Conference Paper

A Method for Determining an Emergency Readmission Time Window for Better Patient Management

Health & Social Care Modelling Group, Westminster Univ., London;
DOI: 10.1109/CBMS.2006.15 Conference: Computer-Based Medical Systems, 2006. CBMS 2006. 19th IEEE International Symposium on
Source: IEEE Xplore

ABSTRACT This paper introduces a 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, hence 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

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    ABSTRACT: The absence of a unified definition of readmissions has motivated the development of a modelling approach, to systematically tackle the issue surrounding the appropriate choice of a time window which defines readmission. The population of discharged patients can be broadly divided in two groups - a group at high risk of readmission and a group at low risk. This approach extends previous work by the authors, without restricting the number of stages, that patients may experience in the community. Using the national data (UK), we demonstrate its usefulness in the case of chronic obstructive pulmonary disease (COPD) which is known to be one of the leading causes of readmission. We further investigate variability in the definition of readmission among 10 strategic health authorities (SHAs) in England and observe that there are differences in the estimated time window across SHAs. The novelty of this modelling approach is the ability of capturing time to readmission that exhibit a non-zero mode and to estimate an appropriate time window based on evidence objectively derived from operational data.
    Computer-Based Medical Systems, 2007. CBMS '07. Twentieth IEEE International Symposium on; 07/2007
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    ABSTRACT: A frequently chosen time window in defining readmission is 28 days after discharge. Yet in the literature, shorter and longer periods such as 14 days or 90-180 days have also been suggested. In this paper, we develop a modeling approach that systematically tackles the issue surrounding the appropriate choice of a time window as a definition of readmission. The approach is based on the intuitive idea that patients who are discharged from hospital can be broadly divided in to two groups-a group that is at high risk of readmission and a group that is at low risk. Using the national data (England), we demonstrate the usefulness of the approach in the case of chronic obstructive pulmonary disease (COPD), stroke, and congestive heart failure (CHF) patients, which are known to be the leading causes of early readmission. Our findings suggest that there are marked differences in the optimal width of the time window for COPD, stroke, and CHF patients. Furthermore, time windows and the probabilities of being in the high-risk group for COPD, stroke, and CHF patients for each of the 29 acute and specialist trusts in the London area indicate wide variability between hospitals. The novelty of this modeling approach lies in its ability to define an appropriate time window based on evidence objectively derived from operational data. Therefore, it can separately provide a unique approach in examining variability between hospitals, and potentially contribute to a better definition of readmission as a performance indicator.
    IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 10/2008; 12(5):644-9. · 1.69 Impact Factor

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May 28, 2014