Trans-sectoral patient pathways in urgent and emergency care: a Study Protocol for a prospective mixed-methods study in Germany (TRANSPARENT Study)

Abstract Introduction Urgent and emergency care in Germany is delivered across multiple, loosely connected sectors. In the absence of coherent, time-resolved data on patient movements between Emergency Medical Services (EMS), out-of-hours primary care, emergency departments (EDs), and inpatient care, inefficiencies and coordination gaps remain difficult to quantify. A process-centric, trans-sectoral analysis is required to characterise real-world patient pathways and identify actionable levers for improvement. The study aims to reconstruct, model, and analyse patient pathways for urgent health complaints across all relevant sectors of the healthcare system in a German model region. Methods and analysis We will employ a mixed-methods observational study design. Routine data from EMS, out-of-hours primary care, EDs, and subsequent inpatient care will be pseudonymized at source, linked via a trusted third party, and analysed within a trusted research environment. Time-stamped event logs will support process mining for discovery, conformance, and performance analysis alongside descriptive statistics with stratification by context, such as setting, time of day, urgency, and patient cohorts. Anonymous cross-sectional surveys of patients and frontline professionals, complemented by quarterly snapshot surveys in out-of-hours primary care and interviews, will provide convergent evidence on the motives, barriers, and coordination of utilisation behavior. Enrolment for surveys is anticipated from the 4 th quarter of 2025; routine data capture covers 1 January–31 December 2026; analyses and dissemination run until 31 December 2027. Strengths and limitations The multiprofessional, trans-sectoral mixed-methods design with triangulation of perspectives, together with comprehensive routine data spanning the acute-care continuum, provides a robust basis to reconstruct time-resolved pathways and validate findings. Limitations include reliance on routinely collected electronic health records and administrative or billing data, which have variable completeness and coding quality, potential misclassification, and structurally induced missingness. Additionally, constraints arise from record linkage across sources, and the exploratory, observational nature of the analyses limits causal inference.