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Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66-0.70) was achieved by the "any HRSNs" outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs., (© 2022. The Author(s).)

Background: Recent clinical trials have established the efficacy of endovascular stroke therapy and intravenous thrombolysis using advanced imaging, particularly computed tomography perfusion (CTP). The availability and utilization of CTP for patients and hospitals that treat acute ischemic stroke (AIS), however, is uncertain., Methods: We performed a retrospective cross-sectional analysis using 2 complementary Medicare datasets, full sample Texas and 5% national fee-for-service data from 2014 to 2017. AIS cases were identified using International Classification of Diseases , Ninth Revision and International Classification of Diseases , Tenth Revision coding criteria. Imaging utilization performed in the initial evaluation of patients with AIS was derived using Current Procedural Terminology codes from professional claims. Primary outcomes were utilization of imaging in AIS cases and the change in utilization over time. Hospitals were defined as imaging modality-performing if they submitted at least 1 claim for that modality per calendar year. The National Medicare dataset was used to validate state-level findings, and a local hospital-level cohort was used to validate the claims-based approach., Results: Among 50 797 AIS cases in the Texas Medicare fee-for-service cohort, 64% were evaluated with noncontrast head CT, 17% with CT angiography, 3% with CTP, and 33% with magnetic resonance imaging. CTP utilization was greater in patients treated with endovascular stroke therapy (17%) and intravenous thrombolysis (9%). CT angiography (4%/y) and CTP (1%/y) utilization increased over the study period. These findings were validated in the National dataset. Among hospitals in the Texas cohort, 100% were noncontrast head CT-performing, 77% CT angiography-performing, and 14% CTP-performing in 2017. Most AIS cases (69%) were evaluated at non-CTP-performing hospitals. CTP-performing hospitals were clustered in urban areas, whereas large regions of the state lacked immediate access., Conclusions: In state-wide and national Medicare fee-for-service cohorts, CTP utilization in patients with AIS was low, and most patients were evaluated at non-CTP-performing hospitals. These findings support the need for alternative means of screening for AIS recanalization therapies.

Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent reference methods., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.