AI Governance for Physical Therapy

Where AI agents enter physical therapy operations

Physical therapy organizations increasingly place AI agents near operational workflows rather than only behind static decision support. Common touchpoints include intake triage and referral routing, appointment and resource scheduling, draft clinical documentation and progress notes, patient education and follow-up messaging, care coordination across referring clinicians, and lookup of protocols or exercise libraries. Each touchpoint can require reading protected health information, writing to documentation platforms, calling scheduling APIs, or invoking therapy planning tools.

Runtime governance is required because these agents act continuously and can change records or initiate communications without a traditional application session owned by a named clinician. Static model review, offline bias testing, or acceptable-use policy alone do not constrain what an agent may retrieve or invoke when a live request arrives. Governance leaders should inventory workflows by data class, system of record, write versus read impact, and whether a human remains in the loop before any patient-facing or chart-altering action is finalized.

Runtime control layers for PT AI agents

Effective programs stack four control layers so that identity, permission scope, tool invocation, and evidence stay aligned across therapy workflows.

Define agent identity before permissions

Treat every AI agent as a first-class principal, not as an anonymous service account shared across clinics or use cases. Assign a stable agent identity that maps to owning team, environment, workflow scope, and risk tier. Separate identities for intake assistants, documentation copilots, and scheduling agents so blast radius stays limited when credentials rotate or a policy fails. Bind identity to authentication material that can be revoked without rebuilding the application, and ensure human supervisors can attribute actions to a specific agent version and configuration.

Avoid running therapy agents under broad EHR, practice management, or messaging credentials that also power batch jobs or human power users. Identity should carry enough context for policy engines to evaluate clinic, role of the requesting clinician when present, patient context boundaries, and allowed tool set. When agents orchestrate other agents, each hop needs its own identity and permission envelope rather than inheriting unrestricted rights from the caller. This model supports least privilege and makes incident investigation feasible when documentation or communications are disputed.

Implement least-privilege permissions and tool-call controls

Translate workflow inventory into explicit permission sets and tool-call policies enforced at runtime, not only at design time. Permissions should name the minimum data domains and operations an agent needs for a single workflow. Documentation agents may need read access to recent encounters and write access to draft notes only. Scheduling agents may need calendar availability and referral metadata without full chart access. Messaging agents should be constrained to approved templates, channels, and populations.

Tool-call governance decides whether an agent may invoke a given API, MCP tool, connector, or automation at the moment of the call. Policies can allow low-risk read tools, require human approval for writes to the legal medical record, and deny outbound tools that export bulk patient lists. Parameter checks matter as much as tool names: an allowed “update note” tool should still reject attempts to overwrite finalized notes, change attribution, or attach files outside policy. Encode denial and approval outcomes as enforceable runtime decisions, and fail closed when identity, context, or policy cannot be evaluated.

Example permission boundaries by workflow

Audit records needed for investigation and oversight

When an AI agent affects physical therapy documentation, patient communications, or care coordination, governance teams need durable evidence of what happened. Useful audit records capture agent identity and version, triggering user or system context when available, workflow or session identifier, policy decision and rule identifiers, tools requested and tools executed, redacted or tokenized input and output summaries appropriate to privacy policy, timestamps, and final outcome including denial, approval, or human override. Store records in a form that supports immutable retention and controlled access for security, compliance, and clinical operations stakeholders.

Audit design should support three practical questions. First, which agent changed or proposed a change to a note, schedule, or message. Second, whether that action was permitted by the policy in force at that time. Third, whether a human reviewed, approved, or corrected the outcome before it became patient-visible or chart-final. Incomplete logs that only store model prompts without tool results or policy decisions are insufficient for regulated healthcare investigation. Align log retention and access with organizational privacy and security programs, and ensure operational teams can reconstruct a sequence of agent steps without reconstructing the full model training pipeline.