A tool for routine monitoring and feedback of morbidities following paediatric cardiac surgery.

Short-term survival after paediatric cardiac surgery has improved significantly over the past 20 years and increasing attention is being given to measuring and reducing incidence of morbidities following surgery. How to best use routinely collected data to share morbidity information constitutes a challenge for clinical teams interested in analysing their outcomes for quality improvement. We aimed to develop a tool facilitating this process in the context of monitoring morbidities following paediatric cardiac surgery, as part of a prospective multi-centre research study in the United Kingdom.We developed a prototype software tool to analyse and present data about morbidities associated with cardiac surgery in children. We used an iterative process, involving engagement with potential users, tool design and implementation, and feedback collection. Graphical data displays were based on the use of icons and graphs designed in collaboration with clinicians.Our tool enables automatic creation of graphical summaries, displayed as a Microsoft PowerPoint presentation, from a spreadsheet containing patient-level data about specified cardiac surgery morbidities. Data summaries include numbers/percentages of cases with morbidities reported, co-occurrences of different morbidities, and time series of each complication over a time window.Our work was characterised by a very high level of interaction with potential users of the tool, enabling us to promptly account for feedback and suggestions from clinicians and data managers. The United Kingdom centres involved in the project received the tool positively, and several expressed their interest in using it as part of their routine practice.

Are we prepared for the next influenza pandemic? Lessons from modelling different preparedness policies against four pandemic scenarios.

In the event of a novel influenza strain that is markedly different to the current strains circulating in humans, the population have little/no immunity and infection spreads quickly causing a global pandemic. Over the past century, there have been four major influenza pandemics: the 1918 pandemic ("Spanish Flu"), the 1957-58 pandemic (the "Asian Flu"), the 1967-68 pandemic (the "Hong Kong Flu") and the 2009 pandemic (the "Swine flu"). To inform planning against future pandemics, this paper investigates how different is the net-present value of employing pre-purchase and responsive- purchased vaccine programmes in presence and absence of anti-viral drugs to scenarios that resemble these historic influenza pandemics. Using the existing literature and in discussions with policy decision makers in the UK, we first characterised the four past influenza pandemics by their transmissibility and infection-severity. For these combinations of parameters, we then projected the net-present value of employing pre-purchase vaccine (PPV) and responsive-purchase vaccine (RPV) programmes in presence and absence of anti-viral drugs. To differentiate between PPV and RPV policies, we changed the vaccine effectiveness value and the time to when the vaccine is first available. Our results are "heat-map" graphs displaying the benefits of different strategies in pandemic scenarios that resemble historic influenza pandemics. Our results suggest that immunisation with either PPV or RPV in presence of a stockpile of effective antiviral drugs, does not have positive net-present value for all of the pandemic scenarios considered. In contrast, in the absence of effective antivirals, both PPV and RPV policies have positive net-present value across all the pandemic scenarios. Moreover, in all considered circumstances, vaccination was most beneficial if started sufficiently early and covered sufficiently large number of people. When comparing the two vaccine programmes, the RPV policy allowed a longer timeframe and lower coverage to attain the same benefit as the PPV policy. Our findings suggest that responsive-purchase vaccination policy has a bigger window of positive net-present value when employed against each of the historic influenza pandemic strains but needs to be rapidly available to maximise benefit. This is important for future planning as it suggests that future preparedness policies may wish to consider utilising timely (i.e. responsive-purchased) vaccines against emerging influenza pandemics.

Analysing barriers to service improvement using a multi-level theory of innovation: the case of glaucoma outpatient clinics.

The development and implementation of innovation by healthcare providers is understood as a multi-determinant and multi-level process. Theories at different analytical levels (i.e. micro and organisational) are needed to capture the processes that influence innovation by providers. This article combines a micro theory of innovation, actor-network theory, with organisational level processes using the 'resource based view of the firm'. It examines the influence of, and interplay between, innovation-seeking teams (micro) and underlying organisational capabilities (meso) during innovation processes. We used ethnographic methods to study service innovations in relation to ophthalmology services run by a specialist English NHS Trust at multiple locations. Operational research techniques were used to support the ethnographic methods by mapping the care process in the existing and redesigned clinics. Deficiencies in organisational capabilities for supporting innovation were identified, including manager-clinician relations and organisation-wide resources. The article concludes that actor-network theory can be combined with the resource-based view to highlight the influence of organisational capabilities on the management of innovation. Equally, actor-network theory helps to address the lack of theory in the resource-based view on the micro practices of implementing change.

School-based vaccination programmes: a systematic review of the evidence on organisation and delivery in high income countries.

BACKGROUND: Many countries have recently expanded their childhood immunisation programmes. Schools are an increasingly attractive setting for delivery of these new immunisations because of their ability to reach large numbers of children in a short period of time. However, there are organisational challenges to delivery of large-scale vaccination programmes in schools. Understanding the facilitators and barriers is important for improving the delivery of future school-based vaccination programmes. METHODS: We undertook a systematic review of evidence on school-based vaccination programmes in order to understand the influence of organisational factors on the delivery of programmes. Our eligibility criteria were studies that (1) focused on childhood or adolescent vaccination programmes delivered in schools; (2) considered organisational factors that influenced the preparation or delivery of programmes; (3) were conducted in a developed or high-income country; and (4) had been peer reviewed. We searched for articles published in English between 2000 and 2015 using MEDLINE and HMIC electronic databases. Additional studies were identified by searching the Cochrane Library and bibliographies. We extracted data from the studies, assessed quality and the risk of bias, and categorised findings using a thematic framework of eight organisational factors. RESULTS: We found that most of the recent published literature is from the United States and is concerned with the delivery of pandemic or seasonal flu vaccination programmes at a regional (state) or local level. We found that the literature is largely descriptive and not informed by the use of theory. Despite this, we identified common factors that influence the implementation of programmes. These factors included programme leadership and governance, organisational models and institutional relationships, workforce capacity and roles particularly concerning the school nurse, communication with parents and students, including methods for obtaining consent, and clinic organisation and delivery. CONCLUSIONS: This is the first time that information has been brought together on the organisational factors influencing the delivery of vaccination programmes in school-based settings. An understanding of these factors, underpinned by robust theory-informed research, may help policy-makers and managers design and deliver better programmes. We identified several gaps in the research literature to propose a future research agenda, informed by theories of implementation and organisational change.

Definition of important early morbidities related to paediatric cardiac surgery.

BACKGROUND: Morbidity is defined as a state of being unhealthy or of experiencing an aspect of health that is "generally bad for you", and postoperative morbidity linked to paediatric cardiac surgery encompasses a range of conditions that may impact the patient and are potential targets for quality assurance. METHODS: As part of a wider study, a multi-disciplinary group of professionals aimed to define a list of morbidities linked to paediatric cardiac surgery that was prioritised by a panel reflecting the views of both professionals from a range of disciplines and settings as well as parents and patients. RESULTS: We present a set of definitions of morbidity for use in routine audit after paediatric cardiac surgery. These morbidities are ranked in priority order as acute neurological event, unplanned re-operation, feeding problems, the need for renal support, major adverse cardiac events or never events, extracorporeal life support, necrotising enterocolitis, surgical site of blood stream infection, and prolonged pleural effusion or chylothorax. It is recognised that more than one such morbidity may arise in the same patient and these are referred to as multiple morbidities, except in the case of extracorporeal life support, which is a stand-alone constellation of morbidity. CONCLUSIONS: It is feasible to define a range of paediatric cardiac surgical morbidities for use in routine audit that reflects the priorities of both professionals and parents. The impact of these morbidities on the patient and family will be explored prospectively as part of a wider ongoing, multi-centre study.

A novel approach to evaluating the UK childhood immunisation schedule: estimating the effective coverage vector across the entire vaccine programme.

BACKGROUND: The availability of new vaccines can prompt policy makers to consider changes to the routine childhood immunisation programme in the UK. Alterations to one aspect of the schedule may have implications for other areas of the programme (e.g. adding more injections could reduce uptake of vaccines featuring later in the schedule). Colleagues at the Department of Health (DH) in the UK therefore wanted to know whether assessing the impact across the entire programme of a proposed change to the UK schedule could lead to different decisions than those made on the current case-by-case basis. This work is a first step towards addressing this question. METHODS: A novel framework for estimating the effective coverage against all of the diseases within a vaccination programme was developed. The framework was applied to the current (August 2015) UK childhood immunisation programme, plausible extensions to it in the foreseeable future (introducing vaccination against Meningitis B and/or Hepatitis B) and a "what-if" scenario regarding a Hepatitis B vaccine scare that was developed in close collaboration with DH. RESULTS: Our applications of the framework demonstrate that a programme-view of hypothetical changes to the schedule is important. For example, we show how introducing Hepatitis B vaccination could negatively impact aspects of the current programme by reducing uptake of vaccines featuring later in the schedule, and illustrate that the potential benefits of introducing any new vaccine are susceptible to behaviour changes affecting uptake (e.g. a vaccine scare). We show how it may be useful to consider the potential benefits and scheduling needs of all vaccinations on the horizon of interest rather than those of an individual vaccine in isolation, e.g. how introducing Meningitis B vaccination could saturate the early (2-month) visit, thereby potentially restricting scheduling options for Hepatitis B immunisation should it be introduced to the programme in the future. CONCLUSIONS: Our results demonstrate the potential benefit of considering the programme-wide impact of changes to an immunisation schedule, and our framework is an important step in the development of a means for systematically doing so.

Use of diagnostic information submitted to the United Kingdom Central Cardiac Audit Database: development of categorisation and allocation algorithms.

OBJECTIVE: To categorise records according to primary cardiac diagnosis in the United Kingdom Central Cardiac Audit Database in order to add this information to a risk adjustment model for paediatric cardiac surgery. DESIGN: Codes from the International Paediatric Congenital Cardiac Code were mapped to recognisable primary cardiac diagnosis groupings, allocated using a hierarchy and less refined diagnosis groups, based on the number of functional ventricles and presence of aortic obstruction. SETTING: A National Clinical Audit Database. Patients Children undergoing cardiac interventions: the proportions for each diagnosis scheme are presented for 13,551 first patient surgical episodes since 2004. RESULTS: In Scheme 1, the most prevalent diagnoses nationally were ventricular septal defect (13%), patent ductus arteriosus (10.4%), and tetralogy of Fallot (9.5%). In Scheme 2, the prevalence of a biventricular heart without aortic obstruction was 64.2% and with aortic obstruction was 14.1%; the prevalence of a functionally univentricular heart without aortic obstruction was 4.3% and with aortic obstruction was 4.7%; the prevalence of unknown (ambiguous) number of ventricles was 8.4%; and the prevalence of acquired heart disease only was 2.2%. Diagnostic groups added to procedural information: of the 17% of all operations classed as "not a specific procedure", 97.1% had a diagnosis identified in Scheme 1 and 97.2% in Scheme 2. CONCLUSIONS: Diagnostic information adds to surgical procedural data when the complexity of case mix is analysed in a national database. These diagnostic categorisation schemes may be used for future investigation of the frequency of conditions and evaluation of long-term outcome over a series of procedures.

Using stochastic simulation modelling to study occupancy levels of decentralised admission avoidance units in Norway.

Identifying alternatives to acute hospital admission is a priority for many countries. Over 200 decentralised municipal acute units (MAUs) were established in Norway to divert low-acuity patients away from hospitals. MAUs have faced criticism for low mean occupancy and not relieving pressures on hospitals. We developed a discrete time simulation model of admissions and discharges to MAUs to test scenarios for increasing absolute mean occupancy. We also used the model to estimate the number of patients turned away as historical data was unavailable. Our experiments suggest that mergers alone are unlikely to substantially increase MAU absolute mean occupancy as unmet demand is generally low. However, merging MAUs offers scope for up to 20% reduction in bed capacity, without affecting service provision. Our work has relevance for other admissions avoidance units and provides a method for estimating unconstrained demand for beds in the absence of historical data.

How many births in sub-Saharan Africa and South Asia will not be attended by a skilled birth attendant between 2011 and 2015?

BACKGROUND: The fifth Millennium Development Goal target for 90% of births in low and middle income countries to have a skilled birth attendant (SBA) by 2015 will not be met. In response to this, policy has focused on increasing SBA access. However, reducing maternal mortality also requires policies to prevent deaths among women giving birth unattended. We aimed to generate estimates of the absolute number of non-SBA births between 2011 and 2015 in South Asia and sub-Saharan Africa, given optimistic assumptions of future trends in SBA attendance. These estimates could be used by decision makers to inform the extent to which reductions in maternal mortality will depend on policies aimed specifically at those women giving birth unattended. METHODS: For each country within South Asia and sub-Saharan Africa we estimated recent trends in SBA attendance and used these as the basis for three increasingly optimistic projections for future changes in SBA attendance. For each country we obtained estimates for the current SBA attendance in rural and urban settings and forecasts for the number of births and changes in rural/urban population over 2011-2015. Based on these, we calculated estimates for the number of non-SBA births for 2011-2015 under a variety of scenarios. RESULTS: Conservative estimates are that there will be between 130 and 180 million non-SBA births in South Asia and sub-Saharan Africa from 2011 to 2015 (90% of these in rural areas). Currently, there are more non-SBA births per year in South Asia than sub-Saharan Africa, but our projections suggest that the regions will have approximately the same number of non-SBA births by 2015. We also present results for each of the six countries currently accounting for more than 50% of global maternal deaths. CONCLUSIONS: Over the next five years, many millions of women within South Asia and sub-Saharan Africa will give birth without an SBA. Efforts to improve access to skilled attendance should be accompanied by interventions to improve the safety of non-attended deliveries.

Machine learning for real-time aggregated prediction of hospital admission for emergency patients.

Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital's emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68-0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.

Does triage to critical care during a pandemic necessarily result in more survivors?

OBJECTIVE: The 2009 H1N1 pandemic reinforced the need for a planned response to increased demand for critical care. Triage protocols have been proposed incorporating the exclusion of specified subgroups of patients from critical care. There have been no studies that explore the theoretical underpinning of triage at referral, and it is not clear under what circumstances triage would confer the intended benefits. We sought to explore the mechanisms whereby triage could lead to fewer deaths across a critical care population in the context of a pandemic. DESIGN: We constructed a mathematical model based on queuing theory to compare the estimated short-term survival achieved by using a critical care service with and without triage at referral. Illustrative scenarios concerning a hypothetical critical care population were constructed to explore the roles of length of stay and critical care survival in determining the impact of triage and to identify "tipping points" of demand at which triage would result in more survivors. SETTING: Not applicable as this was a data-free mathematical modeling exercise. MAIN RESULTS: We identified circumstances in which triage would be expected to result in more survivors and circumstances in which it would not. In some scenarios, excluding patient groups solely on the basis of anticipated length of stay could be effective due to a more efficient use of critical care bed days. CONCLUSIONS: The impact of triage is dependent on the level of demand and on the scale of achievable differences between included and excluded groups in terms of anticipated length of stay and critical care survival. It cannot be assumed that triage can or will result in fewer deaths. It should be remembered that there are considerations other than population-level short-term survival when determining the objectives of triage and its ethical implementation.

Operational analysis of school-based delivery models to vaccinate children against influenza.

Large-scale immunisation programmes against seasonal influenza are characterised by logistical challenges related to the need for vaccinating large cohorts of people in a short amount of time. Careful operational planning of resources is essential for a successful implementation of such programmes. We focused on the process of child vaccination in schools and analysed the staffing and workflow aspects of a school-aged children vaccination programme in England. Our objectives were to document vaccination processes and analyse times and costs associated with different models deployed across England. We collected data through direct non-participatory observations. Statistical data analysis enabled us to identify potential factors influencing vaccine delivery time and informed the development of a tool to simulate vaccination sessions. Using this tool, we carried out scenario analyses and explored trade-offs between session times and costs in different settings. Our work ultimately supported the local implementation of school-based vaccination.

Estimation of potential effects of improved community-based drug provision, to augment health-facility strengthening, on maternal mortality due to post-partum haemorrhage and sepsis in sub-Saharan Africa: an equity-effectiveness model.

BACKGROUND: Maternal mortality in Africa has changed little since 1990. We developed a mathematical model with the aim to assess whether improved community-based access to life-saving drugs, to augment a core programme of health-facility strengthening, could reduce maternal mortality due to post-partum haemorrhage or sepsis. METHODS: We developed a mathematical model by considering the key events leading to maternal death from post-partum haemorrhage or sepsis after delivery. With parameter estimates from published work of occurrence of post-partum haemorrhage and sepsis, case fatality, and the effectiveness of drugs, we used this model to estimate the effect of three potential packages of interventions: 1) health-facility strengthening; 2) health-facility strengthening combined with improved drug provision via antenatal-care appointments and community health workers; and 3) all interventions in package two combined with improved community-based drug provision via female volunteers in villages. The model was applied to Malawi and sub-Saharan Africa. FINDINGS: In the implementation of the model, the lowest risk deliveries were those in health facilities. With the model we estimated that of 2860 maternal deaths from post-partum haemorrhage or sepsis per year in Malawi, intervention package one could prevent 210 (7%) deaths, package two 720 (25%) deaths, and package three 1020 (36%) deaths. In sub-Saharan Africa, we estimated that of 182 000 of such maternal deaths per year, these three packages could prevent 21 300 (12%), 43 800 (24%), and 59 000 (32%) deaths, respectively. The estimated effect of community-based drug provision was greatest for the poorest women. INTERPRETATION: Community provision of misoprostol and antibiotics to reduce maternal deaths from post-partum haemorrhage and sepsis could be a highly effective addition to health-facility strengthening in Africa. Investigation of such interventions is urgently needed to establish the risks, benefits, and challenges of widespread implementation. FUNDING: Institute of Child Health and Faculty of Mathematical and Physical Sciences, University College London, and a donation from John and Ann-Margaret Walton.

Using visualisation methods to analyse referral networks within community health care among patients aged 65 years and over.

Community health care services are considered integral to overcoming future problems in health care. However, this sector faces its own challenges, such as how to organise services to provide coordinated care given: their physical distribution, patients using multiple services, increased patient use and differing patient needs. The aim of this work was to explore, analyse and understand patterns in community referrals for patients aged 65 years and over, and their use of multiple services through data visualisation. Working with a large community provider, these methods helped researchers and service managers to investigate questions that were otherwise difficult to answer from raw data. Each map focuses on a different characteristic of community referrals: patients reusing services, concurrent uses of different services and patterns of subsequent referrals. We apply these methods to routine patient data and discuss their implications in designing of a single point of access - a service for streamlining referrals.

A method for evaluating the cost-benefit of different preparedness planning policies against pandemic influenza.

•Our work presents a unifying method to calculate the net-benefit of different preparedness policies against different pandemic influeunza strains. Unlike previous methods, which have focused on evaluating specific strategies against specific pandemics, our method allows assessment of mass immunisation strategies in presence and absence of antiviral drugs for a large range of pandemic influenza strain characteristics and programme features. Overall, the model described here combines two parts to evaluate different preparedness planning policies against pandemic influenza.•The first part is adaptation of an existing transmission model for seasonal influenza to include generalisation across large number of pandemic influenza scenarios.•The second part is development of a tailor-made health economic model devised in collaboration with colleagues at the UK Department of Health and Social Care.

Exploring the role of mass immunisation in influenza pandemic preparedness: A modelling study for the UK context.

The nature and timing of the next influenza pandemic is unknown. This makes it difficult for policy makers to assess whether spending money now to prepare for mass immunisation in the event of a pandemic is worthwhile. We used simple epidemiological modelling and health economic analysis to identify the range of pandemic and policy scenarios under which plans to immunise the general UK population would have net benefit if a stockpiled vaccine or, alternatively, a responsively purchased vaccine were used. Each scenario we studied comprised a combination of pandemic, vaccine and immunisation programme characteristics in presence or absence of access to effective antivirals, with the chance of there being a pandemic each year fixed. Monetarised health benefits and cost savings from any influenza cases averted were set against the option, purchase, storage, distribution, administration, and disposal costs relevant for each scenario to give a discounted net present value over 10 years for planning to immunise, accounting for the possibility that there may be no pandemic over the period considered. To support understanding and exploration of model output, an interactive visualisation tool was devised and made available online. We evaluated over 29 million combinations of pandemic and policy characteristics. Preparedness plans incorporating mass immunisation show positive net present value for a wide range of scenarios, predominantly in the absence of effective antivirals. Plans based on the responsive purchase of vaccine have wider benefit than plans reliant on the purchase and maintenance of a stockpile if immunisation can start without extensive delays. This finding is not dependent on responsively purchased vaccine being more effective than stockpiled vaccine, but rather is driven by avoiding the costs of storing and replenishing a stockpile.

Paediatric cardiac surgery.

We present a short discussion of the literature concerning institutional volume and outcomes for congenital heart surgery. Although there are limitations to some of the studies discussed, all published studies conclude that higher volumes are associated with better outcomes. We then reflect on some of the arguments that can be made in favour of greater centralisation of this practice and some of the arguments that can be made against centralisation.

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.

Addressing the challenges of knowledge co-production in quality improvement: learning from the implementation of the researcher-in-residence model.

The concept of knowledge co-production is used in health services research to describe partnerships (which can involve researchers, practitioners, managers, commissioners or service users) with the purpose of creating, sharing and negotiating different knowledge types used to make improvements in health services. Several knowledge co-production models have been proposed to date, some involving intermediary roles. This paper explores one such model, researchers-in-residence (also known as 'embedded researchers').In this model, researchers work inside healthcare organisations, operating as staff members while also maintaining an affiliation with academic institutions. As part of the local team, researchers negotiate the meaning and use of research-based knowledge to co-produce knowledge, which is sensitive to the local context. Even though this model is spreading and appears to have potential for using co-produced knowledge to make changes in practice, a number of challenges with its use are emerging. These include challenges experienced by the researchers in embedding themselves within the practice environment, preserving a clear focus within their host organisations and maintaining academic professional identity.In this paper, we provide an exploration of these challenges by examining three independent case studies implemented in the UK, each of which attempted to co-produce relevant research projects to improve the quality of care. We explore how these played out in practice and the strategies used by the researchers-in-residence to address them. In describing and analysing these strategies, we hope that participatory approaches to knowledge co-production can be used more effectively in the future.