Building an Explainability Contract between Clinical Algorithms and Practicing Pharmacists

Clinical algorithms increasingly support medication-risk identification, prescription prioritization, alert management, and other pharmacy activities. Yet explainability is commonly treated as a technical property of a model rather than an accountable relationship among developers, health-care organizations, practising pharmacists, and affected patients. An explanation may appear plausible while omitting uncertainty, local applicability, data limitations, workflow consequences, or mechanisms for professional challenge. This article proposes an explainability contract for pharmacist-facing clinical algorithms. The contract is a non-legal, sociotechnical governance construct that links each algorithmic role in the medication-decision lifecycle to defined parties, pharmacist rights, reciprocal duties, required disclosures, and procedures for challenge, correction, and escalation. Its principal components include intended-use boundaries, data provenance, patient-specific rationale, uncertainty and calibration, local-validity information, professional override, version traceability, and documented resolution of material concerns. The framework distinguishes model output from medication authorization, explanation plausibility from fidelity, technical performance from pharmaceutical usefulness, and implementation from demonstrated benefit. Contract performance would require staged technical, clinical, human-factors, organizational, medication-safety, and equity evaluation. Relevant outcomes include explanation fidelity, appropriate reliance, workflow accessibility, challenge responsiveness, correction completeness, subgroup performance, and recurrence of known failures. The proposed contract does not establish that explanation improves medication decisions, transfer liability to pharmacists, guarantee equitable outcomes, or constitute a validated clinical, legal, regulatory, or deployment standard. Its original contribution is to reposition explainability from optional information supplied by an algorithm into a reciprocal and contestable governance relationship requiring empirical validation.