Shola Adeyemi

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.

Random effects models for operational patient pathways

Patient flow modeling is a growing field of interest in health services research. Several techniques have been applied to model movement of patients within and between health-care facilities. However, individual patient experience during the delivery of care has always been overlooked. In this work, a random effects model is introduced to patient flow modeling and applied to a London Hospital Neonatal unit data. In particular, a random effects multinomial logit model is used to capture individual patient trajectories in the process of care with patient frailties modeled as random effects. Intuitively, both operational and clinical patient flow are modeled, the former being physical and the latter latent. Two variants of the model are proposed, one based on mere patient pathways and the other based on patient characteristics. Our technique could identify interesting pathways such as those that result in high probability of death (survival), pathways incurring the least (highest) cost of care or pathways with the least (highest) length of stay. Patient-specific discharge probabilities from the health care system could also be predicted. These are of interest to health-care managers in planning the scarce resources needed to run health-care institutions.

Towards effective capacity planning in a perinatal network centre

Objective To study the arrival pattern and length of stay (LoS) in a neonatal intensive care/high dependency unit (NICU/HDU) and special care baby unit (SCBU) and the impact of capacity shortage in a perinatal network centre, and to provide an analytical model for improving capacity planning. Methods The data used in this study have been collected through the South England Neonatal Database (SEND) and the North Central London Perinatal Network Transfer Audit between 1 January and 31 December 2006 for neonates admitted and refused from the neonatal unit at University College London Hospital (UCLH). Exploratory data analysis was performed. A queuing model is proposed for capacity planning of a perinatal network centre. Outcome measures Predicted number of cots required with existing arrival and discharge patterns; impact of reducing LoS. Results In 2006, 1002 neonates were admitted to the neonatal unit at UCLH, 144 neonates were refused admission to the NICU and 35 to the SCBU. The model shows the NICU requires seven more cots to accept 90% of neonates into the NICU. The model also shows admission acceptance can be increased by 8% if LoS can be reduced by 2 days. Conclusions The arrival, LoS and discharge of neonates having gestational ages of <27 weeks were the key determinants of capacity. The queuing model can be used to determine the cot capacity required for a given tolerance level of admission rejection.

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.

Models for Extracting Information on Patient Pathways

In this paper, we present a random effects approach to modelling of patient flow. Individual patient experience in care as represented by their pathways through the system is modelled. An application to the University College of London Hospital (UCLH) neonatal unit is presented. Using the multinomial logit random effects model, we demonstrate a methodology to extract useful information on patient pathways. This modelling technique is useful for identifying interesting pathways such as those resulting in high probabilities of death/survival, and those resulting in short or long length of stay. Patient-specific discharge probabilities may also be predicted as a function of the frailties; which are modelled as random effects. In the current climate of healthcare cost concerns these will assist healthcare managers in their task of allocating resources to different departments or units of healthcare institution. Two classes of models are presented; one based on patient pathways in which different random effects distribution assumptions are made and the other in which the random effects are regressed on patient characteristics. Intuitively, with the introduction of individual patient frailties, we can argue that both clinical and operational patient flows are being captured in this modelling framework.

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.

Nonproportional random effects modelling of a neonatal unit operational patient pathways

Modelling patient flow in health care systems is considered to be vital in understanding the operational and clinical functions of the system and may therefore prove to be useful in improving the functionality of the health care system, and most importantly provide an evidence based approach for decision making, particularly in the management of the system. In this paper, we introduce a nonproportional cumulative odds random effects model for patient pathways by violating the proportional assumption of the cumulative odds model. Using the probability integral transform, we have extended this to cases where the random effects are not normal, specifically gamma and exponentially distributions. Some of the advantages of this is that these models depict changes in wellbeing (frailties) of patients as they move from one stage of care to the other in time. This is an hybrid extension of our earlier work by jointly including pathways and covariates to explain probability of transition and discharge, which could easily be used to predict the outcome of the treatment. The models here show that the inclusion of pathways render patients characteristics as insignificant. Thus, pathways provide a source of useful information about transition and discharge than patient characteristics, especially when the model is applied to a London University Neonatal Unit dataset. Bootstrapping was then used to investigate the stability, consistency and generalizability of estimated parameters from the models.

Profiling hospitals based on emergency readmission: A multilevel transition modelling approach

Emergency readmission is seen as an important part of the United Kingdom government policy to improve the quality of care that patients receive. In this context, patients and the public have the right to know how well different health organizations are performing. Most methods for profiling estimate the expected numbers of adverse outcomes (e.g. readmission, mortality) for each organization. A number of statistical concerns have been raised, such as the differences in hospital sizes and the unavailability of relevant data for risk adjustment. Having recognized these statistical concerns, a new framework known as the multilevel transition model is developed. Hospital specific propensities of the first, second and further readmissions are considered to be measures of performance, where these measures are used to define a new performance index. During the period 1997 and 2004, the national (English) hospital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a multilevel model using the complete population dataset could possibly take weeks to estimate the parameters. To resolve the problem, we extract 1000 random samples from the original data, where each random sample is likely to lead to differing hospital performance measures. For computational efficiency a Grid implementation of the model is developed. Analysing the output from the full 1000 sample, we noticed that 4 out of the 5 worst performing hospitals treating cancer patients were in London. These hospitals are known to be the leading NHS Trusts in England, providing diverse range of services to complex patients, and therefore it is inevitable to expect higher numbers of emergency readmissions.

A Random Effects Sensitivity Analysis for Patient Pathways Model

In this paper, we present a random effects approach to modelling of patient pathways with an application to the neonatal unit of a large metropolitan hospital. This approach could be used to identify pathways such as those resulting in high probabilities of death/survival, and to estimate cost of care or length of stay. Patient-specific discharge probabilities could also be predicted as a function of the random effect. We also investigate the sensitivity of our modelling results to random effects distribution assumptions.