A person-time analysis of hospital activity among cancer survivors in England.

BACKGROUND: There are around 2 million cancer survivors in the UK. This study describes the inpatient and day case hospital activity among the population of cancer survivors in England. This is one measure of the burden of cancer on the individual and the health service. METHODS: The national cancer registry data set for England (1990-2006) is linked to the NHS Hospital Episode Statistics (HES) database. Cohorts of survivors were defined as those people recorded in the cancer registry data with a diagnosis of breast, colorectal, lung or prostate cancer before 2007. The person-time of prevalence in 2006 for each cohort of survivors was calculated according to the cancer type, sex, age and time since diagnosis. The corresponding HES episodes of care in 2006 were used to calculate the person-time of admitted hospital care for each cohort of survivors. The average proportion of time spent in hospital by survivors in each cohort was calculated as the summed person-time of hospital activity divided by the summed person-time of prevalence. The analysis was conducted separately for cancer-related episodes and non-cancer-related episodes. RESULTS: Lung cancer survivors had the highest intensity of cancer-related hospital activity. For all cancers, cancer-related hospital activity was highest in the first year following diagnosis. Breast and prostate cancer survivors had peaks of cancer-related hospital activity in the relatively young and relatively old age groups. The proportion of time spent in hospital for non-cancer-related care was much lower than that for cancer-related care and increased gradually with age but was generally constant regardless of time since diagnosis. CONCLUSION: The person-time approach used in this study is more revealing than a simple enumeration of cancer survivors and hospital admissions. Hospital activity among cancer survivors is highest soon after diagnosis. The effect of age on the amount of hospital activity is different for each type of cancer

Praxis in healthcare OR: An empirical behavioural OR study

Operational researchers working in academia commonly struggle in attempts to influence practice and decision making in healthcare amid a growing recognition that behaviour is key to effective operational research (OR). To further our understanding of the behavioural factors that operational researchers working in healthcare consider influence their work’s impact, we interviewed 24 OR practitioners working in academia and with experience of working with the UK National Health Service (NHS). The semi-structured interviews were consented, recorded, transcribed, and analysed thematically using a framework approach. Five dominant themes emerged that highlighted: behavioural challenges concerning flexibility, pivoting and the abandonment of projects; the influence of the evolving ambitions, maturity and behaviours of a practitioner’s OR group; the hidden and changing motivations of host healthcare organisations; the reliance of practitioners on intuition and how their praxis is influenced by their agency within their group and its relationships with healthcare organisations; and how attributes of altruism, broader life experience and creative risk-taking influence an individuals’ praxis. In summary, we identified numerous behavioural factors considered important to success that operate within and across individual projects

Modelling patient flow and outcomes in community healthcare − a fluid approximation of a stochastic queueing system

An ambition of UK healthcare policy in recent decades has been to deliver more care in the community. However, questions remain over the impact of shifting services from acute settings closer to patient homes. This is complicated by a lack of comparable measures, nationally and locally, for evaluating quality across differing community services. In this project we develop a novel patient flow model to aid the evaluation of community services which incorporates patient outcomes. The model includes patient flow dynamics common to community care such as the use of multiple services and possible re-use of services. We represent outcomes as groups which patients may move between during a course of care, which are used to model differentiated service. The model provides insight into the performance of an interrelated healthcare services. We extend a first order fluid approximation of a stochastic queueing system with service reuse to include multiple patient groups. In considering differentiated service, we implement a novel method for dynamically allocating servers across parallel queues, overcoming problems of server inactivity. Furthermore, we develop a new measure of “the flow of outcomes” to evaluate how individual services contribute to the output of outcomes from a system of care over time

Understanding patient flow within community healthcare

In recent decades, community services have been at the forefront of healthcare policy around the world, notably within the NHS Five Year Forward Plan. A stand out challenge for this sector is how services may be organised to provide co-ordinated care given their physical distribution, patients using multiple services, increased volume of referrals and differing patient needs. With a wealth of data available, accessible analysis and collaborative research are key in addressing this challenge. We have worked alongside the North East London Foundation Trust (NELFT) to understand the dynamics of community referrals through novel visualisations of referral data. These methods help researchers and service managers to investigate questions that are otherwise difficult to answer from raw data. The aims of this work were to: understand this complex system, explore concurrent uses of multiple services, and identify common patterns of referrals. Each map focuses on a different aspect of community referrals. Applying these methods to NELFT services, we helped inform their thinking towards the design of a single point of access (SPA) - a service for streamlining referral processes in NELFT community services. We present a selection of these maps here

Understanding patient flow within community healthcare - a novel mapping of sequences and patterns of referral

Background: Community healthcare is a diverse sector consisting a wide range of different services, where patients commonly use a range of services to treat any number of co-morbidities. With a policy focus towards increased care for patients with multiple long term illnesses and increased community provision, the management and planning of these services is a key priority. In this project we worked alongside the North East London Foundation Trust (NELFT) to better understand referrals and patient use within community services for elderly patients, based in Havering. Of interest was how patients concurrently used services, whether common patterns of referrals existed and how sequences of referrals occurred over time. To this end we developed a range of novel maps in collaboration with NELFT clinicians to aid in the design and implementation of a single point of access (SPA) for referrals into community services. / Methods: Using operational research methods, we attempt to better understand the structure of NELFT's community provision. From a non-identifiable patient level dataset we constructed a series of maps exploring patient referrals. We first created a network depiction of referrals where nodes represented services and edges represented a referral between them. In support of this, we plotted the time distribution of concurrent patient referrals at a population level – looking at how long patients remained in one, two, three, four, five and six or greater referrals at the same time as time progressed. Developing further, we analysed common sequences and patterns of referrals. We found common mixtures and orders of services first used by patients, looking at sequences of three, four, five and six referrals. Finally, using a timeline plot, we analysed how subsequent referrals develop after a first referral to a given service. / Results: The network map highlights the intensity and frequency of patient activity within the system as well as its complexity and vastness. The time distribution of patient discharges shows how the number of patients requiring multiple treatments evolves over time and how subsequent referrals overlap. Insight into groups of services with high activity and correlation of referrals is gained by finding common sequences and patterns of referrals, whilst the timeline of subsequent referrals shows how these sequences develop over time. Implications In applying these methods to NELFT services we helped to inform the design of their SPA using a "what if" analysis. This analysis provided information about how referrals may be streamlined to improve access, particularly in light of both areas of high activity formed of multiple services, and the large volume of low use referral paths. These methods highlighted important dynamics of patient flow and referrals within community care to be considered in planning services, and visually depicted them to communicate valuable insight into NELFT community referrals

A systematic literature review of operational research methods for modelling patient flow and outcomes within community healthcare and other settings

An ambition of healthcare policy has been to move more acute services into community settings. This systematic literature review presents analysis of published operational research methods for modelling patient flow within community healthcare, and for modelling the combination of patient flow and outcomes in all settings. Assessed for inclusion at three levels – with the references from included papers also assessed – 25 “Patient flow within community care”, 23 “Patient flow and outcomes” papers and 5 papers within the intersection are included for review. Comparisons are made between each paper’s setting, definition of states, factors considered to influence flow, output measures and implementation of results. Common complexities and characteristics of community service models are discussed with directions for future work suggested. We found that in developing patient flow models for community services that use outcomes, transplant waiting list may have transferable benefits

Use of modelling to inform public health policy: a case study on the blood-borne transmission of variant-CJD

Since the identification of variant Creutzfeldt–Jacob Disease in the late 1980s, the possibility that this disease might be passed on via blood transfusion has presented challenging policy questions for Government and blood services in the UK. This paper discusses the use of mathematical modelling to inform policy in this area of health protection. We focus on the use of a relatively simple analytical model to explore how many such infections might eventually be expected to result in clinical cases under a range of alternative scenarios of interest to policy, and on the potential impact of possible additional counter measures. We comment on the value of triangulating between findings generated using distinct modelling approaches and observational data

Erlang could have told you so—A case study of health policy without maths

Little consideration is given to the operational reality of implementing national policy at local scale. Using a case study from Norway, we examine how simple mathematical models may offer powerful insights to policy makers when planning policies. Our case study refers to a national initiative requiring Norwegian municipalities to establish acute community beds (municipal acute units or MAUs) to avoid hospital admissions. We use Erlang loss queueing models to estimate the total number of MAU beds required nationally to achieve the original policy aim. We demonstrate the effect of unit size and patient demand on anticipated utilisation. The results of our model imply that both the average demand for beds and the current number of MAU beds would have to be increased by 34% to achieve the original policy goal of transferring 240 000 patient days to MAUs. Increasing average demand or bed capacity alone would be insufficient to reach the policy goal. Day-to-day variation and uncertainty in the numbers of patients arriving or leaving the system can profoundly affect health service delivery at the local level. Health policy makers need to account for these effects when estimating capacity implications of policy. We demonstrate how a simple, easily reproducible, mathematical model could assist policy makers in understanding the impact of national policy implemented at the local level