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Background Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. Methods We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID−), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. Results Among the first 1000 participants, 722 were COVID+ and 278 were COVID−. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID− group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID− groups at 3 months. Conclusions Approximately half of COVID+ participants, as compared with one-quarter of COVID− participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. Clinical Trials Registration NCT04610515.

Integrating behavioral health in primary care improves quality of care and outcomes for patients with comorbid conditions. Shortage of a trained behavioral health workforce limits adoption. Professional psychology training programs contribute to recruitment, retention, and development of skilled providers, who value and deliver behavioral health services in primary care. This study interviewed a cohort of established psychology training programs in real-world, state-wide clinical primary care settings and identified their strategies and challenges with teaching practices and program resources that impact the robust quality of training.Between December 2020 and March 2021, we conducted semistructured interviews with 12 licensed psychologists who oversaw nine integrated primary care psychology training programs at the internship and postdoctoral levels across Washington State. Data were analyzed using a thematic approach.Programs taught psychology trainees about integrated primary care via extensive onboarding, modeling and shadowing, structured learning environment, interprofessional education opportunities, flexible and cross-disciplinary supervision, and a psychologically safe space. Teaching challenges included balancing scheduled and curbside supervision, pivoting to telehealth, and aligning trainee expectations and program requirements. Training programs were funded through different and multiple sources, but most lacked a stable funding mechanism, compounded by barriers for psychology trainees to bill for services, a lack of control over organization's budgeting decisions, and instability in funding renewal.Synergistic support from program, organization, and system/policy levels are needed to align teaching activities with clinical practice environments and invest in the growth and sustainability of psychology training programs on integrated primary care. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Recent value-based payment reforms in the U.S. called for empirical data on how primary care practices of varying characteristics fund their integrated behavioral health services. To describe payment strategies used by U.S. primary care practices to fund behavioral health integration and compare strategies between practices with and without hospital affiliation.Baseline data were used and collected from 44 practices participating in a cluster-randomized, pragmatic trial of behavioral health integration. Data included practice characteristics and payment strategies—fee-for-service payment, pay-for-performance incentives, grants, and graduate medical education funds. Descriptive and comparative analyses using Fisher’s exact tests and independent T-tests were conducted. The sample had 26 (59.1%) hospital-affiliated (hospital/health system-owned, academic medical centers and hospital-affiliated practices) and 18 (40.9%) non-hospital-affiliated practices (community health centers/federally qualified health centers and privately-owned practices). Most practices (88.6%) received payments through fee-for-service; 63.6% received pay-for-performance incentives; 31.8% received grant funds. Collaborative Care Management billing (CPT) codes were used in six (13.6%) practices. Over half (53.8%) of hospital-affiliated practices funded their behavioral health services through fee-for-service and pay-for-performance incentives only, as opposed to two-thirds (66.7%) of non-hospital-affiliated practices required additional support from grants and/or general medical education funds. Primary care practices support behavioral health integration through diverse payment strategies. More hospital-affiliated practices compared to non-hospital-affiliated practices funded integrated behavioral health services through fee-for-service and pay-for-performance incentives. Practices without hospital affiliation relied on multiple funding streams including grants and/or general medical education funds, suggesting their approach to financial sustainment may be more precarious or challenging, compared to hospital-affiliated practices.

ObjectiveLung cancer is the most common cause of cancer-related death in the USA. While most patients are diagnosed following symptomatic presentation, no studies have compared symptoms and physical examination signs at or prior to diagnosis from electronic health records (EHRs) in the USA. We aimed to identify symptoms and signs in patients prior to diagnosis in EHR data.DesignCase–control study.SettingAmbulatory care clinics at a large tertiary care academic health centre in the USA.Participants, outcomesWe studied 698 primary lung cancer cases in adults diagnosed between 1 January 2012 and 31 December 2019, and 6841 controls matched by age, sex, smoking status and type of clinic. Coded and free-text data from the EHR were extracted from 2 years prior to diagnosis date for cases and index date for controls. Univariate and multivariable conditional logistic regression were used to identify symptoms and signs associated with lung cancer at time of diagnosis, and 1, 3, 6 and 12 months before the diagnosis/index dates.ResultsEleven symptoms and signs recorded during the study period were associated with a significantly higher chance of being a lung cancer case in multivariable analyses. Of these, seven were significantly associated with lung cancer 6 months prior to diagnosis: haemoptysis (OR 3.2, 95% CI 1.9 to 5.3), cough (OR 3.1, 95% CI 2.4 to 4.0), chest crackles or wheeze (OR 3.1, 95% CI 2.3 to 4.1), bone pain (OR 2.7, 95% CI 2.1 to 3.6), back pain (OR 2.5, 95% CI 1.9 to 3.2), weight loss (OR 2.1, 95% CI 1.5 to 2.8) and fatigue (OR 1.6, 95% CI 1.3 to 2.1).ConclusionsPatients diagnosed with lung cancer appear to have symptoms and signs recorded in the EHR that distinguish them from similar matched patients in ambulatory care, often 6 months or more before diagnosis. These findings suggest opportunities to improve the diagnostic process for lung cancer.

Background: Occupational burnout is a major concern for personal well-being and patient care. We examined burnout among primary care providers (PCPs), medical residents, behavioral health providers (BHPs), nurses, and other clinical and nonclinical primary care team members. Methods: This was a cross-sectional study, nested within a larger randomized trial. Participants completed a validated 9-item burnout measure with 3 domains: depersonalization, emotional exhaustion, and personal accomplishment. Multivariable multilevel linear regression with a random intercept for each practice was used to determine mean differences in burnout across professional roles. Results: Overall burnout rates varied by professional role: PCPs 70%, medical residents 89%, BHPs 59%, nurses 66%, other clinicians 68%, and nonclinical professionals 70%. Compared with nonclinical professionals, residents experienced more burnout in more domains, followed by PCPs. PCPs, residents, and nurses reported significantly worse depersonalization and exhaustion scores. Nonclinical professionals had worse accomplishment scores than all clinical professionals except for residents. This study revealed moderate-to-high levels of burnout among primary care professionals. Discussion: Clinicians may be experiencing aspects of burnout more intensely than their nonclinical colleagues, and this may be most true for residents and PCPs. Based on these variations, interventions to mitigate burnout may need to be tailored by professional role.

Primary care has increasingly adopted integrated behavioral health (IBH) practices to enhance overall care. The IBH Cross-Model Framework clarifies the core processes and structures of IBH, but little is known about how practices vary in the implementation of these processes and structures. This study aimed to describe clusters of clinics using the IBH Cross-Model Framework for a large sample of primary care clinics, as well as contextual variables associated with differences in implementation. Primary care clinics (N = 102) in Minnesota reported their level of implementation across 18 different components of IBH via the site self-assessment (SSA). The components were mapped to all five principles and four of the nine structures of the IBH Cross-Model Framework. latent class analysis was used to identify unique clusters of IBH components from the SSA across the IBH Cross-Model Framework's processes and structures. Latent classes were then regressed onto context variables. A four-class model was determined to be the best fit: Low IBH (39.6%), Structural IBH (7.9%), Partial IBH (29.4%), and Strong IBH (23.1%). Partial IBH clinics were more urban than the other three classes, lower in SES risk than Structural IBH clinics, and located in smaller organizations than Strong IBH clinics. There were no differences between classes in race/ethnicity of the clinic area or practice size. Four groups of IBH implementation were identified representing unique profiles of integration. These clusters may represent patterns of community-based implementation of IBH that indicate easier and more challenging aspects of IBH implementation.

BACKGROUND Digital therapeutics have been increasingly used to improve behavioral healthcare but are underutilized in primary care settings. OBJECTIVE This study explored primary care providers’ challenges and strategies in chronic pain management to identify needs and practice gaps for future development of chronic pain-related digital therapeutics tailored to primary care settings. METHODS Using a human-centered design approach, qualitative semi-structured interviews were conducted with healthcare providers (N = 11) recruited from four primary care clinics in Washington and Colorado between July and October 2021. Providers shared their goals and priorities in chronic pain management, experiences with challenges and strategies used to care for patients, and their perceptions of applying digital therapeutics in clinical practice. Transcripts were analyzed using a thematic analysis approach. RESULTS Our study found four themes related to provider needs: patient-provider alliance, team-based care, tracking and monitoring, and social determinants of health. Providers desired resources to improve chronic pain conversations, pain education and counseling, and goal setting with patients. Providers needed a greater accessibility to multidisciplinary care team consultations and non-pharmacological pain treatments. Healthcare system infrastructure was needed for providers to systematically track and monitor patients’ pain and provide wraparound health and social services for underserved patients. CONCLUSIONS Leveraging digital therapeutics in a feasible, appropriate and acceptable way to aid primary care providers in chronic pain management may require multimodal features that address provider needs at an individual care and clinic/system level.

Professional practice guidelines (PPGs) are intended to promote a high level of professional practice and serve as an educational resource, providing pragmatic guidance in a clinical area for psychologists. Measurement-based care (MBC) is an evidence-based psychological practice with accumulating empirical support and alignment with patient-centered care. In connection with the American Psychological Association's Advisory Committee for Measurement-based Care and the Mental and Behavioral Health Registry, this article outlines various lines of support for the development and implementation of an MBC PPG. In addition to research evidence, we address the demonstrated need of this guideline across three domains: public benefit, professional guidance, and legal and regulatory issues. Consistent with the aspirational spirit of a PPG, this article proposes a draft PPG statement and highlights how an MBC PPG would improve service delivery, facilitate implementation of an evidence-based practice associated with symptom reduction, improved retention, and greater patient satisfaction, as well as create a framework that will better align changes in reimbursement models with patients' and providers' treatment goals. We also identify key future directions and critical gaps in MBC science and implementation that require attention. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

BackgroundLung cancer is the most common cause of cancer-related death in the United States (US), with most patients diagnosed at later stages (3 or 4). While most patients are diagnosed following symptomatic presentation, no studies have compared symptoms and physical examination signs at or prior to diagnosis from electronic health records (EHR) in the United States (US).ObjectiveTo identify symptoms and signs in patients prior to lung cancer diagnosis in EHR data.Study DesignCase-control study.MethodsWe studied 698 primary lung cancer cases in adults diagnosed between January 1, 2012 and December 31, 2019, and 6,841 controls matched by age, sex, smoking status, and type of clinic. Coded and free-text data from the EHR were extracted from 2 years prior to diagnosis date for cases and index date for controls. Univariate and multivariate conditional logistic regression were used to identify symptoms and signs associated with lung cancer. Analyses were repeated excluding symptom data from 1, 3, 6, and 12 months before the diagnosis/index dates.ResultsEleven symptoms and signs recorded during the study period were associated with a significantly higher chance of being a lung cancer case in multivariate analyses. Of these, seven were significantly associated with lung cancer six months prior to diagnosis: hemoptysis (OR 3.2, 95%CI 1.9-5.3), cough (OR 3.1, 95%CI 2.4-4.0), chest crackles or wheeze (OR 3.1, 95%CI 2.3-4.1), bone pain (OR 2.7, 95%CI 2.1-3.6), back pain (OR 2.5, 95%CI 1.9-3.2), weight loss (OR 2.1, 95%CI 1.5-2.8) and fatigue (OR 1.6, 95%CI 1.3-2.1).ConclusionsPatients diagnosed with lung cancer appear to have symptoms and signs recorded in the EHR that distinguish them from similar matched patients in ambulatory care, often six months or more before their diagnosis. These findings suggest opportunities to improve the diagnostic process for lung cancer in the US.

Background Digital therapeutics are growing as a solution to manage pain for patients; yet, they are underused in primary care where over half of the patients with chronic pain seek care. Little is known about how to successfully engage primary care providers in recommending digital therapeutics to their patients. Exploring provider motivations in chronic pain management would potentially help to improve their engagement and inform the development of digital therapeutics. Objective This study examined primary care providers’ motivations for chronic pain management, including their strategies and challenges, to inform the future development of chronic pain-related digital therapeutics tailored to primary care settings. Methods We conducted qualitative semistructured interviews with health care providers recruited from 3 primary care clinics in Washington and 1 clinic in Colorado between July and October 2021. The sample (N=11) included 7 primary care physicians, 2 behavioral health providers, 1 physician assistant, and 1 nurse. Most providers worked in clinics affiliated with urban academic health systems. Guided by the human-centered design approach and Christensen’s Job-to-be-Done framework, we asked providers their goals and priorities in chronic pain management, their experiences with challenges and strategies used to care for patients, and their perceptions of applying digital therapeutics in clinical practice. Transcripts were analyzed using a thematic analysis approach. Results We found that primary care providers were motivated but challenged to strengthen the patient-provider alliance, provide team-based care, track and monitor patients’ progress, and address social determinants of health in chronic pain management. Specifically, providers desired additional resources to improve patient-centered communication, pain education and counseling, and goal setting with patients. Providers also requested greater accessibility to multidisciplinary care team consultations and nonpharmacological pain treatments. When managing chronic pain at the population level, providers need infrastructure and systems to systematically track and monitor patients’ pain and provide wraparound health and social services for underserved patients. Recommendations on digital therapeutic features that might address provider challenges in achieving these motivations were discussed. Conclusions Given the findings, to engage primary care providers, digital therapeutics for chronic pain management need to strengthen the patient-provider alliance, increase access to nonpharmacological treatment options, support population health tracking and management, and provide equitable reach. Leveraging digital therapeutics in a feasible, appropriate, and acceptable way to aid primary care providers in chronic pain management may require multimodal features that address provider motivations at an individual care and clinic or system level.

Purpose: Primary care is challenged with safely prescribing opioids for patients with chronic noncancer pain (CNCP), specifically to address risks for overdose, opioid use disorder, and death. We identify sociotechnical challenges, approaches, and recommendations in primary care to effectively track and monitor patients on long-term opioid therapy, a key component for supporting adoption of opioid prescribing guidelines. Methods: We examined qualitative data (field notes and postintervention interview and focus group transcripts) from 6 rural and rural-serving primary care organizations with 20 clinic locations enrolled in a study evaluating a practice redesign program to improve opioid medication management for CNCP patients. Two independent researchers used content analysis to categorize data into key themes to develop an understanding of sociotechnical factors critical to creating and implementing an approach to tracking and monitoring of patients on long-term opioid therapy in primary care practices. Results: Four factors were critical to developing a tracking and monitoring system. For each we describe common challenges and approaches used by the clinics to overcome then. The first factor, buy-in and participation, was essential for accomplishing the other 3. The other factors occurred sequentially: 1) cohort identification—finding the right patients, 2) data collection and extraction—tracking the right data, and 3) data use—monitoring patients and adjusting care processes. Conclusions: We identified common challenges and approaches to tracking and monitoring patients using long-term opioid therapy for CNCP in primary care. Based on these findings we provide recommendations to build capacity for tracking and monitoring for organizations that are engaged in improving safe opioid-prescribing practices for CNCP in primary care.

Background Most people with alcohol or opioid use disorders (AUD or OUD) are not diagnosed or treated for these conditions in primary care. This study takes a critical step toward quantifying service gaps and directing improvement efforts for AUD and OUD by using electronic health record (EHR) data from diverse primary care organizations to quantify the extent to which AUD and OUD are underdiagnosed and undertreated in primary care practices. Methods We extracted and integrated diagnosis, medication, and behavioral health visit data from the EHRs of 21 primary care clinics within four independent healthcare organizations representing community health centers and rural hospital-associated clinics in the Pacific Northwest United States. Rates of documented AUD and OUD diagnoses, pharmacological treatments, and behavioral health visits were evaluated over a two-year period (2015–2016). Results Out of 47,502 adult primary care patients, 1476 (3.1%) had documented AUD; of these, 115 (7.8%) had orders for AUD medications and 271 (18.4%) had at least one documented visit with a non-physician behavioral health specialist. Only 402 (0.8%) patients had documented OUD, and of these, 107 (26.6%) received OUD medications and 119 (29.6%) had at least one documented visit with a non-physician behavioral health specialist. Rates of AUD diagnosis and AUD and OUD medications were higher in clinics that had co-located non-physician behavioral health specialists. Conclusions AUD and OUD are underdiagnosed and undertreated within a sample of independent primary care organizations serving mostly rural patients. Primary care organizations likely need service models, technologies, and workforces, including non-physician behavioral health specialists, to improve capacities to diagnose and treat AUD and OUD.

BACKGROUND: Patients with a substance use disorder (SUD) often present with co-occurring chronic conditions in primary care. Despite the high co-occurrence of chronic medical conditions and SUD, little is known about whether chronic condition outcomes or related service utilization in primary care varies between patients with versus without documented SUDs. This study examined whether having a SUD influenced the use of primary care services and common chronic condition outcomes for patients with diabetes, hypertension, and obesity. METHODS: A longitudinal cohort observational study examined electronic health record data from 21 primary care clinics in Washington and Idaho to examine differences in service utilization and clinical outcomes for diabetes, hypertension, and obesity in patients with and without a documented SUD diagnosis. Differences between patients with and without documented SUD diagnoses were compared over a three-year window for clinical outcome measures, including hemoglobin A1c, systolic and diastolic blood pressure, and body mass index, as well as service outcome measures, including number of encounters with primary care and co-located behavioral health providers, and orders for prescription opioids. Adult patients (N = 10,175) diagnosed with diabetes, hypertension, or obesity before the end of 2014, and who had ≥2 visits across a three-year window including at least one visit in 2014 (baseline) and at least one visit occurring 12 months or longer after the 2014 visit (follow-up) were examined. RESULTS: Patients with SUD diagnoses and co-occurring chronic conditions were seen by providers more frequently than patients without SUD diagnoses (p’s

Background:Opioids are more commonly prescribed for chronic pain in rural settings in the USA, yet little is known about how the rural context influences efforts to improve opioid medication management.Methods:The Six Building Blocks is an evidence-based program that guides primary care practices in making system-based improvements in managing patients using long-term opioid therapy. It was implemented at 6 rural and rural-serving organizations with 20 clinic locations over a 15-month period. To gain further insight about their experience with implementing the program, interviews and focus groups were conducted with staff and clinicians at the six organizations at the end of the 15 months and transcribed. Team members used a template analysis approach, a form of qualitative thematic analysis, to code these data for barriers, facilitators, and corresponding subcodes.Results:Facilitators to making systems-based changes in opioid management within a rural practice context included a desire to help patients and their community, external pressures to make changes in opioid management, a desire to reduce workplace stress, external support for the clinic, supportive clinic leadership, and receptivity of patients. Barriers to making changes included competing demands on clinicians and staff, a culture of clinician autonomy, inadequate data systems, and a lack of patient resources in rural areas.Discussion:The barriers and facilitators identified here point to potentially unique determinants of practice that should be considered when addressing opioid prescribing for chronic pain in the rural setting.

Objective Develop and implement a prescription opioid registry in 10 diverse health systems across the US and describe trends in prescribed opioids between 2012 and 2018. Materials and Methods Using electronic health record and claims data, we identified patients who had an outpatient fill for any prescription opioid, and/or an opioid use disorder diagnosis, between January 1, 2012 and December 31, 2018. The registry contains distributed files of prescription opioids, benzodiazepines and other select medications, opioid antagonists, clinical diagnoses, procedures, health services utilization, and health plan membership. Rates of outpatient opioid fills over the study period, standardized to health system demographic distributions, are described by age, gender, and race/ethnicity among members without cancer. Results The registry includes 6 249 710 patients and over 40 million outpatient opioid fills. For the combined registry population, opioid fills declined from a high of 0.718 per member-year in 2013 to 0.478 in 2018, and morphine milligram equivalents (MMEs) per fill declined from 985 MMEs per fill in 2012 to 758 MMEs in 2018. MMEs per member declined from 692 MMEs per member in 2012 to 362 MMEs per member in 2018. Conclusion This study established a population-based opioid registry across 10 diverse health systems that can be used to address questions related to opioid use. Initial analyses showed large reductions in overall opioid use per member among the combined health systems. The registry will be used in future studies to answer a broad range of other critical public health issues relating to prescription opioid use.

BACKGROUND AND AIM: There is no gold-standard and considerable heterogeneity in outcome measures used to evaluate treatments for opioid use disorder (OUD) along the opioid treatment cascade. The aim of this study was to develop the US National Institute on Drug Abuse (NIDA) National Drug Abuse Treatment Clinical Trials Network (CTN) opioid use disorder core outcomes set (OUD-COS). DESIGN: Four-round, e-Delphi expert panel consensus study and plenary research group discussion and targeted consultation. SETTING: United States. PARTICIPANTS: A panel of 25 members including clinical practitioners, clinical researchers and administrative staff from the CTN, the network's affiliated clinical and community sites and the NIDA Centre for the CTN. MEASUREMENTS: From a pool of 24 candidate items in four domains (biomedical/disease status; behaviors, symptoms and functioning; opioid treatment cascade; and morbidity and mortality), the panel completed an on-line questionnaire to rank items with defined specification on a 9-point scale for importance, with a standard 70% consensus criterion. FINDINGS: After the fourth round of the questionnaire and subsequent discussion, consensus was reached for five outcomes: two patient-reported (global impression of improvement and incident non-fatal overdose); one clinician-reported (illicit/non-medical drug toxicology); and two from administrative records (duration of treatment and fatal opioid poisoning). CONCLUSIONS: An e-Delphi consensus study has produced the US National Institute on Drug Abuse (NIDA) National Drug Abuse Treatment Clinical Trials Network opioid use disorder core outcomes set (version 1) for opioid use disorder treatment efficacy and effectiveness research.

Background: Understanding the impact of precision medicine on medical practice, patient care, and clinical outcomes is a priority for advancing cancer care. With the recent dramatic increase in the use of tumor genomic testing (TGT), records within EHRs are a rich data source for evaluating the impact of TGT results in real-world clinical practice of care and on patient outcomes. However, extracting TGT results from electronic health records (EHR) is challenging due to a lack of standards to communicate genomic information and an inability to store such information in commonly available EHR systems. Moreover, TGT results are delivered to clinicians in unstructured formats and image-based files (PDFs). We initiated a pilot study to assess the ability of natural language processing (NLP) algorithms to convert EHR unstructured clinical text and PDF-formatted TGT results into research-quality data. Methods: One author (RY) drew a sample of approximately 800 clinical text records from 21 breast cancer patients treated at University of Washington. Sources used for data extraction included medical record notes and PDF reports for two breast cancer gene expression tests: 21-gene Recurrence Score (RS, OncotypeDx) and/or the 70-gene signature (MMP, Mammaprint). A team redacted all PHI and provided records to a commercial collaborator (Pangaeadata.AI, UK), along with definitions of variables to be extracted, but without annotated target answers. Existing NLP algorithms that leverage pre-training, fine-tuning and rules were adapted to extract 26 variables specified by the research team (e.g., age at diagnosis, histology, and RS or MMP dates and scores). The output placed variables into relevant, standardized formats and produced a research quality data set. The extraction strategy depended on the feature and variable characteristics. For example, cancer stage, an ordinal numerical variable, was determined with a rule-based extraction method from outpatient clinic notes and pathology reports, whereas the RS score, a continuous variable, came from OncotypeDx PDF and OCR semi-structured retrieval produced the output. Results/Conclusions: The Pangaea tool obtained an average accuracy of 97.3% with a standard deviation of 3.5% across all 26 variables. The approach is developed based on rules designed and validated by clinical experts, using a model that does not require training, making overfitting likely minimal. Qualitative analysis showed that: 1] algorithms used to electronically extract TGT results provided the same data as manual abstraction by physicians, and 2] context matters, namely, the capability of preliminary semantic understanding in the Pangaea model using contextual words and phrases contributed to high accuracy and can be generalized further with larger datasets. Expansion to other health care data systems is needed to assess scalability of these technologies to create research-quality data fit for use. Citation Format: Rachel Yung, Kari A. Stephens, Meliha Yetisgen, Andrea Burnett-Hartman, Ashwani Tanwar, Guilherme Freire, Atri Sharma, Jingqing Zhang, Vibhor Gupta, Yike Guo, VK Gadi, Larry Kessler. Creating research quality cancer genomic data from electronic health records [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4090.

Background Chronic diseases that drive morbidity, mortality, and health care costs are largely influenced by human behavior. Behavioral health conditions such as anxiety, depression, and substance use disorders can often be effectively managed. The majority of patients in need of behavioral health care are seen in primary care, which often has difficulty responding. Some primary care practices are providing integrated behavioral health care (IBH), where primary care and behavioral health providers work together, in one location, using a team-based approach. Research suggests there may be an association between IBH and improved patient outcomes. However, it is often difficult for practices to achieve high levels of integration. The Integrating Behavioral Health and Primary Care study responds to this need by testing the effectiveness of a comprehensive practice-level intervention designed to improve outcomes in patients with multiple chronic medical and behavioral health conditions by increasing the practice’s degree of behavioral health integration. Methods Forty-five primary care practices, with existing onsite behavioral health care, will be recruited for this study. Forty-three practices will be randomized to the intervention or usual care arm, while 2 practices will be considered “Vanguard” (pilot) practices for developing the intervention. The intervention is a 24-month supported practice change process including an online curriculum, a practice redesign and implementation workbook, remote quality improvement coaching services, and an online learning community. Each practice’s degree of behavioral health integration will be measured using the Practice Integration Profile. Approximately 75 patients with both chronic medical and behavioral health conditions from each practice will be asked to complete a series of surveys to measure patient-centered outcomes. Change in practice degree of behavioral health integration and patient-centered outcomes will be compared between the two groups. Practice-level case studies will be conducted to better understand the contextual factors influencing integration. Discussion As primary care practices are encouraged to provide IBH services, evidence-based interventions to increase practice integration will be needed. This study will demonstrate the effectiveness of one such intervention in a pragmatic, real-world setting. Trial registration ClinicalTrials.gov NCT02868983. Registered on August 16, 2016.

Key Points Question What is the impact of including benchmark prevalence data of common findings in reports of spinal imaging ordered by primary care clinicians? Findings In this randomized clinical trial that included 250 401 adults, no overall decrease in subsequent spine-related health care utilization after the intervention was observed. However, there was a significant decrease in opioid prescriptions at 1 year in the intervention group compared with the control group. Meaning The findings of this study suggest that including epidemiological benchmarks on spinal imaging reports has little impact on subsequent spine-related utilization overall but may reduce subsequent opioid prescriptions., This randomized clinical trial evaluates the impact of including benchmark prevalence data in routine spinal imaging reports on subsequent spine-related health care utilization and opioid prescriptions., Importance Lumbar spine imaging frequently reveals findings that may seem alarming but are likely unrelated to pain. Prior work has suggested that inserting data on the prevalence of imaging findings among asymptomatic individuals into spine imaging reports may reduce unnecessary subsequent interventions. Objective To evaluate the impact of including benchmark prevalence data in routine spinal imaging reports on subsequent spine-related health care utilization and opioid prescriptions. Design, Setting, and Participants This stepped-wedge, pragmatic randomized clinical trial included 250 401 adult participants receiving care from 98 primary care clinics at 4 large health systems in the United States. Participants had imaging of their backs between October 2013 and September 2016 without having had spine imaging in the prior year. Data analysis was conducted from November 2018 to October 2019. Interventions Either standard lumbar spine imaging reports (control group) or reports containing age-appropriate prevalence data for common imaging findings in individuals without back pain (intervention group). Main Outcomes and Measures Health care utilization was measured in spine-related relative value units (RVUs) within 365 days of index imaging. The number of subsequent opioid prescriptions written by a primary care clinician was a secondary outcome, and prespecified subgroup analyses examined results by imaging modality. Results We enrolled 250 401 participants (of whom 238 886 [95.4%] met eligibility for this analysis, with 137 373 [57.5%] women and 105 497 [44.2%] aged >60 years) from 3278 primary care clinicians. A total of 117 455 patients (49.2%) were randomized to the control group, and 121 431 patients (50.8%) were randomized to the intervention group. There was no significant difference in cumulative spine-related RVUs comparing intervention and control conditions through 365 days. The adjusted median (interquartile range) RVU for the control group was 3.56 (2.71-5.12) compared with 3.53 (2.68-5.08) for the intervention group (difference, −0.7%; 95% CI, −2.9% to 1.5%; P = .54). Rates of subsequent RVUs did not differ between groups by specific clinical findings in the report but did differ by type of index imaging (eg, computed tomography: difference, −29.3%; 95% CI, −42.1% to −13.5%; magnetic resonance imaging: difference, −3.4%; 95% CI, −8.3% to 1.8%). We observed a small but significant decrease in the likelihood of opioid prescribing from a study clinician within 1 year of the intervention (odds ratio, 0.95; 95% CI, 0.91 to 1.00; P = .04). Conclusions and Relevance In this study, inserting benchmark prevalence information in lumbar spine imaging reports did not decrease subsequent spine-related RVUs but did reduce subsequent opioid prescriptions. The intervention text is simple, inexpensive, and easily implemented. Trial Registration ClinicalTrials.gov Identifier: NCT02015455

A movement towards integrated behavioral health (IBH) in primary care continues to grow, among an accumulating evidence base for its effectiveness for improving care. However, healthcare organizations struggle to navigate where to target their limited resources for improving integration. We evaluated a cross-model framework of IBH core processes and structures. We used a mixed-methods approach for evaluation of the framework, which included (a) an evaluation survey of national experts and stakeholders, (b) crosswalks with common IBH measures, and (c) a real-world usability test. Five core IBH principles, mapping to 25 processes, and nine clinic structures were defined. Survey responses from 29 IBH domain and policy experts and stakeholders resulted in uniformly high ratings of importance and variable levels of feasibility for measurement, particularly with respect to electronic health record (EHR) systems. A real-world usability test resulted in good uptake and use of the framework across a state-wide effort. An IBH Cross-Model Framework of core principles, processes, and structures generated good acceptability and showed good real-world utility in a state-wide effort to improve IBH across disparate levels of integration in diverse primary care settings. Findings identify feasible areas of measurement, particularly with EHR systems. Next steps include testing the relationship between the individual framework components and patient outcomes to help guide clinics towards prioritizing efforts focused on improving integration.

Background A lack of consensus on the optimal outcome measures to assess the efficacy and effectiveness of interventions for the treatment of opioid use disorder (OUD) has hampered the pooling of research data for evidence synthesis and clinical guidelines. A core outcome set (COS) is a minimum set of outcome measures that are recommended for all studies of a particular condition. The National Drug Abuse Treatment Clinical Trials Network (CTN) Core Outcome Set for OUD (COS-OUD) is a development study to identify core constructs, meaningful outcomes, and their optimal measurement for all efficacy and effectiveness studies of OUD treatment and service delivery. Methods/design Overseen by an expert workgroup, a modified, stepwise, e-Delphi methodology will be used to gain consensus among a panel of clinical practitioners and researchers involved in the treatment of OUD, who are members of the CTN. Sequential rounds of anonymous, online questionnaires will be used to identify, rate the importance of, and refine a core outcome set. A consensus threshold will be achieved if at least 70% of the panel rate the measure as critical for inclusion in the COS-OUD. Where consensus is not reached or there are suggestions for new measures, these will be brought forward to a further round of review prior to a consensus meeting. Products from this study will be communicated via peer-reviewed scientific journals and conferences. Discussion This initiative will develop a COS for OUD intervention trials, treatment studies, and service delivery and will support the pooling of research and clinical practice data and efforts to develop measurement-based care within the OUD treatment cascade. Trial registration http://www.comet-initiative.org/Studies/Details/1579

ObjectiveMultiple clinical prediction rules have been developed, but lack validation. This study aims to identify a set of prediction algorithms for influenza, based on electronic health record (EHR) structured data and clinical notes derived data using Unified Medical Language System (UMLS) driven natural language processing (NLP).Materials and MethodsData were extracted from an enterprise-wide data warehouse for all patients who tested positive for influenza and were seen in ambulatory care between 2009 and 2019 (N = 7,278). A text processing pipeline was used to analyze chart notes for UMLS terms for symptoms of interest to improve data quality completeness. Three models, which step up complexity of the dataset and predictors, were tested with least absolute shrinkage and selection operator (LASSO)-selected parameters to identify predictors for influenza. Receiver operating characteristic (ROC) curves compared test accuracy across the three models.ResultsThree models identified 7, 8, and 10 predictors, and the most complex model performed best. The addition of the UMLS-driven NLP symptoms data improved data quality (false negatives) and increased the number of significant predictors. NLP also increased the strength of the models, as did the addition of two-way predictor interactions.DiscussionThe EHR is a feasible source for offering rapidly accessible datasets for influenza related prediction research that was used to produce a prediction model for influenza. Combining data collected in routine care with data science methods improved a prediction model for influenza, and in the future, could be used to drive diagnostics at the point of care.

Objective Information gaps that accompany hurricanes and floods limit researchers’ ability to determine the impact of disasters on population health. Defining key use cases for sharing complex disaster data with research communities and facilitators, and barriers to doing so are key to promoting population health research for disaster recovery. Materials and Methods We conducted a mixed-methods needs assessment with 15 population health researchers using interviews and card sorting. Interviews examined researchers’ information needs by soliciting barriers and facilitators in the context of their expertise and research practices. Card sorting ranked priority use cases for disaster preparedness. Results Seven barriers and 6 facilitators emerged from interviews. Barriers to collaborative research included process limitations, collaboration dynamics, and perception of research importance. Barriers to data and technology adoption included data gaps, limitations in information quality, transparency issues, and difficulty to learn. Facilitators to collaborative research included collaborative engagement and human resource processes. Facilitators to data and technology adoption included situation awareness, data quality considerations, adopting community standards, and attractive to learn. Card sorting prioritized 15 use cases and identified 30 additional information needs for population health research in disaster preparedness. Conclusions Population health researchers experience barriers to collaboration and adoption of data and technology that contribute to information gaps and limit disaster preparedness. The priority use cases we identified can help address information gaps by informing the design of supportive research tools and practices for disaster preparedness. Supportive tools should include information on data collection practices, quality assurance, and education resources usable during failures in electric or telecommunications systems.

Objective To provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories. Materials and Methods This article describes the tool’s design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix. Results DQe-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators. Discussion Open science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQe-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup. Conclusion EHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQe-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.

Patients receiving long-term opioid therapy for chronic pain and interested in tapering their opioid dose were randomly assigned to a 22-week taper support intervention (psychiatric consultation, opioid dose tapering, and 18 weekly meetings with a physician assistant to explore motivation for tapering and learn pain self-management skills) or usual care (N = 35). Assessments were conducted at baseline and 22 and 34 weeks after randomization. Using an intention to treat approach, we constructed linear regression models to compare groups at each follow-up. At 22 weeks, adjusted mean daily morphine-equivalent opioid dose in the past week (primary outcome) was lower in the taper support group, but this difference was not statistically significant (adjusted mean difference = -42.9 mg; 95% confidence interval, -92.42 to 6.62; P = .09). Pain severity ratings (0-10 numeric rating scale) decreased in both groups at 22 weeks, with no significant difference between groups (adjusted mean difference = -.68; 95% confidence interval, -2.01 to .64; P = .30). The taper support group improved significantly more than the usual care group in self-reported pain interference, pain self-efficacy, and prescription opioid problems at 22 weeks (all P-values .05). This taper support intervention is feasible and shows promise in reducing opioid dose while not increasing pain severity or interference.In a pilot randomized trial comparing a prescription opioid taper support intervention to usual care, lower opioid doses and pain severity ratings were observed at 22 weeks in both groups. The groups did not differ significantly at 22 weeks in opioid dose or pain severity, but the taper support group improved significantly more in pain interference, pain self-efficacy, and perceived opioid problems. These results support the feasibility and promise of this opioid taper support intervention.

ObjectiveAcademic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of data that originate from multiple sources. Enterprise Data Warehouses (EDWs) have emerged as central resources for these data, but they often require an informatician to extract meaningful information, thereby limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying and extracting clinical data from heterogeneous data sources.Materials and MethodsLeaf utilizes a flexible biomedical concept system to define hierarchical items and ontologies. Each Leaf concept contains both textual representations and associated SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.ResultsLeaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an EDW with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org) and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain athttps://github.com/uwrit/leaf.DiscussionLeaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication from existing databases.ConclusionLeaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly integrate with existing enterprise user authentication systems and clinical databases in situ. We demonstrate its unique technical strengths and success alongside its friendly user interface. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks and clinical studies involving large patient cohorts.

Objective Evidence-based therapies for posttraumatic stress disorder are underutilized and at times unavailable in specialty settings. We reviewed the literature on interventions to treat PTSD within primary care to make recommendations on their effectiveness as treatment modalities or ways to improve engagement in specialty care. Method We searched PubMed, PsychInfo, CINHAL, and Cochrane Reviews databases using search terms related to PTSD and primary care. We excluded clinical guidelines and studies of screening only or subthreshold PTSD. Results 524 articles were identified. Twenty-one papers on 15 interventions met review criteria. Seven interventions focus on individual therapies studied via small feasibility studies to prepare for full-scale intervention research. Eight describe treatment programs in primary care based on collaborative care that included medication management, tracking outcomes, referral services, and for some psychotherapy (versus psychotherapy referral). Ten interventions were feasibility studies which precludes meaningful comparison of effect sizes. Of the four RCTs of treatment programs, only two including some psychotherapy found improvements in PTSD symptoms. Conclusion More research is needed to adapt treatment for PTSD to primary care. Collaborative care may be a promising framework for improving the reach of PTSD treatments when psychotherapy is offered within the collaborative care team.

We use prescription of statin medications and prescription of warfarin to explore the capacity of electronic health record data to (1) describe cohorts of patients prescribed these medications and (2) identify cohorts of patients with evidence of adverse events related to prescription of these medications. This study was conducted in the WWAMI region Practice and Research Network (WPRN)., a network of primary care practices across Washington, Wyoming, Alaska, Montana and Idaho DataQUEST, an electronic data-sharing infrastructure. We used electronic health record data to describe cohorts of patients prescribed statin or warfarin medications and reported the proportions of patients with adverse events. Among the 35,445 active patients, 1745 received at least one statin prescription and 301 received at least one warfarin prescription. Only 3 percent of statin patients had evidence of myopathy; 51 patients (17% of those prescribed warfarin) had a bleeding complication. Primary-care electronic health record data can effectively be used to identify patients prescribed specific medications and patients potentially experiencing medication adverse events.

© 2016. Objective: Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results. Methods: Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washington's Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages. Results: We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility. Conclusion: An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.

Diagnostic imaging is often the first step in evaluating patients with back pain and likely functions as a "gateway" to a subsequent cascade of interventions. However, lumbar spine imaging frequently reveals incidental findings among normal, pain-free individuals suggesting that treatment of these "abnormalities" may not be warranted. Our prior work suggested that inserting the prevalence of imaging findings in patients without back pain into spine imaging reports may reduce subsequent interventions. We are now conducting a pragmatic cluster randomized clinical trial to test the hypothesis that inserting this prevalence data into lumbar spine imaging reports for studies ordered by primary care providers will reduce subsequent spine-related interventions.We are using a stepped wedge design that sequentially randomizes 100 primary care clinics at four health systems to receive either standard lumbar spine imaging reports, or reports containing prevalence data for common imaging findings in patients without back pain. We capture all outcomes passively through the electronic medical record. Our primary outcome is spine-related intervention intensity based on Relative Value Units (RVUs) during the following year. Secondary outcomes include subsequent prescriptions for opioid analgesics and cross-sectional lumbar spine re-imaging.If our study shows that adding prevalence data to spine imaging reports decreases subsequent back-related RVUs, this intervention could be easily generalized and applied to other kinds of testing, as well as other conditions where incidental findings may be common. Our study also serves as a model for cluster randomized trials that are minimal risk and highly pragmatic.

PURPOSE: Six key elements of opioid medication management redesign in primary care have been previously identified. Here, we examine the effect of implementing these Six Building Blocks on opioid-prescribing practices. METHODS: Six rural-serving organizations with 20 clinic locations received support for 15 months during the period October 2015 to May 2017 to implement the Six Building Blocks. Patients undergoing long-term opioid therapy (LtOT) at these study sites were compared with patients undergoing LtOT enrolled in a regional health plan who did not receive care at the study sites but who resided in the same primary care service areas (control group). Outcomes were monthly trend in the proportion of patients undergoing LtOT prescribed a ≥100 morphine equivalent dose (MED) of opioids daily and the total number of patients receiving an opioid prescription. An interrupted time series using difference-indifference analysis was used for tests of significance. RESULTS: The proportion of patients prescribed a ≥100 MED of opioids daily decreased 2.2% (11.8% to 9.6%) among patients at the intervention clinics and 1.3% (14.0% to 12.7%) among patients in the control group. The rate of decrease was significantly greater among study patients than among patients in the control group (P = .018). The rate of decrease in the number of patients on LtOT at intervention clinics increased during the intervention period compared with the preintervention period (P