In emergency and critical-care settings, 12-lead ECG interpretation demands rapid decisions; however, the standard waveform-based workflow imposes substantial search and mental integration burden because clinicians must repeatedly compare and integrate fragmented lead signals. This study reframes the bottleneck not as an individual skill issue but as a problem of representation and work distribution, and proposes a pre-review–verification interface framework grounded in distributed cognition that externalizes internal integration into an explicit structure. The proposed framework positions 3D ECG representations as cues for pre-review rather than final conclusions, and links observed cues to corresponding leads and time windows in the 12-lead ECG for evidence verification. To stabilize the joint use of two representations, the framework specifies transition rules, fixes semantic anchors that keep meaning criteria consistent across views (e.g., time windows, lead groups, and scale), and incorporates trust guardrails that route decisions back to verification to prevent over-reliance on automated cues. Instead of conducting user experiments to quantify performance gains, we present a case application and a comparative case analysis using the same input to contrast the conventional 12-lead-only workflow with the proposed 3D-first pre-review–12-lead verification workflow, examining how diagnostic experience is reorganized by design variables. The analysis indicates that the pre-review stage aligns attentional focus, the verification stage explicitly reclaims judgment through evidence checking, and transition rules and semantic anchors mitigate discontinuities and interpretive inconsistencies between representations. This work contributes design knowledge by formalizing an interface framework that enables safer and more consistent clinical decision-making experiences built on 3D ECG transformation and deep learning–based clinical decision support.