BackgroundComorbid depression is a significant challenge for safety-net primary care systems. Team-based collaborative depression care is effective, but complex system factors in safety-net organizations impede adoption and result in persistent disparities in outcomes. Diabetes-Depression Care-management Adoption Trial (DCAT) evaluated whether depression care could be significantly improved by harnessing information and communication technologies to automate routine screening and monitoring of patient symptoms and treatment adherence and allow timely communication with providers.ObjectiveThe aim of this study was to compare 6-month outcomes of a technology-facilitated care model with a usual care model and a supported care model that involved team-based collaborative depression care for safety-net primary care adult patients with type 2 diabetes.MethodsDCAT is a translational study in collaboration with Los Angeles County Department of Health Services, the second largest safety-net care system in the United States. A comparative effectiveness study with quasi-experimental design was conducted in three groups of adult patients with type 2 diabetes to compare three delivery models: usual care, supported care, and technology-facilitated care. Six-month outcomes included depression and diabetes care measures and patient-reported outcomes. Comparative treatment effects were estimated by linear or logistic regression models that used generalized propensity scores to adjust for sampling bias inherent in the nonrandomized design.ResultsDCAT enrolled 1406 patients (484 in usual care, 480 in supported care, and 442 in technology-facilitated care), most of whom were Hispanic or Latino and female. Compared with usual care, both the supported care and technology-facilitated care groups were associated with significant reduction in depressive symptoms measured by scores on the 9-item Patient Health Questionnaire (least squares estimate, LSE: usual care=6.35, supported care=5.05, technology-facilitated care=5.16; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.02); decreased prevalence of major depression (odds ratio, OR: supported care vs usual care=0.45, technology-facilitated care vs usual care=0.33; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.007); and reduced functional disability as measured by Sheehan Disability Scale scores (LSE: usual care=3.21, supported care=2.61, technology-facilitated care=2.59; P value: supported care vs usual care=.04, technology-facilitated care vs usual care=.03). Technology-facilitated care was significantly associated with depression remission (technology-facilitated care vs usual care: OR=2.98, P=.04); increased satisfaction with care for emotional problems among depressed patients (LSE: usual care=3.20, technology-facilitated care=3.70; P=.05); reduced total cholesterol level (LSE: usual care=176.40, technology-facilitated care=160.46; P=.01); improved satisfaction with diabetes care (LSE: usual care=4.01, techn...

There is an abundance of literature that highlights the importance of patient-centered communication with cancer patients requiring surgical intervention. While the need for communication for patients requiring surgery is well understood, less attention is brought to patients with severe mental illnesses. More literature is needed to highlight the importance and application of patient-centered care for patients suffering from both severe mental illness and cancer requiring surgical intervention. It is unclear if poor communication between patients and cancer-care specialists is part of the reason for the underlying discrepancy. Efforts to reduce this discrepancy may be worth considering as a priority for health care systems. We present a case of a 63-year-old man with schizophrenia who received a late cancer diagnosis after a missed screening, resulting in an extensive surgical resection for colon cancer. We explore the possibility of careful communication between the treating physician, patient, and patient's caretakers potentially preventing the delay in his cancer diagnosis. Effective communication is especially important with mental health patients because of its effect on long-term physical and mental outcomes. We hope to further the discussion on how to better cater to this specific population of patients undergoing cancer surgery.

Chat Generative Pre-trained Transformer, also known as ChatGPT, is a new artificial intelligence (AI) program that responds to user inquiry with discourse resembling human language. The range of ChatGPT capabilities caught the interest of the medical world after it demonstrated its ability to pass medical boards examinations. In this case report, we present the clinical treatment of a 22-year-old male diagnosed with treatment-resistant schizophrenia (TRS) and compare the medical management suggested by ChatGPT to current standards of care in order to assess the program's ability to identify the disorder, evaluate potential medical and psychiatric work-up, and develop a treatment plan addressing the distinct nuances of our patient. In our inquiry with ChatGPT, we found that it can accurately identify our patient as having TRS and order appropriate tests to methodically rule out alternative causes of acute psychosis. Furthermore, the AI program suggests pharmacologic treatment options including clozapine with adjuvant medications, and nonpharmacologic treatment options including electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), and psychotherapy which align with current standards of care. Lastly, ChatGPT provides a comprehensive list of side effects associated with antipsychotics and mood stabilizers used to treat TRS. We found both potential for and limitations in the clinical application of ChatGPT to assist in the assessment and management of complex medical conditions. Overall, ChatGPT may serve as a powerful tool to organize medical data in a meaningful and palatable format for medical professionals to reference during patient care.

The literature describing acts of non-suicidal self-mutilation (NSSM) in the adult population is limited. Of the cases that document NSSM, a disproportionate number of these individuals have a history of psychiatric illnesses. Although the motivation to perform NSSM varies across patients, the literature suggests that past self-injurious behaviors, extreme religious delusions, and command hallucinations are the most significant risk factors. The primary forms of NSSM include ocular, genital, and limb mutilation. Limb mutilation is the least common of the three and typically occurs proximal to the wrist or hand. Here, we present a rare case involving a 42-year-old man with schizophrenia who was hospitalized due to osteomyelitis of his autoamputated digits. This case is unique in involving multiple digits of the hand and using a rare amputation method. We aim to compare this case with the existing body of work on NSSM and identify factors that may predispose patients to act on these extreme impulses. We also highlight a novel interventional program that reduces psychiatric and medical comorbidities.