A tool for studying the effects of residents' attributes on patterns of length of stay in long-term care

Understanding the differential pattern of length of stay (LOS) in long-term care (LTC) due to residents' attributes has important practical implications in the management of long-term care. In this paper, we extend a previously developed modelling approach to incorporate residents' attributes. Two applications using data collected by a local authority in England are presented to demonstrate the potential use of this extension. In the study of possible difference in LOS pattern due to gender, our model provides quantitative support to the observations that male residents admitted to NC take more time to settle down and have poorer short-term survival prospect than female residents

A model-based approach to the analysis of patterns of length of stay in institutional long-term care

Understanding the pattern of length of stay in institutional long-term care has important practical implications in the management of long-term care. Furthermore, residents' attributes are believed to have significant effects on these patterns. In this paper, we present a model-based approach to extract, from a routinely gathered administrative social care dataset, high-level length-of-stay patterns of residents in long-term care. This approach extends previous work by the authors to incorporate residents' features. Two applications using data provided by a local authority in England are presented to demonstrate the potential use of this approach

Patients flow: a mixed-effects modelling approach to predicting discharge probabilities

A mixed effects approach hereby introduced to patients flow and length of stay modelling. In, particular, a class of generalized linear mixed models has been used to demonstrate the usefulness of this approach. This modelling technique is used to capture individual patients experience during the process of care as represented by their pathways through the system. The approach could predict the probability of discharge from the system, as well as detect where the system may be going wrong

Measuring and modelling occupancy time in NHS continuing healthcare

Background - Due to increasing demand and financial constraints, NHS continuing healthcare systems seek to find better ways of forecasting demand and budgeting for care. This paper investigates two areas of concern, namely, how long existing patients stay in service and the number of patients that are likely to be still in care after a period of time. Methods - An anonymised dataset containing information for all funded admissions to placement and home care in the NHS continuing healthcare system was provided by 26 (out of 31) London primary care trusts. The data related to 11289 patients staying in placement and home care between 1 April 2005 and 31 May 2008 were first analysed. Using a methodology based on length of stay (LoS) modelling, we captured the distribution of LoS of patients to estimate the probability of a patient staying in care over a period of time. Using the estimated probabilities we forecasted the number of patients that are likely to be still in care after a period of time (e.g. monthly). Results - We noticed that within the NHS continuing healthcare system there are three main categories of patients. Some patients are discharged after a short stay (few days), some others staying for few months and the third category of patients staying for a long period of time (years). Some variations in proportions of discharge and transition between types of care as well as between care groups (e.g. palliative, functional mental health) were observed. A close agreement of the observed and the expected numbers of patients suggests a good prediction model. Conclusions - The model was tested for care groups within the NHS continuing healthcare system in London to support Primary Care Trusts in budget planning and improve their responsiveness to meet the increasing demand under limited availability of resources. Its applicability can be extended to other types of care, such as hospital care and re-ablement. Further work will be geared towards updating the dataset and refining the results

A system for patient management based discrete-event simulation and hierarchical clustering

Hospital Accident and Emergency (A&E) departments in England have a 4 hour target to treat 98% of patients from arrival to discharge, admission or transfer. Managing resources to meet the target and deliver care across the range of A&E services is a huge challenge for A&E managers. This paper develops an intelligent patient management tool to help managers and clinicians better understand patient length of stay and resources within an A&E area. The developed discrete-event simulation model gives a highlevel representation of ambulance arrivals into A&E. The model facilitates analysis in the following ways: visually interactive software showing patient length of stay in the A&E area; patient activity broken down into sub-groups so that intelligence might be gathered on how sub-groups affect the overall length of stay; understanding the number of patient treatment places and nurse resources required. To support ease of inputs for scenario and sensitivity testing, data is entered into the simulation model (Simul8) via Excel spreadsheets. The model discussed in this paper used patient length of stay grouped by A&E diagnosis codes and was limited to ambulance arrivals. The analysis was derived from A&E attendance in 2004 from an English hospital

A comparison of data mining and spatial techniques : an application to property data

The improvement of data management and data capturing techniques has led to the availability of large amounts of data for analysis. This is especially true in the field of spatial data, where data is indexed by location. Traditionally, spatially correlated data has been analysed using methods that rely on the spatial component of the data. This article will compare the results of using traditional spatial methods such as Kriging and geographically weighted regression against the use of other statistical data mining methods, given the large amount of data available. Using a dataset containing property values for the Tshwane Metropolitan area, different spatial and statistical models will be applied for predictive purposes in order to determine which model represents the data most accurately. Finally, these methods will be combined using stacking, to determine whether the combination of models has better predictive abilities than the single models.http://www.sastat.org.za/journal.htmam201

A closed queueing network approach to the analysis of patient flow in health care systems

Objectives: To model patient flow in health care systemswith bed capacity constraints in order to providea useful decision aid for health service managers.Methods: We model the patient flow of health caresystems using a closed queueing network frameworkwith the assumption that the system is always full.Key performance measures of the health care systemare also derived.Results: Using parameters taken from a study of ageriatric department in the UK, we show that the modelis useful in helping service managers to gain betterunderstanding of the behaviour of the system. In addition,we demonstrate that the model could help improvingdecision-making by allowing managers to exploredifferent options and evaluate their impacts onperformance. Our findings highlight the importanceof policy makers taking into account the interactionsbetween different phases of care.Conclusions: We have developed a novel approach tomodelling the flow of patients through health caresystems with constrained bed capacity

Balancing the NHS balanced scorecard!

In the UK, the split between opposition and supporters views of the National Health Service (NHS) performance ratings system is growing. Objective argument and consensus would be facilitated if a methodology was developed which showed the cause and effect relationships between the components of the performance rating system. The NHS hospital trust performance ratings data used in 2002 and 2003 were downloaded from the Department of Health performance rating website. Structural equation modelling was used to construct a causal-loop diagram showing the cause and effect relationships between the 16 common performance indicators in the two years. Scenario testing suggests that indicators of delayed transfer of care and of data quality are compromised if emergency readmissions performance is improved