This chapter frames that “predictive gap” as an opportunity for evolution rather than a failure of existing validated systems. It argues that current models, optimized for rapid assessment of the acute presentation (symptoms, vital signs, consciousness, onset), insufficiently capture key prognostic dimensions such as frailty, comorbidity burden, and physiological reserve, factors that substantially influence deterioration and short-term outcomes. Short-term improvements are discussed through pragmatic strategies that increase “cognitive resolution” without overloading workflow: selective integration of high-yield variables, structured use of physiological and syndrome-specific scores at triage, and early reassessment modules activated by predefined risk criteria. For the medium-to-long term, the chapter provides a critical, evidence-based perspective on digital decision support and AI, emphasizing clinician-in-the-loop systems, transparency, fairness, prospective validation, and robust governance, while cautioning against fully automated or self-triage paradigms.