Presentation on theme: "Using Simulation To Understand Orthopaedic Flow Through Triage"— Presentation transcript:
1 Using Simulation To Understand Orthopaedic Flow Through Triage Ekwutosi Chigbo EzehSupervised by Dr Navid Izady16/10/13University of SouthamptonUniversity Hospital Southampton - Solent NHS – ISTC (CARE UK)Southampton City Clinical Commissioning Group
2 BackgroundWhen patients require orthopaedic assessment they are referred by their GP to an Integrated Medical Assessment and Treatment (IMAT) service for triage to determine the appropriate referral pathwayFor orthopaedics, these routes include physiotherapy, podiatry, rehabilitation programmes, pain management services, and community re-ablement services, as well as orthopaedic surgery (three tier system)Evidence that some patients are routed incorrectly, leading to wastage and poor patient experienceAims: to identify how patients are referred, then triaged then routed; quantify where patients are initially routed incorrectly and subsequently rerouted; use simulation to test alternative pathway designsPathway – clinical (progression of a patients health status)operational (movements of patients through a set of locations in a healthcare segmentCommon feature of a pathway- entrance, exit, path from entrance to exit & a random variety of healthcare elements in between.
3 Providers modelledIn the Southampton Musculoskeletal service (including IMATs, physiotherapy, rheumatology and pain management) served 16,000 patients and provided 38,000 outpatient appointmentsSouthampton City CCG (Tier 1)NHS Solent (Moorgreen Hospital) – community-based outpatient clinics, physio and reablement (Tier 2)Independent Sector Treatment Centre at the Royal South Hants hospital (Tiers 2 & 3)University Hospital Southampton (Tiers 2 & 3)Many others – highly complex patient flow through different sectors with a multiplicity of providers and over 400 pathways, which were modelled as a series of clinicsPathway – clinical (progression of a patients health status)operational (movements of patients through a set of locations in a healthcare segmentCommon feature of a pathway- entrance, exit, path from entrance to exit & a random variety of healthcare elements in between.
4 Patient flow between providers Patients can leave this system to private care or return from private care.
5 ChallengesLimited data available for modelling the whole system (lack of referral numbers; medical conditions recorded; referral destination; no entrance data for cohort)Significant differences in data across providersNo universal identifiers linking dataAppointment scheduling procedures required to model waiting times, but were not availableLack of referral numbersLack of medical conditions recordedLack of referral destinationNo entrance data for cohort
6 Moorgreen Hospital Simul8 Model I60 pathways at moorgreen hospital160 pathways in total!
11 Conclusions Many limitations of model due to data challenges Enhancement of current data available needed to effectively model thisWe found less inefficiency in the system than was perhaps initially perceived by our “client”: the majority of patients are correctly triaged at Tier 1, while 94% of patients referred to Tier 2 attend only the first clinic they are referred toDespite the data limitations, the modelling process highlighted many key issues for the providers to think about
13 NHS perspective Are pathways of care Timely? - treatment delivered within an acceptable waiting period (need to understand rate of deterioration whilst waiting)Effective? - no bounce around, minimal follow upsEfficient? - minimal number of follow ups
14 Commissioning Landscape Multiple Providers within small geographyConfusing entry criteriaOne large teaching hospitalMultiple Signposting “Tier 2” services for GP’sCollaboration difficult across providersCommissioned time points for providers to meet to review cases (“virtual clinics”)Patient experience “confusing”Clinical effectiveness unclear
15 Modelling /OR – comments Brought some clarity on efficiency (minimal follow ups) in Signposting service – pretty efficientClinical Effectiveness hard to ascertain within timescale (no follow ups per provider – needed to be accurately agreed )Model built that allowed for varying scenariosConcerning number of differing outcomes for patients (400+ pathways )No easy way to ID patient through whole system i.e. more confident modelling would require this