Azure Text Analytics
Mine insights in unstructured text using NLP—no machine-learning expertise required—using text analytics, a collection of features from Cognitive Service for Language. Gain a deeper understanding of customer opinions with sentiment analysis. Identify key phrases and entities such as people, places, and organizations to understand common topics and trends. Classify medical terminology using domain-specific, pretrained models. Evaluate text in a wide range of languages. Identify important concepts in text, including key phrases and named entities such as people, events, and organizations. Examine what customers are saying about your brand and analyze sentiments around specific topics through opinion mining. Extract insights from unstructured clinical documents such as doctors' notes, electronic health records, and patient intake forms using text analytics for health.
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Google Cloud Natural Language API
Get insightful text analysis with machine learning that extracts, analyzes, and stores text. Train high-quality machine learning custom models without a single line of code with AutoML. Apply natural language understanding (NLU) to apps with Natural Language API. Use entity analysis to find and label fields within a document, including emails, chat, and social media, and then sentiment analysis to understand customer opinions to find actionable product and UX insights. Natural Language with speech-to-text API extracts insights from audio. Vision API adds optical character recognition (OCR) for scanned docs. Translation API understands sentiments in multiple languages. Use custom entity extraction to identify domain-specific entities within documents, many of which don’t appear in standard language models, without having to spend time or money on manual analysis. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment.
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Oracle Health Clinical Digital Assistant
Help physicians put patients before documentation—returning joy to the practice of medicine, giving them time back for themselves, and enhancing the quality of care by restoring the centrality of the physician-patient relationship. Oracle Health Clinical Digital Assistant is an AI-powered voice assistant that records key elements of the physician-patient encounter to interpret the information, input a draft note into the Oracle Health EHR, and let the physician quickly review and approve the clinical documentation produced. Ask questions in natural language to access patient details and perform frequent clinical workflows. Generate draft clinical notes—based on patient data and patient-physician conversations—for the physician’s review and sign-off. Use voice commands to create and append patient notes and edit pre-drafted notes generated from patient encounters.
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Verana Health
Verana Health is a real‑world data platform that transforms structured and unstructured electronic health record information into de‑identified, curated, disease‑specific data modules via its clinician‑informed and AI‑enhanced VeraQ population health data engine. Aggregating data from strategic partnerships with leading medical registries (including the American Academies of Ophthalmology, Neurology, and Urological Association), it encompasses over 20,000 clinicians and roughly 90 million patient records, providing near real‑time, high‑quality datasets to power real‑world evidence generation, clinical trial site and subject identification, clinician quality reporting, and medical registry management. Accessible through cloud services such as AWS Data Exchange and Amazon Redshift, the platform offers self‑service API access, an intuitive dashboard, and customizable cohort discovery tools, while advanced AI/ML algorithms, robust data quality assessments.
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