Modelling health systems: How health data and simulation can help inform the redesign of our NHS services Collaboration for Leadership in Applied Health Research and Care (Wessex)

Health Systems Analytics Collaboration for Leadership in Applied Health Research and Care (Wessex)

Health Systems Analytics Visualise and diagnose the problem Upfront testing of a change to a process or pathway Strategic reconfiguration of health services Vs Analytics encourages systemic solutions to systemic problems

Applied Analytics Being realistic about the impact of hospital reconfiguration intervention on ED overcrowding

Reorganising a medical pathway

A simplified acute medical pathway Wards grouped by Medicine for old people (MOP) Cardiology Gastro General Medicine Respiratory Might also happen if the system is under pressure

‘What if’ – stream by pLoS We can push cases with a pLoS < 72 hours to a short stay ward We can vary the number of beds in short stay and specialities We vary the accuracy/error rate We can change the case mix in wards Adjust the % of cases that move between speciality wards

What is the model measuring? Waiting times. Average ED trolley waiting time Average transfer delay from AMU Usage of space. Average no. patients on a trolley Average no. of medical bed shortages Occupancy of wards

Using routine data for analytics Our simulated patient population Emergency department The variation in emergency demand that we expect hourly, daily, monthly, annually etc along with mode of arrival to hospital (e.g. unscheduled versus GP referred). + AEC The variation in ambulatory emergency demand that we expect and likelihood of admission to hospital. PAS The profile of a patients journey’s through a hospital. For example, transfers between wards Our simulated hospital

Results (60 bedded short stay) It was estimated that the hospital needed to achieve a 65% rate to reduce ED overcrowding We don’t know in advance how good the hospital will be at streaming patients so we do what is called a sensitivity analysis.

Conclusions Usage of the short stay ward must be tightly controlled. Outcomes are very sensitive to the accuracy of short stay streaming Predictions of LoS needs to be >=70% accurate otherwise benefits will not be realised. Monitor and control Monitor prediction accuracy within the short stay ward Monitor the impact on outliers and between ward transfers Monitor the impact on elective cancellation rates

NIHR CLAHRC Wessex in partnership with University Hospital Southampton NHS Foundation Trust Portsmouth Hospitals NHS Trust Hampshire Hospitals NHS Foundation Trust The Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust Poole Hospital NHS Foundation Trust Isle of Wight NHS Trust Dorset County Hospital NHS Foundation Trust Salisbury NHS Foundation Trust Solent NHS Trust Southern Health NHS Foundation Trust NHS England South Wessex area team NHS Dorset CCG NHS West Hampshire CCG NHS Southampton City CCG NHS Portsmouth CCG NHS North East Hampshire and Farnham CCG NHS North Hampshire CCG NHS South Eastern Hampshire CCG NHS Fareham and Gosport CCG NHS Isle of Wight CCG Health Education Wessex

Extra Slides Collaboration for Leadership in Applied Health Research and Care (Wessex)

Visualise and diagnose Identify key areas to target interventions Achieve a shared vision of the problem Agree actions and a way forward

Upfront testing of change Ask ‘what if’ before you make any risky changes Predict the economic, quality and patient impacts Test out the wider system impact of disinvestment decisions Identify unintended knock-on consequences of change

Strategic reconfiguration Where should services be located to equitably meet patient need? How many locations should you commission? How much capacity should each site have?