A Panel Management Approach Using Prescription Drug Monitoring Program Data for Primary Care Patients with Chronic Pain Treated with Opioids: A Feasibility Study

A Panel Management Approach Using Prescription Drug Monitoring Program Data for Primary Care Patients with Chronic Pain Treated with Opioids: A Feasibility Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Panel Management Approach Using Prescription Drug Monitoring Program Data for Primary Care Patients with Chronic Pain Treated with Opioids: A Feasibility Study Constance van Eeghen, Marianne Burke, Zoe Daudier, Amanda G. Kennedy, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7793877/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This feasibility study explored a process for primary care clinicians to improve chronic pain management related to opioid prescribing practices by using state-based Prescription Drug Monitoring Program (PDMP) data to create panel management reports on patients receiving long term opioid therapy. Methods Conducted across four rural primary care clinics in Northern New England, the study assessed the feasibility of downloading and utilizing PDMP data and the perceived value of panel management reports derived from both PDMP and electronic health record (EHR) data in the care of patients with chronic pain treated with opioids for more than one year. Results The study found that downloading PDMP data was feasible and efficient across all sites. However, EHR review proved more challenging due to inconsistencies in data entry and the unstructured nature of some relevant data fields. Clinicians generally found PDMP data easy to generate and the panel management reports informative and useful for understanding opioid prescribing trends and identifying high-risk patients. Conclusions The findings suggest that while PDMPs are a potential source for panel management reports for patients with chronic pain who are treated with opioids, further study is needed to determine the effectiveness of such efforts to improve care for and safety of patients treated with opioids. Translational Medicine Primary Care chronic pain opioid drug prescription panel management Figures Figure 1 Figure 2 Background The most common symptom encountered in primary care is pain, 1 making primary care clinicians (PCCs) the de facto pain management service in the U.S. With PCCs writing half of all opioid prescriptions and writing a higher proportion of long-term opioid prescriptions, 2, 3 they are the primary managers of a treatment that may lead to opioid use disorder (OUD) and its associated complications, including death. For 30 years, the U.S. population has experienced increased opioid use partly due to prescribed opioids. Although opioid prescribing has declined since 2010, 4 opioid-related deaths have increased in the U.S to 32.6 deaths per 100,000 standard population in 2022, recently decreasing to 31.3 in 2023 5 . The 29% annual increase of deaths from 2020–2021 was likely due to the availability of illicit synthetic opioids (e.g., fentanyl 6 ), lack of compliance by insurers in coverage of care for substance use disorder, barriers to accessing evidence-based treatment, and other secular trends. 7 Opioid prescribing continues to be of interest as current trends show primary care physicians are decreasing opioid prescribing at a decreasing rate (leveling off) while advanced practice primary care clinicians are increasing their prescribing. 8 Treating chronic pain with opioids is challenging. Long-term prescribing is associated with many patient characteristics, including age at initiation (adults aged 18–25 years according to some sources; older adults in others), low socioeconomic status, and poor physical and mental health. 9 , 10 Predictors of prescription opioid misuse for patients with chronic pain are complex, involving multiple psychosocial and mental health factors. 11 In 2021, 3.1% of the population aged 12 years or older (almost 9 million people) reported that they misused prescription pain relievers (primarily but not solely opioids). 12 Most often, they sought relief from physical pain. Healthcare clinicians are caught between under-addressing patients’ pain and over-prescribing a potentially addictive substance. Even a five-day course of opioids is associated with a 10% probability of long-term opioid use one year later, 13 rising to 27.3% for long-acting opioids. Tramadol, promoted as safer than traditional opioids in avoiding addiction, is associated with a one-year probability of long-term use of 13.7%. 11 Although national guidance exists for opioid treatment of chronic, non-cancer pain, 14 there is evidence of divergence between prescribing practice and clinical recommendations for a variety of reasons, 15 including “inherited” patient panels from retiring PCC colleagues. 16 PCCs need data and tools to manage chronic pain appropriately, including opioid prescribing. They must optimize patient outcomes while also following prescribing guidelines, state and local regulations, insurance requirements, and institutional policies. One strategy is “panel management,” which offers a systematic approach to the management of chronic illness. 17 – 19 Panel management is useful in managing chronic conditions such as diabetes mellitus and hypertension. 18 It is characterized by a set of tools and processes to identify patients and provide structured workflows based on evidence-based protocols, especially for those at high risk. Well-established models exist and they highlight the importance of using population health approaches such as the development of chronic disease registries. 20 However, the model depends on customizable, timely data reporting under local control and the ability to benchmark to guide improvement. 21 One possible information source for panel management of patients treated with opioids for chronic pain is state-based Prescription Drug Monitoring Programs (PDMPs) that track controlled substance prescriptions. PDMPs feature data downloads for longitudinal quality improvement (QI) and reporting purposes, although individual prescribers can see data only of patients for whom they prescribe. Such data, when aggregated across clinicians, can provide a practice-wide view of prescribed medications, dosage, prescribing intervals, and concurrent prescriptions of benzodiazepines, which can inform a panel management approach to opioid treatment. We conducted a feasibility study across four primary care clinics in Northern New England (NNE) to assess whether: 1) PCCs and staff could download, merge, and deidentify PDMP data for analysis and 2) PDMP and electronic health record (EHR) panel management reports on opioid prescribing were considered valuable by PCCs. Materials and Methods Study Design This retrospective, observational, feasibility study assessed PCC and staff ability and willingness to collect patient data from the PDMP for primary care practices in three different states and to match a sample of those records with the EHR for chart review. Practices collected PDMP data from the previous 3–6 years (2017–2022) as allowed by individual state PDMP regulations. The downloaded data from each prescriber were combined into a single, de-identified dataset by the site staff to create a complete prescribing history of each patient in the practice. Trained practice staff also extracted EHR data from the previous two years. The research team sought to assess 1) whether these sources provided insights into opioid prescribing patterns and 2) adherence with state-level prescribing regulations and Centers for Disease Control and Prevention (CDC) prescribing guidelines. The research team analyzed deidentified data and generated prescriber- and practice-specific longitudinal trend reports on populations of patients with chronic pain treated with long-term opioids — the specific panel of interest. Prescribers from each clinic reviewed the reports and participated in a structured group conversation about their perspectives on the ease of collecting the data and the usability of the reports. To help guide the work of this study, the research team convened a team of patients and clinicians to advise on a data collection instrument for chart abstraction to include indicators of pain management relevant to the population. The team included two patients with long-term chronic pain and opioid treatment experience, a clinical pharmacist, and a PCC. Patient partners were compensated for their time and contributed to instrument design, interpretation of results, and dissemination efforts. Meeting ten times over this one-year project, the patient/clinician partner team reviewed the chart abstraction data collection instrument and advised on key features of pain and pain management to identify during chart review. They evaluated available patient-reported instruments, highlighted strengths and gaps, and made recommendations for future work on this topic. They collaborated on the development, editing, and review of a poster and an oral presentation on the results of this study. Study Participants/Population Eligible primary care clinics were recruited through the Northern New England CO-OP Practice- and Community-Based Research Network. Each eligible clinic had at least 50 patients across all clinicians that were treated for chronic pain with opioids for at least 90 days. The subjects of analysis were adult patients (18 years+) with chronic pain treated with 1000 or more morphine milligram equivalents 22 (MME) of opioids per year, or 5 or more individual opioid prescriptions in a year, between 2017 and 2022. Patients who were receiving buprenorphine or other medications for OUD were excluded. Patients receiving buprenorphine for chronic pain were included. Procedures, Materials, and Instruments Clinics completed an initial questionnaire to describe their office practice, patient panel size, number and types of clinicians, and typical pain management and assessment tools used with patients (Appendix 1). PDMP Downloads and Data Merge : The research team trained prescribers to download their prescribing history from the PDMP using a written manual and a 10-minute video guide with step-by-step procedures developed for this study (Appendix 2, Part 1). A designated practice staff member combined and deidentified the data into a single dataset to create a complete prescribing history of each patient in the practice (Appendix 2, Part 2). Each practice dataset was delivered to the research team through secure file transfer. From this file, the research team created a roster of 50 patients treated with long-term opioids (LTO Roster) for each practice, assuring proportional representation of each prescriber. LTO Roster records were coded to match patient identifiers held only by the practice behind its organizational firewall. EHR Chart Review : The research team trained clinic staff in EHR chart review, using a written manual (Appendix 3) to accompany the REDCap 23 data entry abstraction instrument (Appendix 4). The team conducted live remote training sessions for two clinic staff members selected by each practice. As part of each training session, chart reviewers practiced reviewing one EHR record matched to the LTO Roster working together and one record separately, cross-comparing results, and discussing any differences. Separate coding of additional records continued until the two reviewers coded the same record consistently, after which they continued independently to complete their review of the remaining patients on the LTO Roster. Reviewers abstracted de-identified EHR data into REDCap, capturing a two-year look-back period prior to the patient’s most recent chronic pain-related visit in 2022. Practice Presentations and Guided Group Discussion : The research team conducted an hour-long presentation and guided discussion session with each practice following the analysis of practice data. The meetings included the prescribers and the practice manager with the research team. A presentation described the controlled substance prescribing trends at the practice, including variation among prescribers in the practice and comparison to other practices in the study. The study team used a semi-structured small group discussion guide to gather the perspective of the participants on the ease of data retrieval and the usefulness of the reports as predictors of acceptability (Appendix 5). Study Measures and Analyses PDMP Data : Each practice dataset provided a de-identified patient number assigned by the practice, year of birth, prescriber identifier, date of prescription, drug name, number of days supplied, and dosing in MMEs. Other than the birth year, no other demographic patient characteristics are provided by the PDMP. The research team summarized the data at the prescriber and practice level. Descriptive statistics included annual sums of opioid MMEs prescribed, number and proportion of high-risk patients (i.e., greater than 90 MME/day or overlap of opioids with benzodiazepines), proportion of prescriptions in multiples of 7-days (to assure that prescriptions are consistently due on a weekday to avoid on-call coverage), and number of patients prescribed with both an opioid and benzodiazepine. Panel management reports included tables and charts highlighting trends in prescribing practices over time. EHR Data : The research team used descriptive statistics to analyze data reported from chart reviews, including the proportion of patients with documentation of legally mandated procedures (use of the PDMP, informed consent, treatment agreement) and CDC-recommended measures (pain, opioid risk, functional status, urine drug testing, and screening for depression). Quantitative PDMP and EHR analyses were conducted using Excel and Stata version 18 (StataCorp, College Station, TX). Feasibility, Ease of Use, and Usefulness : The research team assessed the feasibility of conducting PDMP downloads and extraction from EHRs in real time as participants engaged in the study. The assessment was based on ease of use of the data retrieval process and usefulness of the panel management reports as stated in responses given in the guided discussion sessions after each practice had reviewed their results. After each session, the research team met to review the session responses, organize the qualitative data by practice and theme, and come to consensus on what was learned. Protection of Human Rights and Participation Incentives The Institutional Review Boards at the University of Vermont and the Dartmouth-Hitchcock Medical Center determined that the study was not human subjects research per the regulatory definition under 45 CFR 46.102(d) and that a full review was not needed. Business associate agreements and memoranda of understanding were completed by clinics and the research institution (University of Vermont). Clinics received a stipend for participation and for compensation of the staff who reviewed the EHR, as well as a report on their outcomes in comparison with other study participants. Results Four primary care practices in rural areas of Maine (1 clinic), New Hampshire (1), and Vermont (2) participated. The ownership model, size, and other practice characteristics are shown in Table 1 . Between 4% and 12% of patients used opioids during the observation period available in the PDMP. Patients in the EHR review study sample (48–49 patients per practice successfully matched to the PDMP registry) were more likely to be female, except for site D. The median age ranged from 58–70 (Table 2 ). Table 1 Practice characteristics Practice ID and location A: State 1 B: State 1 C: State 2 D: State 3 Practice ownership model CHC/FQHC * CHC/FQHC Health system Health System Specialty FM † FM Mixed FM & IM ‡ FM Tax status Not-for-profit Not-for-profit Not-for-profit Not-for-profit NCQA - PCMH status § Participating Participating Participating Participating Electronic health record vendor Athena Medent EPIC EPIC Year EHR installed 2015 2017 2013 2011 Clinicians in practice 7 8 13 11 a Patients in practice 5,575 11,465 9,992 8,263 Patients visits per year 3,791 9,909 7,721 6,301 Years of observation in PDMP 6 6 6 3 Number of patients using an opioid in PDMP dataset (%) 636 (11%) 1,378 (12%) 1,170 (12%) 313 (4%) Age of patients in PDMP, mean (SD) 69 (16.1) 63 (15.6) 64 (16.0) 63 (14.6) Insurance mix Percent panel with Medicare 24% 28% IM 35%, FM 19% 23% Percent panel with Medicaid missing 24% missing 5% Patient-facing tools Depression screening PHQ || PHQ PHQ PHQ Initial misuse risk assessment ORT ¶ None ORT, CPAA # ORT Ongoing risk assessment None None None None Informed consent and treatment agreement Yes Yes Yes Yes Pain/functional assessment Custom PEG ** PEG PEG Non-medical roles in practice Psychologist Yes - - - Social work Yes - Yes Yes Pharmacist - - Yes Yes Psychiatry APRN †† Yes - - - MOUD team ‡‡ Yes - - - Community Health Worker Yes - - Yes Health Coach - - Yes - Behavioral Health partner or consulting psychiatrist - Yes Yes Nurse care coordinator - - - Yes * CHC/FQHC Community Health Center/Federally Qualified Health Center † FM Family Medicine ‡ IM Internal Medicine § NCQA - PCMH status || PHQ Patient Health Questionnaire ¶ Opioid Risk Tool # Chronic Pain Assessment Algorithm ** Pain Enjoyment General activity scale †† Advanced Practice Nurse Practitioner ‡‡ MOUD Medication for Opioid Use Disorder a 11 of 23 prescribers in Site D participated in the study. All other practice characteristics reported for Site D represent the entire practice. Table 2 Medical record documentation of best-practice endpoints Practice ID and location A: State 1 B: State 1 C: State 2 D: State 3 Number of records reviewed 49 49 48 49 Age, median (range) 70 (18–90+) 59 (18–90+) 58 (18–90+) 66 (34–90+) Female, proportion 59% 55% 53% 39% Legally mandated endpoints PDMP lookup documented * 94% 72% 100% 90% Treatment agreement 94% 94% 98% 62% CDC-recommended strategies Informed Consent 61% in 2 yrs 18% in 2 yrs 42% in 2 yrs 67% in 2yrs Pill count 12% 12% 2% 12% Urine drug testing 92% 56% 98% 92% Pain measurement 65% Not found 100% 18% Functional status † 57% 60% 2% 41% Depression screen 98% 76% 97% 88% OUD risk assessment, (tool) ‡ Not found Not found 21% (ORT § ) 33% (ORT) Documentation of non-opioid treatments, proportion 53% 44% 40% 59% * PDMP: Prescription Drug Monitoring Program † Assessed with PEG or in clinician notes ‡ OUD: Opioid Use Disorder § ORT: Opioid Risk Tool 1. Overall Feasibility All practices reported that the PDMP download and data collation procedures were feasible and efficient, completing them successfully. Additional training and help were needed in one practice for the de-identification step (Appendix 2, Part 2). Feasibility of the EHR review was not as positive. Although each site was able to complete the chart review process, training the chart reviewers required multiple clarifications and re-training for each practice. Locating the relevant EHR data endpoints was consistently more successful when associated with a structured or mandatory field in the EHR (e.g., report of PDMP lookup or informed consent and treatment agreements). When reviewers looked for non-standardized data that might be found in multiple EHR fields or record sections, such as functional assessments or discussions of non-medical treatments, their ability to find the data was less consistent and reliable. 2. Value of PDMP Data: Ease of Use and Usefulness Panel management reports from the PDMP about opioid prescribing for patients with chronic pain were presented in-person to clinicians at each site in guided group discussions. These were made available in tabular (Table 3 , Table 4 ) and bar chart formats (Fig. 1 , Fig. 2 ). Clinicians described the reports as generally informative and useful, even when summary reports from other sources were available to them. Several clinicians noted that the panel management reports provided novel insights into variation across prescribers in the practice and across time. Some highlighted that the reports provided actionable information for QI and managing patient care. Table 3 Example of trend in opioid prescribing for pain in MME, by anonymized prescriber, 2018–2022 Prescriber Year % change MME (18–22) 2018 2019 2020 2021 2022 Clinician A 305,416 310,398 252,561 261,959 245,404 -20% Clinician B 4,883 115,775 - Clinician C 99,240 125,679 177,528 157,548 111,174 + 12% [Partial practice data shown for anonymity] Clinician Y 1,158 4,655 63,156 136,104 - Clinician Z 1,271,132 1,007,300 683,920 799,567 792,123 -38% Practice Total * 1,961,865 1,695,875 1,348,993 1,631,280 1,526,990 -22% * Totals may not sum because of partial practice data shown Table 4 Example Patient counts by anonymized prescriber, 2018–2022 Prescriber 2018 2019 2020 2021 2022 Clinician A Count of opioid patients 151 103 112 91 80 Count of chronic opioid patients 49 36 33 33 32 Proportion 7 pill increments 36% 46% 47% 47% 49% Count of benzo patients 76 65 51 60 51 Count of overlap patients 16 14 16 16 13 Count of MOUD * patients 0 0 0 21 17 Clinician B Count of opioid patients 8 62 Count of chronic opioid patients 1 22 Proportion 7 pill increments 36% 36% Count of benzo patients 6 43 Count of overlap patients 1 12 Count of MOUD patients 0 0 Clinician C Count of opioid patients 46 56 55 43 38 Count of chronic opioid patients 15 22 21 22 21 Proportion 7 pill increments 66% 76% 77% 74% 79% Count of benzo patients 74 74 70 69 56 Count of overlap patients 17 18 9 12 10 Count of MOUD patients 0 0 0 0 0 * MOUD Medication for Opioid Use Disorder Prescribers and office managers reported that the challenges of opioid prescribing for chronic pain continue to be a high priority and that new strategies for managing care are needed. In response to “Is Panel Management report information useful to your practice?,” two sites agreed with “Yes” and two sites with “Maybe,” with one prescriber noting, “Maybe for some kinds of patients… It’s hard to work it in in the time with patients and keep it a priority” (Table 5 ). The value of these reports was also reflected in comments such as “ We want to be on the same page ” in prescribing opioids for pain and caring for their community and “ patients are aging over time and their past treatment plans, in the context of the whole picture of their health, are no longer working ” (Table 5 ). The panel management reports demonstrated one method of providing the information needed to achieve these goals. Although three practices reported that these data were available in their EHR or PDMP vendor sources, they were not considered flexible in format or actionable for follow-up and they did not provide a practice-level view of prescribing. Table 5 Qualitative Responses to Semi-Structured Guided Group Discussions A State 1 B State 1 C State 2 D State 3 Number of attendees 7 9 8 16 Questions/Themes Is PDMP download easy? Easy; not even memorable Yes; [QI leader] came by and coached each clinician through it; easy Mostly Yes; Took maybe 5 seconds; would be even easier if made into a routine Are PDMP data useful? Not anymore. Have been working on this 5–10 years; concentrated efforts to reduce opioid prescribing. Other aspects are more challenging: decreasing high doses Confirms what is known. [This report] added new info (overlap patients) which is useful and not currently reported. Yes. Is it actionable? What are the guidelines we should use; what project should we do? Community is changing from mill town to “up and coming.” How does this show up in the data? Are there better ways to look at the data? We want to be on the same page as a practice and a community. Consistent approach is important. Data reflects the community. Yes. Interesting; feel for aggregate number is good. Important to separate the 2 populations: pain management and MOUD; would be very useful with an Opioid Council Do you already have PDMP data available? Previously available in annual reviews. QI projects with similar data: UDS, contract, agreement Most of it. Clinicians are very aware of their opioid prescribing and patients treated with opioids already. Not known or accessible or easy to get to. There are some Best Practice Alerts on multiple drugs and Z drugs; possibly a high risk elderly warning exists somewhere. No. Technically possible but not operationalized; only data they see comes from financial DB Are chart review data useful? Confirms what is known. [We] get information through clinical system Confirms what is known Yes. Can get information through clinical system. Have EMR flags based on pt med list Yes. Data would push us to standardized practice Is Panel Management report information available from your own system? Yes Yes Yes. This report is better No Is Panel Management report information useful to your practice Maybe for some kinds of patients. High dose patients "age out" - they die. QI is a challenge. Lots of guidance is available. It's hard to work it in in the time with patients and keep it a priority. Maybe, if it is actionable. There is a sense that patients are aging over time and that past treatment plans, in the context of the whole picture of their health, are no longer working, including opioid prescribing. Their quality of life is important and their opioid prescription is part of that. Yes. High prescribers took over retiring practices and [have] many opioid-using patients. [We need to be] making practice structured. Yes. Data would push us to standardized practice. 3. Value of EHR Data: Ease of Use and Usefulness Clinicians reported that the chart review reports were useful and accurate but not novel. The information usually could be provided through their clinical systems although such reports were not easily available or well organized. Presence of patient-centered measures (e.g., pain, function, and risk stratification) in the EHR review varied within and across practices (Table 2 ). Legally mandated endpoints such as patient treatment agreements and PDMP lookup dates were well documented. Informed consents were often not found, likely because such documentation for a long-term condition would fall outside the 2-year window of the chart review. Discretionary strategies, such as pill counts, pain measures, functional assessments, screening for depression, OUD risk assessments, and offering or discussing non-medical therapy were typically lower and varied widely. In Table 2 , clinicians were given credit for documentation of functional status for use of either a scale such as the “Pain, Enjoyment, and General Activity” (PEG) assessment, or for any non-standardized assessment reflected in the clinic notes. Measures that were structured into designated data fields (e.g., depression screen) were consistently documented (76%-98% across clinics); measures that had no standardized documentation field (functional status) were not (2%-60%). Clinicians noted that consistent use of these tools would be beneficial and regular reports “ would push us to standardize [our] practice ” (Table 5 ). 4. Challenges identified The research team encountered specific challenges in training and supporting practice sites with the data download and chart review requirements. Each state governs the access to and use of PDMPs and may have different restrictions on how long historical data are available, requiring regular downloads to update trend reports in keeping with prescribers’ need to review panel management reports. In addition, healthcare organizations have differing requirements on how their clinical data may be used for QI projects, such as multiple levels of review and approval. Creating precise but not overly laborious instruction for EHR abstraction was an ongoing goal. Even with better instructional materials, however, chart review remained a time-consuming process. Conclusions This study demonstrates that it is feasible to create reports from PDMP downloads for PCCs to use in caring for patients with chronic pain. PCCs found the process easy and the results useful, but not always novel with respect to past reports some had received. However, clinicians found the format of panel management reports valuable. With opioid prescribing largely now in the domain of primary care 3 , 24 , PCCs can leverage panel management strategies that are already familiar to them. The CDC chronic opioid prescribing guidelines of 2016 and 2022 highlighted the importance of specific strategies to support safe and responsible prescribing 14 . For busy clinical teams, access to summary data, peer comparisons, and rosters of patients around which to plan QI programming can be very useful. In this pilot study we demonstrated the feasibility and usefulness of using a widely available data source, the state PDMP, to create helpful data summaries. While EHR data represent medications that were prescribed, PDMP data reflect medications that were actually dispensed at any pharmacy in the state (and often other states as well). There may be important discrepancies between prescribed and dispensed medications. For example, in post-operative outpatient prescribing, while 92% of patients receive an opioid prescription, only 27% of the prescribed MME was consumed 25 . Summary reports available through EHR systems vary not only by vendor, but also by specific institutional customization—this limits the ability to compare data reports across practices. When compared to PDMP vendor reports (which are limited to single clinicians), this approach to panel management reports can provide a practice-level view of the care management of patients with chronic pain treated with opioids and can avoid the problem of double counting patients who receive prescriptions from more than one prescriber. Despite these advantages, reviewing data on opioid prescribing alone does not provide a complete clinical picture. An understanding of the particular context within a practice is essential to account, for example, for prescribers who have recently retired, new clinicians managing legacy patient panels, or prescriber specialization (pain management, opioid use disorder, end of life care, etc.) when interpreting the data. In an effort to create a more complete picture of opioid prescribing in primary care, we also sought to extract patient-reported outcomes from the EHR. Recent reports have highlighted that many patients with chronic pain treated with opioids are interested in reducing their dosage, but are concerned about being able to control their pain, mood, and opioid cravings effectively 26 . The inclusion of patient-reported outcomes on pain management and tolerance is a key part of treatment, as no objective measure of pain exists. In this small sample of four primary care practices in one region of the US, we observed considerable variability in the documentation of pain, functionality, and risk assessment in the EHR. This raises the question of whether a standardized assessment tool that incorporates administrative endpoints (such as those that are legally required) and important patient-reported outcomes (such as pain, function, and perspective on opioid tapering) would be helpful in standardizing care and benchmarking. Reducing opioid doses increases the chance that the patient experience will worsen, although this is not always true 27 . Clinicians and researchers have an obligation to measure the impact of changes in pain management regimens on those they care for, the patients. Strategies to support PCC efforts to improve opioid medication management already indicate the value of identifying and tracking the use of opioids. EHRs cannot generate rosters based on medication dispensing across the state and by multiple prescribers. Those depending on standard PDMP reports will not see trends at the practice level. And those who can conduct manual chart abstraction will face the inefficiencies of unstructured data as well as lacking state-wide information available from pharmacies dispensing Schedule II-IV controlled substances. The use of clinical data to create panel management reports is not new, but the use of clinical data from outside the patient record for panel management may provide a new opportunity to work with patients on the challenges brought by opioid treatment. The PDMP also provides a highly accurate and widely available source of data for research on prescribing patterns of controlled substances. Limitations to this study include uncertain generalizability beyond the four clinics that volunteered to participate, all of whom were in rural, northern New England settings. Further work is needed to assess feasibility across a geographically wider set of clinics with varying needs related to opioid prescription management. Small group, semi-structured discussions elicited feedback from participants willing to voice their opinions in front of their peers and may have been influenced by social desirability bias. Combining the prescribing history of 3–6 years of PDMP data with two years of abstracted EHR data may have created an incomplete picture of compliance with state-level regulations and CDC guidelines. We may have missed documented care that stood just outside the 2-year time horizon of the chart review. Some patients who begin opioid treatment may be lost to follow-up in the PDMP data base due to a change in state of residence, movement to a long-term care or correctional facility, death, or discontinuation of opioid treatment. Use of the PDMP as a source for panel management should include watching for the absence of patients on the population roster and including a follow-up mechanism for those who are missing. In summary, this feasibility study across four primary care clinics in northern New England revealed that while downloading PDMP data was feasible, easy, and efficient, EHR chart review was more challenging due to inconsistencies in data entry. Clinicians found panel management-style reports for patients with chronic pain treated with opioids to be informative and useful. PDMPs may be a helpful source for a panel management approach in caring for patients with chronic pain and treated with opioids, but further research to test its comparative effectiveness, its impact on primary care clinicians of different locations and credentials, and the value of more standardized applications of clinical data across state-based PDMPs and EHRs is needed. Abbreviations CDC Centers for Disease Control and Prevention EHR Electronic Health Record LTO Long-Term Opioids MME Morphine Milligram Equivalents NNE Northern New England ORT Opioid Risk Tool assessment OUD Opioid Use Disorder PCC Primary Care Clinician PEG Pain, Enjoyment, and General Activity assessment PDMP Prescription Drug Monitoring Program Declarations Ethics Approval and Consent to Participate The Institutional Review Boards at the University of Vermont and the Dartmouth-Hitchcock Medical Center determined that the study was not human subjects research per the regulatory definition under 45 CFR 46.102(d) and that a full review was not needed. Consent for Publication – not applicable Availability of Data and Materials Due to the sensitive nature of the data shared by health care clinicians for this study, and based on their respective state laws governing use of prescription drug monitoring data, we assured participants that their raw data would remain confidential and not be shared. Data are not available. The data that have been used are confidential. Open materials statement: the components of the research methodology needed to reproduce the reported procedure(s) and analyses are not publicly available but available on request to the corresponding author. Competing Interests The authors declare that they have no conflicting or competing interests. Funding Statement The research reported here was supported by grant U54 GM115516 from the National Institutes of Health for the Northern New England Clinical and Translational Research Network. Author’s Contributions CvE designed and was the principal investigator conducting the study, including conceptualization; data curation (qualitative); formal analysis (qualitative); funding acquisition; investigation; methodology; project administration; resources; visualization; roles/writing - original draft; and writing - review and editing. MB conducted data curation (qualitative & quantitative); formal analysis (qualitative); investigation; project administration; visualization; roles/writing - original draft; and writing - review and editing. ZD assisted with project administration; resources; and writing – review and editing. AK advised the investigation; resources; and writing - review and editing. NK supported funding acquisition; methodology; and writing - review and editing. BL conducted formal analysis (quantitative); methodology; and writing - review and editing. MM advised on the investigation; resources; and writing - review and editing. DP advised on the investigation; resources; and writing - review and editing. JR assisted with project administration; resources; and writing – review and editing. MS supported project administration; resources; and writing - review and editing. CDM guided conceptualization; data curation (quantitative); formal analysis (quantitative); funding acquisition; investigation; methodology; visualization; roles/writing - original draft; and writing - review and editing. Acknowledgments The authors would like to thank site leaders and practice facilitators of the primary care practices in Maine, New Hampshire, and Vermont that participated in this study. Special recognition goes to the Northern New England CO-OP Practice and Community-Based Research Network (NNE CO-OP PCBRN) at Dartmouth Health, which provided project guidance and support in the execution of this study. Our appreciation as well goes to Dr. Jon Porter, Medical Director and Division Chief of the Comprehensive Pain Program at the University of Vermont Medical Center for his comments regarding patient reported outcomes and Ms. Juvena Hitt, Quality Program Director, Department of Medicine, University of Vermont for archival work. References Framing Opioid Prescribing Guidelines for Acute Pain: Developing the Evidence. Washington, DC: National Academies of Sciences, Engineering, and Medicine; 2020. Weiner SG, Baker O, Rodgers AF, Garner C, Nelson LS, Kreiner PW, et al. Opioid Prescriptions by Specialty in Ohio, 2010-2014. Pain medicine (Malden, Mass). 2018;19(5):978-89. Levy B, Paulozzi L, Mack KA, Jones CM. Trends in Opioid Analgesic-Prescribing Rates by Specialty, U.S., 2007-2012. Am J Prev Med. 2015;49(3):409-13. Guy GP, Jr., Zhang K, Bohm MK, Losby J, Lewis B, Young R, et al. Vital Signs: Changes in Opioid Prescribing in the United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66(26):697-704. Garnett MF, Miniño, A.M. Drug overdose deaths in the United States, 2003–2023. Hyattsville, MD; 2024. Contract No.: NCHS Data Brief, no 522. Mattson CL TL, Quinn K, Kariisa M, Patel P, Davis NL. Trends and Geographic Patterns in Drug and Synthetic Opioid Overdose Deaths — United States, 2013–2019. MMWR Morb Mortal Wkly Rep. 2021;70. Overdose Epidemic Report: American Medical Association; 2024 [Available from: https://end-overdose-epidemic.org/wp-content/uploads/2024/11/24-1177083-Advocacy-2024-Overdose-Report_DIGITAL-2.pdf. Pristell C, Byun H, Huffstetler AN. Opioid Prescribing Has Significantly Decreased in Primary Care. Am Fam Physician. 2024;110(6):572-3. Volkow ND, Jones EB, Einstein EB, Wargo EM. Prevention and Treatment of Opioid Misuse and Addiction: A Review. JAMA Psychiatry. 2019;76(2):208-16. Karmali RN, Bush C, Raman SR, Campbell CI, Skinner AC, Roberts AW. Long-term opioid therapy definitions and predictors: A systematic review. Pharmacoepidemiol Drug Saf. 2020;29(3):252-69. Kaye AD, Jones MR, Kaye AM, Ripoll JG, Galan V, Beakley BD, et al. Prescription Opioid Abuse in Chronic Pain: An Updated Review of Opioid Abuse Predictors and Strategies to Curb Opioid Abuse: Part 1. Pain Physician. 2017;20(2s):S93-s109. What is the scope of prescription drug misuse in the United States? : National Institute of Drug Abuse; 2023 [Available from: https://nida.nih.gov/publications/research-reports/misuse-prescription-drugs/what-scope-prescription-drug-misuse Shah A, Hayes CJ, Martin BC. Characteristics of Initial Prescription Episodes and Likelihood of Long-Term Opioid Use - United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66(10):265-9. Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain--United States, 2016. JAMA. 2016;315(15):1624-45. Mikosz CA, Zhang K, Haegerich T, Xu L, Losby JL, Greenspan A, et al. Indication-Specific Opioid Prescribing for US Patients With Medicaid or Private Insurance, 2017. JAMA Network Open. 2020;3(5):e204514-e. Tong ST, Hochheimer CJ, Brooks EM, Sabo RT, Jiang V, Day T, et al. Chronic Opioid Prescribing in Primary Care: Factors and Perspectives. Ann Fam Med. 2019;17(3):200-6. Bodenheimer T. Transforming Practice. New England Journal of Medicine. 2008;359(20):2086-9. Neuwirth EE, Schmittdiel JA, Tallman K, Bellows J. Understanding panel management: a comparative study of an emerging approach to population care. Perm J. 2007;11(3):12-20. Kaminetzky CP, Nelson KM. In the Office and In-Between: The Role of Panel Management in Primary Care. Journal of general internal medicine. 2015;30(7):876-7. Bodenheimer T, Ghorob, A., Margolius, D. Use Patient Care Registries to Provide Comprehensive Preventive Care 2024 [Available from: https://edhub.ama-assn.org/steps-forward/module/2702192. Watts B, Lawrence RH, Drawz P, Carter C, Shumaker AH, Kern EF. Development and Implementation of Team-Based Panel Management Tools: Filling the Gap between Patient and Population Information Systems. Popul Health Manag. 2016;19(4):232-9. Calculating total daily dose of opioids for safer dosage Centers for Disease Control and Prevention; 2016 [Available from: https://stacks.cdc.gov/view/cdc/38481. Harris PA, Taylor, R., Thielke, R., et al. . Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics. 2009;42(2):377-81. Salvatore PP, Guy GP, Mikosz CA. Changes in Opioid Dispensing by Medical Specialties After the Release of the 2016 CDC Guideline for Prescribing Opioids for Chronic Pain. Pain medicine (Malden, Mass). 2022;23(11):1908-14. Fujii MH, Hodges AC, Russell RL, Roensch K, Beynnon B, Ahern TP, et al. Post-Discharge Opioid Prescribing and Use after Common Surgical Procedure. J Am Coll Surg. 2018;226(6):1004-12. Jabakhanji R, Tokunaga F, Rached G, Vigotsky AD, Griffith J, Schnitzer TJ, et al. Attitudes of chronic pain patients on long-term opioid therapy toward opioid tapering. medRxiv PREPRINT. 2023:2023.12.19.23300217. Hardy CJ, Cochran G, Howey W, Wright E, Wasan AD, Gordon AJ, et al. Impact of Clinician-Facing Interventions to Reduce Opioid Use on Pain Related Outcomes in Primary Care: A Cluster Randomized Trial. Health Services Research and Managerial Epidemiology. 2024;11:23333928241240957. Additional Declarations The authors declare no competing interests. Supplementary Files AppendicesTOCofTrainingMaterialsandDatacollectionInstruments.pdf Appendices TOC of Training Materials and Data collection Instruments App1PraciticeInfoQuestionnaire.pdf App 1 - Pracitice Info Questionnaire App2P1PDMPDownloadData.pdf App 2, P1 - PDMP Download Data App2P2PDMPMergeDeidentifySOP.pdf App 2, P2 - PDMP Merge & De-identify SOP App3ChartreviewSOP81723.pdf App 3 - Chart review SOP 8_17_23 App4ChartReviewsurveyOpioidPrescrREDCap4292023.pdf App 4 - ChartReview_survey OpioidPrescr REDCap 4_29_2023 App5Postpresentationsemistructuredguide.pdf App 5 - Post-presentation semi-structured guide Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7793877","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525586893,"identity":"20df192b-0c74-4c19-a533-20802b700fb0","order_by":0,"name":"Constance van Eeghen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBCDBH4gIQHlGBCnRbKBZC0GB4jVwj8j+dmDDxW1ecbXDh+8wdh2OJqBvXmbBD4tEjfSzA1nnDlebHY7LdkCqCW3gedYGV4tBjwHzKR5244lbrudYybB2JaW2yABZODXcvyb9N9/xxI3z87/BtEi/4aAFvYeM2nGhprEDdI5bEAtNkBbePBrkTjeUybZc+xA4ozbacYWCedsctt40oot8Gnhb2bfJvGjpi6xf3bywxsfyiRy+9kPb7yBTwsUHIZQCUDMRoRyEKgjUt0oGAWjYBSMSAAAnLZJ1ce6QusAAAAASUVORK5CYII=","orcid":"","institution":"University of Vermont","correspondingAuthor":true,"prefix":"","firstName":"Constance","middleName":"van","lastName":"Eeghen","suffix":""},{"id":525586894,"identity":"e3c280a6-275d-4cd3-b1f7-298f6abcd424","order_by":1,"name":"Marianne Burke","email":"","orcid":"","institution":"University of Vermont","correspondingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"","lastName":"Burke","suffix":""},{"id":525586896,"identity":"4297b98e-4859-41ac-8388-dcfa9ba319de","order_by":2,"name":"Zoe Daudier","email":"","orcid":"","institution":"Dartmouth Health","correspondingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"","lastName":"Daudier","suffix":""},{"id":525586898,"identity":"9f9d8f93-435f-4da5-a6dc-8a3f7efe4fc1","order_by":3,"name":"Amanda G. Kennedy","email":"","orcid":"","institution":"University of Vermont","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"G.","lastName":"Kennedy","suffix":""},{"id":525586899,"identity":"255020d7-0194-4b13-8aa9-8ee431f40b35","order_by":4,"name":"Neil Korsen","email":"","orcid":"","institution":"MaineHealth","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"","lastName":"Korsen","suffix":""},{"id":525586900,"identity":"fed3b348-6470-4ca7-a9ef-1b455994b570","order_by":5,"name":"Benjamin Littenberg","email":"","orcid":"","institution":"University of Vermont","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Littenberg","suffix":""},{"id":525586901,"identity":"5ef64224-3e58-49de-b56b-60e5553ac228","order_by":6,"name":"Moira Mulligan","email":"","orcid":"","institution":"University of Vermont","correspondingAuthor":false,"prefix":"","firstName":"Moira","middleName":"","lastName":"Mulligan","suffix":""},{"id":525586902,"identity":"cc38b32f-34b6-41f2-9d12-401d06d9bc2b","order_by":7,"name":"Doug Pomeroy","email":"","orcid":"","institution":"University of Vermont","correspondingAuthor":false,"prefix":"","firstName":"Doug","middleName":"","lastName":"Pomeroy","suffix":""},{"id":525586903,"identity":"7f2da30e-39a0-4e8e-8d83-ed60b3cd5366","order_by":8,"name":"Jennifer Raymond","email":"","orcid":"","institution":"Dartmouth Health","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Raymond","suffix":""},{"id":525586904,"identity":"09268860-fa01-44b0-a3e1-321386a11c5c","order_by":9,"name":"Meagan E. 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Each bar represents an individual primary care provider, with bar height displaying the total MMEs prescribed for each of the years 2018-2022. Note the relevance of on-site context when interpreting these charts: the change in MME/year for Provider Y may be due to the gradual adjustment of opioid treatment for a panel of patients “inherited” from a recently retired prescriber.\u003c/p\u003e\n\u003cp\u003eMOUD: Medication for Opioid Use Disorder\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/2a9a0bee4381871cfc85c219.jpg"},{"id":93025975,"identity":"559d0974-95ab-482c-ad80-645c16b5d393","added_by":"auto","created_at":"2025-10-08 09:25:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186147,"visible":true,"origin":"","legend":"\u003cp\u003eExample graphic of trend in opioid prescribing for pain in MME, by anonymized primary care practice, 2018-2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 Legend:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnual opioid MME prescribed by the practice per 1000 patients in the practice panel.\u003c/p\u003e\n\u003cp\u003eTrends of opioid MME prescribed by clinicians at each participating clinic varied based on the particular histories involved in retiring and newly arrived prescribers and greater awareness of opioid prescribing strategies and monitoring tools. Opioid prescribing per 1000 patients, by 2022, appeared to have converged between 100K-200K MMEs/year.\u003c/p\u003e\n\u003cp\u003eMOUD: Medication for Opioid Use Disorder\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/d4bdf1e9e3daedaa3cce0dc7.jpg"},{"id":93027679,"identity":"238ef76a-e1bd-4d3b-a1a0-3268e4bc0f19","added_by":"auto","created_at":"2025-10-08 09:41:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1291892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/2298991c-d4cf-4230-95a2-1c19f3e027e8.pdf"},{"id":93025974,"identity":"9e735491-e4bd-43c9-8e96-815502f4e936","added_by":"auto","created_at":"2025-10-08 09:25:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33730,"visible":true,"origin":"","legend":"\u003cp\u003eAppendices TOC of Training Materials and Data collection Instruments\u003c/p\u003e","description":"","filename":"AppendicesTOCofTrainingMaterialsandDatacollectionInstruments.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/ce04b91208f5801db0413e41.pdf"},{"id":93024623,"identity":"c30a6abf-583e-48b8-8558-8537d430c3d4","added_by":"auto","created_at":"2025-10-08 09:17:53","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":577898,"visible":true,"origin":"","legend":"\u003cp\u003eApp 1 - Pracitice Info Questionnaire\u003c/p\u003e","description":"","filename":"App1PraciticeInfoQuestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/bed60409fc6f7730bf519ca8.pdf"},{"id":93024628,"identity":"f3855889-a875-409a-8dfd-e3272dadb2e0","added_by":"auto","created_at":"2025-10-08 09:17:53","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":95078,"visible":true,"origin":"","legend":"\u003cp\u003eApp 2, P1 - PDMP Download Data\u003c/p\u003e","description":"","filename":"App2P1PDMPDownloadData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/ed57de5adb5bf28c636f56c3.pdf"},{"id":93026718,"identity":"aff536dd-09d8-4523-b4a9-d9f653ddbcd8","added_by":"auto","created_at":"2025-10-08 09:33:53","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":90726,"visible":true,"origin":"","legend":"\u003cp\u003eApp 2, P2 - PDMP Merge \u0026amp; De-identify SOP\u003c/p\u003e","description":"","filename":"App2P2PDMPMergeDeidentifySOP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/a575a7b701ddb4bacb3f1d08.pdf"},{"id":93024629,"identity":"3c53623d-8f9e-419f-a660-f139b59e2ad8","added_by":"auto","created_at":"2025-10-08 09:17:53","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":214182,"visible":true,"origin":"","legend":"\u003cp\u003eApp 3 - Chart review SOP 8_17_23\u003c/p\u003e","description":"","filename":"App3ChartreviewSOP81723.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/338393c0138ee1189e7b4896.pdf"},{"id":93024620,"identity":"e5f84f3e-6d3d-47e0-9d3c-2f9c45af06a5","added_by":"auto","created_at":"2025-10-08 09:17:53","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":54016,"visible":true,"origin":"","legend":"\u003cp\u003eApp 4 - ChartReview_survey OpioidPrescr REDCap 4_29_2023\u003c/p\u003e","description":"","filename":"App4ChartReviewsurveyOpioidPrescrREDCap4292023.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/b3f8bd270671c06f12285985.pdf"},{"id":93026717,"identity":"9236cc03-3ef9-4fe8-9fdc-523a9407659f","added_by":"auto","created_at":"2025-10-08 09:33:53","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":289990,"visible":true,"origin":"","legend":"\u003cp\u003eApp 5 - Post-presentation semi-structured guide\u003c/p\u003e","description":"","filename":"App5Postpresentationsemistructuredguide.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7793877/v1/c6bf63b5248e23a98248f285.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Panel Management Approach Using Prescription Drug Monitoring Program Data for Primary Care Patients with Chronic Pain Treated with Opioids: A Feasibility Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe most common symptom encountered in primary care is pain,\u003csup\u003e1\u003c/sup\u003e making primary care clinicians (PCCs) the \u003cem\u003ede facto\u003c/em\u003e pain management service in the U.S. With PCCs writing half of all opioid prescriptions and writing a higher proportion of long-term opioid prescriptions,\u003csup\u003e2, 3\u003c/sup\u003e they are the primary managers of a treatment that may lead to opioid use disorder (OUD) and its associated complications, including death. For 30 years, the U.S. population has experienced increased opioid use partly due to prescribed opioids. Although opioid prescribing has declined since 2010,\u003csup\u003e4\u003c/sup\u003e opioid-related deaths have increased in the U.S to 32.6 deaths per 100,000 standard population in 2022, recently decreasing to 31.3 in 2023\u003csup\u003e5\u003c/sup\u003e. The 29% annual increase of deaths from 2020\u0026ndash;2021 was likely due to the availability of illicit synthetic opioids (e.g., fentanyl\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e), lack of compliance by insurers in coverage of care for substance use disorder, barriers to accessing evidence-based treatment, and other secular trends.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Opioid prescribing continues to be of interest as current trends show primary care physicians are decreasing opioid prescribing at a decreasing rate (leveling off) while advanced practice primary care clinicians are increasing their prescribing.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTreating chronic pain with opioids is challenging. Long-term prescribing is associated with many patient characteristics, including age at initiation (adults aged 18\u0026ndash;25 years according to some sources; older adults in others), low socioeconomic status, and poor physical and mental health.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Predictors of prescription opioid misuse for patients with chronic pain are complex, involving multiple psychosocial and mental health factors.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e In 2021, 3.1% of the population aged 12 years or older (almost 9\u0026nbsp;million people) reported that they misused prescription pain relievers (primarily but not solely opioids).\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Most often, they sought relief from physical pain.\u003c/p\u003e\u003cp\u003eHealthcare clinicians are caught between under-addressing patients\u0026rsquo; pain and over-prescribing a potentially addictive substance. Even a five-day course of opioids is associated with a 10% probability of long-term opioid use one year later,\u003csup\u003e13\u003c/sup\u003e rising to 27.3% for long-acting opioids. Tramadol, promoted as safer than traditional opioids in avoiding addiction, is associated with a one-year probability of long-term use of 13.7%.\u003csup\u003e11\u003c/sup\u003e Although national guidance exists for opioid treatment of chronic, non-cancer pain,\u003csup\u003e14\u003c/sup\u003e there is evidence of divergence between prescribing practice and clinical recommendations for a variety of reasons,\u003csup\u003e15\u003c/sup\u003e including \u0026ldquo;inherited\u0026rdquo; patient panels from retiring PCC colleagues.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePCCs need data and tools to manage chronic pain appropriately, including opioid prescribing. They must optimize patient outcomes while also following prescribing guidelines, state and local regulations, insurance requirements, and institutional policies. One strategy is \u0026ldquo;panel management,\u0026rdquo; which offers a systematic approach to the management of chronic illness.\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePanel management is useful in managing chronic conditions such as diabetes mellitus and hypertension.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e It is characterized by a set of tools and processes to identify patients and provide structured workflows based on evidence-based protocols, especially for those at high risk. Well-established models exist and they highlight the importance of using population health approaches such as the development of chronic disease registries.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e However, the model depends on customizable, timely data reporting under local control and the ability to benchmark to guide improvement.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOne possible information source for panel management of patients treated with opioids for chronic pain is state-based Prescription Drug Monitoring Programs (PDMPs) that track controlled substance prescriptions. PDMPs feature data downloads for longitudinal quality improvement (QI) and reporting purposes, although individual prescribers can see data only of patients for whom they prescribe. Such data, when aggregated across clinicians, can provide a practice-wide view of prescribed medications, dosage, prescribing intervals, and concurrent prescriptions of benzodiazepines, which can inform a panel management approach to opioid treatment.\u003c/p\u003e\u003cp\u003eWe conducted a feasibility study across four primary care clinics in Northern New England (NNE) to assess whether: 1) PCCs and staff could download, merge, and deidentify PDMP data for analysis and 2) PDMP and electronic health record (EHR) panel management reports on opioid prescribing were considered valuable by PCCs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003e This retrospective, observational, feasibility study assessed PCC and staff ability and willingness to collect patient data from the PDMP for primary care practices in three different states and to match a sample of those records with the EHR for chart review. Practices collected PDMP data from the previous 3\u0026ndash;6 years (2017\u0026ndash;2022) as allowed by individual state PDMP regulations. The downloaded data from each prescriber were combined into a single, de-identified dataset by the site staff to create a complete prescribing history of each patient in the practice. Trained practice staff also extracted EHR data from the previous two years. The research team sought to assess 1) whether these sources provided insights into opioid prescribing patterns and 2) adherence with state-level prescribing regulations and Centers for Disease Control and Prevention (CDC) prescribing guidelines. The research team analyzed deidentified data and generated prescriber- and practice-specific longitudinal trend reports on populations of patients with chronic pain treated with long-term opioids \u0026mdash; the specific panel of interest. Prescribers from each clinic reviewed the reports and participated in a structured group conversation about their perspectives on the ease of collecting the data and the usability of the reports.\u003c/p\u003e\u003cp\u003eTo help guide the work of this study, the research team convened a team of patients and clinicians to advise on a data collection instrument for chart abstraction to include indicators of pain management relevant to the population. The team included two patients with long-term chronic pain and opioid treatment experience, a clinical pharmacist, and a PCC. Patient partners were compensated for their time and contributed to instrument design, interpretation of results, and dissemination efforts. Meeting ten times over this one-year project, the patient/clinician partner team reviewed the chart abstraction data collection instrument and advised on key features of pain and pain management to identify during chart review. They evaluated available patient-reported instruments, highlighted strengths and gaps, and made recommendations for future work on this topic. They collaborated on the development, editing, and review of a poster and an oral presentation on the results of this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Participants/Population\u003c/h3\u003e\n\u003cp\u003eEligible primary care clinics were recruited through the Northern New England CO-OP Practice- and Community-Based Research Network. Each eligible clinic had at least 50 patients across all clinicians that were treated for chronic pain with opioids for at least 90 days. The subjects of analysis were adult patients (18 years+) with chronic pain treated with 1000 or more morphine milligram equivalents\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (MME) of opioids per year, or 5 or more individual opioid prescriptions in a year, between 2017 and 2022. Patients who were receiving buprenorphine or other medications for OUD were excluded. Patients receiving buprenorphine for chronic pain were included.\u003c/p\u003e\n\u003ch3\u003eProcedures, Materials, and Instruments\u003c/h3\u003e\n\u003cp\u003eClinics completed an initial questionnaire to describe their office practice, patient panel size, number and types of clinicians, and typical pain management and assessment tools used with patients (Appendix 1).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePDMP Downloads and Data Merge\u003c/span\u003e: The research team trained prescribers to download their prescribing history from the PDMP using a written manual and a 10-minute video guide with step-by-step procedures developed for this study (Appendix 2, Part 1). A designated practice staff member combined and deidentified the data into a single dataset to create a complete prescribing history of each patient in the practice (Appendix 2, Part 2). Each practice dataset was delivered to the research team through secure file transfer. From this file, the research team created a roster of 50 patients treated with long-term opioids (LTO Roster) for each practice, assuring proportional representation of each prescriber. LTO Roster records were coded to match patient identifiers held only by the practice behind its organizational firewall.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEHR Chart Review\u003c/span\u003e: The research team trained clinic staff in EHR chart review, using a written manual (Appendix 3) to accompany the REDCap\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e data entry abstraction instrument (Appendix 4). The team conducted live remote training sessions for two clinic staff members selected by each practice. As part of each training session, chart reviewers practiced reviewing one EHR record matched to the LTO Roster working together and one record separately, cross-comparing results, and discussing any differences. Separate coding of additional records continued until the two reviewers coded the same record consistently, after which they continued independently to complete their review of the remaining patients on the LTO Roster. Reviewers abstracted de-identified EHR data into REDCap, capturing a two-year look-back period prior to the patient\u0026rsquo;s most recent chronic pain-related visit in 2022.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePractice Presentations and Guided Group Discussion\u003c/span\u003e: The research team conducted an hour-long presentation and guided discussion session with each practice following the analysis of practice data. The meetings included the prescribers and the practice manager with the research team. A presentation described the controlled substance prescribing trends at the practice, including variation among prescribers in the practice and comparison to other practices in the study. The study team used a semi-structured small group discussion guide to gather the perspective of the participants on the ease of data retrieval and the usefulness of the reports as predictors of acceptability (Appendix 5).\u003c/p\u003e\n\u003ch3\u003eStudy Measures and Analyses\u003c/h3\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePDMP Data\u003c/span\u003e: Each practice dataset provided a de-identified patient number assigned by the practice, year of birth, prescriber identifier, date of prescription, drug name, number of days supplied, and dosing in MMEs. Other than the birth year, no other demographic patient characteristics are provided by the PDMP. The research team summarized the data at the prescriber and practice level. Descriptive statistics included annual sums of opioid MMEs prescribed, number and proportion of high-risk patients (i.e., greater than 90 MME/day or overlap of opioids with benzodiazepines), proportion of prescriptions in multiples of 7-days (to assure that prescriptions are consistently due on a weekday to avoid on-call coverage), and number of patients prescribed with both an opioid and benzodiazepine. Panel management reports included tables and charts highlighting trends in prescribing practices over time.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEHR Data\u003c/span\u003e: The research team used descriptive statistics to analyze data reported from chart reviews, including the proportion of patients with documentation of legally mandated procedures (use of the PDMP, informed consent, treatment agreement) and CDC-recommended measures (pain, opioid risk, functional status, urine drug testing, and screening for depression). Quantitative PDMP and EHR analyses were conducted using Excel and Stata version 18 (StataCorp, College Station, TX).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFeasibility, Ease of Use, and Usefulness\u003c/span\u003e: The research team assessed the feasibility of conducting PDMP downloads and extraction from EHRs in real time as participants engaged in the study. The assessment was based on ease of use of the data retrieval process and usefulness of the panel management reports as stated in responses given in the guided discussion sessions after each practice had reviewed their results. After each session, the research team met to review the session responses, organize the qualitative data by practice and theme, and come to consensus on what was learned.\u003c/p\u003e\n\u003ch3\u003eProtection of Human Rights and Participation Incentives\u003c/h3\u003e\n\u003cp\u003eThe Institutional Review Boards at the University of Vermont and the Dartmouth-Hitchcock Medical Center determined that the study was not human subjects research per the regulatory definition under 45 CFR 46.102(d) and that a full review was not needed. Business associate agreements and memoranda of understanding were completed by clinics and the research institution (University of Vermont). Clinics received a stipend for participation and for compensation of the staff who reviewed the EHR, as well as a report on their outcomes in comparison with other study participants.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFour primary care practices in rural areas of Maine (1 clinic), New Hampshire (1), and Vermont (2) participated. The ownership model, size, and other practice characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Between 4% and 12% of patients used opioids during the observation period available in the PDMP. Patients in the EHR review study sample (48\u0026ndash;49 patients per practice successfully matched to the PDMP registry) were more likely to be female, except for site D. The median age ranged from 58\u0026ndash;70 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePractice characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractice ID and location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA: State 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB: State 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC: State 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD: State 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractice ownership model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHC/FQHC \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCHC/FQHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth System\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecialty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFM \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMixed FM \u0026amp; IM \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTax status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot-for-profit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot-for-profit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot-for-profit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot-for-profit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNCQA - PCMH status \u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParticipating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eParticipating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eParticipating\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElectronic health record vendor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAthena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEPIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEPIC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear EHR installed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinicians in practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatients in practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8,263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatients visits per year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9,909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of observation in PDMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of patients using an\u003c/p\u003e\u003cp\u003eopioid in PDMP dataset (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e636 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,378 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,170 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e313 (4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of patients in PDMP,\u003c/p\u003e\u003cp\u003emean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63 (14.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance mix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercent panel with Medicare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIM 35%, FM 19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercent panel with Medicaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient-facing tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression screening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHQ \u003csup\u003e||\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePHQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePHQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePHQ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial misuse risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eORT\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eORT, CPAA\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eORT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOngoing risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformed consent and treatment agreement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain/functional assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCustom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePEG \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePEG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePEG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-medical roles in practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychologist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePharmacist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychiatry APRN \u003csup\u003e\u0026dagger;\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOUD team\u003csup\u003e\u0026Dagger;\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunity Health Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Coach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBehavioral Health partner or consulting psychiatrist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse care coordinator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* CHC/FQHC Community Health Center/Federally Qualified Health Center\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger; FM Family Medicine\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026Dagger; IM Internal Medicine\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026sect; NCQA - PCMH status\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e|| PHQ Patient Health Questionnaire\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026para; Opioid Risk Tool\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e# Chronic Pain Assessment Algorithm\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e** Pain Enjoyment General activity scale\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger;\u0026dagger; Advanced Practice Nurse Practitioner\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026Dagger;\u0026Dagger; MOUD Medication for Opioid Use Disorder\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003ea 11 of 23 prescribers in Site D participated in the study. All other practice characteristics reported for Site D represent the entire practice.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMedical record documentation of best-practice endpoints\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractice ID and location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA: State 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB: State 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC: State 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD: State 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of records reviewed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, median (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (18\u0026ndash;90+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (18\u0026ndash;90+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (18\u0026ndash;90+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66 (34\u0026ndash;90+)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, proportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegally mandated endpoints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDMP lookup documented \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment agreement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDC-recommended strategies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformed Consent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61% in 2 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18% in 2 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42% in 2 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67% in 2yrs\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePill count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine drug testing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain measurement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot found\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional status \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression screen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOUD risk assessment, (tool) \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot found\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot found\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21% (ORT \u003csup\u003e\u0026sect;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33% (ORT)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDocumentation of non-opioid treatments, proportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* PDMP: Prescription Drug Monitoring Program\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger; Assessed with PEG or in clinician notes\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026Dagger; OUD: Opioid Use Disorder\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026sect; ORT: Opioid Risk Tool\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1. Overall Feasibility\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll practices reported that the PDMP download and data collation procedures were feasible and efficient, completing them successfully. Additional training and help were needed in one practice for the de-identification step (Appendix 2, Part 2).\u003c/p\u003e\u003cp\u003eFeasibility of the EHR review was not as positive. Although each site was able to complete the chart review process, training the chart reviewers required multiple clarifications and re-training for each practice. Locating the relevant EHR data endpoints was consistently more successful when associated with a structured or mandatory field in the EHR (e.g., report of PDMP lookup or informed consent and treatment agreements). When reviewers looked for non-standardized data that might be found in multiple EHR fields or record sections, such as functional assessments or discussions of non-medical treatments, their ability to find the data was less consistent and reliable.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Value of PDMP Data: Ease of Use and Usefulness\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePanel management reports from the PDMP about opioid prescribing for patients with chronic pain were presented in-person to clinicians at each site in guided group discussions. These were made available in tabular (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and bar chart formats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Clinicians described the reports as generally informative and useful, even when summary reports from other sources were available to them. Several clinicians noted that the panel management reports provided novel insights into variation across prescribers in the practice and across time. Some highlighted that the reports provided actionable information for QI and managing patient care.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExample of trend in opioid prescribing for pain in MME, by anonymized prescriber, 2018\u0026ndash;2022\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePrescriber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e% change MME\u003c/p\u003e\u003cp\u003e(18\u0026ndash;22)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e305,416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310,398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e252,561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e261,959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e245,404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115,775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99,240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125,679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e177,528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157,548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e111,174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e[Partial practice data shown for anonymity]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63,156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e136,104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician Z\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,271,132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,007,300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e683,920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e799,567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e792,123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePractice Total \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,961,865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,695,875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,348,993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,631,280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,526,990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e Totals may not sum because of partial practice data shown\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExample Patient counts by anonymized prescriber, 2018\u0026ndash;2022\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrescriber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of opioid patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of chronic opioid patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion 7 pill increments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of benzo patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of overlap patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of MOUD\u003csup\u003e*\u003c/sup\u003e patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of opioid patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of chronic opioid patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion 7 pill increments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of benzo patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of overlap patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of MOUD patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinician C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of opioid patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of chronic opioid patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion 7 pill increments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of benzo patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of overlap patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount of MOUD patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e* MOUD Medication for Opioid Use Disorder\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePrescribers and office managers reported that the challenges of opioid prescribing for chronic pain continue to be a high priority and that new strategies for managing care are needed. In response to \u0026ldquo;Is Panel Management report information useful to your practice?,\u0026rdquo; two sites agreed with \u0026ldquo;Yes\u0026rdquo; and two sites with \u0026ldquo;Maybe,\u0026rdquo; with one prescriber noting, \u003cem\u003e\u0026ldquo;Maybe for some kinds of patients\u0026hellip; It\u0026rsquo;s hard to work it in in the time with patients and keep it a priority\u0026rdquo;\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The value of these reports was also reflected in comments such as \u0026ldquo;\u003cem\u003eWe want to be on the same page\u003c/em\u003e\u0026rdquo; in prescribing opioids for pain and caring for their community and \u0026ldquo;\u003cem\u003epatients are aging over time and their past treatment plans, in the context of the whole picture of their health, are no longer working\u003c/em\u003e\u0026rdquo; (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The panel management reports demonstrated one method of providing the information needed to achieve these goals. Although three practices reported that these data were available in their EHR or PDMP vendor sources, they were not considered flexible in format or actionable for follow-up and they did not provide a practice-level view of prescribing.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQualitative Responses to Semi-Structured Guided Group Discussions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA State 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB State 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC State 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD State 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of attendees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuestions/Themes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIs PDMP download easy?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEasy; not even memorable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes; [QI leader] came by and coached each clinician through it; easy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMostly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes; Took maybe 5 seconds; would be even easier if made into a routine\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAre PDMP data useful?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot anymore. Have been working on this 5\u0026ndash;10 years; concentrated efforts to reduce opioid prescribing. Other aspects are more challenging: decreasing high doses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConfirms what is known. [This report] added new info (overlap patients) which is useful and not currently reported.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes. Is it actionable? What are the guidelines we should use; what project should we do? Community is changing from mill town to \u0026ldquo;up and coming.\u0026rdquo; How does this show up in the data? Are there better ways to look at the data? We want to be on the same page as a practice and a community. Consistent approach is important. Data reflects the community.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes. Interesting; feel for aggregate number is good. Important to separate the 2 populations: pain management and MOUD; would be very useful with an Opioid Council\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDo you already have PDMP data available?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreviously available in annual reviews. QI projects with similar data: UDS, contract, agreement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMost of it. Clinicians are very aware of their opioid prescribing and patients treated with opioids already.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot known or accessible or easy to get to. There are some Best Practice Alerts on multiple drugs and Z drugs; possibly a high risk elderly warning exists somewhere.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo. Technically possible but not operationalized; only data they see comes from financial DB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAre chart review data useful?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConfirms what is known. [We] get information through clinical system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConfirms what is known\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes. Can get information through clinical system. Have EMR flags based on pt med list\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes. Data would push us to standardized practice\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIs Panel Management report information available from your own system?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes. This report is better\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIs Panel Management report information useful to your practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaybe for some kinds of patients. High dose patients \"age out\" - they die. QI is a challenge. Lots of guidance is available. It's hard to work it in in the time with patients and keep it a priority.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaybe, if it is actionable. There is a sense that patients are aging over time and that past treatment plans, in the context of the whole picture of their health, are no longer working, including opioid prescribing. Their quality of life is important and their opioid prescription is part of that.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes. High prescribers took over retiring practices and [have] many opioid-using patients. [We need to be] making practice structured.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes. Data would push us to standardized practice.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3. Value of EHR Data: Ease of Use and Usefulness\u003c/b\u003e\u003c/p\u003e\u003cp\u003eClinicians reported that the chart review reports were useful and accurate but not novel. The information usually could be provided through their clinical systems although such reports were not easily available or well organized. Presence of patient-centered measures (e.g., pain, function, and risk stratification) in the EHR review varied within and across practices (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLegally mandated endpoints such as patient treatment agreements and PDMP lookup dates were well documented. Informed consents were often not found, likely because such documentation for a long-term condition would fall outside the 2-year window of the chart review. Discretionary strategies, such as pill counts, pain measures, functional assessments, screening for depression, OUD risk assessments, and offering or discussing non-medical therapy were typically lower and varied widely. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, clinicians were given credit for documentation of functional status for use of either a scale such as the \u0026ldquo;Pain, Enjoyment, and General Activity\u0026rdquo; (PEG) assessment, or for any non-standardized assessment reflected in the clinic notes. Measures that were structured into designated data fields (e.g., depression screen) were consistently documented (76%-98% across clinics); measures that had no standardized documentation field (functional status) were not (2%-60%). Clinicians noted that consistent use of these tools would be beneficial and regular reports \u0026ldquo;\u003cem\u003ewould push us to standardize [our] practice\u003c/em\u003e\u0026rdquo; (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003e4. Challenges identified\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe research team encountered specific challenges in training and supporting practice sites with the data download and chart review requirements. Each state governs the access to and use of PDMPs and may have different restrictions on how long historical data are available, requiring regular downloads to update trend reports in keeping with prescribers\u0026rsquo; need to review panel management reports. In addition, healthcare organizations have differing requirements on how their clinical data may be used for QI projects, such as multiple levels of review and approval. Creating precise but not overly laborious instruction for EHR abstraction was an ongoing goal. Even with better instructional materials, however, chart review remained a time-consuming process.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that it is feasible to create reports from PDMP downloads for PCCs to use in caring for patients with chronic pain. PCCs found the process easy and the results useful, but not always novel with respect to past reports some had received. However, clinicians found the format of panel management reports valuable.\u003c/p\u003e\u003cp\u003eWith opioid prescribing largely now in the domain of primary care\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, PCCs can leverage panel management strategies that are already familiar to them. The CDC chronic opioid prescribing guidelines of 2016 and 2022 highlighted the importance of specific strategies to support safe and responsible prescribing\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. For busy clinical teams, access to summary data, peer comparisons, and rosters of patients around which to plan QI programming can be very useful. In this pilot study we demonstrated the feasibility and usefulness of using a widely available data source, the state PDMP, to create helpful data summaries.\u003c/p\u003e\u003cp\u003eWhile EHR data represent medications that were prescribed, PDMP data reflect medications that were actually dispensed at any pharmacy in the state (and often other states as well). There may be important discrepancies between prescribed and dispensed medications. For example, in post-operative outpatient prescribing, while 92% of patients receive an opioid prescription, only 27% of the prescribed MME was consumed\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Summary reports available through EHR systems vary not only by vendor, but also by specific institutional customization\u0026mdash;this limits the ability to compare data reports across practices. When compared to PDMP vendor reports (which are limited to single clinicians), this approach to panel management reports can provide a practice-level view of the care management of patients with chronic pain treated with opioids and can avoid the problem of double counting patients who receive prescriptions from more than one prescriber. Despite these advantages, reviewing data on opioid prescribing alone does not provide a complete clinical picture. An understanding of the particular context within a practice is essential to account, for example, for prescribers who have recently retired, new clinicians managing legacy patient panels, or prescriber specialization (pain management, opioid use disorder, end of life care, etc.) when interpreting the data.\u003c/p\u003e\u003cp\u003eIn an effort to create a more complete picture of opioid prescribing in primary care, we also sought to extract patient-reported outcomes from the EHR. Recent reports have highlighted that many patients with chronic pain treated with opioids are interested in reducing their dosage, but are concerned about being able to control their pain, mood, and opioid cravings effectively\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The inclusion of patient-reported outcomes on pain management and tolerance is a key part of treatment, as no objective measure of pain exists. In this small sample of four primary care practices in one region of the US, we observed considerable variability in the documentation of pain, functionality, and risk assessment in the EHR. This raises the question of whether a standardized assessment tool that incorporates administrative endpoints (such as those that are legally required) and important patient-reported outcomes (such as pain, function, and perspective on opioid tapering) would be helpful in standardizing care and benchmarking. Reducing opioid doses increases the chance that the patient experience will worsen, although this is not always true\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Clinicians and researchers have an obligation to measure the impact of changes in pain management regimens on those they care for, the patients.\u003c/p\u003e\u003cp\u003eStrategies to support PCC efforts to improve opioid medication management already indicate the value of identifying and tracking the use of opioids. EHRs cannot generate rosters based on medication dispensing across the state and by multiple prescribers. Those depending on standard PDMP reports will not see trends at the practice level. And those who can conduct manual chart abstraction will face the inefficiencies of unstructured data as well as lacking state-wide information available from pharmacies dispensing Schedule II-IV controlled substances. The use of clinical data to create panel management reports is not new, but the use of clinical data from outside the patient record for panel management may provide a new opportunity to work with patients on the challenges brought by opioid treatment. The PDMP also provides a highly accurate and widely available source of data for research on prescribing patterns of controlled substances.\u003c/p\u003e\u003cp\u003eLimitations to this study include uncertain generalizability beyond the four clinics that volunteered to participate, all of whom were in rural, northern New England settings. Further work is needed to assess feasibility across a geographically wider set of clinics with varying needs related to opioid prescription management. Small group, semi-structured discussions elicited feedback from participants willing to voice their opinions in front of their peers and may have been influenced by social desirability bias. Combining the prescribing history of 3\u0026ndash;6 years of PDMP data with two years of abstracted EHR data may have created an incomplete picture of compliance with state-level regulations and CDC guidelines. We may have missed documented care that stood just outside the 2-year time horizon of the chart review.\u003c/p\u003e\u003cp\u003eSome patients who begin opioid treatment may be lost to follow-up in the PDMP data base due to a change in state of residence, movement to a long-term care or correctional facility, death, or discontinuation of opioid treatment. Use of the PDMP as a source for panel management should include watching for the absence of patients on the population roster and including a follow-up mechanism for those who are missing.\u003c/p\u003e\u003cp\u003eIn summary, this feasibility study across four primary care clinics in northern New England revealed that while downloading PDMP data was feasible, easy, and efficient, EHR chart review was more challenging due to inconsistencies in data entry. Clinicians found panel management-style reports for patients with chronic pain treated with opioids to be informative and useful. PDMPs may be a helpful source for a panel management approach in caring for patients with chronic pain and treated with opioids, but further research to test its comparative effectiveness, its impact on primary care clinicians of different locations and credentials, and the value of more standardized applications of clinical data across state-based PDMPs and EHRs is needed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectronic Health Record\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLTO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLong-Term Opioids\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMorphine Milligram Equivalents\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNNE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNorthern New England\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eORT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOpioid Risk Tool assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOUD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOpioid Use Disorder\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrimary Care Clinician\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePEG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePain, Enjoyment, and General Activity assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePDMP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrescription Drug Monitoring Program\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics Approval and Consent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Boards at the University of Vermont and the Dartmouth-Hitchcock Medical Center determined that the study was not human subjects research per the regulatory definition under 45 CFR 46.102(d) and that a full review was not needed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e – not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDue to the sensitive nature of the data shared by health care clinicians for this study, and based on their respective state laws governing use of prescription drug monitoring data, we assured participants that their raw data would remain confidential and not be shared. Data are not available. The data that have been used are confidential.\u003c/p\u003e\n\u003cp\u003eOpen materials statement: the components of the research methodology needed to reproduce the reported procedure(s) and analyses are not publicly available but available on request to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicting or competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding Statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe research reported here was supported by grant U54 GM115516 from the National Institutes of Health for the Northern New England Clinical and Translational Research Network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor’s Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCvE designed and was the principal investigator conducting the study, including conceptualization; data curation (qualitative); formal analysis (qualitative); funding acquisition; investigation; methodology; project administration; resources; visualization; roles/writing - original draft; and writing - review and editing. MB conducted data curation (qualitative \u0026amp; quantitative); formal analysis (qualitative); investigation; project administration; visualization; roles/writing - original draft; and writing - review and editing. ZD assisted with project administration; resources; and writing – review and editing. AK advised the investigation; resources; and writing - review and editing. NK supported funding acquisition; methodology; and writing - review and editing. BL conducted formal analysis (quantitative); methodology; and writing - review and editing. MM advised on the investigation; resources; and writing - review and editing. DP advised on the investigation; resources; and writing - review and editing. JR assisted with project administration; resources; and writing – review and editing. MS supported project administration; resources; and writing - review and editing. CDM guided conceptualization; data curation (quantitative); formal analysis (quantitative); funding acquisition; investigation; methodology; visualization; roles/writing - original draft; and writing - review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank site leaders and practice facilitators of the primary care practices in Maine, New Hampshire, and Vermont that participated in this study. Special recognition goes to the Northern New England CO-OP Practice and Community-Based Research Network (NNE CO-OP PCBRN) at Dartmouth Health, which provided project guidance and support in the execution of this study. Our appreciation as well goes to Dr. Jon Porter, Medical Director and Division Chief of the Comprehensive Pain Program at the University of Vermont Medical Center for his comments regarding patient reported outcomes and Ms. Juvena Hitt, Quality Program Director, Department of Medicine, University of Vermont for archival work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFraming Opioid Prescribing Guidelines for Acute Pain: Developing the Evidence. Washington, DC: National Academies of Sciences, Engineering, and Medicine; 2020.\u003c/li\u003e\n\u003cli\u003eWeiner SG, Baker O, Rodgers AF, Garner C, Nelson LS, Kreiner PW, et al. Opioid Prescriptions by Specialty in Ohio, 2010-2014. Pain medicine (Malden, Mass). 2018;19(5):978-89.\u003c/li\u003e\n\u003cli\u003eLevy B, Paulozzi L, Mack KA, Jones CM. Trends in Opioid Analgesic-Prescribing Rates by Specialty, U.S., 2007-2012. Am J Prev Med. 2015;49(3):409-13.\u003c/li\u003e\n\u003cli\u003eGuy GP, Jr., Zhang K, Bohm MK, Losby J, Lewis B, Young R, et al. Vital Signs: Changes in Opioid Prescribing in the United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66(26):697-704.\u003c/li\u003e\n\u003cli\u003eGarnett MF, Mini\u0026ntilde;o, A.M. Drug overdose deaths in the United States, 2003\u0026ndash;2023. Hyattsville, MD; 2024. Contract No.: NCHS Data Brief, no 522.\u003c/li\u003e\n\u003cli\u003eMattson CL TL, Quinn K, Kariisa M, Patel P, Davis NL. Trends and Geographic Patterns in Drug and Synthetic Opioid Overdose Deaths \u0026mdash; United States, 2013\u0026ndash;2019. MMWR Morb Mortal Wkly Rep. 2021;70.\u003c/li\u003e\n\u003cli\u003eOverdose Epidemic Report: American Medical Association; 2024 [Available from: https://end-overdose-epidemic.org/wp-content/uploads/2024/11/24-1177083-Advocacy-2024-Overdose-Report_DIGITAL-2.pdf.\u003c/li\u003e\n\u003cli\u003ePristell C, Byun H, Huffstetler AN. Opioid Prescribing Has Significantly Decreased in Primary Care. Am Fam Physician. 2024;110(6):572-3.\u003c/li\u003e\n\u003cli\u003eVolkow ND, Jones EB, Einstein EB, Wargo EM. Prevention and Treatment of Opioid Misuse and Addiction: A Review. JAMA Psychiatry. 2019;76(2):208-16.\u003c/li\u003e\n\u003cli\u003eKarmali RN, Bush C, Raman SR, Campbell CI, Skinner AC, Roberts AW. Long-term opioid therapy definitions and predictors: A systematic review. Pharmacoepidemiol Drug Saf. 2020;29(3):252-69.\u003c/li\u003e\n\u003cli\u003eKaye AD, Jones MR, Kaye AM, Ripoll JG, Galan V, Beakley BD, et al. Prescription Opioid Abuse in Chronic Pain: An Updated Review of Opioid Abuse Predictors and Strategies to Curb Opioid Abuse: Part 1. Pain Physician. 2017;20(2s):S93-s109.\u003c/li\u003e\n\u003cli\u003eWhat is the scope of prescription drug misuse in the United States? : National Institute of Drug Abuse; 2023 [Available from: https://nida.nih.gov/publications/research-reports/misuse-prescription-drugs/what-scope-prescription-drug-misuse\u003c/li\u003e\n\u003cli\u003eShah A, Hayes CJ, Martin BC. Characteristics of Initial Prescription Episodes and Likelihood of Long-Term Opioid Use - United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66(10):265-9.\u003c/li\u003e\n\u003cli\u003eDowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain--United States, 2016. JAMA. 2016;315(15):1624-45.\u003c/li\u003e\n\u003cli\u003eMikosz CA, Zhang K, Haegerich T, Xu L, Losby JL, Greenspan A, et al. Indication-Specific Opioid Prescribing for US Patients With Medicaid or Private Insurance, 2017. JAMA Network Open. 2020;3(5):e204514-e.\u003c/li\u003e\n\u003cli\u003eTong ST, Hochheimer CJ, Brooks EM, Sabo RT, Jiang V, Day T, et al. Chronic Opioid Prescribing in Primary Care: Factors and Perspectives. Ann Fam Med. 2019;17(3):200-6.\u003c/li\u003e\n\u003cli\u003eBodenheimer T. Transforming Practice. New England Journal of Medicine. 2008;359(20):2086-9.\u003c/li\u003e\n\u003cli\u003eNeuwirth EE, Schmittdiel JA, Tallman K, Bellows J. Understanding panel management: a comparative study of an emerging approach to population care. Perm J. 2007;11(3):12-20.\u003c/li\u003e\n\u003cli\u003eKaminetzky CP, Nelson KM. In the Office and In-Between: The Role of Panel Management in Primary Care. Journal of general internal medicine. 2015;30(7):876-7.\u003c/li\u003e\n\u003cli\u003eBodenheimer T, Ghorob, A., Margolius, D. Use Patient Care Registries to Provide Comprehensive Preventive Care 2024 [Available from: https://edhub.ama-assn.org/steps-forward/module/2702192.\u003c/li\u003e\n\u003cli\u003eWatts B, Lawrence RH, Drawz P, Carter C, Shumaker AH, Kern EF. Development and Implementation of Team-Based Panel Management Tools: Filling the Gap between Patient and Population Information Systems. Popul Health Manag. 2016;19(4):232-9.\u003c/li\u003e\n\u003cli\u003eCalculating total daily dose of opioids for safer dosage Centers for Disease Control and Prevention; 2016 [Available from: https://stacks.cdc.gov/view/cdc/38481.\u003c/li\u003e\n\u003cli\u003eHarris PA, Taylor, R., Thielke, R., et al. . Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics. 2009;42(2):377-81.\u003c/li\u003e\n\u003cli\u003eSalvatore PP, Guy GP, Mikosz CA. Changes in Opioid Dispensing by Medical Specialties After the Release of the 2016 CDC Guideline for Prescribing Opioids for Chronic Pain. Pain medicine (Malden, Mass). 2022;23(11):1908-14.\u003c/li\u003e\n\u003cli\u003eFujii MH, Hodges AC, Russell RL, Roensch K, Beynnon B, Ahern TP, et al. Post-Discharge Opioid Prescribing and Use after Common Surgical Procedure. J Am Coll Surg. 2018;226(6):1004-12.\u003c/li\u003e\n\u003cli\u003eJabakhanji R, Tokunaga F, Rached G, Vigotsky AD, Griffith J, Schnitzer TJ, et al. Attitudes of chronic pain patients on long-term opioid therapy toward opioid tapering. medRxiv PREPRINT. 2023:2023.12.19.23300217.\u003c/li\u003e\n\u003cli\u003eHardy CJ, Cochran G, Howey W, Wright E, Wasan AD, Gordon AJ, et al. Impact of Clinician-Facing Interventions to Reduce Opioid Use on Pain Related Outcomes in Primary Care: A Cluster Randomized Trial. Health Services Research and Managerial Epidemiology. 2024;11:23333928241240957.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"54a4e9a4-af79-4ab0-a45c-15ea1ddb2096","identifier":"10.13039/100000002","name":"National Institutes of Health","awardNumber":"U54 GM115516 ","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Vermont","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Primary Care, chronic pain, opioid, drug prescription, panel management","lastPublishedDoi":"10.21203/rs.3.rs-7793877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7793877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis feasibility study explored a process for primary care clinicians to improve chronic pain management related to opioid prescribing practices by using state-based Prescription Drug Monitoring Program (PDMP) data to create panel management reports on patients receiving long term opioid therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eConducted across four rural primary care clinics in Northern New England, the study assessed the feasibility of downloading and utilizing PDMP data and the perceived value of panel management reports derived from both PDMP and electronic health record (EHR) data in the care of patients with chronic pain treated with opioids for more than one year.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe study found that downloading PDMP data was feasible and efficient across all sites. However, EHR review proved more challenging due to inconsistencies in data entry and the unstructured nature of some relevant data fields. Clinicians generally found PDMP data easy to generate and the panel management reports informative and useful for understanding opioid prescribing trends and identifying high-risk patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe findings suggest that while PDMPs are a potential source for panel management reports for patients with chronic pain who are treated with opioids, further study is needed to determine the effectiveness of such efforts to improve care for and safety of patients treated with opioids.\u003c/p\u003e","manuscriptTitle":"A Panel Management Approach Using Prescription Drug Monitoring Program Data for Primary Care Patients with Chronic Pain Treated with Opioids: A Feasibility Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:17:48","doi":"10.21203/rs.3.rs-7793877/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e7004de-6022-45e2-8e84-2a8861a5b5df","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55859378,"name":"Translational Medicine"}],"tags":[],"updatedAt":"2025-10-08T09:17:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:17:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7793877","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7793877","identity":"rs-7793877","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}