Literature Collection

The integration of Artificial Intelligence (AI) in to the field of medicine is offering a new-age of updated diagnostics, prediction and treatment across multiple fields, addressing systemic disease including viral infections and cancer. The fields of Oral Pathology, Dermatology, Psychiatry and Cardiology are shifting towards integrating these algorithms to improve health outcomes. AI trained on biomarkers (e.g. salivary cf DNA) has shown to uncover the genetic linkage to disease and symptom susceptibility. AI-enhanced imaging has increased sensitivity in cancer and lesion detection, as well as detecting functional abnormalities not clinically identified. The integration of AI across fields enables a systemic approach to understanding chronic inflammation, a central driver in conditions like cardiovascular disease, diabetes and neuropsychiatric disorders. We propose that through the use of imaging data with biomarkers like cytokines and genetic variants, AI models can better trace the effects of inflammation on immune and metabolic disruptions. This can be applied to the pandemic response, where AI can model the cascading effects of systemic dysfunctions, refine predictions of severe outcomes and guide targeted interventions to mitigate the multi-systemic impacts of pathogenic diseases.

BACKGROUND: Millions of people with substance use disorders (SUDs) need, but do not receive, treatment. Delivering SUD treatment in primary care settings could increase access to treatment because most people visit their primary care doctors at least once a year, but evidence-based SUD treatments are underutilized in primary care settings. We used an organizational readiness intervention comprised of a cluster of implementation strategies to prepare a federally qualified health center to deliver SUD screening and evidence-based treatments (extended-release injectable naltrexone (XR-NTX) for alcohol use disorders, buprenorphine/naloxone (BUP/NX) for opioid use disorders and a brief motivational interviewing/cognitive behavioral -based psychotherapy for both disorders). This article reports the effects of the intervention on key implementation outcomes. METHODS: To assess changes in organizational readiness we conducted pre- and post-intervention surveys with prescribing medical providers, behavioral health providers and general clinic staff (N = 69). We report on changes in implementation outcomes: acceptability, perceptions of appropriateness and feasibility, and intention to adopt the evidence-based treatments. We used Wilcoxon signed rank tests to analyze pre- to post-intervention changes. RESULTS: After 18 months, prescribing medical providers agreed more that XR-NTX was easier to use for patients with alcohol use disorders than before the intervention, but their opinions about the effectiveness and ease of use of BUP/NX for patients with opioid use disorders did not improve. Prescribing medical providers also felt more strongly after the intervention that XR-NTX for alcohol use disorders was compatible with current practices. Opinions of general clinic staff about the appropriateness of SUD treatment in primary care improved significantly. CONCLUSIONS: Consistent with implementation theory, we found that an organizational readiness implementation intervention enhanced perceptions in some domains of practice acceptability and appropriateness. Further research will assess whether these factors, which focus on individual staff readiness, change over time and ultimately predict adoption of SUD treatments in primary care.

Hearing loss is a significant public health challenge, with prevalence estimates based on projected age demographics rather than actual public health needs. This study aimed to quantify hearing loss using real-world data from primary care and explore local patterns and trends from 2013 to 2022 in Cheshire and Merseyside counties of Northwest England. Annual hearing loss prevalence was measured using an ecological space-time analysis of 2.7 million primary care records from Cheshire and Merseyside Integrated Care System. We applied cluster and outlier analysis with geographically weighted regression to examine local deprivation effects. We detected spatial clusters of high prevalence of hearing loss in Cheshire and an increasing trend in hearing loss prevalence in Halton. Deprivation accounted for up to 35% of hearing loss variance in 2020. Monitoring spatial patterns of hearing loss is crucial for addressing health inequalities and guiding targeted prevention and intervention strategies.