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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.

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.

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.