Modelling Behaviour in Spaces Optimising Built and Urban Spaces for People Movement

Transcription

1 Modelling Behaviour in Spaces Optimising Built and Urban Spaces for People Movement Andy Parker and Shrikant Sharma 18 th February 2013 Building a healthier south west Seminar, Plymouth Buro Happold

2 The NHS Challenge 20 billion in "efficiency savings" by 2015 [ Nicholson challenge : NHS CEO s Annual Report ] Growing demand Under-resourced departments Department of Health reports under-utilised space at 1.72m m 2 ~ 1bn annual expenditure [EC Harris, 2011] Over 80% of this is for acute facilities.

3 Addressing the Challenge: Current approaches to resolve this: Simple cuts: close departments, reduce staff, build less Result: Additional pressure and poorer patient experience [Appleby et al, February 2013] Demand reduction: Urban planning and healthcare linkage is weak Urban planning does not focus on reduction in load on NHS

4 Basic influences on Outcomes Registry Triage

5 Is there a better approach? The idea: modelling of Space (utilisation) Process (efficiency) People (behaviour and demand)

6 Evidence based modelling

7 The modelling process

8 Capturing behaviour Public Environment People Characteristics Design and Management Airports Culture Layout Stadia Age Wayfinding Theatres Gender Visibility Schools Disability Processes Hospitals Group size Management

9 Capturing behaviour SMART Counter Event Counter Blue Counter

10 Capturing process parameters Queen s Hospital, Romford

11 Data capturing: flow patterns Engaging with stakeholders to map all processes and issues Walk-in patients Ambulence patients A&E example

12 Visual mapping of Simulation of Ambulance Entrance/Triage and Major Beds Journey times Travel distances Waiting times Densities Conflicts Interactions Flowrates (bespoke)

13 Visual mapping of To highlight Bottlenecks Conflicts Dead spots Congestions Interactions (bespoke)

14 Visual mapping of

15 Predictive modelling of

16 Predictive modelling of

17 Queen s Hospital A&E Romford Waiting Beds Register Triage s Doctor s Triage process RAT process Register Doctor s Waiting Beds

18 Queen s Hospital A&E Romford Triage process RAT process Waiting Time Waiting space Bed space Doctor s time Stress/

19 Predictive modelling of Existing Patient # s 25% Increase in Patient # s 25% Increase in Patient # s + Triage Nurse + Triage Nurse + RAT Doctor Queens Hospital Romford: Study to improve A&E patient waiting times. Mapping current journey patterns to identify bottlenecks and optimise TRIAGE and RAT processes.

20 Removing the guesswork Simulation of Ambulance Entrance/Triage and Major Beds When tested with 25% increase in patient # s + RAT Doctor > continuous queues of 5 patients and paramedics waiting for RAT doctor (spiking to 10) existing situation has 0 3 paramedics and patients waiting Queens Hospital Romford: Study to improve A&E patient waiting times. Mapping current journey patterns to identify bottlenecks and optimise TRIAGE and RAT processes.

21 Predictive modelling of An Orthopaedic Hospital in UK: Mapping of diverse patient, staff, visitors, beds and FM flows.to optimise corridor widths, bed-lift sizing, visitor lift sizing, stairs, and communal areas.

22 This image cannot currently be displayed. Predictive modelling of An Orthopaedic Hospital in UK: Mapping of visitor flow within the communal areas to assess footfalls, dwell times and queues

23 Novel tools for rapid real-time optioneering and assessments

24 Rapid iterations for conceptual design and planning SMART Spaces

25 Rapid iterations for conceptual design and planning SMART Move

26 Live dashboard to monitor Royal United Hospital Bath Spatial mapping of realtime staff movements

27 Modelling in the urban scale Public health benefits of urban design?

28 SMART Space Optimising the Interface between People and Places Andy Parker and Shrikant Sharma smart.burohappold.com