Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Randomised controlled trials of complex interventions and large-scale transformation of services

Complex interventions and large-scale transformations of services are necessary to meet the health-care challenges of the 21st century. However, the evaluation of these types of interventions is challenging and requires methodological development. Innovations such as cluster randomised controlled trials, stepped-wedge designs, and non-randomised evaluations provide options to meet the needs of decision-makers. Adoption of theory and logic models can help clarify causal assumptions, and process evaluation can assist in understanding delivery in context. Issues of implementation must also be considered throughout intervention design and evaluation to ensure that results can be scaled for population benefit. Relevance requires evaluations conducted under real-world conditions, which in turn requires a pragmatic attitude to design. The increasing complexity of interventions and evaluations threatens the ability of researchers to meet the needs of decision-makers for rapid results. Improvements in efficiency are thus crucial, with electronic health records offering significant potential

Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures

Background and Objective: Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. Methods: Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax ) after the walking, and the HR decay 3 min after (HRR3 ). The use of BN allows the assessment of the patients’ status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient’s 6MWT outcomes. Results: Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax ) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3 ), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. Conclusions: We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care

Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures

Background and Objective Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. Methods Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients’ status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT outcomes. Results Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. Conclusions We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care.Peer ReviewedPostprint (published version

Essays in ‘global’ health utilization: How distance, gender, and stigma condition whether and when we seek care

The landscape of health service use varies across, and even within, countries and health sectors irrespective of payment model or health system, yet the fundamental purpose motivating its study is aimed at overcoming the challenges that prevent better and equitable uptake of effective interventions. This dissertation is fuelled by such purpose. Although each chapter poses a specific utilization question relevant to a unique target population, in its entirety, this work seeks to answer the following cross-cutting questions: (i) what are the factors that encourage and challenge utilization of health services, (ii) under which moderating conditions and through which channels is improved utilization supported, and (iii) how can a better understanding of the antecedents of improved utilization contribute to the design of well-targeted health interventions. In Chapter 1, we show that participation in health insurance increases the probability of meeting medical needs while decreasing the probability of incurring catastrophic out-of-pocket health payments in Ghana. Drawing on nationally representative survey data from India, Chapter 2 offers causal insight into the effect of female empowerment, in the form of marital age, on women’s utilization of cervical and breast screening. Our findings suggest that losses in female empowerment attributed to early marriage partly explain Indian women’s low cervical and breast screening participation. Aiming to contribute a better understanding of health utilization among hard-to-reach groups, Chapter 3 investigates the factors that determine the extent of thought given to screening in a sample of high-risk heavy smokers who attended the first free lung cancer screening program in Italy. We show that individuals with greater life-time smoking exposure, and therefore at higher risk of developing lung cancer, tend to contemplate screening less. Finally, Chapter 4 evaluates the cost-effectiveness of a population-based lung cancer screening program targeting high-risk prior and current heavy smokers (≥20 pack-years) aged between 55 and 74 years, in Italy. In doing so, we explore the economic relevance of programs designed with a view towards improving screening participation within hard-to-reach target populations. We offer evidence that rendering an annual LDCT-based screening – with three varying screening invitation strategies – available to the Italian heavy smoker population is more effective, yet more costly, than current clinical practice from the perspective of the national budget holder. Thus, in seeking to offer insight into the factors that encourage and challenge utilization, the conditions and channels that sustain it, and the design of programs that may, in turn, be sustained by it, this dissertation positions health utilization at centre stage

Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019 : a systematic analysis for the Global Burden of Disease Study 2019

In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation