Welsh Assembly Government

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Welsh Assembly Government
Capacity Requirements to Deliver Maximum Waiting
Times Implementing Good Practice 17 November 2005
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Waiting list Model Introduction and objectives

• A robust model and a vehicle for testing outcomes
• Follows patient flows
• Driven by readily available data
• Reflects an appropriate level of detail
• Will project into future
• Reflects key policy initiatives
• Transparent with respect to key assumptions and
  drivers
• Allow users to engage in what-if dialogue
• Links to financial and capacity models

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Designed for life milestones

• 1a All patients seen within 12 months 2005-06
• 1b All cataract patients seen within 4 months
  Mar 2006
• 1c All patients seen within 12 months for 1st
  OPA Mar 2006
• 2a All angiogram patients seen within 4 months
  Mar 2006
• 2b All revascularisation seen within 6 months
  Mar 2006
• 3a Combined OP and IP/DC wait of max 16 months
  Mar 2007
• 3b Combined OP and IP/DC wait of max 26 weeks
  Mar 2009

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Graph of All Wales Waiting Lists

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Graph of All Wales Waiting Lists

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Graph of All Wales Waiting Lists

Size of list reduces as waiting times reduce
Similar principle regards reduction in numbers as
max waiting times reduce
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Waiting List Analysis

• Small number of specialties contain majority of
  patients
• 7 specialties account for majority of IP/DC
• Concentrate efforts on these specialties
• Additional specialties are material for OP
  waiters
• Again concentrate on these specialties

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Graph of IP/DC waiting List by Specialty

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Graph of OP waiting List by Specialty

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Materiality of Waiting Lists against Activity

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Methodology waiting list dynamics

• Planned vs Unplanned and Urgent vs Routine
  activity
• Patients flows
• Changing Models of Care
• Dynamics of the cohort model
• Referrals (i.e. underlying demand) derived from
  analysis of
• Activity seen
• Activity treated elsewhere
• Change in waiting lists over period

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Patients flows

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Cohort model overview (1)

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Cohort model overview (2)

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Model requirements and Data availability

• Activity analysed and modelled at patient record
  level
• Activity treated and Waiting lists by
• Trust and site
• Specialty
• HRG (Healthcare Resource Group at sub-specialty
  level)
• Includes activity undertaken at
• Welsh NHS Trusts
• English NHS Trusts (for Welsh residents)
• Private sector (under second offer scheme)

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The model key assumptions and drivers

• Demography and other change factors
• Waiting time targets and list management
• Urgent vs routine activity
• Outpatient conversion rates
• Removals other Than Treatment (RoTT) rates
• Performance

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Key driver 1 Demography and other factors

• Change factors implemented in baseline
  projections
• Current volume and profile of activity
• Future impact of demographic change
• Other change factors supported by model (for
  sensitivity and what-if testing)
• Morbidity and equity
• Medical technology and pharmacology
• Social, economic and political
• Evidence base
• Change factors by Region, LHB, Specialty and HRG

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Key driver 2 Maximum waiting time targets

• Waiting time targets
• Profiling between targets
• Headroom and impact of breaches
• PTL slippage (Primary Targeting of Lists)

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Key driver 2 Maximum waiting time targets

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Key driver 3 Urgent vs Routine Activity

• Urgents planned activity regarded as clinically
  urgent
• Routines planned activity to be undertaken in a
  timely fashion as soon as possible but not
  taking precedence over patients waiting longer
  for the same procedures
• Poor recording in datasets but can be identified
  from
• historic data
• discussion with clinicians

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Key driver 4 Conversion rates

• The likelihood of an outpatient referral
  requiring an inpatient or day case admission
• Is not the ratio of OP activity to IP/DC activity
  in any period
• Modelled as the ratio of OP activity seen to
  IP/DC referrals
• Alternative statistical models available
• Rates likely to increase as WTs fall and GPSIs
  impact on GP referral patterns and overall OP
  referral rates fall

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Key driver 5 RoTT rates

• Removals from list for Reasons other Than
  Treatment
• Variety of reasons for RoTT rates
• Impact can be significant
• Clear time dimension short WT lead to low RoTT
• Need to predict future not replicate past
  performance
• Model moves from current to long term lower
  targets

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Model Outputs

• Activity referred, RoTT and seen / treated by
  period
• Waiting list profile at end of period
• Activity shown by
• Trust
• Specialty
• OP, IP and DC
• HRG
• Selected outputs for Orthopaedics

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Output Treatments required by year

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Output Treatments required by year

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Output Treatments required by year

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Output Treatments required by year

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Activity Performance and Capacity Issues

• Activity performance by Trust, Specialty and HRG
• Capacity requirements
• Meeting the targets ring-fencing resources
• Financial vs Physical capacity

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Waiting list Model Summary

• A robust model and vehicle for testing
• Follows patient flows
• Driven by readily available data
• Will project into future
• Reflect key policy initiatives
• Transparent with respect to key assumptions and
  drivers
• Allow users to engage in what-if dialogue
• Links to financial and capacity models
• Reflects an appropriate level of detail

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Waiting lists Scoping the problem

• Waiting list analysis
• Review of shifts over 2-year period
• Identify list by strata
• Effect of previous reductions in waiting times
• Analysis available by Trust and specialty

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Input Maximum waiting times

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Input Urgent referrals (as of new referrals)

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Input Conversion rates

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Input RoTT rates

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Key driver 3 Urgent vs Routine Activity

• Urgents planned activity regarded as clinically
  urgent
• Routines planned activity to be undertaken in a
  timely fashion as soon as possible but not
  taking precedence over patients waiting longer
  for the same procedures
• Poor recording in datasets but can be identified
  from
• historic data
• discussion with clinicians

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Input Urgent referrals (as of new referrals)

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Input Conversion rates

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Key driver 5 RoTT rates

• Removals from list for Reasons other Than
  Treatment
• Variety of reasons for RoTT rates
• Impact can be significant
• Clear time dimension short WT lead to low RoTT
• Need to predict future not replicate past
  performance
• Model moves from current to long term lower
  targets

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Input RoTT rates

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Output Treatments required by year

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Conversion Rates

• Detailed OP data available for 2004-05 12
  months only
• Mapping of individual OP first attendance against
  corresponding IP episode
• Assume all conversions occurred after 18 months
  wait
• Regression analysis to extrapolate actual
  conversions between 1 month and 12 months out to
  18 months
• Generally high correlation of regression
• Extrapolated conversions by Trust for WL top 7
  specialties
• Conversion rates for other minor/immaterial
  specialties supplied from Checklist overarching
  conversion rates

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Examples of conversion regression

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Examples of conversion regression

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Examples of conversion regression

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Examples of conversion regression

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Links to Other Work

• Capacity model identifies gap between current
  activity and that required to achieve targets
• Capacity assumed as 2004-05 funded activity less
  second offer scheme additions
• Financial model based on costing the gap between
  funded and required activity
• Trust average specialty costs used as basis for
  additional activity undertaken within NHS Wales
• Capacity required over NHS availability costed at
  private sector rates for two years, then at avge
  specialty cost
• Costing completed for TO, indicating a financial
  outturn similar to earlier estimates

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Appendices

• Appendix 1 High level model structure
• Appendix 2 Demand profiles

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Appendix 1 - High level model structure (1)

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Appendix 1 - High level model structure (2)

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Appendix 2 Demand profiles

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Waiting list Model Agenda

• Objectives of the study
• Waiting lists scoping the problem
• Methodology waiting list dynamics
• Data requirements and availability
• The model key assumptions and drivers
• Performance and capacity requirements
• Summary

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Waiting List Analysis (1)

• Review of shifts in overall waiting lists over a
  two year period
• Identify waiting lists by strata of waiting
  times
• Note the effect of previous reductions in maximum
  waiting times
• Analysis capable by Trust by Specialty

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Waiting lists Scoping the problem

• Waiting list analysis
• Review of shifts over 2-year period
• Identify list by strata
• Effect of previous reductions in waiting times
• Analysis available by Trust and specialty

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Input Urgent referrals (as of new referrals)

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Input Conversion rates

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Input RoTT rates

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Key driver 3 Urgent vs Routine Activity

• Urgents planned activity regarded as clinically
  urgent
• Routines planned activity to be undertaken in a
  timely fashion as soon as possible but not
  taking precedence over patients waiting longer
  for the same procedures
• Poor recording in datasets but can be identified
  from
• historic data
• discussion with clinicians

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Input Urgent referrals (as of new referrals)

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Input Conversion rates

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Key driver 5 RoTT rates

• Removals from list for Reasons other Than
  Treatment
• Variety of reasons for RoTT rates
• Impact can be significant
• Clear time dimension short WT lead to low RoTT
• Need to predict future not replicate past
  performance
• Model moves from current to long term lower
  targets

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Input RoTT rates

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Output Treatments required by year

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Conversion Rates

• Detailed OP data available for 2004-05 12
  months only
• Mapping of individual OP first attendance against
  corresponding IP episode
• Assume all conversions occurred after 18 months
  wait
• Regression analysis to extrapolate actual
  conversions between 1 month and 12 months out to
  18 months
• Generally high correlation of regression
• Extrapolated conversions by Trust for WL top 7
  specialties
• Conversion rates for other minor/immaterial
  specialties supplied from Checklist overarching
  conversion rates

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Examples of conversion regression

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Examples of conversion regression

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Examples of conversion regression

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Waiting List Analysis (1)

• Review of shifts in overall waiting lists over a
  two year period
• Identify waiting lists by strata of waiting
  times
• Note the effect of previous reductions in maximum
  waiting times
• Analysis capable by Trust by Specialty