Eren Demir

Emergency Readmission Criterion: A Technique for Determining the Emergency Readmission Time Window

A frequently chosen time window in defining readmission is 28 days after discharge. Yet in the literature, shorter and longer periods such as 14 days or 90-180 days have also been suggested. In this paper, we develop a modeling approach that systematically tackles the issue surrounding the appropriate choice of a time window as a definition of readmission. The approach is based on the intuitive idea that patients who are discharged from hospital can be broadly divided in to two groups-a group that is at high risk of readmission and a group that is at low risk. Using the national data (England), we demonstrate the usefulness of the approach in the case of chronic obstructive pulmonary disease (COPD), stroke, and congestive heart failure (CHF) patients, which are known to be the leading causes of early readmission. Our findings suggest that there are marked differences in the optimal width of the time window for COPD, stroke, and CHF patients. Furthermore, time windows and the probabilities of being in the high-risk group for COPD, stroke, and CHF patients for each of the 29 acute and specialist trusts in the London area indicate wide variability between hospitals. The novelty of this modeling approach lies in its ability to define an appropriate time window based on evidence objectively derived from operational data. Therefore, it can separately provide a unique approach in examining variability between hospitals, and potentially contribute to a better definition of readmission as a performance indicator.

Towards an evidence-based decision making healthcare system management: Modelling patient pathways to improve clinical outcomes

The concept of patient flow modelling has attracted managers, commissioners and clinicians to better understand the operational and clinical functions of the healthcare system. In this context, the current study has two objectives: First, to introduce a random effects continuation-ratio logit model, suitable for detecting stage wise transitions, to patient pathways modelling. Second, we aim at advancing our knowledge with regard to the application of modelling techniques to patient pathways. We study individual clinical pathways of chronic obstructive pulmonary disease (COPD) patients, a source of concern for major stakeholders. Data on COPD patients were extracted from the national English Hospital Episodes Statistics dataset. Individual patient pathways from initial admission through to more than four readmissions are captured. We notice that as patients are frequently readmitted, males are more likely to be in the higher risk group than females. Furthermore, the number of previous readmissions has a direct impact on the propensity of experiencing a further readmission. This model is very useful in detecting the most critical threshold at which multiple readmissions are more probable. Clinicians should note that a first readmission signifies a problem in the process of care and if care is not taken this may be the beginning of many subsequent readmissions. Our method could easily be implemented as a decision support tool to determine disease specific probabilities of multiple readmissions. Therefore, this could be a valuable tool for clinicians, health care managers, and policy makers for informed decision making in the management of diseases, which ultimately contributes to improved measures for hospital performance management.

A DECISION SUPPORT TOOL FOR PREDICTING PATIENTS AT RISK OF READMISSION: A COMPARISON OF CLASSIFICATION TREES, LOGISTIC REGRESSION, GENERALIZED ADDITIVE MODELS, AND MULTIVARIATE ADAPTIVE REGRESSION SPLINES

This is the peer reviewed version of the following article: Eren Demir, “Classification Trees, Logistic Regression, Generalized Additive Models, and Multivariate Adaptive Regression Splines” Decision Sciences, Vol 45(5): 849-880, October 2014, which has been published in final form at doi: 10.1111/deci.12094. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

A discrete event simulation model to evaluate the use of community services in the treatment of patients with Parkinson’s disease in the United Kingdom

BackgroundThe number of people affected by Parkinson’s disease (PD) is increasing in the United Kingdom driven by population ageing. The treatment of the disease is complex, resource intensive and currently there is no known cure to PD. The National Health Service (NHS), the public organisation delivering healthcare in the UK, is under financial pressures. There is a need to find innovative ways to improve the operational and financial performance of treating PD patients. The use of community services is a new and promising way of providing treatment and care to PD patients at reduced cost than hospital care. The aim of this study is to evaluate the potential operational and financial benefits, which could be achieved through increased integration of community services in the delivery of treatment and care to PD patients in the UK without compromising care quality.MethodsA Discrete Event Simulation model was developed to represent the PD care structure including patients’ pathways, treatment modes, and the mix of resources required to treat PD patients. The model was parametrised with data from a large NHS Trust in the UK and validated using information from the same trust. Four possible scenarios involving increased use of community services were simulated on the model.ResultsShifting more patients with PD from hospital treatment to community services will reduce the number of visits of PD patients to hospitals by about 25% and the number of PD doctors and nurses required to treat these patients by around 32%. Hospital based treatment costs overall should decrease by 26% leading to overall savings of 10% in the total cost of treating PD patients.ConclusionsThe simulation model was useful in predicting the effects of increased use of community services on the performance of PD care delivery. Treatment policies need to reflect upon and formalise the use of community services and integrate these better in PD care. The advantages of community services need to be effectively shared with PD patients and carers to help inform management choices and care plans.

A simulation-based decision support tool for informing the management of patients with Parkinson’s disease

We describe a decision support toolkit that was developed with the aim of assisting those responsible with the management and treatment of Parkinson’s disease (PD) in the UK. Having created a baseline model and established its face validity, the toolkit captures the complexity of PD services at a sufficient level and operates within a user-friendly environment; that is, an interface was built to allow users to specify their own local PD service and input their own estimates or data of service demands and capacities. The main strength of this decision support tool is the adoption of a team approach to studying the system, involving six PD specialist nurses across the country, ensuring that variety of views and suggestions are taken as well as systems modelling and simulations. The tool enables key decision-makers to estimate the likely impact of changes, such as increased use of community services on activity, cost, staffing levels, skill-mix and utilisation of resources. Such previously unobtainable quantitative information can be used to support business cases for changes in the increased use of community services and its impact on clinical outcomes (disease progression), nurse visits and costing.

Modelling length of stay and patient flows : Methodological case studies from the UK neonatal care services

The number of babies needing neonatal care is increasing mainly because of technological and therapeutic advances. These advances have implied a decreasing neonatal mortality rate for low birth weight infants and also a falling incidence of preterm stillbirth. Given the structural changes in the National Health Service in England, coupled with recession and capacity constraints, the neonatal system is facing some serious challenges, such as nurse shortages and the lack of cots, which could inevitably impact neonates’ length of stay, and the performance of the system as a whole. These constraints have forced neonatal managers to better understand their organisation and operations in order to optimise their systems. As a result, we have developed three unique methodologies based on length of stay modelling, physical patient pathways, and system dynamics modelling. This paper evaluates these techniques applied to neonatal services in London and showcases their usefulness and implications in practice, particularly focusing on patient flow to determine major drivers of the system, which could reduce inefficiencies, improve patient experience, and reduce cost.

Measuring and modelling occupancy time in NHS continuing healthcare

BackgroundDue 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.MethodsAn 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).ResultsWe 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.ConclusionsThe 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.

Data preparation for clinical data mining to identify patients at risk of readmission

Steadily rising numbers of emergency (unplanned) inpatient admissions have been the major source of pressure on the NHS over the past twenty years. There is currently still a strong need for a consistent predictive tool and an automation of the development of re-admission risk profiles, in particular, one that addresses both data preparation and predictive modelling. This paper proposes a data preparation framework for transforming raw transactional clinical data to well-formed data sets so that data mining can be applied. In this framework, rules are created according to the statistical characteristics of the data, the metadata that characterises the host information systems and medical knowledge. These rules can be used for data pre-processing, attribute selection and data transformation in order to generate appropriately prepared data sets. The proposed data preparation framework incorporates automatic methods with heuristic pre-processing treatments for the potential challenges within a large-scaled development and its applicability is not limited to clinical data.

A Decision Support Tool for Health Service Re-design

Many of the outpatient services are currently only available in hospitals, however there are plans to provide some of these services alongside with General Practitioners. Consequently, General Practitioners could soon be based at polyclinics. These changes have caused a number of concerns to Hounslow Primary Care Trust (PCT). For example, which of the outpatient services are to be shifted from the hospital to the polyclinic? What are the current and expected future demands for these services? To tackle some of these concerns, the first phase of this project explores the set of specialties that are frequently visited in a sequence (using sequential association rules). The second phase develops an Excel based spreadsheet tool to compute the current and expected future demands for the selected specialties. From the sequential association rule algorithm, endocrinology and ophthalmology were found to be highly associated (i.e. frequently visited in a sequence), which means that these two specialties could easily be shifted from the hospital environment to the polyclinic. We illustrated the Excel based spreadsheet tool for endocrinology and ophthalmology, however, the model is generic enough to cope with other specialties, provided that the data are available.

Capturing the re-admission process: focus on time window

In the majority of studies on patient re-admissions, a re-admission is deemed to have occurred if a patient is admitted within a time window of the previous discharge date. However, these time windows have rarely been objectively justified. We capture the re-admission process from the community using a special case of a Coxian phase-type distribution, expressed as a mixture of two generalized Erlang distributions. Using the Bayes theorem, we compute the optimal time windows in defining re-admission. From the national data set in England, we defined re-admission for chronic obstructive pulmonary disease (COPD), stroke, congestive heart failure, and hip- and thigh-fractured patients as 41, 9, 37, and 8 days, respectively. These time windows could be used to classify patients into two groups (binary response), namely those patients who are at high risk (e.g. within 41 days for COPD) and low risk of re-admission group (respectively, greater than 41 days). The generality of the modelling framework and the capability of supporting a broad class of distributions enables the applicability into other domains, to capture the process within the field of interest and to determine an appropriate time window (a cut-off value) based on evidence objectively derived from operational data.