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Making Best Use of Beds: elective and emergency care modern.nhs.uk/beds

Making Best Use of Beds: elective and emergency care www.modern.nhs.uk/beds. Overview of content. Context and diagnosis: Background to the work A typical picture of flow through beds in the UK Solution ideas: What we found worked and the effect Key messages for implementation

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Making Best Use of Beds: elective and emergency care modern.nhs.uk/beds

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  1. Making Best Use of Beds: elective and emergency care www.modern.nhs.uk/beds

  2. Overview of content Context and diagnosis: • Background to the work • A typical picture of flow through beds in the UK Solution ideas: • What we found worked and the effect • Key messages for implementation • Resources to support work on beds

  3. Capacity & demand management Theatre Utilisation Booking Diagnostic services Day surgery Improvement programmes Discharge planning Pre-operative assessment Clinical Governance Emergency care IMPROVING FLOW THROUGH BEDS Leadership development Waiting list management Workforce redesign Financial flows Staff experience Access targets Patient experience Performance ratings Clinical quality Beds: a key constraint in the system

  4. Background to the work • Beds long seen as a core problem • Emergency Services Collaborative and Improvement Partnership for Hospitals encouraged focus on whole flow • Waiting for a bed the most common cause of breaching emergency waiting time target • Research of best practice across UK • Package of support to Trusts

  5. Our starting point • The availability of beds within a Trust is a constant problem • Lack of beds is usually the result of a temporary mismatch between the demand for beds and the time at which they are available (capacity) • The root cause of this problem is the variation in patient flows through the Trust

  6. Variation in Discharge - By time of day - By day of week - Seasonal variations Variation in Admission Patterns - particularly for Elective Care Bed availability: a problem of variation ADMISSION IN-PATIENT STAY DISCHARGE Variation in patient pathways and processes. E.g. in Length of Stay

  7. Variation in Discharge - By time of day - By day of week - Seasonal variations Variation in patient pathways and processes. Variation in Length of Stay Variation in Admission Patterns - particularly for Elective Care “We always bring our hips in on Tuesday !” ADMISSION IN-PATIENT STAY DISCHARGE

  8. Variation in admissions

  9. Variation without the weekend effect

  10. Variation in Discharge - By time of day - By day of week - Seasonal variations Variation in patient pathways and processes. Variation in Length of Stay Variation in Admission Patterns - particularly for Elective Care “Mr Smith’s TURP patients always stay five days but Mr Jones only keeps them in for three days ADMISSION IN-PATIENT STAY DISCHARGE

  11. Length of stay by day of admission 9 7.8 7.6 8 7.1 7.0 6.5 7 6.1 6.2 6 5 Average length of stay (days) 4 3 2 1 0 Monday Tuesday Wednesday Thursday Friday Saturday Sunday Variation in length of stay

  12. Variation in Discharge - By time of day - By day of week - Seasonal variations Variation in patient pathways and processes. Variation in Length of Stay Variation in Admission Patterns - particularly for Elective Care “We’re too busy in the morning to think about discharges. They all get done in the afternoon. ADMISSION IN-PATIENT STAY DISCHARGE

  13. Total Admissions & Discharges May 2002 - December 2002 120 Admission Discharges 100 80 60 40 20 0 21/08/2002 01/05/2002 29/05/2002 04/09/2002 18/09/2002 02/10/2002 11/12/2002 15/05/2002 12/06/2002 26/06/2002 10/07/2002 24/07/2002 07/08/2002 16/10/2002 30/10/2002 13/11/2002 27/11/2002 25/12/2002 Discharges vary more than admissions…

  14. Variation within each day

  15. What drives bed availability in the UK? Admission Queues Distress Driven Discharge Custom & Practice Discharge Patient Flow Weekends Holidays Managed Flow

  16. “It’s chaos now! 15 DTA’s in A&E & no free beds - we need to get the wards to discharge ASAP” Bed Occupancy “20 free beds this morning but lots of electives TCI” “Just about got them all in by the end of the day - well done!” “I think we have it all under control now - lets hope next week is better” “We need more beds” 800 780 760 740 720 Beds Occupied 700 680 660 640 620 600 Mo Mo Mo Mo Tu Tu Tu Tu We We We We Th Th Th Th Fr 0 Fr 6 Fr Fr Sa Sa Sa Sa Su Su Su Su 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 12 18 0 6 12 18 0 6 12 18 Day/hour Of Week occupied beds estimated beds available

  17. What can we do about it? Solution ideas

  18. Improvements that worked Short term: • Gaining operational control of beds • Moving discharges earlier in the day Longer term: • Using prediction and scheduling tools • Addressing elective flow variation • Segmenting flows by length of stay • Strategic, improvement led, capacity planning

  19. What would happen if we implemented a few of the recommendations? Restricting ourselves to modest changes…

  20. Reducing the in day beds mismatch Arrivals and discharges by hour: Monday only 30 25 20 number of arrivals or discharges per hour 15 10 5 0 Mo 0 Mo 6 Mo 12 Mo 18 24 hour of week Emer Adm A&E Emer Adm direct Elec Adm Disch

  21. The need for beds during the day This trust needs about 35 more beds at midday than it did at midnight

  22. Move 35 (out of 123) discharges from the afternoon to the morning How moving a few discharges can help discharges: before and after 30 25 20 15 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 before after

  23. Demand and capacity are more balanced Arrivals and discharges by hour: monday only 30 25 20 number of arrivals or discharges per hour 15 10 5 0 Mo 0 Mo 6 Mo 12 Mo 18 Tu 0 hour of week Emer Adm A&E Emer Adm direct Elec Adm Disch

  24. Less of a daily peak in demand for beds

  25. Length of stay by day of admission 9 7.8 7.6 8 7.1 7.0 6.5 7 6.1 6.2 6 Average length of stay (days) 5 4 3 2 1 0 Monday Tuesday Wednesday Thursday Friday Saturday Sunday Variation in length of stay

  26. Aiming for average LOS over the week Length of stay by day of admission 9 8 6.5 6.5 6.5 6.5 6.5 7 6.1 6.2 6 5 Average length of stay (days) 4 3 2 1 0 Monday Tuesday Wednesday Thursday Friday Saturday Sunday

  27. Target short stay patients for a big impact Greatest impact will be seen by concentrating on shorter LOS - usually simple discharges 250 200 150 Number of patients 100 50 0 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 Length of stay (days)

  28. 78 beds required each day to give 99.9% chance of admission Total Admissions 80.0 70.0 60.0 Admissions Average = 49.7 50.0 UPL = 78.1 40.0 30.0 20.0 Beds required each day to give 99.9% chance of admission 80.0 70.0 60.0 Admissions Average = 49.7 50.0 UPL = 67.9 40.0 30.0 20.0 Daily bed requirement reduced from 78 to 68

  29. How to approach implementation: Key messages

  30. Key messages for implementation 1. Look across the whole system: admission to discharge, electives and emergencies 2. Understand the unique pattern of variation at your hospital 3. Understand the main sources of variation including unnecessary queues / carve out 4. Plan for short and long term improvements and manageable changes

  31. Key messages for implementation 5. Map and measure your main flows 6. Concentrate on the 80% of simple discharges first 7. Aim for real time data analysis • Integrate work on beds into existing plans • Respond appropriately to common and special cause variation

  32. Resources to support work on beds All MA materials and UK Department of Health Checklists on the website: www.modern.nhs.uk/beds Also: toolkits produced by the PFC…

  33. Click here to continue

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  35. Five innovations to improve length of stay management and whole system patient flow

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