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Easter 2007 in London

Easter 2007 in London. Defining better measures of emergency readmission. Eren Demir, Thierry Chaussalet, Haifeng Xie chausst@wmin.ac.uk www.healthcareinformatics.org.uk. Who we are. People A bunch of academic staff including Christos Vasilakis A research fellow: Haifeng Xie

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Easter 2007 in London

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  1. Easter 2007 in London

  2. Defining better measures of emergency readmission Eren Demir,Thierry Chaussalet, Haifeng Xiechausst@wmin.ac.uk www.healthcareinformatics.org.uk

  3. Who we are • People • A bunch of academic staff including Christos Vasilakis • A research fellow: Haifeng Xie • A visiting professor (clinician): Peter Millard (Nosokinetics News) • Four PhD research students including Eren Demir, Brijesh Patel and Anthony Codrington-Virtue • Research collaborators in and outside the UK and academia • What do we do? • Application of Decision Support, Simulation, and Data Mining applied to the process of care • Problem domain: Length of stay and cost modelling in long-term care, geriatric services; accident and emergency services • Techniques: Markov/semi-Markov models, data mining, queuing networks, simulation

  4. Outline of presentation • Definition(s) emergency readmission. • The importance of emergency readmission for the National Health Service (NHS). • A method for determining an appropriate time window to classify a readmission as critical readmission. • Application of the methodology to the UK national dataset. • Discussions and Future Work.

  5. Emergency Readmission (ER) • High level of emergency or unplanned (i.e. not scheduled) readmission is potentially associated with poor patient care “I take my car into a garage; if it needs to go back in a short time then that's obviously because they didn't do a good job“ (Clarke, 2003) • Frequent readmissions are highly costly • Readmission rate is an indicator in the performance rating framework for NHS hospitals in the UK • Currently the NHS defines readmission as an emergency or unplanned admission (department) within 28 days following discharge • Lack of consensus in the literature on the appropriate choice of time interval in defining readmission. Clarke, A. (2003). Readmission to hospital: a measure of quality of outcome. British Medical Journal 13, 10-11.

  6. Definition of ER from different sources

  7. Justifying a 28 days interval? • 28 day interval has been justified by constructing a graphical output for the total number of readmissions (Sibbritt, 1995) • Each graph shows an exponential or lognormal shaped distribution • Justification relied solely on visual inspection • Too crude and does not account of variations

  8. Community high risk group Hospital discharge Hospital admission low risk group Modelling framework • For each patient we observe the time between successive hospital admissions • We assume the population of readmitted patients comprises two groups • High risk group ( ) • Low risk group ( ) • We do not know which group the patient belongs to

  9. Modelling framework • Mixture distribution with probability density function (pdf) • where is the probability of a patient being in group , and and are the pdf of time to admission for group and respectively. • Probability of belonging to and can be determined from the posterior probability expressed via the Bayes’ theorem as

  10. General Framework: “time window” • Group membership of a patient with observed time to readmission : assign to if ; and to otherwise. • Optimal time window can be determined by solving • Or given by the time value where that is, where the two corresponding curves intersect.

  11. Optimal time window General Framework - continued • Given time to admission, this approach can be expressed as a mixture distribution in terms of the rates. • Where and are the pdf’s for high and low risk readmission, often assumed to be exponential.

  12. Community high risk of readmission low risk of readmission Hospital Modelling Framework: Alternative approach • Empirical evidence suggests that risk of readmission substantially changes over time • High soon after discharge • Low after a period of time in the community • Assuming that all rates ( ) are constant, time to admission follows a Coxian phase-type distribution

  13. Application to UK National Dataset • National dataset - Hospital Episode Statistics (HES) • Admissions, Discharges; Geographical, Clinical variables • Dataset ranges from 1997 – 2004 (80 million records) • HES captures all the consultant episodes of a patient. • First we focus our study on chronic obstructive pulmonary diseases (COPD), one of the leading causes of early readmission • 962,656 episodes from patients who had the primary diagnosis code corresponding to COPD (J40-J44) • After data cleansing process, a set of 696,911 completed spells were derived.

  14. Observations of calendar years • Using time window of 28 days as currently defined we observe: • Increase in number of admissions between 1998-2003 • Decreasing trend in percentage of readmissions within 28 day interval

  15. Strategic Health Authorities in London

  16. Optimal time window for COPD patients • Nationally, the optimal time window is computed to be about 26 days

  17. COPD Results for SHA’s in London • Fitted to COPD data from the 5 SHA’s in the London area. • Marked difference in the estimated optimal time window among the regions. • Estimated time window is inline with the current 28 day interval for three out five SHA’s • Additional information: Probability of belonging to high risk group can be used as alternative emergency readmission “indicator”

  18. SHA’s in London: COPD and other • Fitted to data from the 5 SHA’s in the London area. • Again marked difference in the estimated optimal time window among the regions • Estimated time window is no longer “inline” with current 28 days • Probability of belonging to high risk group is less variable

  19. Summary and Future work • We developed a simple modelling approach to determining an “optimal” readmission time window • The approach takes account of variations across diseases, regions, etc. • Suggest alternative indicators: “high risk” probability • The measures are “easy” to calculate • More work needed test these indicators • What do we do when mixture of two-phase Coxian do not fit? • More phases…but the meaning is “lost” • Alternative: Use Mixture of Erlang and 2-phase Coxian

  20. phase M phase 1 phase 2 Hospital Hospital phase M-1 phase 2 phase M phase 1 phase M-2 Hospital Hospital Model Extensions • What if mixture of two exponentials does not fit? • More phases…OK if there looking for more than two readmission risk groups • Alternative: Use Mixture of Erlang and 2-phase Coxian

  21. THANK YOU! www.healthcareinformatics.org.uk

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