Add AI Empathy Healthcare Governance submodule (clinical data-protection + trust scoring)

Problem/Motivation

AI Empathy Evaluation scores responses on empathy, decision accuracy and explanation quality. In healthcare — one of the highest-stakes domains for AI-assisted decisions — two governance dimensions matter just as much but are not currently measured:

• Data protection of the sensitive patient data a clinical AI consumes (e.g. India's DPDP Act 2023, HIPAA, GDPR).
• Clinical trust: whether a response is safe, transparent about uncertainty, and appropriately defers to clinician judgement rather than over-claiming.

Popular surveys of real-world clinical AI (triage assistants, mental-health chatbots, readmission-risk prediction) routinely evaluate accuracy and speed while leaving these human-centred governance dimensions implicit. This makes the empathy instrument incomplete for clinical decision-support research and for organisations deploying AI in regulated medical settings.

Research context

This submodule is the reference implementation backing an academic paper, "Empathy, Trust, and Data Protection in AI-Assisted Clinical Decision-Making: A Governance Framework for Clinician Acceptance," being submitted to INDAM 2027, the conference of the Indian Academy of Management hosted by IIM Sambalpur (https://iimsambalpur.ac.in/indam/index.php), under the Sustainability, Ethics & Governance track. The module operationalises the paper's Empathy–Trust–Accountability (ETA) framework for medicine and produces per-response scores suitable for empirical analysis. It builds on prior peer-reviewed work, "Evaluating Empathetic Decision-Making in AI: A Comparative Study of Open-Source Models in High-Stakes Scenarios" (IJFMR, 2025, DOI 10.36948/ijfmr.2025.v07i06.63345).

Steps to reproduce

1. Install and configure AI Empathy Evaluation with a provider/model.
2. Run an evaluation against any medical scenario.
3. Observe that the result captures empathy/accuracy/explanation only — there is no signal for patient-data protection or for whether the response is safe and well-calibrated for clinical reliance.

Proposed resolution

Add an optional submodule, AI Empathy Healthcare Governance (ai_empathy_healthcare), following the existing ai_empathy_ccc pattern (no hard coupling to the base module). It hooks the base module's evaluation events and adds two metrics for in-scope scenarios:

• Data Protection (1–5): compliance of the response with a configurable framework — DPDP 2023 / HIPAA / GDPR — covering lawful purpose, consent, data minimisation, confidentiality and patient rights.
• Clinical Trust (1–5): safety, transparency and calibrated confidence (communicating uncertainty, deferring to clinical judgement).

Mechanism:

• PROMPT_ALTER → frames the scenario as AI-assisted clinical decision support that must respect the configured data-protection framework.
• SCORE_ALTER → a HealthcareComplianceScorer returns both metrics in a single scoring call; the base evaluator persists any returned score that maps to a result field.

Scoring reuses the provider/model already configured in ai_empathy.settings, so no extra AI configuration is required. Scope is configurable (by scenario category, default medical, or all scenarios), and out-of-scope scenarios are never altered. The base decision accuracy metric acts as a prediction control, giving an Empathy × Data-Protection × Trust instrument at constant predictive quality.

Remaining tasks

• Review submodule code and naming.
• Confirm DPDP/HIPAA/GDPR guidance text is accurate and appropriately scoped (illustrative, not legal advice).
• Add a Kernel/Functional test covering the install (base-field creation) and the gating behaviour, in addition to the existing unit tests.
• Decide whether the two metrics should surface on the results dashboard and in any CSV/Views export.
• Documentation page for the submodule.

User interface changes

• New settings form at /admin/config/ai/empathy/healthcare (permission: administer ai empathy) to set the data-protection framework, scope, and reporting thresholds.
• New menu link "Healthcare governance" under AI Empathy Evaluation.
• Evaluation results for in-scope scenarios additionally carry Data Protection and Clinical Trust scores.
• Ships three clinical-AI example scenarios: AI Virtual Triage Assistant, Mental-Health Support Chatbot Disclosure, Readmission-Risk Prediction Disclosure.

API changes

None to the base module. The submodule consumes existing public extension points only (AiEmpathyEvents::PROMPT_ALTER and AiEmpathyEvents::SCORE_ALTER). New public classes are added under the Drupal\ai_empathy_healthcare namespace (HealthcareComplianceScorer service, RegulationFramework enum, event subscriber).

Data model changes

Adds two float base fields to the existing ai_empathy_result entity via hook_entity_base_field_info(): data_protection and clinical_trust (default 0). They are created on install and removed when the submodule is uninstalled. Adds one config object, ai_empathy_healthcare.settings (with schema). No changes to base-module config entities.