The Health Sector Coordinating Council’s (HSCC) Cybersecurity Working Group published the Playbook for AI Clinical Resilience (PAICR), a framework designed to help health delivery organizations manage safety, security and operational risks as artificial intelligence becomes increasingly embedded in clinical operations. The playbook addresses AI use across inpatient, acute care and outpatient workflows, where systems can influence triage, diagnostic assessments, patient monitoring, documentation, scheduling and care coordination.
The 26-page playbook equips cross-functional healthcare teams, spanning clinicians, governance leaders, IT and technical experts, risk managers and compliance officers, to detect, analyze and respond to threats in AI-integrated clinical operations. Accounting for workflow context, decision criticality and automation levels, it guides them from detection through patient safety review and regulatory response to system remediation, protecting patient safety, data integrity, compliance and operational resilience.
Determining how many playbooks are needed to address AI threats to clinical workflow, and of what types, depends on several structural, clinical and technical factors.
The first factor is clinical criticality and patient safety impact. Workflows with high acuity and low tolerance for error, such as emergency triage, ICU monitoring and surgical decision support, warrant distinct, highly detailed playbooks because AI failures in these settings can cause immediate patient harm. Lower-acuity or administrative use cases, such as scheduling optimization, can be grouped under broader operational AI playbooks.
The second factor is the type of AI function. Decision support, automation and engagement each create a different risk profile. Diagnostic and predictive decision support carries risks of misclassification, bias and model drift. Generative AI, used for documentation and chatbots, risks misinformation, hallucinations and leakage of protected health information (PHI). Operational automation, such as scheduling and throughput optimization, risks data integrity problems and workflow disruption.
Monitoring and alerting systems risk alert fatigue, missed alerts and false reassurance. Agentic and tool-using AI introduces a further risk of excessive agency, because these systems can place orders, route cases, send messages, update records or call other tools, meaning the AI takes action rather than merely making a recommendation. Where this applies, playbooks should call for least-privilege tool permissions, allow-listed actions, step-up human approval before any patient-impacting write-back to clinical systems, execution logs, rate limits and an emergency stop.
The third factor is the degree of clinical integration. The tighter the integration with electronic health records (EHRs), order entry, imaging systems or device feeds, the more complex the risk landscape becomes. Deeply embedded tools may require dedicated incident response and downtime playbooks, while loosely coupled tools may be adequately addressed in broader AI governance guidance.
The fourth factor is regulatory status and manufacturer control. Classifying AI by clinical criticality, function, integration and autonomy alone does not distinguish a regulated medical-device function from non-device AI. The same containment or recovery action may be a permissible local adjustment for an administrative model developed by a healthcare delivery organization (HDO), yet an unauthorized modification for a regulated device.
For each AI function, organizations should therefore identify whether it is an FDA-regulated device function, part of a regulated medical device, non-device clinical software or administrative AI. They should also record the manufacturer of record, the intended use, the authorized configuration and the software or model version, and establish which party can lawfully and technically modify the function.
The HSCC document identified that an essential step to help an organization understand what playbooks to develop is to systematically ‘map’ how AI is used across the care delivery lifecycle. “This involves identifying each AI-enabled system, its function (e.g., triage support, diagnostic augmentation, documentation generation, scheduling optimization), the data it ingests and produces, its integration points with EHRs and other clinical systems, and the human decision-makers who rely on its outputs. The mapping should trace data flows, model dependencies, automation levels, and override mechanisms to clarify where AI meaningfully influences clinical, operational, or administrative decisions.”
For AI-enabled medical devices, or any AI managed across a diverse installed base, the inventory should also capture: the manufacturer of record; product and model; device identifier (where applicable); software, firmware, and model version; supported configuration; relevant local customization; hosting and service dependencies; the remote-service pathway; end-of-support status; and the manufacturer’s support or escalation contact.
One method for applying an AI filter through a broader range of clinical workflows – such as home health, claims and payments, retail pharmacy and 14 others – is through use of the HSCC CWG Health Industry Cybersecurity Sector Mapping and Risk Toolkit (SMART). SMART provides healthcare organizations with templates and a methodology to visualize, identify and measure systemic risk posed by third-party technology, software and communications services essential to clinical, administrative and manufacturing workflows.
The PAICR document said that the number and type of AI playbooks should reflect five considerations, including differences in clinical acuity, differences in autonomy level, regulatory divergence, vendor dependency, and the similarity of failure modes. Playbook segmentation should improve clarity during a crisis rather than add complexity.
The goal is operational precision. When an AI-driven risk emerges in admission triage, inpatient monitoring, imaging interpretation or post-discharge chatbots, the organization should know exactly which playbook applies, who leads, how to contain the risk, and how to protect patient safety without hesitation.
Too many playbooks can create confusion during incidents. Organizations should therefore use the smallest number of playbooks, supported by modular annexes, that still enables responders to identify the correct actions and authorities during an incident. The appropriate number depends on organizational scale, clinical risk, architecture and existing incident-response structures.
AI risk response playbooks are predefined, role-based action guides that enable rapid, consistent and compliant handling of incidents in which AI systems in clinical workflows present risks to patient safety, care quality, privacy or compliance. They ensure timely detection of AI-related failures or threats, clear accountability for immediate actions, minimized patient harm and operational disruption, and regulatory alignment with HIPAA, FDA and Joint Commission requirements.
These requirements are legally and operationally distinct. FDA regulation applies only to regulated device functions and regulated entities, HIPAA governs privacy, security and breach obligations, and Joint Commission requirements are accreditation and patient-safety expectations. Playbooks should therefore use separate pathways for each, along with contractual commitments and voluntary frameworks, rather than treating them as one undifferentiated ‘compliance’ category.
When structuring AI risk playbooks, organizations should first integrate them with existing incident response frameworks, including clinical safety events, IT security and quality management.
Organizations should also use a trigger-based activation model, so that playbooks launch only when predefined, observable and instrumentable performance, safety or compliance thresholds are breached, rather than solely on events that can only be discovered through human review or after-the-fact reporting. Triggers such as ‘policy breach’ or ‘conflicts with clinical guidelines’ are difficult behavioral classifications even for human reviewers, and are nearly impossible to detect at machine speed without dedicated instrumentation. Wherever a trigger in this document depends on human reporting, it should be treated as a signal that specialized monitoring is needed before the playbook can run reliably.
The PAICR document mentioned that responsibilities should be assigned across multiple roles, including data science, clinical governance, IT security, compliance, leadership and communications. For regulated or safety-critical products, these should also include clinical engineering or biomedical engineering and the manufacturer of record, with product security, quality, regulatory and medical-safety representatives as applicable. Healthcare delivery organization (HDO) leaders retain authority over local clinical operations, while the manufacturer retains authority over manufacturer-controlled product changes and supported configurations.
Playbooks should be built for explainability and transparency, so that actions are defensible to clinicians, patients and regulators. They should also be subject to version control and ongoing testing through tabletop and functional exercises. Tabletop exercises help leadership, cybersecurity, privacy, legal, communications, and clinical and operations teams walk through decision-making and coordination.
Functional exercises test whether response procedures actually work in practice, covering detection, escalation, account controls, vendor notification, communications, documentation and continuity of operations. For exercises involving regulated or safety-critical products, organizations should include clinical engineering and the manufacturer or service provider. They should use tabletop or controlled test environments unless the manufacturer and HDO have jointly approved live testing, and should not alter production devices or validated configurations solely to run an exercise.
Also, organizations should measure response times, recurrence and corrective actions through metrics and post-incident review.
The PAICR document detailed that an AI risk response playbook should define its title, scope and purpose, naming the specific AI risk scenario it covers, such as model drift, bias or a data breach in training data. It should also set trigger conditions based on measurable, model- and use-case-specific metrics, such as sensitivity, specificity, false-negative rate or deviation from the validated baseline. These are established before deployment and approved by the clinical and product owner, and no universal threshold is implied. The playbook should document critical clinical workflows and AI integration points as well.
The immediate actions section separates what the healthcare delivery organization (HDO) can decide locally from what requires the manufacturer or system owner. Detection relies on monitoring alerts and clinician reports, ideally supported by runtime telemetry so problems surface before clinical impact. Before suspending or reverting a safety-critical or regulated AI function, the HDO must document a clinical risk assessment confirming that the fallback workflow is safe, unless immediate action is needed to prevent imminent harm.
Investigation involves root cause analysis, cross-functional engagement and notification of the manufacturer. Changes to manufacturer-controlled functionality must be performed or authorized by the manufacturer. Return to service requires two gates: manufacturer confirmation that the version and configuration are supported, and HDO approval that local workflows and patient-safety controls are validated. If full remediation is delayed, a documented risk acceptance with compensating controls, executive approval and an expiration date is required.
The remaining components cover accountability, communication and learning. Roles and responsibilities, along with a clear escalation path, assign ownership of each step, with patient-harm events typically led by clinical leadership and data breaches by security and privacy officers.
The communication plan uses a notification decision matrix tied to event type and each party’s legal obligations, covering internal updates, HIPAA breach notification, device reporting, manufacturer obligations, vendor notice, law enforcement and public communications. Each party handles its own obligations without delaying urgent patient-safety actions, and patient notification is a multidisciplinary decision rather than a privacy-only one. A post-incident review then captures lessons learned, process updates and retraining needs.
