AI Clinician Assistant

AI Ambient Scribes are companies that use AI to ambiently capture clinical encounters and generate notes and/or transcripts. Notably we do not include simple dictation tools or virtual/remote scribes here, but we do include platforms that integrate AI scribing with human oversight, where the human element typically acts as a verification layer before the clinical note reaches the physician-user for approval. AI Ambient Scribe core capabilities are to: - Ambiently capture both patient consultations and clinicians' dictation - Convert these audio recordings into written transcripts through speech-to-text technology - Synthesize a clinical note from the transcript by employing artificial intelligence, notably a large language model (LLM) specifically tuned for this application Additionally, Ambient Scribes are increasingly building additional capabilities, including prompts to address care gaps or coding suggestions.

AI Clinical Summary solutions use AI to ingest clinical information and generate a summarized output. There are a wide range of use cases for these solutions, including: pre-charting; referral intake; surfacing care and diagnosis gaps; quality measurement; curating real world evidence; clinical trial matching; clinical registry submissions; clinical documentation improvement; and medical record review for insurance and legal needs. There is overlap between this category and clinical decision support, care gap and contract performance analytics, patient intake, and clinical documentation improvement (CDI), as the generation of summaries is often paired with specific workflow-focused solutions.

The AI Inbox Management category focuses on solutions that automate and streamline the management of clinical inboxes within healthcare settings, reducing administrative burdens on providers and improving communication workflows. These tools integrate directly with EHR systems to process various types of patient communications, documents, and inquiries. By using AI to categorize and prioritize tasks—such as e-faxes, lab results, referrals, and patient messages—these platforms help clinicians efficiently manage their in-baskets and ensure timely responses to urgent needs. A key advantage of AI inbox management is its ability to reduce physician burnout by minimizing repetitive manual tasks and enabling faster triage of patient messages. These tools often include advanced features like sentiment analysis to assess patient satisfaction and context-aware response generation to help clinicians draft personalized messages. Automating the routing of messages and updates to patient charts also enhances care coordination and ensures that critical information is promptly addressed, contributing to better patient outcomes and more efficient healthcare operations.

The AI Surgical Clinical Decision Support category encompasses tools designed to enhance surgical procedures through advanced data analysis and real-time insights. These platforms integrate video monitoring and data streams from surgical tools, life support systems, and other instruments to provide surgeons and clinical teams with valuable feedback during procedures. By analyzing various data inputs, these tools can offer actionable insights that improve surgical precision, patient safety, and overall outcomes. They assist in real-time decision-making, allowing providers to make adjustments during operations based on up-to-the-minute data. A significant benefit of these systems is their potential to reduce variability in surgical practices and improve outcomes across a wide range of procedures. By providing clinical teams with AI-driven insights, they can better predict complications, identify optimal strategies, and ensure that surgical workflows align with best practices.

Clinical Pathways and Reference products are designed to guide healthcare providers through evidence-based, standardized treatment protocols for specific conditions or disease states. They are built to improve clinical decision-making, enhance the quality of care, reduce variability in treatment, and ensure that patients receive care aligned with the latest medical guidelines. Clinical pathways products typically integrate into electronic health record (EHR) systems to deliver real-time, actionable insights during patient encounters. In order to be considered a clinical pathways product, a product some of the following capabilities: - Evidence-Based Guidelines: The product must provide up-to-date, evidence-based treatment protocols that are regularly updated in alignment with the latest clinical research and guidelines. - Condition-Specific Pathways: It should offer predefined, condition-specific pathways for managing diseases such as cancer, cardiovascular conditions, or chronic illnesses, covering various stages of treatment and care. - Personalization: The product must be able to tailor pathways based on individual patient data, such as demographics, comorbidities, and treatment history, ensuring that recommendations are patient-specific. - EHR Integration: Integration with electronic health record systems (e.g., Epic, Cerner, Allscripts) can allow clinicians to access and apply pathways directly within their workflow. - Real-Time Decision Support: The product should provide real-time clinical decision support, offering recommendations during patient consultations, minimizing delays in care delivery. - Reporting and Compliance: It should include features for tracking and documenting clinical adherence to pathways, generating reports on pathway utilization, and ensuring compliance with regulatory or payer requirements.

Products in the Point of Care Clinical Decision Support category directly help physicians during their pre-visit, in-visit, and post-visit workflows. They focus on improving patient care quality by making in-session suggestions and diagnoses to assist the doctor. Some of these tools may assist with intake or coding, but the core value add is helping doctors with their decision-making at the point of care.

Smart EHR UI products sit on top of the EHR and other data sources, surfacing and summarizing workflow-specific, relevant information to clinicians, but also write back to the EHR in useful ways. They use AI and machine learning, in addition to NLP and advanced search capabilities, to deliver a more useful interface for specific workflows than the more fully-featured EHR beneath it.