescriptions filled. The patient’s operating surgeon prescribed significantly smaller average opioid prescriptions in total morphine milligram equivalents compared to all other provider groups.ConclusionsSurgeons should be aware that their surgical patients may be receiving opioid prescriptions from a wide variety of medical providers postoperatively, and that these other providers may be prescribing larger prescriptions. The findings of this study emphasize the importance of collaboration across medical specialties to mitigate the risks of prolonged opioid use after hand surgery.

APRNs' controlled substance prescribing and readiness following Florida legislative changes.

Two years after the Florida legislature expanded APRN prescribing to include schedule II-IV drugs in 2017, we studied APRN utilization of this prescriptive authority. Study results reveal that Florida APRNs are meeting the educational requirements to prescribe and apply the use of these drugs in practice, improving patient access to care.

The “Strawman” in the Process of Review and Evaluation of Complaints Regarding Controlled Substance Prescriptions

In 2017, AB 474 greatly changed laws surrounding prescriptions of controlled substances (CS) medications in the State of Nevada. The law, governing the Nevada State Boards of healthcare providers w...

Telemental health policies for college students during COVID-19

Federal and institutional policy changes have accelerated the use of telemental health to care for college students distant from their mental health providers during the early part of the COVID-19 pandemic. Temporary measures have made telemental health more readily available, including relaxing of regulations related to interstate licensure, controlled substance prescribing, patient privacy, and reimbursement. Though early efforts are underway to sustain these changes during and in the wake of the pandemic, there are important areas in which federal and institutional policy are still lacking. Additional steps are needed to successfully implement and sustain telemental health for college students include ensuring student access to technology and Internet; proactive outreach to optimize the student’s home environment, addressing concerns about safety and confidentiality; developing the means to track rapidly shifting telemental health policy changes; and developing centralized resources that enable remote providers to become familiar with involuntary commitment laws and emergency protocols.

“Doctor and pharmacy shopping”: A fading signal for prescription opioid use monitoring?

BackgroundThe term “doctor and pharmacy shopping” colloquially describes patients with high multiple provider episodes (MPEs)-a threshold count of distinct prescribers and/or pharmacies involved in prescription fulfillment. Opioid-related MPEs are implicated in the global opioid crisis and heavily monitored by government databases such as U.S. state prescription drug monitoring programs (PDMPs). We applied a widely-used MPE definition to examine U.S. trends from a large, commercially-insured population from 2010 to 2017. Further, we examined the proportion of enrollees identified as “doctor shoppers” with evidence of a cancer diagnosis to examine the risk of false positives. MethodsUsing a large, commercially-insured population, we identified patients with opioid-related MPEs: opioid prescriptions (Schedule II-V, no buprenorphine) filled from ≥5 prescribers AND ≥ 5 pharmacies within the past 90 days (“5x5x90d”). Quarterly rates per 100,000 enrollees (two specifications) were calculated between 2010 and 2017. We examined the trend in a recently published all-payer, 7 state cohort from the U.S. Centers for Disease Control and Prevention for comparison. Cancer-related ICD-9/10-CM codes were used. ResultsQuarterly MPE rates declined by approximately 73 % from 18.2–4.9 per 100,000 enrollee population with controlled substance prescriptions. In 2017, nearly one fifth of these commercially-insured enrollees identified by the 5x5x90d algorithm were diagnosed with cancer. Approximately 8% of this sample included patients with ≥ 1 buprenorphine prescriptions. ConclusionsOpioid “shopping” flags are a long-standing but rapidly fading PDMP signal. To avoid unintended consequences, such as identifying legitimate medical encounters requiring high healthcare utilization or opioid treatment, while maintaining vigilance, more nuanced and sophisticated approaches are needed.

Clinical decision making involving prescription drug monitoring programs: A factorial, vignette-based study among student pharmacists

ObjectivesPrescription drug monitoring programs (PDMPs) are state-maintained databases that providers may reference when deciding to prescribe or dispense controlled substances. As more states begin to mandate PDMP use at the point of care, it is imperative to assess how pharmacists use PDMP information when determining whether to fill a controlled substance prescription (CSP). The objective of this study was to evaluate which factors affected fourth-year student pharmacists’ decision to fill an opioid prescription, their level of confidence in their decision making, and familiarity with the PDMP. MethodsWe used a 24 factorial design to present a series of text-based vignettes to fourth-year student pharmacists. Each participant received 8 vignettes (5 randomly selected, 3 fixed), representing a hypothetical hydrocodone-acetaminophen combination prescription with varying levels of the following dichotomous factors: doctor shopping, dosage, pharmacy shopping, and concurrent benzodiazepine prescription. Participants were asked to decide whether or not they would fill each of the hypothetical prescriptions they received. A multilevel model was used to measure the association between each of the vignette factors, age, race, sex, experience with PDMP, and the decision to refuse to fill a prescription. Each vignette response served as an independent observation. ResultsA total of 87 participants yielded 696 vignette responses. Participants were significantly more likely to refuse to fill prescriptions with doctor shopping (adjusted odds ratio [aOR] 19.86 [95% CI 10.78–36.58]), pharmacy shopping (6.78 [4.13–11.12]), dosage (1.83 [1.16–2.90]), or if the student pharmacist was of female sex (1.73 [1.02–2.93]). Concomitant benzodiazepine use was not associated with a no-fill decision (1.45 [0.92–2.27]). ConclusionThis study reveals that student pharmacists’ decision to fill a prescription is dependent on both prescription characteristics and a patient’s CSP history. The importance of PDMP history cannot be downplayed and suggests that PDMP use may be effective in informing patient care decisions. Still, the variability in filling decision highlights the need to teach a formulaic approach to CSP dispensing in colleges of pharmacy.

Retrospective Chart Review of Advanced Practice Pharmacist Prescribing of Controlled Substances for Pain Management at the Harry S. Truman Memorial Veterans' Hospital.

The US Department of Health and Human Services' 5-point strategy to combat the opioid overdose public health crisis includes improved pain management. There is a shortage of adequately trained health care providers in pain management. Advanced practice pharmacists may be able to help fill that void. The objective of this project was to identify the impact of an advanced practice pharmacist with controlled substance prescriptive authority on morphine milligram equivalent dose (MME) and compliance with opioid risk mitigation. In March 2020, a single-site retrospective chart review was conducted of patients who were prescribed controlled substances from July 1, 2018 to January 31, 2020. Patients received care through the outpatient Pharmacy Pain Clinic in-person or via telephone who were enrolled at the Harry S. Truman Memorial Veterans' Hospital in Columbia, Missouri, or associated outlying outpatient clinics. Patients were included if they were referred to the Pharmacy Pain Clinic and prescribed a Schedule II or III opioid medication. A 2-sided t test was conducted to compare MME, and a Fisher exact test was used to compare adherence to opioid risk mitigation. Patients seen in Pharmacy Pain Clinic had a statistically significant reduction in MME from consult (93 MME) to discharge (31 MME) (P < .01). There was also a statistically significant (P < .01) improvement in use of opioid risk mitigation strategies, including urine drug screen, informed consent, naloxone, prescription drug monitoring program checks, and stratification tool for opioid risk mitigation dashboard reviews. An advanced practice pharmacist with controlled substance prescriptive authority improved patient care with demonstrated statistically significant differences in MME and adherence with opioid risk mitigation from consult to discharge. Health care teams should look to add advanced practice pharmacists to their team as medication experts to deliver comprehensive medication management, which can include controlled substance prescribing and management.

Obstetrician-gynecologist perceptions and utilization of prescription drug monitoring programs: A survey study.

Query of Prescription Drug Monitoring Programs (PDMPs) is recommended before prescribing opioids by the US Centers for Disease Control and Prevention, to inform clinical practice and aid diversion prevention. Many states mandate prescriber PDMP use; however, little is known about PDMP perception of utility and use among Obstetricians-Gynecologists (OB/GYN), who are the primary provider for most women during pregnancy.This study examined OB/GYN perceptions and utilization of their state PDMP.Survey items were developed by expert consensus. A voluntary anonymous survey was emailed to a random sample of 5000 OB/GYNs (adjusted participants n = 1470, minus unread/refusals). Responses were stratified by state policy environment, where response frequency distributions were compared for OB/GYNs practicing in states with mandatory vs voluntary PDMP query.Adjusted response rate was 27% (n = 397). Most OB/GYNs (78%) were registered with their PDMP. The majority agreed that “…mandating physician use of the PDMP was a good idea” (51.4% mandatory state vs 58.3% voluntary state). Respondents in mandatory states reported that the primary purpose of the PDMP was “to allow the physician to verify medications that the patient is being prescribed” less frequently than those in voluntary states (38.3% vs 52.8%). Several report speaking with patients about controlled substance prescriptions after viewing PDMP reports (27.8% in mandatory vs 26.3% in voluntary states). In qualitative responses, reported frustration with PDMPs was evident.OB/GYNs are diverse in their perceptions regarding the utility and purpose of PDMPs. Tailored education is needed regarding clinical utility of PDMPs for OB/GYN practice.

Characterization of gabapentin use in Kentucky after reclassification as a Schedule V controlled substance

BackgroundOwing to increasing concern over the potential for gabapentin misuse, gabapentin was reclassified as a schedule V controlled substance in Kentucky (July 2017). ObjectiveThis study aimed to characterize gabapentin use among Kentucky residents in the first year after its scheduling. MethodsThis study used Kentucky All Schedule Prescription Electronic Reporting data (2018). Gabapentin use was defined as having at least 1 dispensed gabapentin prescription, and high-dose gabapentin use was defined as an average daily dose of more than 3600 mg at the patient level. The prevalence of gabapentin use, concurrent use of gabapentin with opioid analgesics (OAs) (gabapentin-OA), and gabapentin with OAs and benzodiazepines (BDZs) (gabapentin-OA-BDZ) were assessed. Estimated prevalence rate ratio and its 95% CI were reported to compare gabapentin use rates across different demographic groups. ResultsA total of 16% of all 2018 controlled substance prescriptions were for gabapentin, and approximately 20% of Kentucky residents with controlled substance prescriptions received gabapentin at least once in 2018. The overall prevalence of gabapentin use was 63 per 1000 residents, with the highest rates among residents aged 55-64 years (126.9 per 1000). The prevalence of gabapentin use was higher in females (74.6 per 1000) versus males (50.6 per 1000) and in residents living in the Appalachian region (88.57 per 1000) versus Central (51.78 per 1000) and Delta (66.41 per 1000) regions. Among gabapentin users, 1% were high-dose users; 27.4% and 11.9% received gabapentin-OA or gabapentin-OA-BDZ concurrently, respectively. As the average daily dose increased from less than 900 mg to high-dose, the percentage of concurrent gabapentin-OA use increased from 13.4% to 50.7%. ConclusionGabapentin is widely prescribed in Kentucky, with higher rates of use observed in females, those older than 55 years and individuals living in the Appalachian region. Concurrent use of gabapentin and OAs is common, especially in those receiving high-dose gabapentin. Future studies are needed to assess the risks associated with gabapentin use.

Merging statewide data in a public/university collaboration to address opioid use disorder and overdose

ObjectiveDescribe methods to compile a unified database from disparate state agency datasets linking person-level data on controlled substance prescribing, overdose, and treatment for opioid use disorder in Connecticut.MethodsA multidisciplinary team of university, state and federal agency experts planned steps to build the data analytic system: stakeholder engagement, articulation of metrics, funding to establish the system, determination of needed data, accessing data and merging, and matching patient-level data.ResultsStakeholder meetings occurred over a 6-month period driving selection of metrics and funding was obtained through a grant from the Food and Drug Administration. Through multi-stakeholder collaborations and memoranda of understanding, we identified relevant data sources, merged them and matched individuals across the merged dataset. The dataset contains information on sociodemographics, treatments and outcomes. Step-by-step processes are presented for dissemination.ConclusionsCreation of a unified database linking multiple sources in a timely and ongoing fashion may assist other states to monitor the public health impact of controlled substances, identify and implement interventions, and assess their effectiveness.

Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose

Background: Prescription Drug Monitoring Programs (PDMPs) collect controlled substance prescriptions dispensed within a state. Many PDMP programs perform targeted outreach (i.e., “unsolicited reporting”) for patients who exceed numerical thresholds, however, the degree to which patients at highest risk of fatal opioid overdose are identified has not been compared with one another or with a predictive model. Methods: A retrospective analysis was performed using statewide PDMP data for Maryland residents aged 18 to 80 years with an opioid fill between April to June 2015. The outcome was opioid-related overdose death in 2015 or 2016. A multivariable logistic regression model and three PDMP thresholds were evaluated: (1) multiple provider episodes; (2) high daily average morphine milligram equivalents (MME); and (3) overlapping opioid and benzodiazepine prescriptions. Results: The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3% of total deaths) and had a higher death rate (7.12 per 1000) when the risk score cutoff (0.0030) was selected for a comparable size of high-risk individuals (n = 15,881) than those meeting the overlapping opioid/benzodiazepine prescriptions (n = 17,440; 33.2% of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6% of total deaths; 4.09 deaths per 1000) thresholds. Conclusions: The predictive model identified more individuals at risk of fatal opioid overdose as compared with PDMP thresholds commonly used for unsolicited reporting. PDMP programs could improve their targeting of unsolicited reports to reach more individuals at risk of overdose by using predictive models instead of simple threshold-based approaches.

Barriers, solutions, and effect of using pharmacogenomics data to support opioid prescribing.

Opioid use and misuse are continued issues facing clinicians across all aspects of health care. As clinicians struggle to effectively manage opioid prescribing, pharmacogenomics (PGx) further offers the prescriber an improved ability to understand the potential for an individual patient's genetics to influence opioid efficacy and safety. When PGx data are available at the point of initial prescribing, clinicians can apply that data to drug therapy selection. However, barriers continue to exist relative to PGx data sharing and interpretation, which have created difficulties for widespread PGx implementation. This article briefly describes potential barriers to PGx data integration, strategies to overcome those barriers, and the potential positive effect of successful data sharing on opioid prescribing. Prescription drug monitoring programs (PDMPs) have been successfully operationalized to share controlled substance prescribing data across health care settings. Such data sharing enables clinicians to, among other things, better understand risks associated with misuse. Because a relatively limited volume of PGx data is currently pertinent to opioid prescribing, such PGx data could be added to PDMPs as a way to communicate genetic information within current technology platforms. Not only would this integrate into existing clinical workflow models where PDMP data are accessed at this point of prescribing and/or dispensing, but associated clinical guidance for PGx data interpretation in the context of opioids could be integrated into the workflow process. Such clinical decision support could be provided directly through the PDMP interface for uniformity or could be provided via systems that access PDMP data. Clinical, economic, and policy implications of the inclusion of PGx data within PDMPs are also discussed. Through harnessing PDMP for data sharing, multiple barriers to PGx implementation could be mitigated, and clinicians may have better access to PGx data to optimize opioid prescribing. DISCLOSURES: No outside funding supported this study. Bright has a patent pending related to opioid use disorder risk assessment that includes genetic information and was a collaborator on funded research projects with pharmacogenomics-related companies. Petry has been a consultant to the North Dakota Department of Health and has received grants from IGNITE I and IGNITE II (NIH), unrelated to this work. The other authors are aware of no financial conflicts of interest.

The lock-in loophole: Using mixed methods to explain patient circumvention of a Medicaid opioid restriction program

Background Lock-in programs are proliferating among private and public payers to restrict access to controlled substance prescriptions and enhance care coordination for patients exhibiting high-risk use of, primarily, opioids. Patients enrolled in lock-in programs are required to seek opioids from a designated provider and pharmacy for insurance coverage of their opioid and benzodiazepine prescriptions. Lock-in program restrictions are often circumvented by patients through out-of-pocket cash purchases of opioid prescriptions, undermining the program’s intended function. This study sought to construct and explain trajectories of Medicaid-covered and cash pay opioid prescription fills among adults enrolled in an opioid lock-in program. Methods: We used sequential explanatory mixed methods, which involved a quantitative retrospective cohort analysis of opioid fill trajectories using North Carolina Medicaid administrative claims data linked with state prescription drug monitoring program data, followed by qualitative semi-structured interviews with North Carolina pharmacists. The quantitative component included adults enrolled in the North Carolina Medicaid lock-in program between 10/1/2010-3/31/2012. The qualitative component included a maximum variation sample of community pharmacists in North Carolina delivering care to lock-in patients. Quantitative outcomes included group-based trajectories of monthly Medicaid-covered and cash pay opioid prescription fills six months before and after LIP enrollment, and qualitative analyses generated themes explaining observed trajectories. Results: Two-thirds of subjects exhibited reduced Medicaid-covered opioid prescription fills and no increase in cash pay fills after lock-in enrollment, with one-third exhibiting increased cash pay fills after lock-in. Pharmacists attributed increases in cash pay fills primarily to illicit behaviors, while some cash pay behavior likely reflected new unintended barriers to care. Conclusions: Lock-in programs appear to reduce prescription opioid use for most enrolled patients. However, lock-in programs may have limited capacity to deter illicit behaviors among patients intent on abusing, misusing, or diverting these medications and may introduce new access barriers to necessary care for some.

Key Opportunities for the COVID-19 Response to Create a Path to Sustainable Telemedicine Services

Key Opportunities for the COVID-19 Response to Create a Path to Sustainable Telemedicine Services