Ken Fullerton

Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems

Effective resource requirement forecasting is necessary to reduce the escalating cost of care by ensuring optimum utilization and availability of scarce health resources. Patient hospital length of stay (LOS) and thus resource requirements depend on many factors including covariates representing patient characteristics such as age, gender, and diagnosis. We therefore propose the use of such covariates for better hospital capacity planning. Likewise, estimation of the patients expected destination after discharge will help in allocating scarce community resources. Also, probable discharge destination may well affect a patients LOS in hospital. For instance, it might be required to delay the discharge of a patient so as to make appropriate care provision in the community. A number of deterministic models such as ratio-based methods have failed to address inherent variability in complex health processes. To address such complexity, various stochastic models have therefore been proposed. However, such models fail to consider inherent heterogeneity in patient behavior. Therefore, we here use a phase-type survival tree for groups of patients that are homogeneous with respect to LOS distribution, on the basis of covariates such as time of admission, gender, and disease diagnosed; these homogeneous groups of patients can then model patient flow through a care system following stochastic pathways that are characterized by the covariates. Our phase-type model is then extended by further growing the survival tree based on covariates representing outcome measures such as treatment outcome or discharge destinations. These extended phase-type survival trees are very effective in modeling interrelationship between a patients LOS and such outcome measures and allow us to describe patient movements through an integrated care system including hospital, social, and community components. In this paper, we first propose a generalization of the Coxian phase-type distribution to a Markov process with more than one absorbing state; we call this the multi-absorbing state phase-type distribution. We then describe how the model can be used with the extended phase-type survival tree for forecasting hospital, social, and community care resource requirements, estimating cost of care, predicting patient demography at a given time in the future, and admission scheduling. We can, thus, provide a stochastic approach to capacity planning across complex heterogeneous care systems. The approach is illustrated using a five year retrospective data of patients admitted to the stroke unit of the Belfast City Hospital.

Is it beneficial to increase the provision of thrombolysis?— a discrete-event simulation model

BACKGROUND Although Thrombolysis has been licensed in the UK since 2003, it is still administered only to a small percentage of eligible patients. AIM We consider the impact of investing the impact of thrombolysis on important acute stroke services, and the effect on quality of life. The concept is illustrated using data from the Northern Ireland Stroke Service. DESIGN Retrospective study. METHODS We first present results of survival analysis utilizing length of stay (LOS) for discharge destinations, based on data from the Belfast City Hospital (BCH). None of these patients actually received thrombolysis but from those who would have been eligible, we created two initial groups, the first representing a scenario where they received thrombolysis and the second comprising those who do not receive thrombolysis. On the basis of the survival analysis, we created several subgroups based on discharge destination. We then developed a discrete event simulation (DES) model, where each group is a patient pathway within the simulation. Coxian phase type distributions were used to model the group LOS. Various scenarios were explored focusing on cost-effectiveness across hospital, community and social services had thrombolysis been administered to these patients, and the possible improvement in quality of life, should the proportion of patients who are administered thrombolysis be increased. Our aim in simulating various scenarios for this historical group of patients is to assess what the cost-effectiveness of thrombolysis would have been under different scenarios; from this we can infer the likely cost-effectiveness of future policies. RESULTS The cost of thrombolysis is offset by reduction in hospital, community rehabilitation and institutional care costs, with a corresponding improvement in quality of life. CONCLUSION Our model suggests that provision of thrombolysis would produce moderate overall improvement to the service assuming current levels of funding.

Modelling costs of bed occupancy and delayed discharge of post-stroke patients

Stroke is the major cause of disability in the UK, costing the economy £7 billion per annum. Prolonged length of stay (LOS) in hospital is considered to be an inefficient use of resources and is in part due to bed blocking. We present results of survival analyses utilising LOS and destination as outcome measures, based on data from the Belfast City Hospital. On the basis of these results we have created a number of groups, clustered by age, gender, diagnosis and destination. The groups were then used to form the basis of a simulation model, where each group is a patient pathway within the simulation. Various scenarios were explored focusing on the potential cost-efficiency gains should LOS, prior to discharge to a Private Nursing Home (PNH) be reduced. We then consider a possible queue for patients awaiting discharge to a Private Nursing Home with a focus on the financial gains should this queue be reduced.

Using mixed phase-type distributions to model patient pathways

Modeling length of stay (LOS) in hospital is an important aspect of developing integrated models that describe and predict movements of patients. However patient pathways and LOS distributions are highly heterogeneous, particularly with regard to patient diagnosis, age, gender and outcome. We here use a mixed Coxian phase-type distribution (MC-PH distribution) to describe such heterogeneity in terms of covariates, where a Coxian phase-type survival tree is used to estimate parameters for the MC-PH distribution. Multiple absorbing states (such as discharge to home, discharge to private nursing home, or death) are considered, and, based on the MC-PH distribution, expressions presented for key performance indicators of interest. The approach is illustrated using data for stroke patients from the Belfast City Hospital.

A multi-phase DES modelling framework for patient-centred care

We provide a framework for simulating the entire patient journey across different phases (such as diagnosis, treatment, rehabilitation and long-term care) and different sectors (such as GP, hospital, social and community services), with the aim of providing better understanding of such processes and facilitating evaluation of alternative clinical and care strategies. A phase-type modelling approach is used to promote better modelling and management of the specific elements of a patient pathway, using performance measures such as clinical outcomes, patient quality of life, and cost. The approach is illustrated using stroke disease. Approximately 5% of the United Kingdom National Health Service budget is spent treating stroke disease each year. There is an urgent need to assess whether existing services are cost-effective or new interventions could increase efficiency. This assessment can be made using models across primary and secondary care; in particular we evaluate the cost-effectiveness of thrombolysis (clot busting therapy), using discrete event simulation. Using our model, patient quality of life and the costs of thrombolysis are compared under different regimes. In addition, our simulation framework is used to illustrate the impact of internal discharge queues, which can develop while patients are awaiting placement. Probabilistic Sensitivity Analysis of the value parameters is also carried out.

Costing Mixed Coxian Phase-type Systems with Poisson Arrivals

Previously, we developed a modeling framework which classifies individuals with respect to their length of stay (LOS) in the transient states of a continuous-time Markov model with a single absorbing state; phase-type models are used for each class of the Markov model. We here add costs and obtain results for moments of total costs in (0, t], for an individual, a cohort arriving at time zero and when arrivals are Poisson. Based on stroke patient data from the Belfast City Hospital we use the overall modelling framework to obtain results for total cost in a given time interval.

An Extended Mixture Distribution Survival Tree for Patient Pathway Prognostication

Mixture distribution survival trees are constructed by approximating different nodes in the tree by distinct types of mixture distributions to improve within node homogeneity. Previously, we proposed a mixture distribution survival tree-based method for determining clinically meaningful patient groups from a given dataset of patients’ length of stay. This article extends this approach to examine the interrelationship between length of stay in hospital, outcome measures, and other covariates. We describe an application of this approach to patient pathway and examine the relationship between length of stay in hospital and/or treatment outcome using five-years’ retrospective data of stroke patients.