Literature Collection

BACKGROUND: In the last decade there has been an increase in the development and marketing of digital therapeutic (DTx) products aiming to prevent, manage, or treat a medical disorder or disease. Health insurance coverage for these products is not well established, and payers are facing increasing pressure to include these products as a covered benefit. OBJECTIVE: To examine factors and characteristics that could drive health insurance coverage of DTx products from US payers' and coverage decision-makers' perspectives. METHODS: This was a qualitative noninterventional, cross-sectional study conducted from August 2022 to October 2022. Virtual focus group meetings with pharmacy benefit managers/directors or medical directors representing a range of health insurance organizations were held following a semistructured interview guide. Convenience and snowball sampling techniques were used to identify participants. Transcripts were coded and analyzed with Atlas.ti software to identify common themes and subthemes. RESULTS: Five focus group meetings and 1 individual interview were held from August to October 2022. Participants (n = 22) were mostly pharmacists (n = 18, 85%) with more than 15 years of experience (n = 18, 85%). Some participants indicated that DTx products for diabetes (n = 6, 29%), mental/behavioral health (n = 3, 14%), and substance abuse disorders (n = 3, 14%) were already covered by their organizations. The topics generating the most comments grouped by code were issues around the evidence for DTx (67 unique comments) and barriers for coverage (60 unique comments). Participants indicated they want to have evidence of effectiveness that is similar to traditional pharmaceutical products. Barriers for coverage included a need to revise benefit policies, exclusion of nonprescription products, and mechanisms for billing. DTx products with an indication for mental/behavioral health were viewed as most likely to be reimbursed. Coverage of DTx products may occur under either the pharmacy or medical benefit. CONCLUSIONS: Health care payers stated that evidence of effectiveness was a necessary condition for health insurance coverage of DTx products. Given these are relatively new in health care, payers had more questions than answers regarding how these products will be integrated into health benefits.

Egypt is experiencing an increasing drug problem (cannabis, heroin, amphetamines, pharmaceutical opioids, synthetic cannabinoids). Whilst harm reduction and addiction treatment are provided by government and non-governmental organizations in Egypt, very little is known about physicians experience of handling patient substance use and substance use disorder (SUD) in primary care. A cross-sectional national study of 392 Egyptian Family Physicians (FPs) & General Practitioners (GPs) explored their knowledge, attitude and professional practice regarding management of SUD in primary care, as a first step toward identifying professional development support needs and informing general practice. Findings underscore the need for greater depth of addiction training during undergraduate medical education and in continuing professional development, so that Egyptian FPs/GPs can better prevent harmful substance use, detect patients with SUD, intervene and support those in treatment. This study provides unique information which will inform further development and scale of evidence based SUD brief intervention and treatment within Egyptian primary care.

OBJECTIVE: The American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine endorse checklist use to improve obstetric care. However, there is limited research into development, implementation, and sustained use of perinatal emergency checklists to inform individual institutions. This study aimed to investigate the development and implementation of perinatal emergency checklists in diverse hospital settings in the United States. STUDY DESIGN: A qualitative study was conducted individually with clinicians from three health care systems. The participants developed and implemented institution-tailored perinatal emergency checklists. Interview transcriptions were coded using the Consolidated Framework for Implementation Research. RESULTS: The study sites included two health care systems and one individual hospital. Delivery volumes ranged from 3,500 to 48,000 deliveries a year. Interviews were conducted with all 10 participants approached. Checklists for 19 perinatal emergencies were developed at the three health care systems. Ten of the checklist topics were the same at all three institutions. Participants described the checklists as improving patient care during crises. The tools were viewed as opportunities to promote a shared mental model across clinical roles, to reduce redundancy and coordinate obstetric crisis management. Checklist were developed in small groups. Implementation was facilitated by those who developed the checklists. Participants agreed that simulation was essential for checklist refinement and effective use by response teams. Barriers to implementation included limited clinician availability. There was also an opportunity to strengthen integration of checklists workflow early in perinatal emergencies. Participants articulated that culture change took time, active practice, persistence, reinforcement, and process measurement. CONCLUSION: This study outlines processes to develop, implement, and sustain perinatal emergency checklists at three institutions. Participants agreed that multiple, parallel implementation tactics created the culture shift for integration. The overview and specific Consolidated Framework for Implementation Research components may be used to inform adaptation and sustainability for others considering implementing perinatal emergency checklists. KEY POINTS: · Perinatal emergency checklists reduce redundancy and coordinate obstetric crisis management.. · Perinatal emergency simulation is essential for checklist refinement and effective team use.. · Integrations of perinatal emergency checklists requires culture change and process measurement..

BACKGROUND: Major depressive disorder (MDD) and insulin resistance-related conditions are major contributors to global disability. Their co-occurrence complicates clinical outcomes, increasing mortality and symptom severity. AIMS: In this study, we investigated the association of insulin resistance-related conditions and related polygenic scores (PGSs) with MDD clinical profile and treatment outcomes, using primary care records from UK Biobank. METHOD: We identified MDD cases and insulin resistance-related conditions, as well as measures of depression treatment outcomes (e.g. resistance) from the records. Clinical-demographic variables were derived from self-reports, and insulin resistance-related PGSs were calculated using PRS-CS. Univariable analyses were conducted to compare sociodemographic and clinical variables of MDD cases with (IR+) and without (IR-) lifetime insulin resistance-related conditions. Multiple regressions were performed to identify factors, including insulin resistance-related PGSs, potentially associated with treatment outcomes, adjusting for confounders. RESULTS: Among 30 919 MDD cases, 51.95% were IR+. These had more antidepressant prescriptions and classes utilisation and longer treatment duration than patients without insulin resistance-related conditions (P < 0.001). IR+ participants showed distinctive depressive profiles, characterised by concentration issues, loneliness and inadequacy feelings, which varied according to the timing of MDD diagnosis relative to insulin resistance-related conditions. After adjusting for confounders, insulin resistance-related conditions (i.e. cardiovascular diseases, hypertension, non-alcoholic fatty liver disease, obesity/overweight, prediabetes and type 2 diabetes mellitus) were associated with antidepressant non-response/resistance and longer treatment duration, particularly when MDD preceded insulin resistance-related conditions. No significant PGS associations were found with antidepressant treatment outcomes. CONCLUSIONS: Our findings support an integrated treatment approach, prioritising both psychiatric and metabolic health, and public health strategies aimed at early intervention and prevention of insulin resistance in MDD.

The public health impact of alcohol-associated liver disease (ALD), a serious consequence of problematic alcohol use, and alcohol use disorder (AUD) is growing, with ALD becoming a major cause of alcohol-associated death overall and the leading indication for liver transplantation in the United States. Comprehensive care for ALD often requires treatment of AUD. Although there is a growing body of evidence showing that AUD treatment is associated with reductions in liver-related morbidity and mortality, only a minority of patients with ALD and AUD receive this care. Integrated and collaborative models that streamline both ALD and AUD care for patients with ALD and AUD are promising approaches to bridge this treatment gap and rely on multidisciplinary and interprofessional teams and partnerships. Here, we review the role of AUD care in ALD treatment, the effects of AUD treatment on liver-related outcomes, the impact of comorbid conditions such as other substance use disorders, obesity, and metabolic syndrome, and the current landscape of integrated and collaborative care for ALD and AUD in various treatment settings. We further review knowledge gaps and unmet needs that remain, including the role of precision medicine, the application of harm reduction approaches, the impact of health disparities, and the need for additional AUD treatment options, as well as further efforts to support implementation and dissemination.

Healthcare is plagued with many problems that Artificial Intelligence (AI) can ameliorate or sometimes amplify. Regardless, AI is changing the way we reason towards solutions, especially at the frontier of public health applications where autonomous and co-pilot AI integrated systems are now rapidly adopted for mainstream use in both clinical and non-clinical settings. In this regard, we present empirical analysis of thematic concerns that affect patients within AI integrated healthcare systems and how the experience of care may be influenced by the degree of AI integration. Furthermore, we present a fairly rigorous mathematical model and adopt prevailing techniques in Machine Learning (ML) to develop models that utilize a patient's general information and responses to a survey to predict the degree of AI integration that will maximize their experience of care. We model the patient's experience of care as a continuous random variable on the open interval ([Formula: see text]) and refer to it as the AI Affinity Score which encapsulates the degree of AI integration that the patient prefers within a chosen healthcare system. We present descriptive statistics of the distribution of the survey responses over key demographic variables viz. Age, Gender, Level of Education as well as a summary of perceived attitudes towards AI integrated healthcare in these categories. We further present the results of statistical tests conducted to determine if the variance across distributions of AI Affinity Scores over the identified groups are statistically significant and further assess the behavior of any independent distribution of AI Affinity Scores using a Bayesian nonparametric model.

OBJECTIVE: To determine whether the presence of integrated behavioral health care (IBH) in a pediatric practice is associated with improved implementation of Safe Environment for Every Kid (SEEK), an evidence-based approach to prevention of child maltreatment. METHODS: Pediatric primary care practices across the United States (n = 44) expressed interest in participating in a longitudinal multisite trial. Half of the practices included IBH. Semi-structured interviews were conducted at different points in time with 49 practice leaders, primary care professionals, behavioral health professionals, and nursing and administrative staff. Quantitative data on implementation stage and phase, proportion of activities completed at each stage, and length of time to complete each stage were collected by the Stages of Implementation Completion measure. RESULTS: Qualitative data revealed several instances in which IBH facilitated the adoption and implementation of SEEK and where SEEK supported IBH. However, apart from a longer duration devoted to program startup among IBH practices, none of the quantitative differences in rate of program startup, better completion of implementation activities, more tasks completed within each stage, and greater competency were statistically significant. CONCLUSION: Integrated behavioral health care in pediatric primary care settings may help to facilitate the implementation of interventions like SEEK designed to address social determinants of health and reduce the risk of child maltreatment. However, the current study did not find evidence, based on quantitative analyses, that IBH significantly affected the uptake of Project SEEK and that more research may be warranted.