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Apparent vs. Real Admission Rates. Dr Rod Jones (ACMA) Healthcare Analysis & Forecasting Camberley, UK [email protected] Mobile: 07890 640399. Aims. Discuss the technical issues Suggest an approach to clinically meaningful comparisons. From experience. The benchmarks are flawed

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Apparent vs real admission rates

Apparent vs. Real Admission Rates

Dr Rod Jones (ACMA)

Healthcare Analysis & Forecasting

Camberley, UK

[email protected]

Mobile: 07890 640399

Supporting your commitment to excellence


Aims

  • Discuss the technical issues

  • Suggest an approach to clinically meaningful comparisons

Supporting your commitment to excellence


From experience
From experience

  • The benchmarks are flawed

    • Supposed differences are often artefacts of the benchmark!

    • Capitation formula allocation to PCT and subsequent PbR payment to Trusts rely on different assumptions financial asymmetry

    • Serious problems with the Data Standards

  • What works?

    • Adjust for age &deprivation (IMD)

    • Analyse using both HRG and OPCS procedure code

      • HRG are composites & the language of finance

        • HRG ‘X’ does not mean the same thing at different sites

      • Some OPCS procedure codes do not map to a HRG!

    • Look at the trend over time

      • Step changes & trends

    • Use FCE (not Spell) especially for procedures

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From experience contd
From experience (contd)

  • Small differences are impossible to implement & measure

    • Concentrate on high volume

    • Anything within ± 2 SD of expected can be left alone

  • Add EL + EM for final analysis

    • EL/EM boundary is not the same in all hospitals

  • NHS site-based processes of counting & coding are different

    • Each site has a unique signature (especially small PCT run units!)

    • Analyse (EL & EM) zero day stay admissions separately

    • Greater effect on the ‘diagnosis-based’ HRG and on specific ‘procedure-based’ HRG

  • Net off financial effect of over- and under- before deciding to take action

Supporting your commitment to excellence


Index of multiple deprivation
Index of Multiple Deprivation

Intervention rates are only as good as the adjustment used to account for deprivation

IMD is very important and is highly non-linear

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The danger of averaging modifiable area unit property
The danger of averaging (Modifiable Area Unit Property)

The average IMD for this LSOA is 29.9 The HRG described by red line has an apparent rate of 3 but a real rate of 3.7 for the benchmark

Supporting your commitment to excellence


Imd key points
IMD Key Points

  • IMD gives excellent correlations for all acute events

    • IP, OP, DNA rate, etc

    • The multiple criteria appear to give a ‘balanced’ view

    • Capitation formula only uses a single measure of deprivation

  • IMD is highly non-linear

    • Aggregated values damp down the effect of IMD

    • Cannot use averaged data, i.e. LSOA, ward, LA

    • Must use Output Area data and sum up over area required

    • Capitation formula uses ward averages & assumes linear effects

    • Better Value indicators use LSOA average placed into 5 bands

  • IMD analysis is site specific

    • Related to the site-specific nature of counting & coding

    • Explains why Dr Foster & capitation formula analysis are flawed

Supporting your commitment to excellence


Counting coding
Counting & Coding

  • Is ‘national average’ a valid benchmark?

    • High level of ambiguity over data definitions

      • Mainly in zero day admissions

      • Particular HRG appear vulnerable

      • Show up as an apparent intervention rate problem

    • Moderate ambiguity in the coding process

      • Each hospital site is shifted ± relative to the average

      • Diagnosis more so than procedure

      • Higher for emergency admission

    • Plus real differences in the underlying interventions

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Opcs procedure excess as sd
OPCS Procedure – excess as SD

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Hospital based analysis
Hospital-based analysis

  • Output areas are the fundamental census units

    • Around 300 head of population

    • Homogeneous socio-economic groups

    • Full census data

  • Each OA can be mapped to

    • LSOA, Ward, Local Authority, PCT

    • Hospital catchment area

    • PCT is the sum of multiple hospital catchments

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In conclusion
In Conclusion

  • To make real & lasting change you need to understand the real issues

  • Need to do specific local analysis

  • Suggested approach

    • Use both OPCS & HRG codes to do a first sweep

      • Use LA level data, EM+EL combined, Zero day stay included

      • Will detect high profile coding/activity differences

    • Select codes for detailed analysis

      • Zero day stay separate, OA data

      • Analyse by hospital site and with IMD adjustment

    • Agree an approach to deal with counting/coding issues

      • Suggest net off over- and under- to get overall difference

      • Negotiate if net difference is +ve

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