Literature Collection

To identify whether medical homes in FQHCs have advantages over other group and individual medical practices in caring for people with severe mental illness. Models estimated the effect of the type of medical home on monthly service utilization, medication adherence, and total Medicaid spending over a 4-year period for adults aged 18 or older with a major depressive disorder (N = 65,755), bipolar disorder (N = 19,925), or schizophrenia (N = 8501) enrolled in North Carolina's Medicaid program. Inverse probability of treatment weights (IPTW) were used to adjust for nonrandom assignment of patients to practices. Generalized estimating equations for repeated measures were used with gamma distributions and log links for the continuous measures of medication adherence and spending, and binomial distributions with logit links for binary measures of any outpatient or any emergency department visits. Adults with major depression or bipolar disorders in FQHC medical homes had a lower probability of outpatient service use than their counterparts in individual and group practices. The probability of emergency department use, medication adherence, and total Medicaid spending were relatively similar across the three settings. This study suggests that no one type of medical practice setting-whether FQHC, other group, or individual-consistently outperforms the others in providing medical home services to people with severe mental illness.

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.