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

HEALTH INFORMATION STANDARDISATION AS A BASIS FOR LEARNING HEALTH SYSTEMS

PhD ThesisStandardisation of healthcare has been the focus of hospital management and clinicians since the 1990’s. Electronic health records were already intended to provide clinicians with real-time access to clinical knowledge and care plans while also recording and storing vast amounts of patient data. It took more than three decades for electronic health records to start to become ubiquitous in all aspects of healthcare. Learning health systems are the next stage in health information systems whose potential benefits have been promoted for more than a decade - yet few are seen in clinical practice. Clinical care process specifications are a primary form of clinical documentation used in all aspects of healthcare, but they lack standardisation. This thesis contends that this lack of standardisation was inherited by electronic health records and that this is a significant issue holding back the development and adoption of learning health systems. Standardisation of clinical documents is used to mitigate issues in electronic health records as a basis for enabling learning health systems. One type of clinical document, the caremap, is standardised in order to achieve an effective approach to containing resources and ensuring consistency and quality. This led not only to improved clinicians’ comprehension and acceptance of the clinical document, but also to reduced time expended in developing complicated learning health systems built using the input of clinical experts

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)

Ecology and emergence: Understanding factors that drive variation in process quality and clinical outcomes in general practice

Clinical practice variation (CPV), where differences in healthcare delivery do not reflect differences in patient preferences or clinical need, is considered a hallmark of poor quality care. 'Unwarranted' variation is the focus of mounting policy attention and a growing body of literature, but remains poorly explained and theorised, with ways of determining when variation is warranted only weakly developed. Many assertions around CPV remain under-explored and untested. Much of the literature operates on the assumption that the legitimacy of variation depends on its source or cause, and that variation in processes of care will lead to related variation in outcomes. This doctoral research focuses on two overarching questions relating to CPV in Australian general practice: (1) what is CPV, and how can it be best conceptualised and understood; and (2) what can routinely-collected clinical data tell us about the phenomenon of CPV in general practice? Accordingly, this thesis explores the operationalisation of CPV as a theoretical construct and also examines variation in a series of clinical performance measures for coronary heart disease (CHD) and diabetes. Together, these lines of inquiry constitute a mixed-methods 'sense-making' exercise that seeks an incremental interplay between literature and data, to shed light on the phenomenon of CPV. Data are drawn from a unique dataset of aggregate reporting metrics, using extracted electronic medical record data, among an affiliated group of 36 general practice clinics serving approximately 189,848 patients over a 5-year period. These data are examined descriptively and ultimately analysed using Qualitative Comparative Analysis (QCA) against an empirically derived explanatory framework. Theory development draws on complexity science, especially complex adaptive systems theory, and the disciplines of social epidemiology and health ecology. Results show that a series of discourses have strongly shaped thinking about CPV, converging around a normative 'bad apples' approach to understanding variation. However, CPV may also contribute to healthcare quality in ways that are not well considered, especially in primary care settings. I demonstrate that there may be unconventional but more illuminating ways to conceptualise variation that enable our collective understanding to progress. These include using an ecological framework to conceive CPV as an emergent property of coupled, complex adaptive systems, and employing an equity lens to distinguish between CPV in processes and outcomes of care. In descriptive analyses, I find that variation frequently behaves differently across different measures, with crucial system information contained in the interstices of the data. Contrary to common assumptions, relationships between processes and outcomes of care are not straightforward. Using a framework of factors associated with CPV in general practice management of diabetes and CHD, I confirm that causality is complex and multifactorial, operating at a number of levels. Employing the case-based configurational method of QCA, I show that there may be no single or primary cause for CPV. Instead, clinics can arrive at a particular outcome via multiple independent causal pathways which are themselves multifactorial. These multi-component causes may be defined as much by the interactions between component elements as by individual elements themselves. The same factor may have differential effects within different combinations, or at different scales. These findings suggest that relying on causal explanations to demarcate unwarranted variation may be insufficient. However, both theory and methods require continued development to ensure an adequate understanding of the role and representation of warranted and unwarranted variation in performance measurement systems. Case-based configurational methods such as QCA may have substantial utility in helping to explain and delineate these phenomena