Methods for Analyzing Hospital Length of Stay with Application to Inpatients Dying in Southern Thailand

Abstract

This study investigated length of stay (LOS) for patients who died in hospital in Southern Thailand from 2000 to 2003 with respect to principal diagnosis and demographic, geographic and hospital size factors. The computerized data of 40,498 mortality cases were obtained from the Ministry of Public Health from 167 hospitals in 14 provinces of Southern Thailand between October 2000 and September 2003 with information on age, gender, principal diagnosis, province and hospital size. Logistic and linear regression with log-transformed LOS was used to analyze the data. Patients with injuries as principal diagnosis had shortest LOS, whereas cancer patients had the longest LOS. Older patients, particularly females, had higher LOS for all diagnoses. LOS increased with hospital size except in the North and North West. Small hospitals in the South West region had the lowest LOS whereas large hospitals in the North West had the highest. The highest proportion of bed days (11.2%) occurred in males aged less than 60 diagnosed with infectious diseases. Males aged less than 60 diagnosed with injuries and digestive diseases, and aged at least 60 diagnosed with COPD, and aged less than 60 diagnosed with infectious diseases, accounted for more than double those for female patients in the same disease groups. Both logistic regressions with LOS at least 1 week as the outcome and linear regression on appropriately log transformed LOS gave consistent results. Providing suitable palliative care or allowing patients to select the place for spending their final time of life, especially for patients with chronic diseases, which can reduce hospital resource utilization.

Factors affecting length of stay in teaching hospitals of a middle-income country

“…The reason for increased LOS along with age could be the presence of chronic disease in older ages, which required a longer hospital stay. A Lim study in south Thailand showed that age affected LOS, and with increasing age LOS increases as well (17). The current study results indicated that marital status affects LOS, and married patients had longer stays than bachelors, which is similar to the results of other studies (18, 19).…”

Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients

“…They claimed that stroke severity is an important predictor of both acute and total LOS. Some studies have reported that patient demographics and hospital attributes were the two major factors that contributed to identifying patient LOS [3], and the most useful patient feature for predicting LOS was patient's age [32]. In many studies, age has been found to be a very significant predictor of LOS [5].…”

“…There has been considerable interest in controlling hospital costs, particularly in cardiac diseases; thus, hospitals try to make LOS as short as possible [2]. The length of hospital stay is an actual parameter applied to identify health care resource utilization, health cost, and severity of illness [3]. The use of LOS is highly predictive of inpatient costs as a marker of resource utilization [4].…”

“…Providing an efficient and accurate model to predict LOS for different types of diseases is one of the issues considered by researchers. Obviously, developing models for predicting and determining LOS in hospitals can be very useful for hospital management, particularly for prioritizing health care policies and promoting health services, comprising the appropriate allocation of health care resources according to differences in patients' LOS along with considering patients' health status and social-demographic features [3]. Ideally, better prediction models are needed to facilitate the decision-making process and cannot be replaced by judgment.…”

Optimal package pricing in healthcare services

“…In the first model, we assume X as a random variable denoting LoS, which follows a log-normal distribution with parameters l and r. The parameter r is the shape parameter of X, while e l is the scale parameter of X, where X!1. It has been widely known that distribution of LoS is rightly skewed (Atienza, 2005;Atienza, Garc ıa-Heras, Muñoz-Pichardo, & Villa, 2008;Hellervik & Rodgers, 2007;Lim & Tongkumchum, 2009) and empirical evidence for log-normal distribution for LoS in a hospital is provided by Marazzi et al (1998). In the second model, we assume that the LoS follows an Erlang-K distribution with scale parameter k and shape parameter h.…”