AI plus human care
by Counsel Health
· Based in United States →
— AI-enabled, physician-supervised virtual care company on a mission to multiply the world's clinical capacity.
Subscription-based, with a per-visit option for non-members.
Overview
Counsel Health is an AI-enabled, physician-supervised virtual care company that aims to multiply the world’s clinical capacity. They deliver high-quality care through a simple chat interface, combining medical AI with board-certified physicians to provide continuous, secure, and always-available guidance. Counsel Health focuses on asynchronous care, allowing patients to message back and forth with medical AI and add a physician with one click for personalized treatment plans, prescriptions, or referrals. The company has developed its own EMR and integrates large language models for tasks like intake and documentation. Counsel Health partners with health plans, employers, and consumer health solutions to offer members direct access to clinicians for personalized, evidence-based guidance on everyday health concerns.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-enabled chat interface for instant medical advice
- One-click access to board-certified physicians
- Personalized treatment plans
- Prescription management
- Lab ordering and results review
- Medication management
- Asynchronous messaging-based care
- Proprietary clinician cockpit for streamlined physician workflows
- Integration with Oura data for personalized care (with consent)
- Counsel Studio for embedding AI-enabled care into existing digital experiences
Use Cases
- Personalized care
- Instant medical advice
- Health results review
- Lab ordering
- Medication management
- Chronic care management (hypertension, obesity, hyperlipidemia, prediabetes)
What Physicians Need to Know
Evidence Base
AI-powered clinical decision support (CDS) tools are built on peer-reviewed research, established clinical guidelines, and vast amounts of clinical data, including millions of patient cases and medical literature. They leverage machine learning algorithms and natural language processing to analyze patient data in real-time and provide evidence-based recommendations. The systems are designed to offer actionable insights and patient-specific information at the point of care. Some systems integrate with curated medical content using retrieval-augmented generation (RAG) approaches to deliver rapid, context-aware clinical insights.
Clinical Validation Studies
Rigorous, end-to-end clinical validation is crucial for AI-CDSS to minimize risks and maximize accuracy. This validation is an iterative process to ensure long-term accuracy, adapt to new data, and incorporate user feedback. Studies have shown that AI-based CDSS can improve diagnostic accuracy and optimize treatment selection, leading to better patient outcomes and reduced medical errors. One study indicated that AI in critical care environments was associated with a diagnostic accuracy of 92%, compared to 78% for clinicians. While AI tools can improve clinical decision-making and documentation quality, translating these gains into measurable short-term patient benefits in primary care can be challenging due to the rarity of serious outcomes. However, AI has been safely integrated into real clinical workflows without undermining patient trust or clinician autonomy.
Alert Fatigue Management
AI-powered CDS tools are designed to reduce alert fatigue by shifting from reactive alerts to real-time, context-aware decision-making. Traditional CDS tools often lead to alert fatigue due to excessive frequency and low relevance of alerts, causing clinicians to override them. AI optimization helps identify which alerts are most predictive of harm, which are routinely ignored, and which should be re-timed, reworded, tiered, or suppressed based on context. The goal is to deliver higher-value alerts to the right person, at the right time, in the right format, balancing sensitivity and specificity to improve patient outcomes and clinician experience. Designing systems that account for user characteristics and workflow demands, particularly by leveraging AI for adaptive alerting, can help overcome alert fatigue.
Override Rate Data
Tracking override rates is a key metric to determine if clinicians trust and find value in AI clinical decision support tools. Traditional CDS alerts are often overridden, with some reports indicating a 96% override rate. AI-driven CDS aims to achieve different results by being better integrated into workflows and providing more relevant, context-aware information.
Drug Interaction Checking
AI-powered tools revolutionize drug interaction checking by accessing vast, up-to-date databases covering drug-drug, drug-food, and drug-condition contraindications. These systems analyze medication combinations in real-time, factoring in patient-specific data like age, weight, kidney function, allergies, and medical history. They can identify potential adverse drug interactions, dosing issues, and flag allergy conflicts, suggesting safer alternatives or dose adjustments. AI models can process large datasets quickly, screening thousands of drugs within milliseconds, which is crucial in busy clinical settings. Studies have shown AI models to be highly accurate in predicting drug-drug interactions. This technology acts as a supportive layer of safety, complementing the expertise of healthcare teams.
Differential Diagnosis Support
AI assists clinicians in building differential diagnoses faster by surfacing likely conditions from notes and labs with oversight. These tools analyze patient symptoms, history, lab results, and imaging to generate a ranked list of possible diagnoses with reasoning and evidence. They can help reduce cognitive load, catch conditions a human might miss, and prioritize the most likely diagnoses at the point of care. Clinical-grade differential diagnosis AI systems show their chain-of-thought and cite peer-reviewed sources for transparency and verifiability. They can also identify rare diseases faster than traditional methods.
Guideline Update Frequency
AI-powered CDS tools require continuous updates to remain safe, effective, and trusted, especially in fast-evolving fields like genetics and infectious disease. Real-time or near-real-time literature surveillance and continuous content updates are a baseline expectation for AI-driven healthcare solutions. Some systems employ a clinician-in-the-loop update approach, leveraging both humans and AI to monitor thousands of journals. By continually updating real-world data and clinical guidelines, intelligent CDSS helps ensure care conforms to established standards and is backed by scientific evidence.
Clinical Workflow Integration
AI-powered CDS tools are designed to integrate intuitively into the clinical workflow, aiming to minimize administrative burdens and enable more focused patient care. The technology should adapt to the clinician, not the other way around, to ensure non-intrusive support. Effective integration means delivering timely, context-relevant information at the point of care. AI-driven systems can optimize clinician workflow by providing faster, tailored responses and reducing time spent on manual information retrieval. They can also support broad decision support across many care areas and are often deeply integrated with existing electronic health record (EHR) systems.
Decision Audit Trail
For clinical decision support software, audit trails must include details such as source data, processing steps, and inference activity. A tamper-evident audit logging mechanism that records system inputs, retrieved evidence, and inference steps is crucial for retrospective review, improving traceability and clinician trust. Maintaining audit trail documentation for data handling, model versions, and training events is also a HIPAA regulatory requirement. This ensures transparency, verifiability, and accountability of AI-generated recommendations.
Physician Tip
Embrace AI as a powerful cognitive aid, not a replacement for your clinical judgment. Focus on tools that provide transparent, evidence-based recommendations with clear citations, allowing you to verify the logic. Prioritize systems that seamlessly integrate into your existing workflow, reducing friction and alert fatigue. Actively provide feedback on AI tools to ensure continuous improvement and alignment with real-world clinical needs. Remember that human oversight is essential for reviewing AI suggestions and making final treatment decisions.
AI clinical decision support tools are increasingly designed for deep integration with Electronic Health Record (EHR) systems to access comprehensive patient data, including current medications, allergies, medical history, and lab results. This integration allows for real-time analysis and personalized recommendations at the point of care. Some solutions are specifically optimized for existing EHR platforms like Epic. The effectiveness of these tools often depends on access to longitudinal patient data, enabling richer clinical histories and more accurate recommendations. Integration also extends to incorporating AI-generated insights alongside conventional search results to enhance point-of-care decision-making.
Details
| Category | Clinical Decision Support & Reference, Telehealth & Virtual Care |
| Pricing |
Subscription-based, with a per-visit option for non-members.
|
| Deployment | Cloud-based (Kubernetes cluster in a private cloud environment); mobile app (iOS, Android); embeddable platform (Counsel Studio) |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Unknown AI-estimated Counsel Health's AI is designed to assist physicians and does not provide medical or clinical advice independently. Physicians handle diagnosis, prescriptions, referrals, and diagnostics. |
| Integrations | |
| EHR | Not specified |
| Specialties | Family Medicine, Internal Medicine, Pediatrics |
What the Web Says
The integration of AI with human care in healthcare is viewed as a promising development, with the potential to enhance various aspects of medical practice, from administrative tasks to diagnosis and treatment planning. While AI offers benefits like increased efficiency, accuracy, and accessibility, there are also significant concerns regarding its limitations, potential for bias, and the impact on the patient-provider relationship. The consensus among many experts is that AI should serve as a supportive tool for human clinicians rather than a replacement.
Overall: MixedStrengths
- Improved diagnostic accuracy and prediction of health risks.
- Automation of administrative tasks, reducing clinician burnout and freeing up time for patient care.
- Enhanced efficiency and cost-effectiveness in healthcare delivery.
- Increased accessibility to health information and potentially care, especially in underserved areas.
- Consistent and comprehensive information delivery, with some studies even suggesting higher empathy ratings for AI in text-based interactions.
- Ability to identify rare diagnoses and patterns that human doctors might miss.
Limitations
- Risk of misdiagnosis or delayed care due to overreliance on AI.
- Concerns about the erosion of trust in physician expertise and the patient-provider relationship.
- Potential for AI to exacerbate health disparities if underlying data is biased or not inclusive.
- Lack of transparency in AI's decision-making processes, making it difficult for clinicians to understand or interpret diagnoses and treatment plans.
- Limited contextual understanding, emotional, and psychological factors that human doctors can grasp.
- Challenges with implementation, workflow adaptation, and the need for continuous human oversight to catch errors and prevent bias.
Based on reviews from: Reddit, G2, PMC, Athenahealth, Facebook, Doctronic, Mayo Clinic, The Washington Post, AARP, Becker's Hospital Review, Fierce Healthcare, Mashable, Ground Truths
Last updated: 2026-09-23
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Press & Coverage
Business Wire
New Empathy Research Reveals AI Is Already Part of How People Grieve
Empathy's research highlights that people are already using AI in their grief process, particularly for administrative tasks, but prefer 'AI + Human Care' for the more challenging emotional aspects. The company emphasizes using AI to support human care teams, not replace them, and has introduced features like LifeVault Conversations for AI-guided difficult conversations.
2026-06
SonderMind
How AI Is Changing Therapy - SonderMind
SonderMind advocates for a 'hybrid approachu2014AI plus human care' in mental health, where AI assists clients between sessions and streamlines therapist's administrative tasks, allowing for more focused human interaction. The article emphasizes that AI should be a bridge to better mental healthcare, not a replacement for human connection and empathy.
2025-10
BCG
Consumers Are Ready for AI-Enabled Health Care. Health Systems Need to Be, Too.
This article reveals that nearly 60% of consumers are already using generative AI for health, with a preference for hybrid 'AI-plus-human care' models over clinician-only interactions. It highlights the urgency for health systems to integrate trusted AI into core journeys while addressing concerns about privacy, reliability, and personalization.
2026-04
StartUp Health Insights
Avo Raises $10M Series A for Clinical AI Platform | StartUp Health Insights: Week of Mar 31, 2026
This report mentions myStoria, a Kitchener, ON, Canada-based 'AI-plus-human care coordination platform for reproductive health,' which raised $1.6M in funding.
2026-03
Journal of Family and Society Research
Artificial Intelligence in Child and Adolescent Mental Health: Prevention, Diagnosis, and Treatment in Hybrid Humanu2012AI Care Models
This peer-reviewed article discusses the role of AI in child and adolescent mental health, emphasizing hybrid human-AI care models for prevention, diagnosis, and treatment. It notes that patients trust AI tools more when a clinician is involved, and new policies are making 'AI-plus-human care' safer and more sustainable.
2025-12
Physician AI Tools
Medical AI Tools for Physicians | Physician AI Tools
This directory highlights Counsel Health as an 'AI plus human care' company, an AI-enabled, physician-supervised virtual care platform combining medical AI with board-certified physicians for continuous, secure, and always-available guidance.
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Offcall
Medicine Is Headed Toward Semi-Autonomous Care. Are Doctors Ready?
This article features Counsel Health's CMO, Rishi Khakhkhar, who explains why the company is building 'AI plus human care' models rather than aiming to replace physicians entirely, even as AI demonstrates superior performance in narrow tasks.
unknown
Roshni Online
AI Companions in Mental Health 2026: Real Support, Honest Limits.
This blog post discusses how AI companions provide mental health support in 2026, outlining their limits and advocating for an 'AI plus human care' model with clear escalation protocols. It also touches on new regulations for AI mental health chatbots in 2026 across various regions.
2026-07
Videos
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Frequently Asked Questions
AI clinical decision support tools are designed to augment, not replace, human expertise. They can streamline tasks like reviewing charts, integrating lab results, and surfacing recent clinical evidence during patient visits, allowing you to focus more on direct patient care. Successful integration often involves systems that are user-friendly and tailored to specific health system needs, ensuring they complement your existing electronic health records (EHRs) and clinical practices.
Compliance with regulations like HIPAA is crucial, requiring lawful authority for data use and clear data governance. Ethical concerns include potential algorithmic bias, ensuring data privacy and security, and maintaining transparency in how AI recommendations are generated. It's essential to remember that the final decision for patient care always rests with the human physician, and patients should be informed when AI is involved in their diagnosis or treatment.
Traditional clinical decision support systems, such as computerized alerts, reminders, and order sets embedded in EHRs, have been in use for decades. While these are effective for well-defined, safety-critical decisions, AI-powered systems excel at synthesizing narrative information, building differential diagnoses, and drafting assessments by processing large datasets and identifying subtle patterns. The best approach often combines both deterministic rules-based systems and AI for comprehensive support.
The cost of AI clinical decision support tools varies significantly. Individual subscriptions for AI-powered references can range from approximately $38 to $119 per month, or $399 to $699 per year. For enterprise-level solutions, costs can range from $200 to $600 per clinician per month, or $2 to $15 per patient per month, depending on factors like practice size, features, and integration complexity. Hidden costs can include data normalization, clinical validation studies, and ongoing model tuning and updates.
AI systems are limited by the quality and completeness of the data they are trained on, meaning they may not account for atypical presentations, rare conditions, or the nuances of individual patient history and nonverbal cues. They lack real-world experience, empathy, and the ability to personalize care based on complex comorbidities or patient preferences. Physicians should be cautious and apply their clinical judgment, especially in complex or unusual cases, as over-reliance on AI could potentially erode clinical skills.
AI plus human care improves patient outcomes by augmenting human knowledge, reducing inefficiencies, and enabling more personalized treatments. AI can rapidly analyze vast amounts of data to identify disease markers, predict outcomes, and suggest evidence-based treatment options, leading to faster and more accurate diagnoses. This allows clinicians to make more informed decisions, tailor treatments to individual needs, and intervene proactively, ultimately enhancing the quality and safety of care.
Many physicians report low levels of training and understanding regarding the risks and professional responsibilities associated with AI. Recommendations include continuous education programs that evolve with technological advancements, emphasizing the development of diagnostic and decision-making skills. Training through workshops, online courses, and hands-on experience is suggested to help physicians integrate AI tools strategically and responsibly into their practice.
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| Last Verified: 2026-09-23 | First Added: 2026-09-23
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