Informatics for Health 2017 : advancing both science and practice

Conference report, The Informatics for Health congress, 24-26 April 2017, in Manchester, UK.Introduction : The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. Discussion : The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. Conclusions : Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.Publisher PDFPeer reviewe

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

The Relationship between Cardiometabolic Disorders and Schizophrenia: From Early-Life Origins to the Development of a Cardiometabolic Risk Prediction Algorithm for Young People with Psychosis

My thesis considers the theme of comorbidity between cardiometabolic disorders and schizophrenia by focussing on three key aspects: the nature of association between cardiometabolic disorders and schizophrenia; the potential for common underlying biological mechanisms for the comorbidity; and the prediction of cardiometabolic risk in young adults with psychosis. On the nature of association between cardiometabolic disorders and schizophrenia, using longitudinal repeat measure data from a large birth cohort, I found that disruption to glucose-insulin homeostasis through childhood/adolescence is associated with increased risk of psychosis in early-adulthood; may not be fully explained by common sociodemographic and lifestyle factors; and may be specific to it. On the mechanisms of association between cardiometabolic disorders and schizophrenia, I used a range of genetic and observational epidemiological methods to examine whether inflammation and shared genetic liability may be common underlying biological mechanisms for the comorbidity. Using birth cohort data, I show that genetic risk for type 2 diabetes is associated with psychosis-risk in adulthood, and vice versa. I also show that genetic risk for type 2 diabetes may influence psychosis risk by increasing systemic inflammation. Using summary data from large genome-wide association studies (GWAS), I show a thread of evidence for shared genetic overlap between schizophrenia, cardiometabolic and inflammatory traits. Finally, using Mendelian randomization, I show evidence supporting that inflammation may be a common cause for insulin resistance and schizophrenia. On the prediction of cardiometabolic risk in young adults with psychosis, I performed a systematic review of cardiometabolic risk prediction algorithms and explored their predictive performance in a sample of young people at risk of developing psychosis. In doing so, I show that none are likely to be suitable for this population. Then, using patient data, I developed and externally validated the Psychosis Metabolic Risk Calculator (PsyMetRiC), the first cardiometabolic risk prediction algorithm specifically tailored for young people with psychosis. Together, my work suggests that cardiometabolic disorders and schizophrenia share aetiologic mechanisms, namely inflammation and shared genetic liability. I have shown that it is possible to accurately predict cardiometabolic risk in young people with psychosis using a tool tailored for the population. Such tools can in future become valuable resources for clinicians to reduce the risk of long-term cardiometabolic morbidity and mortality in people with schizophrenia.National Institute for Health Research (NIHR) Doctoral Research Fellowshi

Biobank participation of persons with epilepsy in South Wales

Introduction: The Swansea Neurology Biobank (SNB) has collected thousands of DNA bio-samples from people with epilepsy in South Wales. Analysis of biobank participation is important to optimise future recruitment for epilepsy research, meta-data analysis and gene / biomarker discovery. This will lead to a high-quality platform for the collection of biological specimens and data. Method: Participation data was extracted from over 2,500 patient records during SNB screening between 2016 and 2018. Biobank participation rates were calculated and linked to epilepsy prevalence using linked, anonymised primary care within the Secure Anonymised Information Linkage databank. Demographics, epilepsy characteristics and social deprivation status (measured using the Welsh Index of Multiple Deprivation – WIMD) were combined at a small geographical (Lower Super Output Area) scale. Factors hypothesised to influence biobank participation were analysed using bivariate and multivariate statistics. A proportion of biobank participants completed a questionnaire assessing attitudes to biobank consent. Results: 12.5% of people with epilepsy seen at epilepsy clinics within the Swansea area were represented in the SNB in 2018. Epilepsy prevalence in the study area (0.92%) was higher than the all Wales epilepsy prevalence (0.85%) and was highest in the most deprived areas. Older patients were more likely to donate compared to the youngest age grouping. Generalised onset epilepsy was underrepresented in the SNB with only 19% having generalised epilepsy. Nearly 20% of patients did not attend their appointment with the majority (59%) coming from the most deprived areas. A large proportion of non-attenders who had generalised epilepsy were diagnosed with Juvenile Myoclonic Epilepsy. Participation rates were lower in more deprived areas when compared to less deprived areas (36% WIMD quintile 1 compared to 41% quintile 4 and 5). Biobank participants were generally positive about biobank donation but there were uncertainties related to the broad reach of the consent process. Conclusion: Our results highlight the difficulty in encouraging research participation at levels representative of the local epilepsy population. Despite higher epilepsy prevalence in more deprived areas, participation rates are lower and non-attendance rates are higher. Mapping of epilepsy participation enables the identification of these low participation areas enabling focused recruitment strategies. Working with primary care and bringing services to the community may improve recruitment when compared to hospital clinic based recruitment

Neurological and Mental Disorders

Mental disorders can result from disruption of neuronal circuitry, damage to the neuronal and non-neuronal cells, altered circuitry in the different regions of the brain and any changes in the permeability of the blood brain barrier. Early identification of these impairments through investigative means could help to improve the outcome for many brain and behaviour disease states.The chapters in this book describe how these abnormalities can lead to neurological and mental diseases such as ADHD (Attention Deficit Hyperactivity Disorder), anxiety disorders, Alzheimer’s disease and personality and eating disorders. Psycho-social traumas, especially during childhood, increase the incidence of amnesia and transient global amnesia, leading to the temporary inability to create new memories.Early detection of these disorders could benefit many complex diseases such as schizophrenia and depression

Statistical Analysis of Infectious Diseases in Nursing and Genomic Data

In a variety of settings, including the medical field, it is common for the number of variables gathered to far exceed the sample size. Along with a high dimension, many of these included variables are often correlated. This can pose problems for traditional methods. Much of the time, the data cannot be utilized completely as is, but instead requires previous research to guide researchers to choose relevant predictors prior to model selection. Traditional methods such as logistic regression and mixed models cannot necessarily converge and struggle with identifiability when the number of measurements collected approach or become larger than the number of patients in the study. Machine-learning techniques, including Random Forests and the newly developed Fuzzy Forests method, can accommodate data with high dimensionality. We concentrate on decision trees in particular because of their relative ease of use, availability and predictive ability. Random Forest is a widely used, parallelizable and computationally efficient method; however it does not acknowledge any correlation between variables leading to a preference for correlated predictors. Fuzzy Forest, on the other hand, explicitly explores the correlation structure among the variables, leading to unbiased variable importance measures. Fuzzy Forest, along with Random Forest, is utilized in three applications; smoking cessation in health care workers, re-arrest among homeless ex-offenders and genetic predictors of lithium response in individuals with Bipolar disorder