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Introduction: 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. Method: 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. Results: 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. Conclusion: 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. Keywords: behavioral health, integrated primary care, interprofessional education, psychology, workforce, Integrated primary care, in which teams of medical providers and their behavioral health counterparts work closely together to address population health needs, has emerged in response to calls for a [...]

Background: People of color (POC), especially those who also hold social identities associated with disadvantage (non–English-speaking, female, older, lower socioeconomic level), continue to be underserved in the health system, which can result in poorer care and worsened health outcomes. Most disparity research in traumatic brain injury (TBI) focuses on the impact of single factors, which misses the compounding effect of belonging to multiple historically marginalized groups. Objective: To examine the intersectional impact of multiple social identities vulnerable to systemic disadvantage following TBI on mortality, opioid usage during acute hospitalization, and discharge location. Methods: Retrospective observational design utilizing electronic health records merged with local trauma registry data. Patient groups were defined by race and ethnicity (POC or non-Hispanic White), age, sex, type of insurance, and primary language (English-speaking vs non–English-speaking). Latent class analysis (LCA) was performed to identify clusters of systemic disadvantage. Outcome measures were then assessed across latent classes and tested for differences. Results: Over an 8-year period, 10 809 admissions with TBI occurred (37% POC). LCA identified a 4-class model. Groups with more systemic disadvantage had higher rates of mortality. Classes with older populations had lower rates of opioid administration and were less likely to discharge to inpatient rehabilitation following acute care. Sensitivity analyses examining additional indicators of TBI severity demonstrated that the younger group with more systemic disadvantage had more severe TBI. Controlling for more indicators of TBI severity changed statistical significance in mortality for younger groups. Conclusion: Results demonstrate significant health inequities in the mortality and access to inpatient rehabilitation following TBI along with higher rates of severe injury in younger patients with more social disadvantages. While many inequities may be related to systemic racism, our findings suggested an additive, deleterious effect for patients who belonged to multiple historically disadvantaged groups. Further research is needed to understand the role of systemic disadvantage for individuals with TBI within the healthcare system. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung cancer is the most common cause of cancer related death in the US, but survival is far better when people are diagnosed at an earlier stage. There are currently no clinical quality measures that are routinely used to measure the quality or timeliness of diagnosis of lung cancer in the US. We used Natural Language Processing (NLP) to extract information on the symptoms and signs that had been recorded in the electronic medical records of patients presenting in ambulatory care over the 2 years prior to their diagnosis with lung cancer. We found that the time from the first recorded symptoms/signs associated with lung cancer to diagnosis was 570 days. The time intervals from chest CT or chest X-ray imaging to diagnosis, and from specialist consultation to diagnosis were shorter—at 43 and 72 days, respectively. Advanced techniques such as NLP can be used to extract detailed information from electronic medical records, that could potentially be used to create clinical quality measures with the goal of improving the timeliness of diagnosis of this cancer. The diagnosis of lung cancer in ambulatory settings is often challenging due to non-specific clinical presentation, but there are currently no clinical quality measures (CQMs) in the United States used to identify areas for practice improvement in diagnosis. We describe the pre-diagnostic time intervals among a retrospective cohort of 711 patients identified with primary lung cancer from 2012–2019 from ambulatory care clinics in Seattle, Washington USA. Electronic health record data were extracted for two years prior to diagnosis, and Natural Language Processing (NLP) applied to identify symptoms/signs from free text clinical fields. Time points were defined for initial symptomatic presentation, chest imaging, specialist consultation, diagnostic confirmation, and treatment initiation. Median and interquartile ranges (IQR) were calculated for intervals spanning these time points. The mean age of the cohort was 67.3 years, 54.1% had Stage III or IV disease and the majority were diagnosed after clinical presentation (94.5%) rather than screening (5.5%). Median intervals from first recorded symptoms/signs to diagnosis was 570 days (IQR 273–691), from chest CT or chest X-ray imaging to diagnosis 43 days (IQR 11–240), specialist consultation to diagnosis 72 days (IQR 13–456), and from diagnosis to treatment initiation 7 days (IQR 0–36). Symptoms/signs associated with lung cancer can be identified over a year prior to diagnosis using NLP, highlighting the need for CQMs to improve timeliness of diagnosis. [ABSTRACT FROM AUTHOR]

Objective: To investigate the risk of progression to rheumatoid arthritis (RA) in patients who were cyclic citrullinated peptide (CCP) antibody positive without RA at initial presentation.Methods: We performed a retrospective cohort study of CCP+ individuals seen at a US tertiary care system between 2009 and 2018 who were without RA or other systemic rheumatic disease by medical record review at the time of CCP antibody positivity. Progression to classifiable RA was determined through medical record review. We investigated the risk of progression to RA overall and stratified by CCP antibody level (low: >1 to 2× the upper limit of normal [ULN]; medium: >2 to 3× ULN; high: >3× ULN). Multivariable Cox regression estimated the hazard ratio (HR) and 95% confidence interval (95% CI) for RA by CCP antibody level.Results: We identified 340 CCP+ patients who were without RA or other rheumatic disease at baseline. During 1,047 person-years of follow-up, 73 patients (21.5%) developed RA. The risk of progression to RA increased with CCP antibody level, with 46.0% (95% CI 34.7-55.3) of patients with high-level CCP antibodies progressing to RA by 5 years. Compared to low CCP antibody level, medium (HR 3.00 [95% CI 1.32-6.81]) and high (HR 4.83 [95% CI 2.51-9.31]) CCP antibody levels were strongly associated with progression to RA, adjusting for age, sex, body mass index, smoking, family history of RA, and rheumatoid factor level.Conclusion: Among CCP+ patients without RA, the risk for progression to RA increased substantially with increasing CCP antibody level. This study provides further support for close monitoring for development of RA among CCP+ patients and identifying strategies to mitigate this risk. [ABSTRACT FROM AUTHOR]

Objective: To assess knowledge of rheumatoid arthritis (RA) risk factors among unaffected first-degree relatives (FDRs) and to study whether a personalized RA education tool increases risk factor knowledge.Methods: We performed a randomized controlled trial assessing RA educational interventions among 238 FDRs. The web-based Personalized Risk Estimator for RA (PRE-RA) tool displayed personalized RA risk results (genetics, autoantibodies, demographics, and behaviors) and educated about risk factors. Subjects were randomly assigned to a Comparison arm (standard RA education; n = 80), a PRE-RA arm (PRE-RA alone; n = 78), or a PRE-RA Plus arm (PRE-RA and a one-on-one session with a trained health educator; n = 80). The RA Knowledge Score (RAKS), the number of 8 established RA risk factors identified as related to RA, was calculated at baseline and post-education (immediate/6 weeks/6 months/12 months). We compared RAKS and its components at each post-education point by randomization arm.Results: At baseline before education, few FDRs identified behavioral RA risk factors (15.6% for dental health, 31.9% for smoking, 47.5% for overweight/obesity, and 54.2% for diet). After education, RAKS increased in all arms, higher in PRE-RA and PRE-RA Plus than Comparison at all post-education points (P < 0.05). PRE-RA subjects were more likely to identify risk factors than those who received standard education (proportion agreeing that smoking is a risk factor at 6 weeks: 83.1% in the PRE-RA Plus arm, 71.8% in the PRE-RA arm, and 43.1% in the Comparison arm; P < 0.05 for PRE-RA versus Comparison).Conclusion: Despite being both familiar with RA and at increased risk, FDRs had low knowledge about RA risk factors. A web-based personalized RA education tool successfully increased RA risk factor knowledge. [ABSTRACT FROM AUTHOR]

Objective: To determine the effect of disclosure of rheumatoid arthritis (RA) risk personalized with genetics, biomarkers, and lifestyle factors on health behavior intentions.Methods: We performed a randomized controlled trial among first-degree relatives without RA. Subjects assigned to the Personalized Risk Estimator for Rheumatoid Arthritis (PRE-RA) group received the web-based PRE-RA tool for RA risk factor education and disclosure of personalized RA risk estimates, including genotype/autoantibody results and behaviors (n = 158). Subjects assigned to the comparison arm received standard RA education (n = 80). The primary outcome was readiness for change based on the trans-theoretical model, using validated contemplation ladder scales. Increased motivation to improve RA risk-related behaviors (smoking, diet, exercise, or dental hygiene) was defined as an increase in any ladder score compared to baseline, assessed immediately, 6 weeks, and 6 months post-intervention. Subjects reported behavior change at each visit. We performed intent-to-treat analyses using generalized estimating equations for the binary outcome.Results: Subjects randomized to PRE-RA were more likely to increase ladder scores over post-intervention assessments (relative risk 1.23, 95% confidence interval [95% CI] 1.01, 1.51) than those randomized to nonpersonalized education. At 6 months, 63.9% of PRE-RA subjects and 50.0% of comparison subjects increased motivation to improve behaviors (age-adjusted difference 15.8%; 95% CI 2.8%, 28.8%). Compared to nonpersonalized education, more PRE-RA subjects increased fish intake (45.0% versus 22.1%; P = 0.005), brushed more frequently (40.7% versus 22.9%; P = 0.01), flossed more frequently (55.7% versus 34.8%; P = 0.004), and quit smoking (62.5% versus 0.0% among 11 smokers; P = 0.18).Conclusion: Disclosure of RA risk personalized with genotype/biomarker results and behaviors increased motivation to improve RA risk-related behaviors. Personalized medicine approaches may motivate health behavior improvements for those at risk for RA and provide rationale for larger studies evaluating effects of behavior changes on clinical outcomes, such as RA-related autoantibody production or RA development. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effect of providing comprehensive personalized risk information on concern for chronic disease development.Methods: Unaffected first-degree relatives (FDRs) of rheumatoid arthritis (RA) patients (n = 238) were randomly allocated to: 1) disclosure of RA risk personalized to demographics, genetics, biomarkers, and behaviors using a web-based tool (PRE-RA arm, n = 78); 2) PRE-RA with interpretation by a health educator (PRE-RA Plus arm, n = 80); and 3) standard RA education (Comparison arm, n = 80). Concern for developing RA was assessed at baseline and immediately, 6 weeks, 6 months, and 12 months post-intervention.Results: FDRs randomized to PRE-RA arms were less concerned about developing RA than the Comparison arm at all post-intervention assessments (p < 0.05). Among those concerned about RA risk at baseline, the PRE-RA (OR = 4.7, 95%CI 1.5-14.4) and PRE-RA Plus (OR = 5.2, 95%CI 1.6-17.3) arms were more likely to have reassurance 6 months post-intervention than the Comparison arm.Conclusion: A comprehensive tool provided reassurance to those at risk for developing a chronic disease, with or without interpretation from a health educator, compared to standard education.Practice Implications: Individuals may be more likely to be reassured using a personalized chronic disease risk disclosure tool than a standard non-personalized approach. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic disrupted how primary care patients with chronic pain received care. Our study sought to understand how long-term opioid therapy (LtOT) for chronic pain changed over the course of the pandemic overall and for different demographic subgroups., Methods: We used data from electronic health records of 64 primary care clinics across Washington state and Idaho to identify patients who had a chronic pain diagnosis and were receiving long-term opioid therapy. We defined 10-month periods in 2019 to 2021 as prepandemic, early pandemic and late pandemic and used generalized estimating equations analysis to compare across these time periods and demographic characteristics., Results: We found a proportional decrease in LtOT for chronic pain in the early months of the pandemic (OR = 0.94, P = .007 ) followed by an increase late pandemic (OR = 1.08, P = .002 ). Comparing late pandemic to prepandemic, identifying as Asian or Black, having fewer comorbidities, or living in an urban area were associated with higher likelihood of being prescribed LtOT., Discussion: The use of LtOT for chronic pain in primary care has increased from before to after the COVID-19 pandemic with racial/ethnic and geographic disparities. Future research is needed to understand these disparities in LtOT and their effect on patient outcomes., Competing Interests: Conflict of interest: None., (© Copyright 2024 by the American Board of Family Medicine.)

Objective: Lung 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., Design: Case-control study., Setting: Ambulatory care clinics at a large tertiary care academic health centre in the USA., Participants, Outcomes: We 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., Results: Eleven 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)., Conclusions: Patients 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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

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., (©Kris Pui Kwan Ma, Kari A Stephens, Rachel E Geyer, Maria G Prado, Brenda L Mollis, Susan M Zbikowski, Deanna Waters, Jo Masterson, Ying Zhang. Originally published in JMIR Formative Research (https://formative.jmir.org), 03.02.2023.)