Maria Barton

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.

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.

Using phase-type models to cost stroke patient care across health, social and community services

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.

Knowledge of Food Production Methods Informs Attitudes toward Food but Not Food Choice in Adults Residing in Socioeconomically Deprived Rural Areas within the United Kingdom.

OBJECTIVE Understand food choice, from the perspective of people residing in socioeconomically deprived rural neighborhoods. METHODS Focus groups (n = 7) were undertaken within a community setting involving 42 adults (2 males and 40 females) recruited through voluntary action groups. Data were recorded, transcribed verbatim, and content analyzed. RESULTS Attitudes to food and health were influenced by knowledge of food production and processing. Healthful foods were considered those which were fresh and unprocessed, and taste was taken as an indicator of how the food had been produced. Despite negative views of food production, processed foods were consumed. Explanations for this tension between what people wanted to eat (unprocessed food) and what they actually chose to eat (processed food) were attributed to lifestyle compression. CONCLUSIONS AND IMPLICATIONS Dietary health promotion initiatives targeted at deprived rural dwellers should consider perceived issues regarding food production and processing that may influence views on food.

Using Markov Systems to Plan Stroke Services

We have previously used Markov models to describe movements of patients between hospital states. The distribution of costs at any time and in a given time interval were also previously evaluated and expressions found for the corresponding means and variances. In this paper we extend our previous model to a system that includes on-off costs of making a transition from one state to another; previously costs were per day for the appropriate state. Such transition costs allow us to evaluate the overall costs of therapy or a clinical intervention where an operation or other intervention may be an option. This model can be used to determine costs for the entire system for different strategies thus facilitating a systems approach to the planning of healthcare and a holistic approach to costing. Such models can also help us to assess the complex relationship between hospital and community care where there may be possible trade-offs between hospital treatment costs and community care costs. Such a scenario is commonly encountered in stroke disease where care may include a long period of rehabilitation or residence in a nursing home.

Discovery of value streams for Lean Healthcare

In recent years there has been considerable interest in the possibility of improving healthcare by using ideas from manufacturing and engineering, such as lean thinking. In this paper we describe a lean systems framework for healthcare improvement, where we propose to discover high-level patient pathways using a Markov phase-type model that employs readily available, patient administrative data. Such models can be used to identify major patient pathways and Lean value streams as clusters. The ideas are illustrated using a case study for emergency patient care and a case study for stroke patients.

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.