Heidi Health vs Google Med-PaLM 2

Similar category, different focus. These tools serve overlapping but distinct needs. Comparability 65/100 Comparability is an AI-graded 0–100 score of how directly these two tools compete — higher means a more apples-to-apples comparison.

Heidi Health

by Heidi Health

Google Med-PaLM 2

by Google

AI Verdict

Heidi Health is an AI medical scribe and care partner platform focused on automating clinical documentation and patient communication, offering direct EHR integrations and multilingual support. Google Med-PaLM 2, while also providing clinical decision support and summarization, is a foundational large language model with advanced reasoning and multimodal capabilities, making it more suitable for complex medical Q&A and research. While both offer clinical decision support, Heidi Health is an end-user application for clinicians, whereas Med-PaLM 2 is a developer tool for building healthcare AI applications.

Choose Heidi Health if…

Choose Heidi Health if your primary need is to automate clinical documentation and patient communication across a diverse patient population. It excels at real-time ambient encounter capture in over 110 languages, generating structured clinical notes, and managing follow-up communications, making it ideal for busy outpatient settings, especially in primary care and family medicine, seeking to reduce administrative burden and improve patient engagement. Its freemium model also allows for easy adoption and evaluation of its core scribing capabilities.

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Choose Google Med-PaLM 2 if…

Choose Google Med-PaLM 2 if your focus is on advanced clinical decision support, in-depth medical Q&A, and the analysis of complex, multimodal medical data. This tool is particularly well-suited for specialized medical research, education, and clinical care scenarios where expert-level medical reasoning and the interpretation of medical images (like X-rays and CT scans) are critical for diagnosis and personalized treatment recommendations. It is designed for healthcare organizations seeking to leverage a comprehensive medical knowledge base for complex problem-solving and evidence-based insights.

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When choosing between Heidi Health and Google Med-PaLM 2, physicians should consider their primary needs: real-time clinical workflow integration or advanced medical knowledge and reasoning. Heidi Health excels as an AI medical scribe and care partner, automating documentation, evidence search, and patient communication in over 110 languages. Its key practical difference lies in its direct integration into the clinical workflow, offering features like ambient encounter capture, structured note generation, and EHR push-to-chart capabilities. Physicians seeking to streamline their daily administrative tasks, improve documentation efficiency, and enhance patient communication will find Heidi Health particularly beneficial, especially given its freemium model and tiered pricing for various levels of support and features.

Google Med-PaLM 2, on the other hand, is a specialized large language model fine-tuned for healthcare, focusing on advanced clinical decision support, summarization, and medical Q&A. Its strength lies in its expert-level medical knowledge and reasoning, demonstrated by its high accuracy on medical licensing exams and its developing multimodal capabilities for interpreting medical images and other complex data. The key practical difference here is Med-PaLM 2’s depth of medical intelligence and analytical power, rather than direct real-time scribing. Physicians primarily needing a powerful tool for complex case analysis, comprehensive medical literature synthesis, and evidence-based recommendations, particularly in specialties like critical care or radiology where multimodal data interpretation is crucial, would benefit more from Med-PaLM 2. Access to Med-PaLM 2 is currently more restricted, available through Google’s MedLM API and select partnerships, requiring approval for medical use cases.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Quick Comparison

Feature	Heidi Health	Google Med-PaLM 2
Pricing	Freemium with paid tiers	Contact for pricing
Deployment	Cloud-based, with optional wearable hardware for on-device encryption and offline use.	Cloud-based (Google Cloud Vertex AI)
BAA Available	Unknown	Yes AI-estimated
HIPAA	Unknown	Yes AI-estimated

Head-to-Head

AI-generated assessment across six dimensions based on each tool's documented features and compliance posture. Grounded in public data — not a substitute for hands-on evaluation.

Usability

Heidi Health

Heidi Health offers a clear user interface, fast setup, and strong multilingual support, making it easy for clinicians to integrate into their daily workflow. While some user feedback mentions occasional issues with customer service and reliability, the overall sentiment points to a product that is generally easy to use and helps reduce documentation burden. In contrast, Google Med-PaLM 2 is primarily a foundational large language model accessed via API, suggesting a higher technical barrier for direct clinical use and requiring integration into other applications for practical usability.

Clinical Value

Google Med-PaLM 2

Google Med-PaLM 2 demonstrates superior clinical value through its expert-level performance on medical licensing exams (86.5% accuracy on USMLE-style questions) and its advanced multimodal capabilities, including interpreting medical images like X-rays and CT scans. Heidi Health provides clinical decision support with citations and generates structured clinical notes, but its core value lies more in automating documentation and communication rather than advanced diagnostic reasoning or multimodal analysis.

Pricing & Value

Heidi Health

Heidi Health offers a transparent freemium model with clearly defined paid tiers, allowing users to understand costs upfront and scale according to their needs. Google Med-PaLM 2's pricing is not publicly available and requires contacting Google for details, with access currently limited to select Google Cloud customers, making its value proposition less accessible and transparent for individual practitioners or smaller clinics.

Enterprise Readiness

Tie

Both tools demonstrate aspects of enterprise readiness. Heidi Health offers custom pricing for enterprise clients, HIPAA compliance, BAA, and EHR integrations with major systems like Epic and Athenahealth. Google Med-PaLM 2 is available through Google's MedLM API and select healthcare partnerships, is HIPAA compliant, has a BAA, and is designed for integration into complex healthcare workflows, indicating its suitability for enterprise deployment.

Innovation

Google Med-PaLM 2

Google Med-PaLM 2 stands out for its innovation in achieving expert-level performance on medical licensing exams and its developing multimodal capabilities to interpret various medical data types, including images. Heidi Health demonstrates innovation in its ambient encounter capture across 110+ languages and its new agentic AI system for automating follow-up tasks, but Med-PaLM 2's foundational AI advancements and multimodal approach represent a more significant leap in core AI capabilities for healthcare.

Support & Docs

Tie

Heidi Health offers priority support for its higher-paid tiers and provides documentation. However, some user reviews have reported negative experiences with customer service and response times. Google, as a large technology company, typically provides extensive documentation and support for its enterprise-level products and APIs, though specific details for Med-PaLM 2's direct user support are not readily available, making a definitive comparison challenging without more specific information for both.

Feature-by-Feature

Detail beyond the Quick Comparison summary. For pricing, deployment, BAA, FDA, and HIPAA see the Overview tab.

Feature	Heidi Health	Google Med-PaLM 2
Company	Heidi Health	Google
Tagline	Your AI Care Partner for Modern Clinical Practice	A large language model specifically built for healthcare, fine-tuned with medical data to assist with clinical decision support, summarization, and medical Q&A.
Categories	Clinical Decision Support & Reference, Documentation & Scribing	Clinical Decision Support & Reference, Documentation & Scribing, Medical Education & Training
Supported Specialties	Family Medicine, Hospital Medicine, Internal Medicine, Mental Health, Primary Care, Allied Health, Specialists (200+ specialties including psychiatry, emergency medicine, dentistry, gastroenterology, dermatology, reproductive endocrinology, rheumatology, obesity medicine)	Critical Care, Internal Medicine, Radiology
Key Features	Ambient Encounter Capture (110+ languages), Structured Clinical Notes Generation, Heidi Evidence (Clinical Decision Support with citations), Heidi Comms (AI-powered Patient Communications), EHR Push-to-Chart Integrations (athenahealth, Epic, eClinicalWorks), AI-assisted Coding, Customizable Note Structures, Task Management, Remote hardware device for offline capture, Voice dictation, Cross-platform (web, mobile)	Expert-level medical question answering (86.5% accuracy on USMLE-style questions), Advanced clinical reasoning capabilities, Comprehensive medical knowledge base, Multi-step problem solving, Medical literature synthesis and analysis, Multimodal data analysis (interprets X-rays, mammograms, CT scans, clinical data, patient histories, genetic information), Evidence-based recommendations, Continuous learning and updates, Medical summarization, Symptom analysis
Multilingual Support	110+ languages for ambient encounter capture	Multilingual capabilities (not specified how many languages)

Videos

Demos, reviews, and walkthroughs featuring Heidi Health and Google Med-PaLM 2.

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Frequently Asked Questions

How do these tools integrate with existing EHR systems and clinical workflows?

Heidi Health offers EHR push-to-chart integrations with systems like athenahealth, Epic, and eClinicalWorks, and is designed to be an EHR-agnostic solution. It focuses on supporting workflows across clinical context, EHRs, external evidence, and patient communication, aiming to reduce time spent in EHRs. Google Med-PaLM 2, available through Google's MedLM API and select partnerships, is being explored to enhance EHR solutions by summarizing patient records and providing a deeper understanding of patient history within systems like MEDITECH Expanse.

What is the accuracy and sensitivity of each tool in clinical documentation and decision support?

Heidi Health demonstrates strong note accuracy across specialties, producing notes that often require minimal editing for routine encounters. Its adaptive voice recognition technology learns from clinician styles to enhance documentation accuracy over time. Google Med-PaLM 2 achieved 86.5% accuracy on USMLE-style questions and its answers were preferred by physicians over human answers across eight of nine clinical utility axes in a pairwise study. It also demonstrated 85% accuracy in controlled clinical trials for interpreting symptoms and suggesting differential diagnoses.

What is the typical learning curve for physicians adopting these AI tools?

Heidi Health has a minimal learning curve, with most physicians adapting to the platform within days and becoming productive within their first session. While creating and instructing AI to use templates effectively can require some learning, Heidi's template builder allows uploading example notes to extrapolate instructions. Information on the specific learning curve for Google Med-PaLM 2 is not explicitly detailed, but its application is currently through Google Cloud's Vertex AI and limited partnerships, suggesting a more integrated, developer-assisted implementation.

What kind of customer support and resources are available for physicians using these platforms?

Heidi Health offers priority support for its Clinician tier and has received positive feedback for its responsive customer support, particularly through its chat function, and provides training sessions. However, some users have reported negative experiences with slow response times and unhelpful support. Google Med-PaLM 2 is available to a limited number of Google Cloud customers, and its access requires approval for medical use cases, implying a more direct, enterprise-level support model through Google Cloud.

Can these tools be customized for specific medical specialties or unique clinical workflows?

Heidi Health offers customizable note structures and templates, allowing clinicians to build formats that match their documentation workflow across over 200 specialties. It supports localized clinical terminology and has strong specialty customization, particularly with oncology templates. Google Med-PaLM 2 is a large language model fine-tuned with medical data for general healthcare applications, but its multimodal capabilities are developing to interpret various medical images and data, which could support diverse specialties.

What evidence or customer references are available to demonstrate the real-world value and effectiveness of these tools?

Heidi Health has numerous customer testimonials highlighting reduced charting time, improved documentation quality, and enhanced patient engagement. It supports over 2.5 million consults weekly across 190 countries, with clinicians reporting significant time savings. Google Med-PaLM 2 has been tested by a limited group of Google Cloud clients, including HCA and Mayo Clinic, to explore use cases and gather feedback. Research indicates physicians preferred Med-PaLM 2 answers over physician-generated answers in a pairwise study.