DEMAND AND CAPACITY MODELLING

Transcription

1 DEMAND AND CAPACITY MODELLING How we used demand and capacity modelling to develop a robust and credible recovery plan Piers Young Deputy Chief Operating Officer (Elective Care)

2 CONTENTS Brief history of our data quality issues The essential data requirements for demand and capacity analysis The theory behind demand and capacity modelling Our approach Demand and capacity training Trials and tribulations with demand and capacity modelling Our RTT Incomplete Trajectory The final RTT Improvement plan The role of outsourcing with RTT improvement programmes

3 BRIEF HISTORY Our PAS migration exposed a discrepancy between current RTT performance and historical performance In 2013 our total incompletes waiting list size was just over 120,000 patients Worked with Cymbio and MBI Healthcare to validate, review PTL scripts and reporting functions Through extensive validation our waiting list was reduced to 54,000 patients.

4 Lack of understanding of capacity gaps and detailed demand and capacity models Imbalance between demand and capacity This became one of the key work streams of our RTT improvement programme

5 Follow-Up Outpatient Waiting List Follow-Up Outpatient Requests ROTT Additions to Waiting List Minimum Capacity Req'd Attendances DNAs DNAs Discharged Cancellations Cancellations Discharged Total Waiting List Removals Last Year Plan Follow-Up Outpatient Waiting List Clearance 17,319 21,144 Demand From Clearance 1, Capacity Required 1,682 17,228 21,014 Expected Attendances 1,359 21,323 22,519 Expected DNAs ,228 18,194 Expected DNAs Discharged 122 4,095 4,325 Expected Cancellations 481 1,552 1,639 Expected Cancellations Discharged 88 6,101 6,443 Total Removals 1,570 1,118 1,181 19,898 21,014 30,000 25,000 20,000 15,000 10,000 5,000 0 Core Additional Steady State Clearance Current Required Admitted Waiting List Admitted Waiting List Decisions to Admit Of Which Urgent Of Which Routine ROTT Additions to Waiting List Minimum Capacity Req'd Admissions Cancellations Cancellations Discharged Total Waiting List Removals Last Year Plan Admitted Waiting List Clearance 3,313 3,499 Waiting List Size Maximum Sustainable Size 379 2,979 3,146 Reduction Required Demand From First OP Clearance 302 3,097 3,271 Clearance Capacity Required 744 3,202 3,378 Expected Admissions 622 2,676 2,823 Expected Cancellations Expected Cancellations Discharged Total Removals 720 3,100 3,271 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Core Additional Steady State Clearance Current Required External consultancy companies helped validate a number of our information (clinic templates, admitted capacity) Chose to use the NHS Improvement IST Flow Through demand and capacity model

6 THE ESSENTIALS IST models basic but give the right and relevant outputs Shows weekly variation in demand - allows you to understand what sustainable capacity looked like Highlights gaps and bottlenecks Includes sustainable waiting list sizes We could see the impact of high DNA and cancellation rates and rebookings Planning tab allows to build scenarios e.g. docs strikes and see effects on waiting lists

7 SUSTAINABLE WAITING LIST SIZES Outlines the theoretical volume of patients you can afford to have on your waiting list to deliver the required waiting time for that stage of treatment Takes into account patient choice, reasonable notice and urgency Weeks Elapsed Referrals Patients Waiting Patients Seen Patients still Waiting Cumulative Waiting List

8 SUSTAINABLE WAITING LIST SIZES

9 SUSTAINABLE WAITING LIST SIZES

10 OUR APPROACH Developed detailed demand and capacity models for each of the main specialties Held validation sessions for each specialty to review data and assumptions with clinical input Each specialty provided extra capacity and redesign plans quantifying effects on a weekly basis IST provided extra training to teams Business Partners and Performance Managers support CCG input in some specialty areas around redesign and demand management No assumptions included on future demand management or impact of validation

11 RTT INCOMPLETE TRAJECTORIES Not an exact science more of an illustrative guide Incomplete trajectories rely on very accurate clock stop data for non-admitted and admitted We used sustainable waiting list targets, and clearance times for the stage of treatments from the D&C models Pieced together each specialty model to create Trust aggregate incomplete trajectory

12 RTT INCOMPLETE TRAJECTORIES Key information used to build specialty and Trust trajectory: Current incomplete waiting list and backlog position Sustainable waiting list sizes for the key stages of treatment based on D&C models RTT recovery plans from D&C models with timelines Target incomplete waiting list sizes and backlog positions

13 RTT INCOMPLETE TRAJECTORY

14 RTT INCOMPLETE TRAJECTORY

15 THE FINAL RESULT RTT TRAJECTORY

16 WHAT COULD POSSIBLY GO WRONG? Not enough capacity at specialty level Time pressures - Havering CCG Legal Directions requesting a system-wide plan by September 2016 Data quality issues Misunderstandings in terminology across teams Needed to ensure diagnostic D&C was completed too

17 WHAT ELSE WAS REQUIRED Infrastructure, programme and governance Outline of our approach Data quality and reporting RTT Governance and Clinical Harm Resumption of reporting Capacity and capability Programme Management Organisational Development/ Clinical engagement Training and development Resourcing and finance Endorsement from Chairs

18 BEST LAID PLANS. At the end of February: 36,000 patients on our waiting list, (4,900 patients waiting over 18 weeks) A performance of 86% (8% ahead of plan) We monitor progress against plan weekly We have had to review a number specialty plans Development of speciality performance scorecards

19 WHAT NEXT 1st OP waiting list size Lastest 1st OP Varince to date from plan Oct-16 Nov-16 Dec-16 WL Value % 2 Surgery Surgical 1st OP WL 1,969 1,748 1,546 1, % 3 Urology Urology 1st OP WL 1,772 1,803 1,593 1,593 (638) -67% 5 Orthopaedics Orthopaedics 1st OP WL 2,168 1,986 1,780 1, % 6 ENT Ear Nose and Th1st OP WL 2,092 1,369 1,467 1, % 7 Ophthalmology Ophthalmology 1st OP WL 3,111 2,738 2,495 2,495 (657) -36% 8 Max Facs Maxillo-Facial 1st OP WL 1,163 1,090 1,060 1, % 9 Neurosurgery Neurosurgery 1st OP WL (87) -15% 10 Pain Mgt Pain Manageme1st OP WL 1,105 1, (349) -59% 11 Gastroenterology Gastroenterolog 1st OP WL 1,852 1,617 1,420 1, % 12 Endocrine Endocrinology 1st OP WL (251) -54% 13 Cardiology Cardiology 1st OP WL 2,541 2,643 2,603 2,603 (336) -15% 14 Dermatology Dermatology 1st OP WL 1, % 15 Respiratory Respiratory Med1st OP WL % 16 Neurology Neurology 1st OP WL 2,527 2,277 2,175 2,175 (807) -59% 17 Rheumatology Rheumatology 1st OP WL 1,240 1,137 1,202 1,202 (671) -127% 18 Gynaecology Gynaecology 1st OP WL 1,181 1,189 1,099 1, % 19 sub total Sub total ,440 (423) -2% Developed monitoring tools Embedding understanding Continued with training on rules, outcome forms and D&C models Changes to our management teams 17/18 challenges do even more with even less System response required to clock starts and referral behaviour

20 TO OUTSOURCE OR NOT TO OUTSOURCE? Can be a valuable part of any recovery plan The stigma and reputation of outsourcing Requires a change of mindset by staff and patients Clinical criteria can restrict access significantly Patient choice and trust Competition for IS capacity 10-15% success rate Team required to do outsourcing including pre-assessment criteria Buy-in from consultants from the outset Has to be reactive to demand Impact or perceived impact on Trust

21 MISSION IMPOSSIBLE? Embed capacity (19 consultant posts) Embed knowledge and skills Cannot do more of the same physically or financially Must design pathways around local population Must have agreed system pathways Reduce variation and duplication Use tools and support that is available

22 ANY QUESTIONS? Piers Young Deputy Chief Operating Officer (Elective) Barking, Havering and Redbridge University Hospitals NHS Trust T: ext 3534 M: E: W: