Trend Analysis of Length of Stay Data via Phase-Type Models

2011 ◽  
Vol 2 (3) ◽  
pp. 37-51 ◽  
Author(s):  
Truc Viet Le ◽  
Chee Keong Kwoh ◽  
Kheng Hock Lee ◽  
Eng Soon Teo
Keyword(s):  
Length Of Stay ◽  
Temporal Trends ◽  
Developed Countries ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Important Indicator ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Hospital Resources ◽  
The Common

The populations in many developed countries throughout the world are aging rapidly and the number of geriatric patients is expected to rise steeply in those countries. This will exert greater pressures on the management of hospital resources as a result. Hospital length of stay (LOS) is an important indicator of hospital activity and management because of its direct relation to resource consumption. Planning of hospital resources according to identified trends of LOS is, thus, an effective way to meet such future needs. In this paper, the authors propose a method to analyze the temporal trends of LOS based on the Coxian phase-type distributions, a special type of continuous-time Markov process. By fitting and regressing the probabilities of discharge from each phase of the distribution on time, the authors have found a growing trend in the proportion of long-staying patients in their sample of stroke patients from a general hospital in Singapore. The authors compare the yearly, quarterly and monthly trends over the same period to see the common pattern. The datasets were also robustified by bootstrapping to aid the analysis.

Healthcare Data Mining

2012 ◽  
Vol 3 (3) ◽  
pp. 44-66 ◽  
Author(s):  
Ali Azari ◽  
Vandana P. Janeja ◽  
Alex Mohseni
Keyword(s):  
Data Mining ◽  
Length Of Stay ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Early Interventions ◽  
Healthcare Data ◽  
Data Mining Approach ◽  
Hospital Resources ◽  
Measure Of Performance ◽  
Training Sets

A model to predict the Length of Stay (LOS) for hospitalized patients can be an effective tool for measuring the consumption of hospital resources. Such a model will enable early interventions to prevent complications and prolonged LOS and also enable more efficient utilization of manpower and facilities in hospitals. In this paper, the authors propose an approach for Predicting Hospital Length of Stay (PHLOS) using a multi-tiered data mining approach. In their aproach, the authors form training sets, using groups of similar claims identified by k-means clustering and perfom classification using ten different classifiers. The authors provide a combined measure of performance to statistically evaluate and rank the classifiers for different levels of clustering. They consistently found that using clustering as a precursor to form the training set gives better prediction results as compared to non-clustering based training sets. The authors have also found the accuracies to be consistently higher than some reported in the current literature for predicting individual patient LOS. Binning the LOS to three groups of short, medium and long stays, their method identifies patients who need aggressive or moderate early interventions to prevent prolonged stays. The classification techniques used in this study are interpretable, enabling them to examine the details of the classification rules learned from the data. As a result, this study provides insight into the underlying factors that influence hospital length of stay. They also examine the authors’ prediction results for three randomly selected conditions with domain expert insights.

scholarly journals Physicians’ Ability to Predict Hospital Length of Stay for Patients Admitted to the Hospital from the Emergency Department

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Gregory Mak ◽  
William D. Grant ◽  
James C. McKenzie ◽  
John B. McCabe
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Prediction Accuracy ◽  
Pediatric Patients ◽  
Age Groups ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Medical Complications ◽  
Future Studies ◽  
Hospital Resources

Accurate predictions of patient length of stay (LOS) in the hospital can effectively manage hospital resources and increase efficiency of patient care. A study was done to assess emergency medicine physicians' ability of predicting the LOS of patients who enter the hospital through the ER. Results indicate that EM physicians are relatively accurate with their pediatric patients than any other age groups. In addition, as actual hospital LOS increases, the prediction accuracy decreases. Possible reasons may be due increasing medical complications associated with increasing age and this may lead to overall longer stays. Other variables such as the admitted service of the patient are not statistically significant in predicting LOS in this study. Future studies should be done in order to determine other variables that may affect LOS predictions.

Modeling hospital length of stay by Coxian phase-type regression with heterogeneity

2012 ◽  
Vol 31 (14) ◽  
pp. 1502-1516 ◽  
Author(s):  
Xiaoqin Tang ◽  
Zhehui Luo ◽  
Joseph C. Gardiner
Keyword(s):  
Length Of Stay ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Phase Type

scholarly journals Pharmacologically treated diabetes and hospitalization among older Norwegians receiving homecare services from 2009 to 2014: a nationwide register study

2021 ◽  
Vol 9 (1) ◽  
pp. e002000
Author(s):  
Anne-Siri Fismen ◽  
Jannicke Igland ◽  
Tonje Teigland ◽  
Grethe Seppola Tell ◽  
Truls Ostbye ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Home Care ◽  
Temporal Trends ◽  
Home Care Services ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Policy Makers ◽  
Care Services ◽  
And Gender ◽  
Rate Ratios

IntroductionThe aim was to assess whether annual hospitalization (admissions, length of stay and total days hospitalized) among persons >65 years receiving home care services in Norway were higher for persons with diabetes than those without diabetes. Given the growing prevalence of diabetes, this issue has great importance for policy makers who must plan for meeting these needs.Research design and methodsData were obtained from national Norwegian registries, and the study population varied from 112 487 to 125 593 per calendar year during 2009–2014. Diabetes was defined as having been registered with at least one prescription for blood glucose lowering medication. Overall and cause-specific hospitalization were compared, as well as temporal trends in hospitalization. Hospitalization outcomes for persons with and without diabetes were compared using log-binomial regression or quantile regression, adjusting for age and gender. Results are reported as incidence rate ratios (IRRs).ResultsHigher total hospitalization rates (IRR 1.17; 95% CI 1.12 to 1.22) were found among persons with, versus without, diabetes, and this difference remained stable throughout the study period. Similar reductions over time in hospital length of stay were observed among persons with and without diabetes, but total annual days hospitalized decreased significantly (p=0.001) more among those with diabetes than among those without diabetes.ConclusionsAmong older recipients of home care services in Norway, diabetes was associated with a higher overall risk of hospitalization and increased days in the hospital. Given the growing prevalence of diabetes, it is important for policy makers to plan for meeting these needs.

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