Using Simulation to Manage Unscheduled Care
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Dr. Paul Schmidt presented on using simulation to manage unscheduled care at Portsmouth Hospitals NHS Trust. The current system had functional divides between the emergency department and acute medicine unit that led to operational inefficiencies. Simulation was used to test an integrated model that aligned demand with focused services to simplify patient flow. The new model reduced transfers, wait times and ambulance turnaround times. It showed potential staffing and bed capacity savings that require further testing and organizational change.
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Using Simulation to Manage Unscheduled Care
- 1. Using Simulation to Manage Unscheduled Care Guest Speaker: Dr Paul Schmidt Consultant Acute Physician, Portsmouth Hospitals NHS Trust Powerful. Flexible. Fast. SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com
- 2. Presenters Paul Schmidt Consultant Acute Physician, Portsmouth Hospitals NHS Trust SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com
- 3. Housekeeping 1. Audio 2. Q & A Recording available on SIMUL8Healthcare.com SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com
- 4. Introduction to the case study Overview of context and challenges for unscheduled care operations Principles for a rational operational strategy Use of Modelling and Simulation Outcomes Lessons learnt and next steps
- 5. ACUTE NHSTRUST South Coast of England ▪ 600,000 catchment ▪ 105, 000 ED attendances ▪ 40,000 inpatients Emergency Clinical Service Centre (CSC) ▪ Emergency Department ▪ Acute Medicine Unit ▪ Single management structure since 2010 ▪ Clinical directors for ED and AMU
- 6. Increasing and changing nature of demand Trauma & injuries True new non-trauma emergencies Chronic medical co-morbidities with acute deterioration Frail elderly Ambulant and exercising choice Patients with chaotic lives ED access block Acute medicine EmergencyAmbulatory Care (AEC) (Dis)continuity of care?
- 7. Asplin et al (2002)
- 8. Functional divide designed for clinicians rather than patients Operational inefficiencies Not aligned to patients Inefficient use of skills Complex patient flow Failure to meet targets Cost overruns Low morale Staff turnover Failure to recruit Damage to reputation
- 9. Create focused Services around customers with similar requirements requiring similar processes The greater the pooling of demand and processors, the more effective a given system will be in minimising queues. (Erlang queue theory) Service time variation increases the length of the queue Reduced service time variation increases resource utilisation Very high utilisation levels increase risk that peaks in demand could cause exponential growth in queue length Avoid cut-outs unless completely justified. Only use for unique processes/services
- 10. MedicalAcute Med UnitED OutliersOutliers MedicalAcute Med UnitED MedicalAcute Med UnitED Outliers Sorting (streaming)Prioritising (triage)ED Queues Acute Med UnitED Medical Outliers GP ED vs GP admissions Choosing outliers
- 11. Inconsistent overburdening (mura) of staff at different times Very high staff utilisation or process constraint (muri) Non-value adding activities (muda) (waiting times, clinical rework, unnecessary transfers) Inventory (patients for beds) Batching and overproduction (consultant WRs) Desynchronised flow (to specialist beds) Unnecessary complexity
- 12. Add value Feel valued Use skills Work in a good team STAFF PATIENTS Continuity Good communication No unnecessary waits Good quality care Trust in staff OPERATION Fewer moves Simpler processes Good use of skills Less work in progress High staff utilisation Patient-centred Less variation CORPORATE Safety, avoid critical events Impact on elective services Financial constraints Meet targets Reputation ALIGNMENT
- 13. Flow/ED access block/4-hour breaches Patient safety: direct and overnight admissions Bed capacity allocation Staffing and staff utilisation Job content of consultants Staff buy-in and identification with new organisation Cultures different in ED andAMU
- 14. Understand demand SegmentationTime Test designs Simulation models Key performance indicators Coherent redesign Simplify & cut waste Stream to skills Patient access Fewest transfers Align Co-production Synchronise flow
- 16. 86.7% by 4 hrs 94.6% 96.7%
- 17. • 3-stage to 2-stage process • All patients through ED (including GP referrals and Ambulatory patients) • Patients separated by their expected total LOS • ≤ 48 hrs – Short-Stay • > 48 hrs – Medical Spec • Assessment (and resources) bought forward to ED • MAU becomes Short-Stay ward – for execution of care only
- 19. Alignment of Demand and Services in an Integrated Model (All Emergency CSC Activity) Injuries Own Transport 20,919 999 calls 7,944 Trauma & Orthopaedic CSC Surgical Spec Wards 4,957 cases Gastro 2409 Uro/Vasc 215 Cardioresp 107 Neuro 79 Various 182 Acute Medicine Short stay ward 13819 cases Cardioresp 5028 Neuro 2316 Adverse reactions 1744 Unknown/misc 1414 Gastrointestinal 1026 Uro/Vasc 454 Multi-systems 452 Skin/soft tissue 443 Metabolic 288 Mental health 268 Skin/soft tissue trauma 194 Various 192 Trauma /Injury Unit 28,906 cases Soft tissue injuries 21578 Multi-trauma Orthopaedic problems 7328 Falls that needs injuries treated or ruled out Non Trauma Assessment Unit 29,603 cases Cardioresp 9033 Gastrointestinal 6781 Neuro 6413 Adverse reactions 2326 Unknown/misc 2174 Urology/Vascular 1124 Metabolic 986 Multisystem 672 Mental health 94 Ambulatory Unit 29,916 cases Skin/ soft tissue 8099 Unknown/misc 7265 Cardioresp 5300 ENT 1674 Eye 1549 Neuro 1476 Uro/Vasc 883 Adverse reactions 858 Obs/gynae 842 Dental 725 Multi-system 671 Mental health 574 Non-trauma: Own transport Own Transport 31,021 Non-Trauma Ambulance Transport 999 calls 28,541 Urgent GP amb 1,827 Medical/DMOP Spec Wards 12,186 cases Cardioresp 3750 Neuro 2704 Gastro 1352 Metabolic 497 Multisystem 375 Uro/Vasc 354 Unknwn/Misc 347 Adv reactions 145 43241912 23,817 7,833 20,970 92561964 26632993 9523 Trauma Obs ward 1902 210 210 239 GP Calls 10,000 Diverted 2,000 5500 3500 Patient access Streaming into focused services Co-production by ED and acute physicians Consultant decision prior to admission Assessment Cells Single transfer
- 23. 00:00 01:12 02:24 03:36 04:48 06:00 0 24 48 72 96 120 144 168 Real Sim New Average Std Dev Lower CI Upper CI Real 02:38 0.01 02:36 02:40 Sim 02:38 0.04 02:30 02:45 New 02:29 0.02 02:23 02:34 Wed Thurs Fri Sat SunTuesMon
- 24. 0 100 200 300 400 500 600 Real Sim NEW Difference = 4000 fewer breaches 4 hr target
- 25. 00:00 00:28 00:57 01:26 01:55 02:24 02:52 0 24 48 72 96 120 144 168 Real Sim NEW Average Std Dev Max Min Real 01:16 00:20 02:07 00:36 Sim 00:57 00:35 02:32 00:18 NEW 00:32 00:19
- 27. 0 200 400 600 800 1000 1200 1400 ED/AC to Home Home from MAU/ Short Stay Non-Medical Admission* Specialty Minutes Existing Merged
- 29. • Mean ED LOS reduced by 12 min/patient • Time to nurse assessment reduced by 9 min/patient • Time to doctor assessment almost halved • Doctor workload reduced by 6.6% - equivalent of having 2.5 extra doctors • Queues reduced: ED and AMU • Transfers of care reduced by 16.8% • Time to end destination reduced for all destinations – more than halved for medical specialties
- 30. Interaction with Specialties ▪ Constraints imposed by specialty bed supply ▪ Impact of reduced transfers ▪ Synchronised flow Staffing ▪ Optimised rotas ▪ Staff utilisation ▪ Job content Bed capacity ▪ How many? ▪ Where?
- 33. Actual bed provision Average Bed Use Utilisation Existing Integrated Existing Integrated Existing Integrated 4 hour assessment cells 40 55 17.2 21.8 43% 39.70% Short stay beds 67 50 41.3 31.83 61.60% 63.60% Specialty bed provision (excl Rehab) 742 758 699.3 713.7 94.20% 94.20% Total inpatient beds (excl Rehab) 809 808 757.8 767.33*
- 34. Gives confidence improvement is possible Requires whole hospital change, not tinkering with parts Simplify and redirect flow Staffing models will have to changes Change staff rotas before making capital investments Right size the capacity at each stage.
- 35. South Central Strategy HealthAuthority for project funding James Newbold, data analyst for untiring efforts creating and testing the SIMUL8 models
- 36. Questions? Please forward any topics you would like to see covered to: claire.c@simul8.com Continue the discussion on SIMUL8 in Health – LinkedIn Group SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com Join us in 2015 for