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

Markov model-based clustering for efficient patient care

Phase-type distributions were used to carry out model-based clustering of patients using the time spent by the patients in hospital, with maximum likelihood estimation of the model parameters. These parameters were allowed to vary with covariates so that the probability of cluster membership was dependent on these covariates. Expressions for the cluster membership probabilities and corresponding distributions of length of stay in care were found where the membership probabilities can be updated to take account of length of stay to date. The approach was applied to data on geriatric patients from an administrative database of a London hospital. The age of the patients at admission to care and the year of admission were included as covariates. Differential effects of these covariates on the various parameters of the fitted model were demonstrated, and interpretations of these effects made. The clusters here corresponded to patient pathways, with different length of stay distributions, varying care needs and different associated costs. By using the membership probabilities to assign patients to such clusters, care may thus be suited to their predicted pathway. Such an approach might be used in association with healthcare process improvement technologies, such as Lean Thinking or Six Sigm

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

Analysis of stopping criteria for the EM algorithm in the context of patient grouping according to length of stay

The expectation maximisation (EM) algorithm is an iterative maximum likelihood procedure often used for estimating the parameters of a mixture model. Theoretically, increases in the likelihood function are guaranteed as the algorithm iteratively improves upon previously derived parameter estimates. The algorithm is considered to converge when all parameter estimates become stable and no further improvements can be made to the likelihood value. However, to reduce computational time, it is often common practice for the algorithm to be stopped before complete convergence using heuristic approaches. In this paper, we consider various stopping criteria and evaluate their effect on fitting Gaussian mixture models (GMMs) to patient length of stay (LOS) data. Although the GMM can be successfully fitted to positively skewed data such as LOS, the fitting procedure often requires many iterations of the EM algorithm. To our knowledge, no previous study has evaluated the effect of different stopping criteria on fitting GMMs to skewed distributions. Hence, the aim of this paper is to evaluate the effect of various stopping criteria in order to select and justify their use within a patient spell classification methodology. Results illustrate that criteria based on the difference in the likelihood value and on the GMM parameters may not always be a good indicator for stopping the algorithm. In fact we show that the values of the difference in the variance parameters should be used instead, as these parameters are the last to stabilise. In addition, we also specify threshold values for the other stopping criteria

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

Measurement of CP observables in B± → D(⁎)K± and B± → D(⁎)π± decays

Measurements of CP observables in B ± →D (⁎) K ± and B ± →D (⁎) π ± decays are presented, where D (⁎) indicates a neutral D or D ⁎ meson that is an admixture of D (⁎)0 and D¯ (⁎)0 states. Decays of the D ⁎ meson to the Dπ 0 and Dγ final states are partially reconstructed without inclusion of the neutral pion or photon, resulting in distinctive shapes in the B candidate invariant mass distribution. Decays of the D meson are fully reconstructed in the K ± π ∓ , K + K − and π + π − final states. The analysis uses a sample of charged B mesons produced in pp collisions collected by the LHCb experiment, corresponding to an integrated luminosity of 2.0, 1.0 and 2.0 fb −1 taken at centre-of-mass energies of s=7, 8 and 13 TeV, respectively. The study of B ± →D ⁎ K ± and B ± →D ⁎ π ± decays using a partial reconstruction method is the first of its kind, while the measurement of B ± →DK ± and B ± →Dπ ± decays is an update of previous LHCb measurements. The B ± →DK ± results are the most precise to date

THE ROLE OF MINERAL NUTRITION ON YIELDS AND FRUIT QUALITY IN GRAPEVINE, PEAR AND APPLE

ABSTRACT Fertilization of temperate fruit trees, such as grapevine ( Vitis spp.), apple ( Malus domestica), and pear ( Pyrus communis) is an important tool to achive maximum yield and fruit quality. Fertilizers are provided when soil fertility does not allow trees to express their genetic potential, and time and rate of application should be scheduled to promote fruit quality. Grapevine berries, must and wine quality are affected principally by N, that regulate the synthesis of some important compounds, such as anthocyanins, which are responsible for coloring of the must and the wine. Fermenation of the must may stop in grapes with low concentration of N because N is requested in high amount by yeasts. An N excess may increase the pulp to peel ratio, diluting the concentration of anthocyanins and promoting the migration of anthocyanins from berries to the growing plant organs; a decrease of grape juice soluble solid concentration is also expected because of an increase in vegetative growth. Potassium is also important for wine quality contributing to adequate berry maturation, concentration of sugars, synthesis of phenols and the regulation of pH and acidity. In apple and pear, Ca and K are important for fruit quality and storage. Potassium is the most important component of fruit, however, any excess should be avoided and an adequate K:Ca balance should be achieved. Adequate concentration of Ca in the fruit prevents pre- and post-harvest fruit disorders and, at the same time, increases tolerance to pathogens. Although N promotes adequate growth soil N availability should be monitored to avoid excessive N uptake that may decrease fruit skin color and storability

First observation of forward Z→bbˉZ \rightarrow b \bar{b} production in pppp collisions at s=8\sqrt{s}=8 TeV

The decay Z→bb¯ is reconstructed in pp collision data, corresponding to 2 fb −1 of integrated luminosity, collected by the LHCb experiment at a centre-of-mass energy of s=8 TeV. The product of the Z production cross-section and the Z→bb¯ branching fraction is measured for candidates in the fiducial region defined by two particle-level b -quark jets with pseudorapidities in the range 2.220 GeV and dijet invariant mass in the range 4520$GeV and dijet invariant mass in the range$45 < m_{jj} < 165$GeV. From a signal yield of$5462 \pm 763$$Z \rightarrow b \bar{b}$events, where the uncertainty is statistical, a production cross-section times branching fraction of$332 \pm 46 \pm 59$pb is obtained, where the first uncertainty is statistical and the second systematic. The measured significance of the signal yield is 6.0 standard deviations. This measurement represents the first observation of the$Z \rightarrow b \bar{b}$production in the forward region of$pp$ collisions