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Demir, Eren; Chaussalet, Thierry J.; Xie, Haifeng; Millard, Peter H.
Publisher: IEEE Computer Society
Languages: English
Types: Part of book or chapter of book
Subjects: UOW3
This paper introduces an modelling approach to determining the appropriate width of a time window within which an admission is classified as a readmission. The approach is based on an intuitive idea that patients, who are discharged from hospital, can be broadly considered as consisting of two groups - a group that is at high risk of readmission and a group that is at low risk. Using national data from the London area (UK), we demonstrate its usefulness in the case of chronic obstructive pulmonary diseases (COPD), one of the leading causes of early readmission. Although marked regional differences exist for the optimal width of the time window for COPD patients, our findings are largely inline with figures used by the government, hance provide some support for the use of 28 days as the time window for defining COPD readmissions. The novelty of this modelling approach lies in its ability to estimate an appropriate time window based on evidence objectively derived from operational data. Therefore, it can provide a means of monitoring performance for hospitals, and can potentially contribute to the better management of patient care.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] M. C. Ashton, H. D. Kuykendall, L. M. Johnson, P. N. Wray, and L. Wu, “The association between the quality of inpatient care and early readmission”, Annals of Internal Medicine, 1995, 122, pp. 415-421.
    • [2] M. Chambers, and A. Clarke, “Measuring readmission rates”, British Medical Journal, 1990, 301, pp. 1134- 1136.
    • [3] D. R. Cox, “A use of complex probabilities in the theory of stochastic processes”, Proceedings of the Cambridge Philosophical Society, 1955, 51, pp. 313- 319.
    • [4] D. Hand, H. Mannila and P. Smyth, Principles of Data Mining, MIT Press, 2001.
    • [5] Healthcare Commission, 2005 performance ratings, http://ratings2005.healthcarecommission. org.uk/home.asp, Accessed 6 February 2006.
    • [6] C. F. Ko, T. K. K. Yu, and T. P. S. Ko, “A survey of hospital readmission in elderly patients”, Hong Kong Medical Journal, 1996, 2, pp. 258-262.
    • [7] G. Lyratzopoulos, D. Havely, I. Gemmell, and A. Cook, “Factors influencing emergency medical readmission risk in a UK district general hospital: A prospective study”, BMC Emergency Medicine, 2005, 5, pp. 1-9.
    • [8] S. I. McClean, “The two-stage model of personnel behaviour”, Journal of Royal Statistics Society A, 1976, 139, pp. 205-217.
    • [9] R Development Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2005.
    • [10] G. Schwarz, “Estimating the dimension of a model,” Annal of Statistics, 1978, 6, pp. 461-464.
    • [11] D. W. Sibbritt, “Validation of a 28 day interval between discharge and readmission for emergency readmission rates”, Journal of Quality in Clinical Practice, 1995, 15, pp. 211- 220.
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