Length of stay
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A common statistic associated with length of stay is the average length of stay (ALOS), a mean calculated by dividing the sum of inpatient days by the number of patients admissions with the same diagnosis-related group classification. A variation in the calculation of ALOS can be to consider only length of stay during the period under analysis.
Length of stay is typically highly skewed, so statistical approaches taking that into account are recommended. While the mean length of stay is useful from the point of view of costs, it may be a poor statistic in terms of representing a typical length of stay; the median may be preferred.
It is useful to be able to predict an individual's expected length of stay or to model length of stay to determine factors that affect it. Various analyses have sought to model length of stay in different condition contexts. This has usually been done with regression models, but Markov chain methods have also been applied. Within regression approaches, linear, log-normal and logistic regression approaches have been applied, but have been criticised by other researchers. Carter & Potts (2014) instead recommend use of negative binomial regression.
Length of stay is commonly used as a quality metric. The prospective payment system in U.S. Medicare for reimbursing hospital care promotes shorter length of stay by paying the same amount for procedures, regardless of days spent in the hospital.
The term "average length of stay" (ALOS) is also applicable to other industries, e.g. entertainment, event marketing, trade show and leisure. ALOS is used to determine the length of time an attendee is expected to spend on a site or in a venue and is part of the calculation used to determine the gross sales potential for selling space to vendors etc. and affects everything from parking to sanitation, staffing and food and beverage. Almost all operational aspects can be altered by an attendee's ALOS.
- Carter, E.M., Potts, H.W.W. (4 April 2014). "Predicting length of stay from an electronic patient record system: a primary total knee replacement example". BMC Medical Informatics & Decision Making 14: 26. doi:10.1186/1472-6947-14-26. Retrieved 23 April 2014.
- Xie, H., Chaussalet, T.J., Millard, P.H. (January 2005). "A continuous time Markov model for the length of stay of elderly people in institutional long-term care". Journal of the Royal Statistical Society: Series A 168 (1): 51–61. doi:10.1111/j.1467-985X.2004.00335.x.
- Faddy, M.J., Graves, N., Pettitt, A.N. (2009). "Modeling length of stay in hospital and other right skewed data : comparison of phase-type, gamma and log-normal distributions". Value In Health 12 (2): 309–314. Retrieved 23 April 2014.