The AI Care Standard
Evaluation Framework™
The AI Care Standard
Evaluation Framework™
Assess AI Solutions Against the AI Care Standard™
The Evaluation Framework is a structured tool for assessing whether AI that communicates with patients meets the expectations of the AI Care Standard™.
The section on the right outlines the core evaluation areas. Expand each one to see what the Standard requires and why it matters for patient safety.
Use the Framework to evaluate existing solutions, compare vendors, or identify risks early in development.
Evaluate an AI Tool
#1 Comprehensive Model Training
Consider utilizing data from multiple health systems across multiple regions. This may require licensing PHI-redacted data or using public data sets like MIMIC-IV. Use diverse data across age, ethnicity, disease state, and treatment setting (e.g. ER data is very different from Primary Care).
#2 Clinically verified Model Prediction
#3 Clinically Reviewed Model Validation
#4 Protecting Patients
#5 Personalization
#6 Patient Agency
#1 Training Data & Model Foundations
Patient-facing AI systems operate in environments where information can directly influence understanding, behavior, and health decisions. Errors, omissions, or overconfident responses can create real clinical, emotional, and reputational harm. This section evaluates whether the AI system is designed and governed to communicate with patients safely and accurately, using clinically grounded information and appropriate safeguards. It assesses the foundational requirements for responsible patient-facing use before examining more advanced capabilities.
#2 Model Predictions & Input Integrity
Patient-facing AI systems must be able to generate responses that are safe and appropriate even when the information they receive is incomplete, inconsistent, or unclear. This section evaluates how the AI system handles the quality of it's inputs, including whether safeguards are in place to prevent unsafe or misleading outputs when input data are fragmented, contradictory, or poorly standardized.
#3 Model Validation & Ongoing Oversight
Even well-designed AI systems can produce errors, degrade over time, or behave unpredictably as clinical data, populations, and workflows evolve. Without ongoing validation, unsafe outputs may go undetected until harm occurs. This section evaluates whether the AI system is validated before deployment, monitored during real-world use, and periodically reassessed to ensure patient-facing communication remains accurate, safe, and aligned with current clinical standards.
#4 Protecting Patients in Vulnerable Situations
Patient-facing AI systems may encounter situations involving serious diagnoses, emotional distress, safety concerns, or medical urgency. In these moments, inappropriate automation can cause harm if systems fail to escalate, defer, or adjust communication appropriately. This section evaluates whether the AI system can recognize sensitive or high-risk situations, and whether it responds with safeguards such as escalation to human care teams, real-world resources or delayed disclosure when needed.
#5 Personalized to the Individual Patient
Patient-facing AI should adapt to the individual patient’s health context, language needs, and communication preferences. Generic or one-size-fits-all responses can undermine trust, increase confusion, and push patients toward unsafe alternatives. This section evaluates whether the AI system personalizes communication using appropriate patient-specific information and supports accessibility across language, literacy, and ability needs.
#6 Empowering Patient Agency
Patient-facing AI should help patients understand their health information - not replace their own decision-making. Transparency about sources, evidence, and limitations is essential for informed decision-making and patient trust. This section evaluates whether the AI system provides clear sourcing, evidence traceability, and mechanisms to verify patient understanding. This supports patients as active participants in their care.
#7 Disclosure
Patients should never be uncertain about whether information was generated by AI. Clear disclosure helps set appropriate expectations, supports informed decision-making, and reinforces trust. This section evaluates whether AI-generated content is clearly identified, appropriately framed, and positioned as supportive — not authoritative — relative to clinician judgment. Disclosure should be consistent, understandable to patients, and appropriate to the clinical context.
#8 Improving Care Team Efficiency
Patient-facing AI systems do not operate in isolation. Their outputs can directly affect clinician workload, inbox volume, alert burden, and downstream workflows. Systems that generate excessive alerts, low-value escalations, or poorly timed interruptions can undermine clinical judgment and contribute to burnout. This section evaluates whether the AI system meaningfully supports care team efficiency, or whether it introduces unnecessary noise, duplicate work or operational burden that outweighs its benefits.
The AI Care Standard
Evaluation Framework™
Assess AI Solutions Against the AI Care Standard™
The Evaluation Framework is a structured tool for assessing whether AI that communicates with patients meets the expectations of the AI Care Standard™.
The section on the right outlines the core evaluation areas. Expand each one to see what the Standard requires and why it matters for patient safety.
Use the Framework to evaluate existing solutions, compare vendors, or identify risks early in development.
Evaluate an AI Tool
#1 Training Data & Model Foundations
Patient-facing AI systems operate in environments where information can directly influence understanding, behavior, and health decisions. Errors, omissions, or overconfident responses can create real clinical, emotional, and reputational harm. This section evaluates whether the AI system is designed and governed to communicate with patients safely and accurately, using clinically grounded information and appropriate safeguards. It assesses the foundational requirements for responsible patient-facing use before examining more advanced capabilities.
#2 Model Predictions & Input Integrity
Patient-facing AI systems must be able to generate responses that are safe and appropriate even when the information they receive is incomplete, inconsistent, or unclear. This section evaluates how the AI system handles the quality of it's inputs, including whether safeguards are in place to prevent unsafe or misleading outputs when input data are fragmented, contradictory, or poorly standardized.
#3 Model Validation & Ongoing Oversight
Even well-designed AI systems can produce errors, degrade over time, or behave unpredictably as clinical data, populations, and workflows evolve. Without ongoing validation, unsafe outputs may go undetected until harm occurs. This section evaluates whether the AI system is validated before deployment, monitored during real-world use, and periodically reassessed to ensure patient-facing communication remains accurate, safe, and aligned with current clinical standards.
#4 Protecting Patients in Vulnerable Situations
Patient-facing AI systems may encounter situations involving serious diagnoses, emotional distress, safety concerns, or medical urgency. In these moments, inappropriate automation can cause harm if systems fail to escalate, defer, or adjust communication appropriately. This section evaluates whether the AI system can recognize sensitive or high-risk situations, and whether it responds with safeguards such as escalation to human care teams, real-world resources or delayed disclosure when needed.
#5 Personalized to the Individual Patient
Patient-facing AI should adapt to the individual patient’s health context, language needs, and communication preferences. Generic or one-size-fits-all responses can undermine trust, increase confusion, and push patients toward unsafe alternatives. This section evaluates whether the AI system personalizes communication using appropriate patient-specific information and supports accessibility across language, literacy, and ability needs.
#6 Empowering Patient Agency
Patient-facing AI should help patients understand their health information - not replace their own decision-making. Transparency about sources, evidence, and limitations is essential for informed decision-making and patient trust. This section evaluates whether the AI system provides clear sourcing, evidence traceability, and mechanisms to verify patient understanding. This supports patients as active participants in their care.
#7 Disclosure
Patients should never be uncertain about whether information was generated by AI. Clear disclosure helps set appropriate expectations, supports informed decision-making, and reinforces trust. This section evaluates whether AI-generated content is clearly identified, appropriately framed, and positioned as supportive — not authoritative — relative to clinician judgment. Disclosure should be consistent, understandable to patients, and appropriate to the clinical context.
#8 Improving Care Team Efficiency
Patient-facing AI systems do not operate in isolation. Their outputs can directly affect clinician workload, inbox volume, alert burden, and downstream workflows. Systems that generate excessive alerts, low-value escalations, or poorly timed interruptions can undermine clinical judgment and contribute to burnout. This section evaluates whether the AI system meaningfully supports care team efficiency, or whether it introduces unnecessary noise, duplicate work or operational burden that outweighs its benefits.
#1 Comprenhensive model trainning
#2 Clinically verified Model Prediction
#3 Clinically Reviewed Model Validation
#4 Protecting Patients
#5 Personalization
#6 Patient Agency
Assess AI Solutions Against the AI Care Standard™
The Evaluation Framework is a structured tool for assessing whether AI that communicates with patients meets the expectations of the AI Care Standard™.
The section on the right outlines the core evaluation areas. Expand each one to see what the Standard requires and why it matters for patient safety.
Use the Framework to evaluate existing solutions, compare vendors, or identify risks early in development.
Evaluate an AI Tool
#1 Training Data & Model Foundations
Patient-facing AI systems operate in environments where information can directly influence understanding, behavior, and health decisions. Errors, omissions, or overconfident responses can create real clinical, emotional, and reputational harm. This section evaluates whether the AI system is designed and governed to communicate with patients safely and accurately, using clinically grounded information and appropriate safeguards. It assesses the foundational requirements for responsible patient-facing use before examining more advanced capabilities.
#2 Model Predictions & Input Integrity
Patient-facing AI systems must be able to generate responses that are safe and appropriate even when the information they receive is incomplete, inconsistent, or unclear. This section evaluates how the AI system handles the quality of it's inputs, including whether safeguards are in place to prevent unsafe or misleading outputs when input data are fragmented, contradictory, or poorly standardized.
#3 Model Validation & Ongoing Oversight
Even well-designed AI systems can produce errors, degrade over time, or behave unpredictably as clinical data, populations, and workflows evolve. Without ongoing validation, unsafe outputs may go undetected until harm occurs. This section evaluates whether the AI system is validated before deployment, monitored during real-world use, and periodically reassessed to ensure patient-facing communication remains accurate, safe, and aligned with current clinical standards.
#4 Protecting Patients in Vulnerable Situations
Patient-facing AI systems may encounter situations involving serious diagnoses, emotional distress, safety concerns, or medical urgency. In these moments, inappropriate automation can cause harm if systems fail to escalate, defer, or adjust communication appropriately. This section evaluates whether the AI system can recognize sensitive or high-risk situations, and whether it responds with safeguards such as escalation to human care teams, real-world resources or delayed disclosure when needed.
#5 Personalized to the Individual Patient
Patient-facing AI should adapt to the individual patient’s health context, language needs, and communication preferences. Generic or one-size-fits-all responses can undermine trust, increase confusion, and push patients toward unsafe alternatives. This section evaluates whether the AI system personalizes communication using appropriate patient-specific information and supports accessibility across language, literacy, and ability needs.
#6 Empowering Patient Agency
Patient-facing AI should help patients understand their health information - not replace their own decision-making. Transparency about sources, evidence, and limitations is essential for informed decision-making and patient trust. This section evaluates whether the AI system provides clear sourcing, evidence traceability, and mechanisms to verify patient understanding. This supports patients as active participants in their care.
#7 Disclosure
Patients should never be uncertain about whether information was generated by AI. Clear disclosure helps set appropriate expectations, supports informed decision-making, and reinforces trust. This section evaluates whether AI-generated content is clearly identified, appropriately framed, and positioned as supportive — not authoritative — relative to clinician judgment. Disclosure should be consistent, understandable to patients, and appropriate to the clinical context.
#8 Improving Care Team Efficiency
Patient-facing AI systems do not operate in isolation. Their outputs can directly affect clinician workload, inbox volume, alert burden, and downstream workflows. Systems that generate excessive alerts, low-value escalations, or poorly timed interruptions can undermine clinical judgment and contribute to burnout. This section evaluates whether the AI system meaningfully supports care team efficiency, or whether it introduces unnecessary noise, duplicate work or operational burden that outweighs its benefits.