Modelling Patient Duration of Stay to Facilitate Resource Management of Geriatric Hospitals

A fundamental aspect of health care management is the effective allocation of resources. This is of particular importance in geriatric hospitals where elderly patients tend to have more complex needs. Hospital managers would benefit immensely if they had advance knowledge of patient duration of stay in hospital. Managers could assess the costs of patient care and make allowances for these in their budget. In this paper, we tackle this important problem via a model which predicts the duration of stay distribution of patients in hospital. The approach uses phase-type distributions conditioned on a Bayesian belief network.

Using Coxian Phase-Type Distributions to Identify Patient Characteristics for Duration of Stay in Hospital

Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. This paper considers the use of Coxian phase-type distributions for modelling patient duration of stay for the elderly in hospital and investigates the potential for using the resulting distribution as a classifying variable to identify common characteristics between different groups of patients according to their (anticipated) length of stay in hospital. The identification of common characteristics for patient length of stay groups would offer hospital managers and clinicians possible insights into the overall management and bed allocation of the hospital wards.

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

Modelling patient flow in health care systems is vital in understanding the system activity and may therefore prove to be useful in improving their functionality. An extensively used measure is the average length of stay which, although easy to calculate and quantify, is not considered appropriate when the distribution is very long-tailed. In fact, simple deterministic models are generally considered inadequate because of the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service.

Simulating Coxian Phase-type Distributions for Patient Survival

Coxian phase-type distributions are a special type of Markov model that can be used to represent survival times in terms of phases through which an individual may progress until they eventually leave the system completely. Previous research has considered the Coxian phase-type distribution to be ideal in representing patient survival in hospital. However, problems exist in fitting the distributions. This paper investigates the problems that arise with the fitting process by simulating various Coxian phase-type models for the representation of patient survival and examining the estimated parameter values and eigenvalues obtained. The results indicate that numerical methods previously used for fitting the model parameters do not always converge. An alternative technique is therefore considered. All methods are influenced by the choice of initial parameter values. The investigation uses a data set of 1439 elderly patients and models their survival time, the length of time they spend in a UK hospital.

Effect of tailored practice and patient care plans on secondary prevention of heart disease in general practice: cluster randomised controlled trial

Objective: To test the effectiveness of a complex intervention designed, within a theoretical framework, to improve outcomes for patients with coronary heart disease. Design: Cluster randomised controlled multicentre trial. Setting: General practices in Northern Ireland and the Republic of Ireland, regions with different healthcare systems. Participants: 903 patients with established coronary heart disease registered with one of 48 practices. Intervention: Tailored care plans for practices (practice based training in prescribing and behaviour change, administrative support, quarterly newsletter), and tailored care plans for patients (motivational interviewing, goal identification, and target setting for lifestyle change) with reviews every four months at the practices. Control practices provided usual care. Main outcome measures: The proportion of patients at 18 month follow-up above target levels for blood pressure and total cholesterol concentration, and those admitted to hospital, and changes in physical and mental health status (SF-12). Results: At baseline the numbers (proportions) of patients above the recommended limits were: systolic blood pressure greater than 140 mm Hg (305/899; 33.9%, 95% confidence interval 30.8% to 33.9%), diastolic blood pressure greater than 90 mm Hg (111/901; 12.3%, 10.2% to 14.5%), and total cholesterol concentration greater than 5 mmol/l (188/860; 20.8%, 19.1% to 24.6%). At the 18 month follow-up there were no significant differences between intervention and control groups in the numbers (proportions) of patients above the recommended limits: systolic blood pressure, intervention 98/360 (27.2%) v control, 133/405 (32.8%), odds ratio 1.51 (95% confidence interval 0.99 to 2.30; P=0.06); diastolic blood pressure, intervention 32/360 (8.9%) v control, 40/405 (9.9%), 1.40 (0.75 to 2.64; P=0.29); and total cholesterol concentration, intervention 52/342 (15.2%) v control, 64/391 (16.4%), 1.13 (0.63 to 2.03; P=0.65). The number of patients admitted to hospital over the 18 month study period significantly decreased in the intervention group compared with the control group: 107/415 (25.8%) v 148/435 (34.0%), 1.56 (1.53 to 2.60; P=0.03). Conclusions: Admissions to hospital were significantly reduced after an intensive 18 month intervention to improve outcomes for patients with coronary heart disease, but no other clinical benefits were shown, possibly because of a ceiling effect related to improved management of the disease. Trial registration: Current Controlled Trials ISRCTN24081411.

Copying letters to patients with cystic fibrosis (CF): Letter content and patient perceptions of benefit

Background: Copying letters involves generating an extra copy of all correspondence between healthcare professionals about the patient, to the patient.

Birthweight and the risk of childhood-onset type 1 diabetes: a meta-analysis of observational studies using individual patient data

Aims/hypothesis: We investigated whether children who are heavier at birth have an increased risk of type 1 diabetes. Methods: Relevant studies published before February 2009 were identified from literature searches using MEDLINE, Web of Science and EMBASE. Authors of all studies containing relevant data were contacted and asked to provide individual patient data or conduct pre-specified analyses. Risk estimates of type 1 diabetes by category of birthweight were calculated for each study, before and after adjustment for potential confounders. Meta-analysis techniques were then used to derive combined ORs and investigate heterogeneity between studies. Results: Data were available for 29 predominantly European studies (five cohort, 24 case-control studies), including 12,807 cases of type 1 diabetes. Overall, studies consistently demonstrated that children with birthweight from 3.5 to 4 kg had an increased risk of diabetes of 6% (OR 1.06 [95% CI 1.01-1.11]; p=0.02) and children with birthweight over 4 kg had an increased risk of 10% (OR 1.10 [95% CI 1.04-1.19]; p=0.003), compared with children weighing 3.0 to 3.5 kg at birth. This corresponded to a linear increase in diabetes risk of 3% per 500 g increase in birthweight (OR 1.03 [95% CI 1.00-1.06]; p=0.03). Adjustments for potential confounders such as gestational age, maternal age, birth order, Caesarean section, breastfeeding and maternal diabetes had little effect on these findings. Conclusions/interpretation: Children who are heavier at birth have a significant and consistent, but relatively small increase in risk of type 1 diabetes. © 2010 Springer-Verlag.


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