Statistical Thought Leadership for Business Innovation in Healthcare: A Mixed-Methods Study

Abstract

Statistical methods shape what counts as “evidence” in medicine, but their impact depends on how findings are disseminated, translated into workflow, and contested within organizational and societal contexts. This mixed-methods manuscript integrates a quantitative quasi-experimental evaluation with qualitative inquiry to examine how statistical application supports healthcare innovation while producing new forms of measurement work, incentives, and inequities. Using a synthetic multi-hospital panel (48 months; 12 hospitals; innovation rollout at month 25) we illustrate difference-in-differences estimates for an AI-enabled sepsis early-warning pathway and key outcomes (mortality, time-to-antibiotics, length of stay, readmissions). Complementary interviews and focus groups (n=44 participants in the illustrative design) are analyzed thematically to surface sociological mechanisms—trust, audit culture, invisible data work, and algorithmic accountability—that mediate implementation and equity. Findings demonstrate how rigorous statistical design, transparent reporting, and open dissemination can accelerate innovation, but only when paired with sociotechnical governance that protects patients, supports clinicians, and prevents metric-driven distortions.

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