Nottinghamshire Healthcare #

Given the other published materials, what should be the priority for Nottinghamshire Healthcare? There follows the key elements of the strategy followed by more detail about each.

• Population Health Intelligence
• Driving innovation and quality
• Whole system demand and capacity intelligence
• Intelligent decision support

Population health intelligence #

• Analytics should consider:
  • Mental and physical wellness
  • Factors that contribute to wellbeing such as loneliness and employment
  • Inequalities in health outcomes and service provision
• Consider impactability:
  • Start with a robust evidence base
  • Evaluate what treatments work for whom
  • Feed evaluation information back to clinicians to guide their clinical practice
  • Robust evaluation and high quality clinical practice should feed back into research through peer reviewed publication

Driving innovation and quality #

• Produce a strategy across QI, evaluation, research, audit, CDU, and applied information and facilitate joint working between them
• Workforce plan across clinicians, managers, and analysts to embed QI, statistical, and data science methods in everyday practice
• Provide a joined up service, provoke curiosity across the workforce and triage questions across the range of data and analytic services with a single point of access
• Analytics should be provided at a whole system level and should be easily shared, reproducible, and self service
  • Data and analysis should contribute to “intelligent transparency”
    • Accessible
    • Comprehensible
    • Usable
    • Assessable- that is, open for inspection

Whole system demand and capacity intelligence #

• Contribute to building capacity in the ICS to routinely carry out demand and capacity modelling
• Develop a system wide approach to demand and capacity, including models of patient flow and early warning systems

Intelligent decision support #

• Provide legible, real time, analytically robust PROMS, CROMS, and PREMS to all staff who need them
• Continue to use and build on success of data science methods such as forecasting and text mining
• Consider implementing CogStack to support text mining of clinical notes in the EHR

Objectives #

Appendix A: Related areas

• Research & Evaluation
• Data architecture
  • Metadata and documentation of data
  • Data should be self service where possible
  • Supporting analytical teams to access data from the data warehouse
• Analytical capacity plan
  • What can be stopped or optimised, and how we can work better
• Workforce plan
  • Data skills for the whole workforce, not just analysts
  • Consider analytic capacity at an ICS level