Divisions Adventist Health is a faith-based, nonprofit integrated health system serving more than 80 communities on the West Coast and in Hawaii. Founded on Seventh-day Adventist heritage and values, Adventist Health provides care in hospitals, clinics, home care agencies, hospice agencies and joint-venture retirement centers in both rural and urban communities.Its headquarters are in Roseville, California. As of 2020 Adventist Health operates 22 hospitals in California, Hawaii, and Oregon.
Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home is inconsistent. Objective: To describe the design and planned evaluation of a conversational agent (chatbot) that guides patients through the remission phase following diabetic limb reconstruction. Methods: This protocol describes a conversational agent (chatbot) that turns remission guidance into daily actions, grounded in clinical expertise and established care guidelines. Walking is dosed like a drug, with careful titration based on tissue response. The agent integrates automatic data capture (smartphone step counts, skin temperature, shoe step streams, smartwatch step streams, Bluetooth thermometry when available, and app session timestamps) with manual patient entries (shoe wear time, skin redness persistence, and symptom checks). It doses walking activity, guides footwear break-in, prompts photo-confirmed concerns, following clinician-informed rules and escalation pathways. We define data quality checks for missingness and physiologic plausibility, and the agent reinforces reducing weight-bearing activity when risk signals appear. We outline device drift. The study is designed as a single-arm feasibility pilot (n = 30) to assess engagement, safety, and implementation fidelity. Results: No clinical outcome results are reported because this is a protocol study and enrollment has not yet begun. This study presents the prespecified sensing-to-decision workflow, escalation logic, and pilot endpoints, along with internal technical verification procedures (e.g., message delivery reliability, data completeness checks, and rule-engine consistency testing). Conclusions: A remission chatbot is a plausible method to extend specialist support into the home, reflecting integration of clinical expertise with digital health tools. This protocol defines how feasibility, safety, and usability will be evaluated. Clinical efficacy should be confirmed in future studies.
Opsoclonus and myoclonus are known paraneoplastic manifestations of malignancy. The most common antibodies found are anti-neuronal antibodies (Hu, Ri, and Ma). The most common affiliated malignancies are small-cell lung cancers, breast cancers, or gynecological malignancies. Gastric cancer is uncommon. Other antibodies have been cited, but are rare. Isolated opsoclonus without significant myoclonus is also rare. While both glutamic acid decarboxylase (GAD) and myelin oligodendrocyte glycoprotein (MOG) antibodies have individually been reported to be associated with opsoclonus, reports of a combination of these two in a patient with gastric cancer with isolated reversible opsoclonus are scarce. In this case, we follow a patient who presented as such and demonstrated significant symptomatic improvement after the administration of intravenous immunoglobulin.
Pancreatic ductal adenocarcinoma (PDAC) and biliary tract cancers (BTCs) remain highly lethal gastrointestinal malignancies because of late presentation, marked molecular heterogeneity, and limited durable benefit from conventional systemic therapy. This narrative review summarizes recent advances in both diseases, focusing on practice-informing clinical trials, biomarker-driven treatment strategies, and translational insights into tumor biology and resistance. In PDAC, progress includes refinement of perioperative management, broader germline and somatic testing, recognition of DNA damage repair-deficient subsets, and development of KRAS-directed therapies and rational combination strategies. In BTCs, especially intrahepatic cholangiocarcinoma, comprehensive molecular profiling has expanded precision oncology through actionable alterations such as FGFR2 rearrangements, IDH1 mutations, HER2 amplification/overexpression, BRAF V600E, NTRK fusions, and MSI-high/dMMR status. Immunotherapy has a clearer role in selected BTC populations, whereas in PDAC benefit remains largely restricted to rare biomarker-defined subsets. Across both diseases, circulating tumor DNA is emerging as a promising tool for prognostication, minimal residual disease assessment, response monitoring, and early resistance detection. Contemporary care increasingly depends on early molecular profiling, individualized treatment sequencing, and integration of targeted therapies, biomarker-guided immunotherapy, and clinical trials.
Background: Timely management of stroke patients with large vessel occlusion (LVO) eligible for endovascular reperfusion therapy (ERT) is essential for optimal outcomes. In 2023, our facility’s average Door-In Door-Out (DIDO) time was 202 minutes, exceeding the Joint Commission benchmark of ≤120 minutes. Delays stemmed from lag in CTA result notification due to reliance on radiologist interpretation, inefficiencies between LVO identification and transfer decision, and a cumbersome transfer process requiring multiple calls to thrombectomy-capable centers. To address these issues, we launched a quality improvement initiative aimed at reducing DIDO times to ≤120 minutes, improving efficiency in LVO detection and decision-making through AI automation, strengthening communication with a Comprehensive Stroke Center (CSC), and ensuring only appropriate patients were transferred for ERT. Methods: Viz.ai software was integrated with our PACS system and stroke workflow to enable real-time imaging analysis and automated LVO alerts. Multidisciplinary training for ED providers, teleneurologists, and radiologists emphasized AI-driven workflow use and faster decision-making. In partnership with a CSC, we developed standardized transfer protocols and direct communication pathways to minimize delays. Continuous improvement was supported by case reviews, DIDO tracking dashboards, and workflow evaluations to identify and address bottlenecks. Results: Following Viz.ai implementation and CSC collaboration in early 2024, our average DIDO time dropped from 202 minutes to 113 minutes, meeting the benchmark. Time from CTA completion to LVO identification was reduced by 84%, from 45 minutes to 7 minutes. Time from LVO diagnosis to transfer decision improved by 70%, from 30 to 9 minutes. These gains resulted from automated alerts that bypassed delays from manual interpretation. Standardized transfer protocols reduced coordination time and eliminated multiple phone calls. Transfers were expedited, and unnecessary ones avoided, ensuring access to ERT for appropriate candidates. Conclusion: Integrating Viz.ai and partnering with a CSC significantly improved the timeliness and efficiency of LVO stroke care. These efforts reduced DIDO times, streamlined transfer processes, and created a scalable model for delivering timely stroke treatment aligned with national standards.
