Best AI tools for< Ehr Specialist >

The AI Documentation Assistant by Netsmart is a market-leading artificial intelligence documentation tool designed to streamline the documentation process in human services. It utilizes AI technology to enhance EHR systems, improve documentation quality, reduce errors, and increase staff efficiency. The tool caters to various sectors such as behavioral health, addiction treatment, child and family services, IDD, autism, and direct service providers. By leveraging AI capabilities, the assistant aims to save time, boost staff productivity, and enhance the overall quality of care provided.

Healthray is a Next-Gen AI Hospital Management System that offers a comprehensive suite of healthcare software solutions, including Hospital Information Management System (HIMS), EMR Software, EHR Software, Pharmacy Management System (PMS), and Laboratory Information Management System (LIMS). The platform leverages cutting-edge AI technology to streamline operations, elevate patient care, and optimize administrative efficiency for healthcare providers. Healthray caters to a wide range of medical specialties and offers advanced functionalities to revolutionize traditional healthcare practices. With a focus on digital healthcare solutions and AI integration, Healthray aims to transform the healthcare industry by providing innovative tools for doctors and hospitals.

S10.AI is an AI-powered medical scribe application designed to streamline medical documentation processes for healthcare professionals. It offers seamless integration with any EMR system, providing accurate and efficient transcription of patient conversations. The application saves time, ensures confidentiality, and adapts to various medical templates and workflows. S10.AI is praised for its precision, efficiency, and support, making it a valuable asset for practitioners looking to enhance administrative tasks without compromising patient care.

Suki Assistant is an enterprise-grade AI assistant designed for clinicians in the healthcare industry. It offers ambient documentation, dictation, ICD-10 and HCC coding, and question answering capabilities. Suki ensures content is clinician-reviewed before being sent to the EHR, minimizing the risk of errors. With deep integrations with major EHR systems, Suki sets the standard for EHR integrations in the healthcare sector. The application aims to save clinicians time, allowing them to focus more on patient care.

Sunoh is a medical AI scribe that uses ambient listening technology to convert natural conversations between healthcare providers and patients into clinical documentation. It offers a unique and immersive experience for both doctors and patients, making the documentation of clinical notes faster and more efficient than ever before. Sunoh can be used with your EHR to accelerate your documentation.

Healthcare IT News is an AI-powered platform that provides the latest news and updates in the healthcare IT industry. The platform covers a wide range of topics including video analytics, artificial intelligence, cloud computing, EHR, government & policy, interoperability, patient engagement, population health, precision medicine, privacy & security, and telehealth. It offers insights, articles, and special projects related to AI, ML intelligence, cybersecurity, and more. Healthcare IT News aims to keep healthcare professionals informed about the latest trends and developments in the field.

TORTUS is an AI application designed for doctors and clinicians, offering an AI interface for Electronic Health Records (EHR). It aims to provide faster, better, and kinder care by automating tasks such as record transcription, note generation, and clinical coding. The application is fully compliant with healthcare regulations and prioritizes data security. O.S.L.E.R. is positioned as a digital worker for digital work, enhancing patient outcomes and relieving clinician burnout through clinician-AI co-working.

Medvise is an AI-powered medical scribe and coding engine designed to eliminate administrative tasks in the medical field. It offers accurate and efficient medical charting, real-time scribe capabilities, automated data entry, and AI-powered medical coding. Medvise ensures compliance with medical coding and billing standards, highlights documentation gaps, and provides suggestions for improvement. The platform integrates seamlessly with EHR systems and offers an API Gateway for custom integrations. Medvise aims to enhance patient care, increase revenue, and streamline healthcare documentation processes across various specialties.

DeepScribe is an AI medical scribe application that leverages advanced speech recognition technology to capture clinical conversations with extreme accuracy. It empowers clinicians and health systems with real-time AI insights, offers customization options to match provider workflows, and ensures trust and safety through features that promote AI transparency. DeepScribe is designed to save time, improve accuracy, drive adoption, and maximize revenue for doctors, hospitals, and health systems. The application is built for enterprise use, allowing users to unlock the power of AI and modernize their patient data strategy.

Supanote is an AI therapy notes application that offers HIPAA-compliant automated progress notes to help therapists save time and focus on their clients. The tool is designed for therapists, social workers, psychologists, and coaches to streamline the note-taking process. Supanote generates accurate notes in custom formats, ensuring data security and compliance with top-tier security measures. It is specifically trained for psychotherapy and coaching use-cases, providing responsible and high-quality notes while respecting client privacy. The application supports various session formats, including in-person, online sessions, dictation, and audio recordings.

Upheal is an AI therapy notes application designed for therapists, psychiatrists, and coaches to streamline the process of creating progress notes, treatment plans, and session analytics. It offers comprehensive client plans, integrations with third-party tools, and scheduling features to enhance workflow efficiency. Upheal's AI technology helps professionals save time and stay more present during sessions, ultimately improving the quality of their work and life. The application supports various therapy formats, client types, languages, and progress notes templates, making it a versatile tool for mental health professionals worldwide.

Regard is an AI-powered healthcare solution that automates clinical tasks, making it easier for clinicians to focus on patient care. It integrates with the EHR to analyze patient records and provide insights that can help improve diagnosis and treatment. Regard has been shown to improve hospital finances, patient safety, and physician happiness.

TheraPulse is an AI-powered assistant designed for mental health professionals to streamline their documentation process. It offers session transcription, progress notes generation, and insightful summaries, saving professionals valuable time and enhancing client care. With secure data storage and compatibility with various EHR systems, TheraPulse aims to revolutionize mental health practice management through AI technology.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

The repository 'LLM-for-Healthcare' provides a comprehensive survey of large language models (LLMs) for healthcare, covering data, technology, applications, and accountability and ethics. It includes information on various LLM models, training data, evaluation methods, and computation costs. The repository also discusses tasks such as NER, text classification, question answering, dialogue systems, and generation of medical reports from images in the healthcare domain.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

Voice-to-text, Vision-to-text transcription, Transcript-to-‘Clinical format’ integrated with CDS. Writes clinical notes, referral letter, generate PDF,prepare discharge summary. (Ultimate aid for clinicians)