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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.
Subject(s)
Hospitals , Humans , Qualitative Research , Patient Satisfaction , Digital Technology , Electronic Health RecordsABSTRACT
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. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.
Subject(s)
Artificial Intelligence , Learning Health System , Humans , Electronic Health Records , HospitalsABSTRACT
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.
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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.
Subject(s)
Acute Kidney Injury , Models, Statistical , Humans , Creatinine , Retrospective Studies , PrognosisABSTRACT
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.
Subject(s)
Pediatric Obesity , Infant , Child , Humans , Pediatric Obesity/prevention & control , Cross-Sectional Studies , Parents , Body Weight , Overweight , OptimismABSTRACT
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.
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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.
Subject(s)
Information Storage and Retrieval , Public Health , Australia , Child, Preschool , Humans , Obesity/epidemiology , Queensland/epidemiologyABSTRACT
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.
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Noncommunicable Diseases , Social Media , Telemedicine , Humans , Noncommunicable Diseases/epidemiology , Noncommunicable Diseases/prevention & control , Public Health , Delivery of Health CareABSTRACT
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.
Subject(s)
Pediatric Obesity , Adolescent , Body Weight , Child , Humans , Learning , Native Hawaiian or Other Pacific Islander , Pediatric Obesity/prevention & controlABSTRACT
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.
Subject(s)
Pediatric Obesity , Australia/epidemiology , Birth Weight , Child , Female , Humans , Infant , Pediatric Obesity/diagnosis , Pediatric Obesity/epidemiology , Pregnancy , Prospective Studies , Reproducibility of ResultsABSTRACT
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.
Subject(s)
Pediatric Obesity , Australia , Child , Exercise , Health Status , Humans , Native Hawaiian or Other Pacific Islander , Pediatric Obesity/prevention & controlABSTRACT
BACKGROUND: Maori and Pacific Islander people are a priority population originating from Australasia. Maori 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. The objective of this systematic review was to identify and evaluate global prevention or treatment interventions for overweight or obesity that targeted Maori and Pacific Islander children and adolescents (aged 2-17 years). METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The databases PubMed, EMBASE, Scopus, Web of Science and CINAHL were searched from inception to August 2018. Study quality and risk of bias was assessed using a modified Downs and Black Quality Checklist for Health Care Intervention Studies. Studies were included if RCT/intervention/case control/ or prevention study designs. The study group was defined under the search term 'Oceanic Ancestry Group'. RESULTS: Of the initial 94 articles identified, six were included describing two prevention and three treatment interventions. Interventions were heterogenous in setting, design, length and outcomes. Four interventions were implemented in New Zealand. Most studies were of 'fair' quality. One study recruited an exclusive population of Maori and Pacific Islander participants. In the five studies that recruited mixed populations, one performed sub-group analysis on Maori and Pacific Islander participants. No study reported an improvement in anthropometric outcomes post-intervention in complete or sub-group analysis. Improvements in cardiometabolic or psychological secondary outcomes were inconsistent across all studies. CONCLUSIONS: There is a lack of evidence to recommend specific intervention characteristics to optimise obesity prevention or treatment outcomes for Maori and Pacific Islander children. Future research requires greater consideration of cultural values and beliefs, community engagement, exclusive targeting of Maori and Pacific Islander children and families, and sub-group analyses for mixed-population studies. Incorporating co-design principles during study design and implementation can maximise the cultural specificity of interventions and may contribute to improved health and weight-related outcomes for this at-risk, priority population. TRIAL REGISTRATION: PROSPERO CRD42019121790 (26 March 2019).
Subject(s)
Behavior Therapy/methods , Exercise Therapy/methods , Native Hawaiian or Other Pacific Islander , Pediatric Obesity/prevention & control , Pediatric Obesity/therapy , Adolescent , Australasia , Case-Control Studies , Child , Child, Preschool , Female , Humans , Male , Pediatric Obesity/ethnology , Randomized Controlled Trials as Topic , Risk Factors , Treatment OutcomeABSTRACT
An amendment to this paper has been published and can be accessed via the original article.
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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.
Subject(s)
Child Health Services/organization & administration , Health Services Research/methods , Hospitals, Pediatric/organization & administration , Adolescent , Child , Humans , QueenslandSubject(s)
Mobile Applications , Humans , Health Facilities , Delivery of Health Care , Chronic DiseaseABSTRACT
OBJECTIVE: To determine the global availability of a multicomponent tool predicting overweight/obesity in infancy, childhood, adolescence or adulthood; and to compare their predictive validity and clinical relevance.Design/SettingThe PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. The databases PubMed, EMBASE, CINAHL, Web of Science and PsycINFO were searched. Additional articles were identified via reference lists of included articles. Risk of bias was assessed using the Academy of Nutrition and Dietetics' Quality Criteria Checklist. The National Health and Medical Research Council's Levels of Evidence hierarchy was used to assess quality of evidence. Predictive performance was evaluated using the ABCD framework. SUBJECTS: Eligible studies: tool could be administered at any life stage; quantified the risk of overweight/obesity onset; used more than one predictor variable; and reported appropriate prediction statistical outcomes. RESULTS: Of the initial 4490 articles identified, twelve articles (describing twelve tools) were included. Most tools aimed to predict overweight and/or obesity within childhood (age 2-12 years). Predictive accuracy of tools was consistently adequate; however, the predictive validity of most tools was questioned secondary to poor methodology and statistical reporting. Globally, five tools were developed for dissemination into clinical practice, but no tools were tested within a clinical setting. CONCLUSIONS: To our knowledge, a clinically relevant and highly predictive overweight/obesity prediction tool is yet to be developed. Clinicians can, however, act now to identify the strongest predictors of future overweight/obesity. Further research is necessary to optimise the predictive strength and clinical applicability of such a tool.
Subject(s)
Global Health , Obesity/etiology , Risk Assessment/methods , Adolescent , Adult , Anthropometry , Body Mass Index , Child , Humans , Infant , Pediatric Obesity , Reproducibility of Results , Risk FactorsABSTRACT
BACKGROUND: Wearables hold potential to improve chronic disease self-management in conditions like cystic fibrosis (CF) through remote monitoring, early detection of illness and motivation. Little is known about the acceptability and sustainability of integrating wearables into routine care from the perspectives of people with CF (pwCF) and their treating clinicians. METHODS: A cross-sectional qualitative study involving semi-structured interviews with adult pwCF and focus groups comprising members of a CF multidisciplinary team (MDT) were conducted at a specialist CF centre in Australia. A phenomenological orientation underpinned the study. Inductive thematic analysis was performed using the Framework method. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. RESULTS: Nine pwCF and eight members of a CF MDT, representing six clinical disciplines, participated in the study. Eight themes were inductively generated from the data, of which four were identified from each group. PwCF valued wearables for providing real-time data to motivate healthy behaviours and support shared goal-setting with healthcare providers. Wearables did not influence adherence to CF-specific self-management practices and had some hardware limitations. Members of the CF MDT recognised potential benefits of remote monitoring and shared goal-setting, but advised caution regarding data accuracy, generating patient anxiety in certain personality traits, and lack of evidence supporting use in CF self-management. CONCLUSIONS: Perspectives on integrating wearables into CF care were cautiously optimistic, with emerging risks related to patient anxiety and lack of evidence moderating acceptance.
Subject(s)
Cystic Fibrosis , Qualitative Research , Self-Management , Wearable Electronic Devices , Humans , Cystic Fibrosis/therapy , Cystic Fibrosis/psychology , Adult , Female , Male , Cross-Sectional Studies , Middle Aged , Health Personnel/psychology , Young Adult , Focus Groups , Motivation , AustraliaABSTRACT
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.