Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representati...
Background Effective management of hyperlipidemia is crucial for preventing cardiovascular diseases. However, traditional monitoring systems often lack efficiency, particularly in navigating electronic health records ...
Background Effective management of hyperlipidemia is crucial for preventing cardiovascular diseases. However, traditional monitoring systems often lack efficiency, particularly in navigating electronic health records (EHRs) to identify and track at-risk patients. Objective This study aims to evaluate the impact of a Hyperlipidemia Medication Reminder System (HLD-MRS) implemented in a district hospital to streamline EHR navigation and enhance patient management. Methods A pre-post prospective study was conducted at a district hospital. This study designed the HLD-MRS to integrate patient data, guideline recommendations, and risk factor assessments into a single interface, simplifying EHR navigation. This study compared EHR navigation times and patient tracking rates before and after HLD-MRS implementation across the Cardiology, Endocrinology, and Family Medicine departments. Results Among 750 patients with follow-up appointments, 334 were identified as having HLD or being at high-risk status. The HLD-MRS significantly reduced mean EHR navigation time from 83.9 s (SD = 8.6) to 53.8 s (SD = 6.6) across all departments (p < 0.001). The proportion of actively monitored HLD patients increased from 42.5 % to 56.6 %, while cases with unknown or withheld assessment status decreased from 57.5 % to 28.4 %. Conclusion The HLD-MRS improved efficiency in EHR navigation and increased the frequency of active patient monitoring. This guideline-driven decision support system shows promise for enhancing hyperlipidemia care in high-volume outpatient settings.
Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined1,2. This poses a significant challenge, since modern therapeutics such as chemo...
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Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined1,2. This poses a significant challenge, since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor3. Patients with a CUP diagnosis routinely undergo an extensive diagnostic work-up of pathology, radiology, endoscopy, laboratory tests, and clinical correlation in an attempt to determine the primary origin. Such exploration is not only time and resource consuming, but it might significantly delay administration of the suitable treatment. Despite extensive diagnostic work-ups the primary may never be determined in many cases. Recent work has focused on using genomics and transcriptomics for identification of tumor origins4–6. However, genomic testing is not conducted for every patient and lacks clinical penetration in low resource settings. Herein, to overcome these challenges, we present a deep learning-based computational pathology algorithm-TOAD-that can provide a differential diagnosis for CUP using routinely acquired histology slides. We used 17,486 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to simultaneously identify the tumor as primary or metastatic and predict its site of origin. We tested our model on an internal test set of 4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 while on our external test set of 662 cases from 202 different hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a dataset of 717 CUP cases from 151 different medical centers and identified a subset of 290 cases for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 50% of cases (κ=0.4 when adjusted for agreement by chance) and a top-3 agreement of 75%. Our proposed method can be used
Objective: Heart rate variability (HRV) has been proven to be an important indicator of physiological status for numerous applications. Despite the progress and active developments made in HRV metric research over the...
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