Evaluation of a health system's implementation of a monkeypox care model under the RE-AIM framework.

Objective: Emerging infectious diseases challenge healthcare systems to implement new models of care. We aim to evaluate the rapid implementation of a new care model for monkeypox in our health system. Design: This is a retrospective case series evaluation under the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework of implementation of a testing and care model for monkeypox in a large, integrated health system. Methods: Atrium Health implemented education of providers, testing protocols, and management of potential monkeypox cases using electronic health record (EHR) data capabilities, telehealth, and collaboration between multiple disciplines. The first 4 weeks of care model implementation were evaluated under the RE-AIM framework. Results: One hundred fifty-three patients were tested for monkeypox by 117 unique providers at urgent care, emergency departments, and infectious disease clinics in our healthcare system between 18 July 2022 and 14 August 2022. Fifty-eight monkeypox cases were identified, compared with 198 cases in the state during the time period, a disproportionate number compared with the health system service area, and 52 patients were assessed for need for tecovirimat treatment. The number of tests performed and providers sending tests increased during the study period. Conclusion: Implementation of a dedicated care model leveraging EHR data support, telehealth, and cross-disciplinary collaboration led to more effective identification and management of emerging infectious diseases and is important for public health. Plain Language Summary: Impact of care model implementation on monkeypox New infectious diseases challenge health systems to implement new care practices. Our health system responded to this challenge by implementing a care model for education, testing, and clinical care of monkeypox patients. We analyzed results from implementing the model. We were able to identify a disproportionate number of monkeypox cases compared with the rest of our state by using our model to educate medical providers, encourage testing, and ensure patients had access to best disease care. Implementation of care models for testing and management of new diseases will improve patient care and public health.

Increasing collaboration on substance use disorder research with primary care practices through the National Drug Abuse Treatment Clinical Trials Network.

BACKGROUND: The National Drug Abuse Treatment Clinical Trials Network (CTN) called for its national nodes to promote the translation of evidence-based interventions from substance use disorder (SUD) research into clinical practices. This collaborative demonstration project engaged CTN-affiliated practice-based research networks (PBRNs) in research that describes aspects of opioid prescribing in primary care. METHODS: Six PBRNs queried electronic health records from a convenience sample of 134 practices (84 participants) to identify the percent of adult patients with an office visit who were prescribed an opioid medication from October 1, 2015, to September 30, 2016, and, of those, the percent also prescribed a sedative in that year. Seven PBRNs sent an e-mail survey to a convenience sample of 108 practices (58 participants) about their opioid management policies and procedures during the project year. RESULTS: Of 561,017 adult patients with a visit to one of the 84 clinics in the project year, 22.9% (PBRN range 3.1%-25.4%) were prescribed opioid medications, and 52.1% (PBRN range 8.5%-60.6%) of those were prescribed a sedative in the same year. Of the 58 practices returning a survey (45.3% response rate), 98.1% had formal written treatment agreements for chronic opioid therapy, 68.5% had written opioid prescribing policies, and 43.4% provided reports to providers with feedback on opioid management. Only 24.1% were providing buprenorphine for OUD. CONCLUSION: CTN-affiliated PBRNs demonstrated their ability to collaborate on a project related to opioid management; results highlight the important role for PBRNs in OUD treatment, research, and the need for interventions and additional policies addressing opioid prescribing in primary care practice.

Traditional Risk Indices as Predictors of Future Utilization and Charges in the Context of Population Health for an Uninsured Cohort.

INTRODUCTION: The uninsured population presents unique challenges to the application of an integrated approach to population health. Our objective is to compare and test population risk indices for identifying a cohort of uninsured patients at high-risk for avoidable healthcare utilization and costs. METHODS: Patients who had a least one visit at a safety-net clinic, had a primary address in Mecklenburg County, were aged 18-74, and had the most recent healthcare visit coded as 'uninsured' were identified in the baseline period. The five risk indices used were: the HHS Hierarchical Conditions Category (HCC), the Charlson Comorbidity Index (CCI), Total Cost Index, Total Inpatient Visits Index, and Total Emergency Department Visits Index. First, agreement across the five indices was analyzed. Then, the accuracy of the five risk indices was tested in predicting future utilization and costs for the subsequent 12-month follow-up period. RESULTS: Kappa statistics and percent overlap values showed below average to poor agreement between indices when comparing scorers.The strongest predictors of being in the 90th percentile of total cost during the 12 months follow-up period were the Total Cost Index at baseline (C statistic=0.75) and the HCC (C-statistic=0.73). The CCI and Total Inpatient Visit Index's demonstrated the lowest accuracy for predicting an unnecessary ED visit (C-statistic=0.51, for both). DISCUSSION/CONCLUSION: Prior cost and ED utilization were key in predicting their corresponding 12-month metrics. In contrast, the Total Inpatient Visit Index had the worst predictive performance for future hospitalization rates. Some indices were similarly predictive as compared to insured cohorts but others showed contrasting results.

Adapting community based participatory research (CBPR) methods to the implementation of an asthma shared decision making intervention in ambulatory practices.

OBJECTIVE: Translating research findings into clinical practice is a major challenge to improve the quality of healthcare delivery. Shared decision making (SDM) has been shown to be effective and has not yet been widely adopted by health providers. This paper describes the participatory approach used to adapt and implement an evidence-based asthma SDM intervention into primary care practices. METHODS: A participatory research approach was initiated through partnership development between practice staff and researchers. The collaborative team worked together to adapt and implement a SDM toolkit. Using the RE-AIM framework and qualitative analysis, we evaluated both the implementation of the intervention into clinical practice, and the level of partnership that was established. Analysis included the number of adopting clinics and providers, the patients' perception of the SDM approach, and the number of clinics willing to sustain the intervention delivery after 1 year. RESULTS: All six clinics and physician champions implemented the intervention using half-day dedicated asthma clinics while 16% of all providers within the practices have participated in the intervention. Themes from the focus groups included the importance of being part the development process, belief that the intervention would benefit patients, and concerns around sustainability and productivity. One year after initiation, 100% of clinics have sustained the intervention, and 90% of participating patients reported a shared decision experience. CONCLUSIONS: Use of a participatory research process was central to the successful implementation of a SDM intervention in multiple practices with diverse patient populations.

A trans-disciplinary approach to the evaluation of social determinants of health in a Hispanic population.

BACKGROUND: Individual and community health are adversely impacted by disparities in health outcomes among disadvantaged and vulnerable populations. Understanding the underlying causes for variations in health outcomes is an essential step towards developing effective interventions to ameliorate inequalities and subsequently improve overall community health. Working at the neighborhood scale, this study examines multiple social determinates that can cause health disparities including low neighborhood wealth, weak social networks, inadequate public infrastructure, the presence of hazardous materials in or near a neighborhood, and the lack of access to primary care services. The goal of this research is to develop innovative and replicable strategies to improve community health in disadvantaged communities such as newly arrived Hispanic immigrants. METHODS/DESIGN: This project is taking place within a primary care practice-based research network (PBRN) using key principles of community-based participatory research (CBPR). Associations between social determinants and rates of hospitalizations, emergency department (ED) use, and ED use for primary care treatable or preventable conditions are being examined. Geospatial models are in development using both hospital and community level data to identify local areas where interventions to improve disparities would have the greatest impact. The developed associations between social determinants and health outcomes as well as the geospatial models will be validated using community surveys and qualitative methods. A rapidly growing and underserved Hispanic immigrant population will be the target of an intervention informed by the research process to impact utilization of primary care services and designed, deployed, and evaluated using the geospatial tools and qualitative research findings. The purpose of this intervention will be to reduce health disparities by improving access to, and utilization of, primary care and preventative services. DISCUSSION: The results of this study will demonstrate the importance of several novel approaches to ameliorating health disparities, including the use of CBPR, the effectiveness of community-based interventions to influence health outcomes by leveraging social networks, and the importance of primary care access in ameliorating health disparities.

Modeling the exposure-response relationship of etanercept in the treatment of patients with chronic moderate to severe plaque psoriasis.

Modeling exposure-response relationships adds significant value to comprehending and interpreting both efficacy and safety data. An exposure-response model was developed using generalized nonlinear mixed-effects methodologies to correlate etanercept exposure with a 75% or greater reduction from baseline in the psoriasis area and severity index (PASI75). Three randomized trials of psoriasis patients were pooled for analysis. Three empirical exposure measures-cumulative dose, predicted cumulative area under the curve, and predicted trough concentration-were evaluated for their predictive capabilities. The predicted cumulative area under the curve model demonstrated the best ability via simulation to reproduce the data and was used to assess the following covariates: age, baseline psoriasis area and severity index, duration of psoriasis disease, prior systemic or phototherapy, race, sex, and weight. The final model was composed by scrutinizing the confidence intervals of a nonparametric bootstrap and included race and sex effects on baseline logit, baseline psoriasis area and severity index and prior systemic or phototherapy effects on maximum drug effect, a weight effect on apparent potency, and an age effect on the rate of drug effect. The model identified covariates predictive of data trends and adequately characterized by simulation the PASI75 over the entire clinical trial design space. In combination with a statistical subgroup analysis, the exposure-response model indicated that dose adjustment was not necessary for etanercept in any patient subpopulation with moderate to severe plaque psoriasis.

Population pharmacokinetic analysis of anidulafungin, an echinocandin antifungal.

The objective of this analysis was to describe the pharmacokinetic characteristics of anidulafungin in patients with serious fungal disease based on pharmacokinetic data collected during four recently completed or ongoing Phase II/III clinical studies. A total of 600 anidulafungin plasma samples from 225 patients across the four studies were available for analysis. Patients received daily intravenous infusions of 50, 75, or 100 mg anidulafungin, preceded by a loading dose that was twice the daily dose. The analysis population consisted of 129 patients with esophageal candidiasis, 87 with invasive candidiasis, 7 with invasive aspergillosis, and 2 with azole refractory mucosal candidiasis. A population analysis approach was used to develop a steady-state pharmacokinetic model for anidulafungin, assess the significance of possible covariates, and determine the amount of intersubject and random residual variability. A two-compartment model with first-order elimination provided the best fit to the data. The clearance of anidulafungin was influenced by weight and gender, and subjects in the invasive candidiasis study had a typical clearance that was approximately 30% higher than subjects from other studies. Weight was determined to be a predictor of the central volume of distribution. The covariates on clearance accounted for less than 20% of the intersubject variability and therefore are deemed to be of little clinical relevance. There was no evidence that the presence of rifampin or metabolic substrates, inhibitors, or inducers of cytochrome p450 influenced the clearance of anidulafungin. This indicates that dosing adjustments are not necessary when anidulafungin is administered in the presence of medications falling into these classifications.