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Evaluating automatically generated text is generally hard due to theinherently subjective nature of many aspects of the output quality. Thisdifficulty is compounded in automatic consultation note generation by differingopinions between medical experts both about which patient statements should beincluded in generated notes and about their respective importance in arrivingat a diagnosis. Previous real-world evaluations of note-generation systems sawsubstantial disagreement between expert evaluators. In this paper we propose aprotocol that aims to increase objectivity by grounding evaluations inConsultation Checklists, which are created in a preliminary step and then usedas a common point of reference during quality assessment. We observed goodlevels of inter-annotator agreement in a first evaluation study using theprotocol; further, using Consultation Checklists produced in the study asreference for automatic metrics such as ROUGE or BERTScore improves theircorrelation with human judgements compared to using the original human note.

Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation