DATA ANALYST, CENTRE OF EXCELLENCE

Job Description:A. Clinical registry development and management

• Develop and maintain registry data dictionaries, metadata, coding specifications, case definitions, validation rules, standard operating procedures, data-entry guidance and change-control records.
• Design data-capture workflows and support appropriate extraction, integration or collection processes from clinical, operational and research systems.
• Monitor registry participation, case ascertainment, completeness, timeliness, consistency, accuracy and follow-up status; identify root causes of gaps and support practical corrective actions with hospital teams.
• Prepare periodic registry reports that highlight outcomes, trends, case mix, variation, performance signals, data-quality issues and opportunities for clinical audit, research or service improvement.
• Support preparation of registry data for approved clinical audit, research, quality improvement, benchmarking, presentations and peer-reviewed publication.

B. Hospital-performance, operational-efficiency and VBHC analytics

• Analyse complex hospital and service-line data to identify factors affecting operational performance, efficiency, capacity, patient flow, outcomes and value.
• Develop analytical approaches to examine patient pathways and episode-of-care performance, including referral, admission, diagnostic, procedural, discharge and follow-up stages where data are available.
• Support hospital teams to identify unnecessary variation, bottlenecks, delays, avoid able utilisation, rework, complications, readmissions and other drivers of reduced efficiency or patient value.
• Develop relevant measures for value-based healthcare, balancing patient outcomes and experience with resource utilisation and cost information where available, valid and appropriately governed.
• Produce actionable analyses to support improvement in length of stay, theatre and procedure-room use, bed management, outpatient flow, emergency-department processes, diagnostic turnaround, discharge planning, service access and other locally agreed priorities.
• Apply appropriate stratification, risk adjustment, case-mix considerations and statistical methods when comparing hospitals, teams, patient populations or clinical pathways.
• Distinguish association from causation and communicate the limitations, assumptions, uncertainty and potential confounders of analyses clearly.
• Partner with quality-improvement and operational leaders to define baseline performance, monitor interventions and assess whether changes are associated with measurable improvement.

C. Advanced analytics, big data and AI-enabled methods

• Work competently with large, multi-source and potentially high-volume datasets, including structured and semi-structured health data, using efficient and reproducible approaches.