Background: Clinical practice for detection, evaluation, management and support of patients and families dealing with Mild Cognitive Impairment/Dementia is not well standardized. Earlier detection affords patients and their family caregivers greater opportunity to come to terms with the challenge of MCI and to arrange coping plans for the present and future. Methods: We undertook an intervention to improve standardization of MCI care that included clinician education, experiential learning of mindful self-compassion skills and EPIC-based supportive tools for clinical decision support, documentation, orders and patient education. These interventions were carried out in a step-wedge approach with two of our FM clinics (SR first followed by GEN) in 6 month time periods. 6-9 clinicians in each clinic participated. Results: Educational and experiential mindful self-compassion experience interventions were well received and resulted in improvements in self-reported comfort and skill in dementia-related clinical skills. Screening improved from 51-79% of those eligible who were up-to-date with screening, comparing pre-and post data in the first clinic. Initial use of recommended follow up evaluation tools appears to be low. Conclusion: While this is a mid-study preliminary report the results indicate that education is effective as assessed by self-report among participating clinicians. While preliminary, the first comparisons of before and after intervention at the first clinic indicate that screening improved substantially. Further analysis comparing control clinic with first intervention clinic and 2nd clinic before and after will be completed soon. Further analysis comparing performance of study clinicians vs. non-study clinicians will also be carried out. Further evaluation of use of recommended evaluation tools will also be done.

BACKGROUND: Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms. METHODS: From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents. RESULTS: Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship). CONCLUSION: Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.

Background Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms. Methods From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents. Results Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship). Conclusion Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.

PURPOSE: Pain accounts for up to 78% of emergency department (ED) patient visits and opioids remain a primary method of treatment despite risks of addiction and adverse effects. While prior acupuncture studies are promising as an alternative opioid-sparing approach to pain reduction, successful conduct of a multi-center pilot study is needed to prepare for a future definitive randomized control trial (RCT). METHODS: Acupuncture in the Emergency Department for Pain Management (ACUITY) is funded by the National Center for Complementary and Integrative Health. The objectives are to: conduct a multi-center feasibility RCT, examine feasibility of data collection, develop/deploy a manualized acupuncture intervention and assess feasibility/implementation (barrier/facilitators) in 3 EDs affiliated with the BraveNet Practice Based Research Network.Adults presenting to a recruiting ED with acute non-emergent pain (e.g., musculoskeletal, back, pelvic, noncardiac chest, abdominal, flank or head) of ≥4 on a 0-10-point Numeric Rating Scale will be eligible. ED participants (n = 165) will be equally randomized to Acupuncture or Usual Care.At pre-, post-, and discharge time-points, patients will self-assess pain and anxiety using the Numeric Rating Scale. Pain, anxiety, post-ED opioid use and adverse events will be assessed at 1 and 4 weeks. Opioid utilization in the ED and discharge prescriptions will be extracted from patients electronic medical records.Acupuncture recipients will asked to participate in a brief qualitative interview about 3 weeks after their discharge. ED providers and staff will also be interviewed about their general perspectives/experiences related to acupuncture in the ED and implementation of acupuncture in ACUITY. RESULTS: Recruitment began on 5/3/21. As of 12/7/21: 84 patients have enrolled, the responsive acupuncture intervention has been developed and deployed, and 26 qualitative interviews have been conducted. CONCLUSION: Successful conduct of ACUITY will provide the necessary framework for conducting a future, multi-center, definitive RCT of acupuncture in the ED. CLINICAL TRIALSGOV: NCT04880733 https://clinicaltrials.gov/ct2/show/NCT04880733.

BACKGROUND: Multimodal lifestyle interventions, employing food as medicine, stand as the recommended first-line treatment for obesity. The Shared Medical Appointment (SMA) model, where a physician conducts educational sessions with a group of patients sharing a common diagnosis, offers an avenue for delivery of comprehensive obesity care within clinical settings. SMAs, however, are not without implementation challenges. We aim to detail our experience with three implementation models in launching a virtual integrative health SMA for weight management. METHODS: Eligible patients included individuals 18 years of age or older, having a body mass index (BMI) of 30 kg/m2 or 27 kg/m2 or greater with at least one weight related comorbidity. The Practical, Robust Implementation and Sustainability Model (PRISM), Plan, Do, Study, Act (PDSA), and the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) models were applied to guide the implementation of the Supervised Lifestyle Integrative Medicine (SLIM) program, a virtually delivered, lifestyle medicine focused SMA program, in a weight management clinic within a major health system. We describe how these models, along with attendance for the initial cohorts, were used for decision-making in the process of optimizing the program. RESULTS: 172 patients completed the SLIM program over two years. Attendance was lowest for sessions held at 8:00 AM and 4:00 PM compared to sessions at 10:00 AM, 1:00 PM, and 3:00 PM, leading to only offering midday sessions (p = 0.032). Attendance data along with feedback from patients, facilitators, and administrative partners led to changes in the curriculum, session number and frequency, session reminder format, and intake visit number. CONCLUSION: The use of implementation and quality improvement models provided crucial insight for deployment and optimization of a virtual, lifestyle medicine focused SMA program for weight management within a large healthcare system.

Objective

To evaluate the effects of medical comorbidity on anxiety treatment outcomes.

Methods

Data were analyzed from 1004 primary care patients enrolled in a trial of a collaborative care intervention for anxiety. Linear-mixed models accounting for baseline characteristics were used to evaluate the effects of overall medical comorbidity (two or more chronic medical conditions [CMCs] versus fewer than two CMCs) and specific CMCs (migraine, asthma, and gastrointestinal disease) on anxiety treatment outcomes at 6, 12, and 18 months.

Results

At baseline, patients with two or more CMCs (n = 582; 58.0%) reported more severe anxiety symptoms (10.5 [95% confidence interval {CI} = 10.1-10.9] versus 9.5 [95% CI = 9.0-10.0], p = .003) and anxiety-related disability (17.6 [95% CI = 17.0-18.2] versus 16.0 [95% CI = 15.3-16.7], p = .001). However, their clinical improvement was comparable to that of patients with one or zero CMCs (predicted change in anxiety symptoms = -3.9 versus -4.1 at 6 months, -4.6 versus -4.4 at 12 months, -4.9 versus -5.0 at 18 months; predicted change in anxiety-related disability = -6.4 versus -6.9 at 6 months, -6.9 versus -7.3 at 12 months, -7.3 versus -7.5 at 18 months). The only specific CMC with a detrimental effect was migraine, which was associated with less improvement in anxiety symptoms at 18 months (predicted change = -4.1 versus -5.3).

Conclusions

Effectiveness of the anxiety intervention was not significantly affected by the presence of multiple CMCs; however, patients with migraine displayed less improvement at long-term follow-up. Trial Registration ClinicalTrials.com Identifier: NCT00347269.

BACKGROUND: Effective primary care necessitates follow-up actions by the patient beyond the visit. Prior research suggests room for improvement in patient adherence. OBJECTIVE: This study sought to understand patients views on their primary care visits, the plans generated therein, and their self-reported adherence after 3 months. METHODS: As part of a large multisite cluster randomized pragmatic trial in 3 health care organizations, patients completed 2 surveys-the first within 7 days after the index primary care visit and another 3 months later. For this analysis of secondary outcomes, we combined the results across all study participants to understand patient adherence to care plans. We recorded patient characteristics and survey responses. Cross-tabulation and chi-square statistics were used to examine bivariate associations, adjusting for multiple comparisons when appropriate. We used multivariable logistic regression to assess how patients intention to follow, agreement, and understanding of their plans impacted their plan adherence, allowing for differences in individual characteristics. Qualitative content analysis was conducted to characterize the patients self-reported plans and reasons for adhering (or not) to the plan 3 months later. RESULTS: Of 2555 patients, most selected the top box option (9=definitely agree) that they felt they had a clear plan (n=2011, 78%), agreed with the plan (n=2049, 80%), and intended to follow the plan (n=2108, 83%) discussed with their provider at the primary care visit. The most common elements of the plans reported included reference to exercise (n=359, 14.1%), testing (laboratory, imaging, etc; n=328, 12.8%), diet (n=296, 11.6%), and initiation or adjustment of medications; (n=284, 11.1%). Patients who strongly agreed that they had a clear plan, agreed with the plan, and intended to follow the plan were all more likely to report plan completion 3 months later (P<.001) than those providing less positive ratings. Patients who reported plans related to following up with the primary care provider (P=.008) to initiate or adjust medications (P≤.001) and to have a specialist visit were more likely to report that they had completely followed the plan (P=.003). Adjusting for demographic variables, patients who indicated intent to follow their plan were more likely to follow-through 3 months later (P<.001). Patients reasons for completely following the plan were mainly that the plan was clear (n=1114, 69.5%), consistent with what mattered (n=1060, 66.1%), and they were determined to carry through with the plan (n=887, 53.3%). The most common reasons for not following the plan were lack of time (n=217, 22.8%), having decided to try a different approach (n=105, 11%), and the COVID-19 pandemic impacted the plan (n=105, 11%). CONCLUSIONS: Patients initial assessment of their plan as clear, their agreement with the plan, and their initial willingness to follow the plan were all strongly related to their self-reported completion of the plan 3 months later. Patients whose plans involved lifestyle changes were less likely to report that they had completely followed their plan. TRIAL REGISTRATION: ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/study/NCT03385512. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/30431.