Artificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals.

Objective: To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals.

Methods: The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5 million. We conducted a participatory action research study with multidisciplinary healthcare professionals, managers, data analysts, consumer representatives and industry professionals (n=16) across three phases: (1) defining the problem, (2) current workflow and co-designing a future workflow and (3) developing proof-of-concept AI-based workflows.

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

Background: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare.

The Impact of Digital Hospitals on Patient and Clinician Experience: Systematic Review and Qualitative Evidence Synthesis.

Background: The digital transformation of health care is advancing rapidly. A well-accepted framework for health care improvement is the Quadruple Aim: improved clinician experience, improved patient experience, improved population health, and reduced health care costs. Hospitals are attempting to improve care by using digital technologies, but the effectiveness of these technologies is often only measured against cost and quality indicators, and less is known about the clinician and patient experience.

How do Twitter users feel about telehealth? A mixed-methods analysis of experiences, perceptions and expectations.

Background: Telehealth use has increased considerably in the last years and evidence suggests an overall positive sentiment towards telehealth. Twitter has a wide userbase and can enrich our understanding of telehealth use by users expressing their personal opinions in an unprompted way. This study aimed to explore Twitter users' experiences, perceptions and expectations about telehealth over the last 5 years.

Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.

Background: There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset.

Healthier Together: Implementation and evaluation of a co-designed, culturally tailored childhood obesity community prevention program for Māori and Pacific Islander children and families.

Issue Addressed: Co-designed and culturally tailored preventive initiatives delivered in childhood have high potential to close the cross-cultural gap in health outcomes of priority populations. Māori and Pacific Islander people living in Australia exhibit a higher prevalence of overweight and obesity and higher rates of multimorbidity, including heart disease, cancer and diabetes.

Methods: This mixed-methods, pilot implementation and evaluation study, aimed to evaluate the implementation of a community-based, co-designed and culturally tailored childhood obesity prevention program, using quantitative (pre-post anthropometric measurement, pre-post health behaviour questionnaire) and qualitative (semi-structured interview) methods.

Real-world data for precision public health of noncommunicable diseases: a scoping review.

Background: Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention.

PREVIDE: A Qualitative Study to Develop a Decision-Making Framework (PREVention decIDE) for Noncommunicable Disease Prevention in Healthcare Organisations.

Noncommunicable diseases (NCDs), including obesity, remain a significant global public health challenge. Prevention and public health innovation are needed to effectively address NCDs; however, understanding of how healthcare organisations make prevention decisions is immature. This study aimed to (1) explore how healthcare organisations make decisions for NCD prevention in Queensland, Australia (2) develop a contemporary decision-making framework to guide NCD prevention in healthcare organisations.

Understanding the Digital Disruption of Health Care: An Ethnographic Study of Real-Time Multidisciplinary Clinical Behavior in a New Digital Hospital.

Background: Understanding electronic medical record (EMR) implementation in digital hospitals has focused on retrospective "work as imagined" experiences of multidisciplinary clinicians, rather than "work as done" behaviors. Our research question was "what is the behavior of multidisciplinary clinicians during the transition to a new digital hospital?"

Objectives: The aim of the study is to: (1) Observe clinical behavior of multidisciplinary clinicians in a new digital hospital using ethnography. (2) Develop a thematic framework of clinical behavior in a new digital hospital.

"We'd be really motivated to do something about it": A qualitative study of parent and clinician attitudes towards predicting childhood obesity in practice.

Issue Addressed: In Australia, one in four (24.9%) children live with overweight or obesity (OW/OB). Identifying infants at risk of developing childhood OW/OB is a potential preventive pathway, but its acceptability is yet to be investigated in Australia.

Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases.

Non-communicable diseases (NCDs) remain the largest global public health threat. The emerging field of precision public health (PPH) offers a transformative opportunity to capitalize on digital health data to create an agile, responsive and data-driven public health system to actively prevent NCDs. Using learnings from digital health, our aim is to propose a vision toward PPH for NCDs across three horizons of digital health transformation: Horizon 1-digital public health workflows; Horizon 2-population health data and analytics; Horizon 3-precision public health.

Machine learning models for diabetes management in acute care using electronic medical records: A systematic review.

Background: Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to predict and potentially prevent adverse patient outcomes. There is increasing interest in the application of these models in digital hospitals to improve clinical decision-making and chronic disease management, particularly for patients with diabetes. The potential of ML models using electronic medical records (EMR) to improve the clinical care of hospitalised patients with diabetes is currently unknown.

Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital.

Objective: A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation.

Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia.

Background: Global action to reduce obesity prevalence requires digital transformation of the public health sector to enable precision public health (PPH). Useable data for PPH of obesity is yet to be identified, collated and appraised and there is currently no accepted approach to creating this single source of truth. This scoping review aims to address this globally generic problem by using the State of Queensland (Australia) (population > 5 million) as a use case to determine (1) availability of primary data sources usable for PPH for obesity (2) quality of identified sources (3) general implications for public health policymakers.

Digital health and precision prevention: shifting from disease-centred care to consumer-centred health.

Digital disruption and transformation of health care is occurring rapidly. Concurrently, a global syndemic of preventable chronic disease is crippling healthcare systems and accelerating the effect of the COVID-19 pandemic. Healthcare investment is paradoxical; it prioritises disease treatment over prevention.

Response to 'Systematic reviews do not always capture context of real-world intervention programmes for childhood obesity' by Wild et al., 2021 in BMC Public Health.

In a correspondence to BMC Public Health, Wild et al. respond to our systematic review that synthesised results of interventions to prevent or treat childhood obesity in Māori and Pacific Islanders. Our review included the Whānau Pakari study as one of six included studies - a multidisciplinary intervention for Māori children and adolescents living with obesity led by their research team.

i-PATHWAY: Development and validation of a prediction model for childhood obesity in an Australian prospective birth cohort.

Aim: To develop and validate a model (i-PATHWAY) to predict childhood (age 8-9 years) overweight/obesity from infancy (age 12 months) using an Australian prospective birth cohort.

Methods: The Transparent Reporting of a multivariable Prediction model for individual Prognosis or Diagnosis (TRIPOD) checklist was followed. Participants were n = 1947 children (aged 8-9 years) from the Raine Study Gen2 - an Australian prospective birth cohort - who had complete anthropometric measurement data available at follow up.

Healthier Together: Co-design of a culturally tailored childhood obesity community prevention program for Māori & Pacific Islander children and families.

Issue Addressed: Children of Māori & Pacific Islander descent living in Australia have a greater prevalence of overweight/obesity and an increased risk of adverse health outcomes. This study aimed to co-design Healthier Together, a community-based, childhood overweight/obesity prevention program tailored to Māori & Pacific Islander cultures.

Methods: Co-design involved a three-phase, iterative, participatory and experience-based process, guided by the Te Ara Tika: Guidelines for Māori Research Ethics to promote respect and equity.

Correction to: Building a Children's Health Service and System Research Strategy: development and integration in an Australian paediatric healthcare setting.

An amendment to this paper has been published and can be accessed via the original article.

Building a Children's Health Service and System Research Strategy: development and integration in an Australian paediatric healthcare setting.

Background: Health services and systems research (HSSR) strategies dedicated to paediatric health care and service delivery are limited. Strategies are available but are outdated and yet to be optimised for use in a paediatric health system. We aim to describe the development and integration of a Children's Health Service and System Research Strategy (CHSSR-S) in Children's Health Queensland (CHQ), a large specialist quaternary hospital and health service caring for children and young people in Queensland and northern New South Wales, Australia.

Interventions to prevent or treat childhood obesity in Māori & Pacific Islanders: a systematic review.

Background: Māori and Pacific Islander people are a priority population originating from Australasia. Māori and Pacific Islander children exhibit greater risk of obesity and associated morbidities compared to children of other descent, secondary to unique cultural practices and socioeconomic disadvantage. Despite these known risk factors, there is limited synthesised evidence for preventing and treating childhood obesity in this unique population.