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. OBJECTIVE: This study aims to conduct a systematic review and qualitative evidence synthesis to assess the clinician and patient experience of digital hospitals. METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) guidelines were followed. The PubMed, Embase, Scopus, CINAHL, and PsycINFO databases were searched from January 2010 to June 2022. Studies that explored multidisciplinary clinician or adult inpatient experiences of digital hospitals (with a full electronic medical record) were included. Study quality was assessed using the Mixed Methods Appraisal Tool. Data synthesis was performed narratively for quantitative studies. Qualitative evidence synthesis was performed via (1) automated machine learning text analytics using Leximancer (Leximancer Pty Ltd) and (2) researcher-led inductive synthesis to generate themes. RESULTS: A total of 61 studies (n=39, 64% quantitative; n=15, 25% qualitative; and n=7, 11% mixed methods) were included. Most studies (55/61, 90%) investigated clinician experiences, whereas few (10/61, 16%) investigated patient experiences. The study populations ranged from 8 to 3610 clinicians, 11 to 34,425 patients, and 5 to 2836 hospitals. Quantitative outcomes indicated that clinicians had a positive overall satisfaction (17/24, 71% of the studies) with digital hospitals, and most studies (11/19, 58%) reported a positive sentiment toward usability. Data accessibility was reported positively, whereas adaptation, clinician-patient interaction, and workload burnout were reported negatively. The effects of digital hospitals on patient safety and clinicians' ability to deliver patient care were mixed. The qualitative evidence synthesis of clinician experience studies (18/61, 30%) generated 7 themes: inefficient digital documentation, inconsistent data quality, disruptions to conventional health care relationships, acceptance, safety versus risk, reliance on hybrid (digital and paper) workflows, and patient data privacy. There was weak evidence of a positive association between digital hospitals and patient satisfaction scores. CONCLUSIONS: Clinicians' experience of digital hospitals appears positive according to high-level indicators (eg, overall satisfaction and data accessibility), but the qualitative evidence synthesis revealed substantive tensions. There is insufficient evidence to draw a definitive conclusion on the patient experience within digital hospitals, but indications appear positive or agnostic. Future research must prioritize equitable investigation and definition of the digital clinician and patient experience to achieve the Quadruple Aim of health care.

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. METHODS: Mixed-methods study with sequential complementary quantitative and qualitative phases was used for analysis stages comprising (1) a quantitative semiautomated analysis and (2) a qualitative research-led thematic analysis. A machine learning model was used to establish the data set with relevant English language tweets from 1 September 2017 to 1 September 2022 relating to telehealth using predefined search words. Results were integrated at the end. RESULTS: From the initial 237,671 downloaded tweets, 6469 had a relevancy score above 0.8 and were input into Leximancer and 595 were manually analysed. Experiences, perceptions and expectations were categorised into three domains: experience with telehealth consultation, telehealth changes over time and the purpose of the appointment. The most tweeted experience was expectations for telehealth consultation in comparison to in-person consultations. Users mostly mentioned the hope that waiting times for the consultations to start to be less than in-person, more telehealth appointments to be available and telehealth to be cheaper. Perceptions around the use of telehealth in relation to healthcare delivery changes brought about by the COVID-19 pandemic were also expressed. General practitioners were mentioned six times more than other healthcare professionals. CONCLUSION/IMPLICATIONS: This study found that Twitter users expect telehealth services to be better, more affordable and more available than in-person consultations. Users acknowledged the convenience of not having to travel for appointments and the challenges to adapt to telehealth. PATIENT OR PUBLIC CONTRIBUTION: An open data set with 237,671 tweets expressing users' opinions in an unprompted way was used as a source for telehealth service users, caregivers and members of the public experiences, perceptions and expectations of telehealth.

Healthier Together: Implementation and evaluation of a co-designed, culturally tailored childhood obesity community prevention program for Maori 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. Maori 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. Sessions relating to healthy eating, physical activity and positive parenting practices were delivered to families residing in Brisbane (Australia) over 8-weeks. RESULTS: Data were collected from a total of 66 children (mean age 11, SD 4) and 38 parents (mean age 40, SD 8) of Maori and Pacific Islander background, from July 2018 to November 2019. Anthropometric changes included a reduction in Body Mass Index (BMI) z-score among 59% of children (median change -0.02, n = 38, p = 0.17) and BMI among 47% of adults (median change +0.06 kg/m2 , n = 18, p = 0.64). Significant improvements (p < 0.05) in self-reported health behaviours from pre- to post-program included increased vegetable consumption among children, decreased discretionary food intake of children, decreased discretionary drink consumption among both children and adults, increased minutes of daily physical activity among adults and increased parental confidence in the healthy diets of their children. Qualitative data revealed participants valued the inclusion of all family members, learning of practical skills and cultural tailoring delivered by the Multicultural Health Coaches. CONCLUSIONS: This study provides preliminary evidence that the Healthier Together program improved self-reported health behaviours and physical activity levels among Maori and Pacific Islander children and their families in the short-term; however, due to the small sample size, these results must be interpreted carefully. The program empowered change via cultural tailoring and accessibility; however, long-term implementation and evaluation with a larger cohort is needed to validate the observed health behaviour improvements and their sustainability. SO WHAT?: The co-design framework that informed program development and key learnings of implementation will provide guidance to health practitioners, health workers, public health professionals and policy makers to develop inclusive and pragmatic co-design solutions for priority cultural populations in Australia. Health outcomes will improve as a result, promoting health equity for future generations.

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. Additionally, AKI prediction models are known to have a high rate of false positive (FP) events regardless of baseline methods. This warrants further exploration of FP events to provide insight into potential underlying reasons. OBJECTIVE: The first aim of this study was to assess the variance in performance of ML models using three methods of baseline sCr on a retrospective dataset. The second aim was to conduct an error analysis to gain insight into the underlying factors contributing to FP events. MATERIALS AND METHODS: The Intensive Care Unit (ICU) patients of the Medical Information Mart for Intensive Care (MIMIC)-IV dataset was used with the KDIGO (Kidney Disease Improving Global Outcome) definition to identify AKI episodes. Three different methods of estimating baseline sCr were defined as (1) the minimum sCr, (2) the Modification of Diet in Renal Disease (MDRD) equation and the minimum sCr and (3) the MDRD equation and the mean of preadmission sCr. For the first aim of this study, a suite of ML models was developed for each baseline and the performance of the models was assessed. An analysis of variance was performed to assess the significant difference between eXtreme Gradient Boosting (XGB) models across all baselines. To address the second aim, Explainable AI (XAI) methods were used to analyse the XGB errors with Baseline 3. RESULTS: Regarding the first aim, we observed variances in discriminative metrics and calibration errors of ML models when different baseline methods were adopted. Using Baseline 1 resulted in a 14% reduction in the f1 score for both Baseline 2 and Baseline 3. There was no significant difference observed in the results between Baseline 2 and Baseline 3. For the second aim, the FP cohort was analysed using the XAI methods which led to relabelling data with the mean of sCr in 180 to 0 days pre-ICU as the preferred sCr baseline method. The XGB model using this relabelled data achieved an AUC of 0.85, recall of 0.63, precision of 0.54 and f1 score of 0.58. The cohort size was 31,586 admissions, of which 5,473 (17.32%) had AKI. CONCLUSION: In the absence of a widely accepted method of baseline sCr, AKI prediction studies need to consider the impact of different baseline methods on the effectiveness of ML models and their potential implications in real-world implementations. The utilisation of XAI methods can be effective in providing insight into the occurrence of prediction errors. This can potentially augment the success rate of ML implementation in routine care.

"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. This study aimed to (1) investigate the acceptability of predicting childhood OW/OB with parents of infants (aged 0-2 years) and clinicians and (2) explore key language to address stigma and maximise the acceptability of predicting childhood OW/OB in practice. METHODS: This was a cross-sectional and qualitative design, comprising individual semi-structured interviews. Participants were multidisciplinary paediatric clinicians (n = 18) and parents (n = 13) recruited across public hospitals and health services in Queensland, Australia. Data were analysed under the Framework Method using an inductive, thematic approach. RESULTS: Five main themes were identified: (1) Optimism for prevention and childhood obesity prediction, (2) parent dedication to child's health, (3) adverse parent response to risk for childhood obesity, (4) language and phrasing for discussing weight and risk and (5) clinical delivery. Most participants were supportive of using a childhood OW/OB prediction tool in practice. Parents expressed dedication to their child's health that superseded potential feelings of judgement or blame. When discussing weight in a clinical setting, the use of sensitive (ie, "overweight", "above average", "growth" versus "obesity") and positive, health-focused language was mostly supported. CONCLUSIONS: Multidisciplinary paediatric clinicians and parents generally accept the concept of predicting childhood OW/OB in practice in Queensland, Australia. SO WHAT?: Clinicians, public health and health promotion professionals and policymakers can act now to implement sensitive communication strategies concerning weight and obesity risk.

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. METHODS: The setting was the go-live of a greenfield 182-bed digital specialist public hospital in Queensland, Australia. Participants were multidisciplinary clinicians (allied health, nursing, medical, and pharmacy). Clinical ethnographic observations were conducted between March and April 2021 (approximately 1 month post-EMR implementation). Observers shadowed clinicians in real-time performing a diverse range of routine clinical activities and recorded any clinical behavior related to interaction with the digital hospital. Data were analyzed in two phases: (1) content analysis using machine learning (Leximancer v4.5); (2) researcher-led interpretation of the text analytics to generate contextual meaning and finalize themes. RESULTS: A total of 55 multidisciplinary clinicians (41.8% allied health, 23.6% nursing, 20% medical, 14.6% pharmacy) were observed across 58 hours and 99 individual patient encounters. Five themes were derived: (1) Workflows for clinical documentation; (2) Navigating a digital hospital; (3) Digital efficiencies; (4) Digital challenges; (5) Patient experience. There was no observed harm attributable to the digital transition. Clinicians primarily used blended digital and paper workflows to achieve clinical goals. The EMR was generally used seamlessly. New digital workflows affected clinical productivity and caused frustration. Digitization enabled multitasking, clinical opportunism, and benefits to patient safety; however, clinicians were hesitant to trust digital information. CONCLUSION: This study improves our real-time understanding of the digital disruption of health care and can guide clinicians, managers, and health services toward digital transformation strategies based upon "work as done."

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. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation. RESULTS: Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear. CONCLUSIONS: Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.

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. Cross-sectional and qualitative design, comprising individual semi-structured interviews. Participants (n = 14) were recruited from two organisations: the state public health care system (CareQ) and health promotion/disease prevention agency (PrevQ). Participants held executive, director/manager or project/clinical lead roles. Data were analysed in two phases (1) automated content analysis using machine learning (Leximancer v4.5) (2) researcher-led interpretation of the text analytics. Final themes were consolidated into a proposed decision-making framework (PREVIDE, PREvention decIDE) for NCD prevention in healthcare organisations. Decision-making was driven by four themes: Data, Evidence, Ethics and Health, i.e., data, its quality and the story it tells; traditional and non-traditional sources of evidence; ethical grounding in fairness and equity; and long-term value generated across multiple determinants of health. The strength of evidence was directly proportional to confidence in the ethics of a decision. PREVIDE can be adapted by public health practitioners and policymakers to guide real-world policy, practice and investment decisions for obesity prevention and with further validation, other NCDs and priority settings (e.g., healthcare).

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. This perspective provides a high-level strategic roadmap for public health practitioners and policymakers, health system stakeholders and researchers to achieving PPH for NCDs. Two multinational use cases are presented to contextualize our roadmap in pragmatic action: ESP and RiskScape (USA), a mature PPH platform for multiple NCDs, and PopHQ (Australia), a proof-of-concept population health informatics tool to monitor and prevent obesity. Our intent is to provide a strategic foundation to guide new health policy, investment and research in the rapidly emerging but nascent area of PPH to reduce the public health burden of NCDs.

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. OBJECTIVE: The aim was to systematically identify and critically review the published literature examining the development and validation of ML models using EMR data for improving the care of hospitalised adult patients with diabetes. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were followed. Four databases were searched (Embase, PubMed, IEEE and Web of Science) for studies published between January 2010 to January 2022. The reference lists of the eligible articles were manually searched. Articles that examined adults and both developed and validated ML models using EMR data were included. Studies conducted in primary care and community care settings were excluded. Studies were independently screened and data was extracted using Covidence® systematic review software. For data extraction and critical appraisal, the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was followed. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Quality of reporting was assessed by adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. The IJMEDI checklist was followed to assess quality of ML models and the reproducibility of their outcomes. The external validation methodology of the studies was appraised. RESULTS: Of the 1317 studies screened, twelve met inclusion criteria. Eight studies developed ML models to predict disglycaemic episodes for hospitalized patients with diabetes, one study developed a ML model to predict total insulin dosage, two studies predicted risk of readmission, and one study improved the prediction of hospital readmission for inpatients with diabetes. All included studies were heterogeneous with regard to ML types, cohort, input predictors, sample size, performance and validation metrics and clinical outcomes. Two studies adhered to the TRIPOD guideline. The methodological reporting of all the studies was evaluated to be at high risk of bias. The quality of ML models in all studies was assessed as poor. Robust external validation was not performed on any of the studies. No models were implemented or evaluated in routine clinical care. CONCLUSIONS: This review identified a limited number of ML models which were developed to improve inpatient management of diabetes. No ML models were implemented in real hospital settings. Future research needs to enhance the development, reporting and validation steps to enable ML models for integration into routine clinical care.

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. METHODS: Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation. RESULTS: A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed. CONCLUSION: Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.

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. METHODS: The Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Unique search strategies were implemented for 'designed' (e.g. surveys) and 'organic' (e.g. electronic health records) data sources. Only primary sources of data (with stratification to Queensland) with evidence-based determinants of obesity were included. Primary data source type, availability, sample size, frequency of collection and coverage of determinants of obesity were extracted and curated into an evidence map. Data source quality was qualitatively assessed. RESULTS: We identified 38 primary sources of preventive data for obesity: 33 designed and 5 organic. Most designed sources were survey (n 20) or administrative (n 10) sources and publicly available but generally were not contemporaneous (> 2 years old) and had small sample sizes (10-100 k) relative to organic sources (> 1 M). Organic sources were identified as the electronic medical record (ieMR), wearables, environmental (Google Maps, Crime Map) and billing/claims. Data on social, biomedical and behavioural determinants of obesity typically co-occurred across sources. Environmental and commercial data was sparse and interpreted as low quality. One organic source (ieMR) was highly contemporaneous (routinely updated), had a large sample size (5 M) and represented all determinants of obesity but is not currently used for public health decision-making in Queensland. CONCLUSIONS: This review provides a (1) comprehensive data map for PPH for obesity in Queensland and (2) globally translatable framework to identify, collate and appraise primary data sources to advance PPH for obesity and other noncommunicable diseases. Significant challenges must be addressed to achieve PPH, including: using designed and organic data harmoniously, digital infrastructure for high-quality organic data, and the ethical and social implications of using consumer-centred health data to improve public health.

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. This is an inefficient break-fix model versus a person-centred predict-prevent model. It is easy to reward and invest in acute health systems because activity is easily measured and therefore funded. Social, environmental and behavioural health determinants explain ~70% of health variance; yet, we cannot measure these community data contemporaneously or at population scale. The dawn of digital health and the digital citizen can initiate a precision prevention era, where consumer-centred, real-time data enables a new ability to count and fund population health, making disease prevention 'matter'. Then, precision decision making, intervention and policy to target preventable chronic disease (e.g. obesity) can be realised. We argue for, identify barriers to, and propose three horizons for digital health transformation of population health towards precision prevention of chronic disease, demonstrating childhood obesity as a use case. Clinicians, researchers and policymakers can commence strategic planning and investment for precision prevention of chronic disease to advance a mature, value-based model that will ensure healthcare sustainability in Australia and globally.

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. The primary outcome was childhood overweight or obesity (age 8-9 years), defined by age- and gender-specific cut-offs. Multiple imputation was performed to handle missing data. Predictors were selected using 2000 unique backward stepwise logistic regression models. Predictive performance was assessed via: calibration, discrimination and decision-threshold analysis. Internal validation of i-PATHWAY was conducted using bootstrapping (1000 repetitions) to adjust for optimism and improve reliability. A clinical model was developed to support relevance to practice. RESULTS: At age 8-9 years, 18.9% (n = 367) of children were classified with overweight or obesity. i-PATHWAY predictors included: weight change (0-1 year); maternal pre-pregnancy body mass index (BMI); paternal BMI; maternal smoking during pregnancy; premature birth; infant sleep patterns; and sex. After validation, predictive accuracy was acceptable: calibration slope = 0.956 (0.952-0.960), intercept = -0.052 (-0.063, -0.048), area under the curve = 0.737 (0.736-0.738), optimised sensitivity = 0.703(0.568-0.790), optimised specificity = 0.646 (0.571-0.986). The clinical model retained acceptable predictive accuracy without paternal BMI. CONCLUSIONS: i-PATHWAY is a simple, valid and clinically relevant prediction model for childhood overweight/obesity. After further validation, this model can influence state and national health policy for overweight/obesity screening in the early years.

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 Maori and Pacific Islanders. Our review included the Whanau Pakari study as one of six included studies - a multidisciplinary intervention for Maori children and adolescents living with obesity led by their research team. Our review suggested that future research can incorporate stronger co-design principles when designing culturally-tailored interventions to maximise cultural specificity, enhance engagement, facilitate program ownership and contribute to improved health and weight-related outcomes. We commend Whanau Pakari and the team of Wild et al. on their sustained commitment to addressing obesity in priority populations and agree that systematic reviews struggle to capture real-world context of interventions for complex diseases such as obesity. In this article, we respond sequentially to the comments made by Wild et al. and (1) clarify the scope of our review article (2) reiterate our commendation of mixed-methods approaches that capture real-world context (3) explain a referencing error that caused a misinterpretation of our results (4) clarify our interpretation of some Whanau Pakari characteristics (5) welcome partnership to facilitate shared learning with Wild et al.

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

ISSUE ADDRESSED: Children of Maori & 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 Maori & Pacific Islander cultures. METHODS: Co-design involved a three-phase, iterative, participatory and experience-based process, guided by the Te Ara Tika: Guidelines for Maori Research Ethics to promote respect and equity. Following traditional oratory customs of Maori & Pacific Islander cultures, "talanoa" facilitated the collaborative program design with recruited Maori & Pacific Islander consumers, cultural advisors and health professionals. Co-design formulated program objectives, session plans, resources and evaluation tools. RESULTS: Co-design developed a 9-week community-based childhood overweight/obesity prevention program providing culturally tailored education across four themes: (a) nutrition (b) physical activity (c) positive parenting practices (d) culture and health. Strong community engagement developed a program highly tailored to the local Maori & Pacific Islander population. CONCLUSIONS: Co-design methodology promotes equity and inclusion of all stakeholders, acknowledges and caters to diversity and creates a medium for openness, respect and shared purpose. Community-led participatory approaches are pivotal to engaging and empowering communities to successfully improve health behaviours, particularly in tackling childhood overweight/obesity. SO WHAT?: Healthier Together is culturally significant to ensure relevance, effectiveness and sustainability. It is relevant and potentially adaptable to other priority populations across Australia and globally. Ultimately, the delivery of culturally tailored health care will contribute to a reduction in the health inequity experienced amongst priority populations.

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. METHODS: The CHSSR-S was developed using an inductive, bottom-up, participatory systems approach across three phases: (1) Identifying local HSSR capacity; (2) Development; (3) Integration. A HSSR "Champion" was appointed to lead all phases. Clinical, research and system-based stakeholders (n = 14) were individually identified, contacted and participated in dedicated meetings and a workshop to iteratively design the CHSSR-S. A health system-wide CHSSR-S governance committee was established to drive phase three. Health system integration was achieved by multicomponent, action-based strategies. RESULTS: The final CHSSR-S comprised ten Research Priorities and three Research Enablers, and was successfully integrated within CHQ via a range of platforms. Research Priorities included: (1) Population Health; (2) Adolescent and Young Adult (AYA) Cancer; (3) Indigenous Health; (4); Mental Health; (5) Nutrition and Obesity; (6) Rare Neurodevelopmental Disorders; (7) Sepsis; (8) Screening, surveillance and monitoring; (9) Innovation; and (10) Electronic Medical Record (EMR). Research Priorities were supported by three Research Enablers: (1) Data; (2); Evaluation and Health Economics; and (3) Policy. CONCLUSIONS: The CHSSR-S is the first known paediatric HSSR strategy developed and integrated within a large dedicated paediatric health system. The CHSSR-S may be used to guide global paediatric healthcare systems to prioritise HSSR in their local setting to optimise health service delivery and patient outcomes.