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Background: Digital health offers unprecedented opportunities to enhance health service delivery across vast geographic regions. However, these benefits can only be realized with effective capabilities and clinical leadership of the rural healthcare workforce. Little is known about how rural healthcare workers acquire skills in digital health, how digital health education or training programs are evaluated and the barriers and enablers for high quality digital health education and training., Objective: To conduct a scoping review to identify and synthesize existing evidence on digital health education and training of the rural healthcare workforce., Inclusion Criteria: Sources that reported digital health and education or training in the healthcare workforce in any healthcare setting outside metropolitan areas., Methods: We searched for published and unpublished studies written in English in the last decade to August 2023. The databases searched were PubMed, Embase, Scopus, CINAHL and Education Resources Information Centre. We also searched the grey literature (Google, Google Scholar), conducted citation searching and stakeholder engagement. The JBI Scoping Review methodology and PRISMA guidelines for scoping reviews were used., Results: Five articles met the eligibility criteria. Two case studies, one feasibility study, one micro-credential and one fellowship were described. The mode of delivery was commonly modular online learning. Only one article described an evaluation, and findings showed the train-the-trainer model was technically and pedagogically feasible and well received. A limited number of barriers and enablers for high quality education or training of the rural healthcare workforce were reported across macro (legal, regulatory, economic), meso (local health service and community) and micro (day-to-day practice) levels., Conclusions: Upskilling rural healthcare workers in digital health appears rare. Current best practice points to flexible, blended training programs that are suitably embedded with interdisciplinary and collaborative rural healthcare improvement initiatives. Future work to advance the field could define rural health informatician career pathways, address concurrent rural workforce issues, and conduct training implementation evaluations., Review Registration Number: Open Science Framework: https://doi.org/10.17605/OSF.IO/N2RMX ., (© 2024. The Author(s).)

Background: Digital healthcare aims to deliver on the quadruple aim: enhance patient experiences, improve population health, reduce costs and improve provider experiences. Despite large investments, it is unclear how advancing digital health enables these healthcare aims., Objective: Our objectives were to: 1) measure the correlation between digital capability and health system outcomes mapped to the quadruple aim, and 2) measure the longitudinal impact of electronic medical record implementations upon health system outcomes., Materials and Methods: We undertook two studies: 1) Digital health correlational study investigating the association among healthcare system capability and healthcare aims, and 2) Digital hospital longitudinal study investigating outcomes pre and post electronic medical record implementation., Results: Digital health capability was associated with lower staff turnover. Digitising healthcare services was associated with decreased medication errors, decreased nosocomial infections, increased hospital activity, and a transient increase in staff leave., Discussion: These results suggest positive impacts on the population health and healthcare costs aim, minimal impacts on the provider experience aim and no observed impacts to the patient experience aim., Conclusion: These findings should provide confidence to healthcare decision-makers investing in digital health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (©Amir Kamel Rahimi, Oliver Pienaar, Moji Ghadimi, Oliver J Canfell, Jason D Pole, Sally Shrapnel, Anton H van der Vegt, Clair Sullivan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.08.2024.)

Introduction: Hospitals are increasingly turning to patients for valuable feedback regarding their care experience. A common method to collect this information is patient reported experience measures (PREMs) surveys. Health care workers report qualitative PREMs as more interesting, relevant, and informative than quantitative survey responses. However, a major barrier to utilising qualitative PREMs data to drive quality improvements is a lack of resources to analyse the data. This scoping review aimed to review the methods used to analyse qualitative PREMs survey data from routine hospital care., Methods: We utilised the JBI scoping review methodology, and searched four databases for articles from 2013 to 2023 which analysed qualitative PREMs survey data from routine care in hospitals. Study characteristics were extracted, as well as the analysis method - specifically, whether the study used traditional manual analysis methods in which the researcher reads the text and categorise the data, or automated methods utilising computers and algorithms to read and categorise the data., Results: From 960 unique articles, 123 went through full-text review and 54 were deemed eligible. 75.9 % used only manual content analysis methods to analyse the qualitative responses, 16.7 % of studies used a combination of manual and automated methods, and only 7.4 % used exclusively automated methods. Automated methods were used in 27.5 % of studies published 2019-2023, compared to 14.3 % of studies published 2013-2018. All bar one study using automated methods focused on investigating the validity of the automated methodology or used it to complement manual content analysis., Conclusion: The studies included in this review show a transition from traditional time-consuming manual analyses to computerised methods enabling analysis at a larger scale. As the volume of PREMs data collected grows, efficient and effective ways to analyse qualitative PREMs data at scale are required to enable health services to capture the patient voice and drive consumer-centred improvements in care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine., (©Alan J Robertson, Andrew J Mallett, Zornitza Stark, Clair Sullivan. Originally published in JMIR Bioinformatics and Biotechnology (https://bioinform.jmir.org), 13.06.2024.)

Background: For many countries, especially those outside the USA without incentive payments, implementing and maintaining electronic medical records (EMR) is expensive and can be controversial given the large amounts of investment. Evaluating the value of EMR implementation is necessary to understand whether or not, such investment, especially when it comes from the public source, is an efficient allocation of healthcare resources. Nonetheless, most countries have struggled to measure the return on EMR investment due to the lack of appropriate evaluation frameworks., Methods: This paper outlines the development of an evidence-based digital health cost-benefit analysis (eHealth-CBA) framework to calculate the total economic value of the EMR implementation over time. A net positive benefit indicates such investment represents improved efficiency, and a net negative is considered a wasteful use of public resources., Results: We developed a three-stage process that takes into account the complexity of the healthcare system and its stakeholders, the investment appraisal and evaluation practice, and the existing knowledge of EMR implementation. The three stages include (1) literature review, (2) stakeholder consultation, and (3) CBA framework development. The framework maps the impacts of the EMR to the quadruple aim of healthcare and clearly creates a method for value assessment., Conclusions: The proposed framework is the first step toward developing a comprehensive evaluation framework for EMRs to inform health decision-makers about the economic value of digital investments rather than just the financial value., (© 2024. The Author(s).)

Background: Clinical pharmacists perform activities to optimise medicines use and prevent patient harm. Historically, clinical pharmacy quality indicators have measured individual activities not linked to patient outcomes., Aim: To determine the proportion of patients who receive a pharmaceutical care bundle (PCB) (consisting of a medication history, medication review, discharge medication list and medicines information on the discharge summary) as well as investigate the relationship between delivery of this PCB and patient outcomes., Method: Pharmaceutical care bundle activities were defined within state-wide (Queensland, Australia) clinical information systems and datasets were linked. An observational study using routinely recorded data was performed at ten participating sites for adult patients who had a non-same day hospital stay. The association between extent of PCB delivery and three patient outcomes were investigated: length of stay (LOS), unplanned readmission, and mortality., Results: In total 283,813 patient hospital stays were evaluated. The delivery of the PCB occurred in 26.9% of patients at the ten participating hospital sites, ranging from 0.6 to 61.2% across sites. Patients with a longer LOS were more likely to receive delivery of the complete PCB (P < 0.001). There was no correlation between PCB and hospital standardised mortality ratio (r = 0.03, p = 0.93). Higher rates of delivery of the PCB were associated with lower rates of unplanned readmission within 30 days (r = - 0.993, p < 0.001)., Conclusion: A complete PCB was delivered to 26.9% of patients and was associated with a significantly lower rate of unplanned readmission within 30 days., (© 2024. Crown.)

Objectives: With an increasing focus on the digitalization of health and care settings, there is significant scope to learn from international approaches to promote concerted adoption of electronic health records., Materials and Methods: We review three large-scale initiatives from Australia, Canada, and England, and extract common lessons for future health and social care transformation strategy., Results: We discuss how, despite differences in contexts, concerted adoption enables sharing of experience and learning to streamline the digital transformation of health and care., Discussion and Conclusion: Concerted adoption can be accelerated through building communities of expertise and partnerships promoting knowledge transfer and circulation of expertise; commonalities in geographical and cultural contexts; and commonalities in technological systems., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Despite the significant advances in understanding the genetic architecture of epilepsy, many patients do not receive a molecular diagnosis after genomic testing. Re-analysing existing genomic data has emerged as a potent method to increase diagnostic yields-providing the benefits of genomic-enabled medicine to more individuals afflicted with a range of different conditions. The primary drivers for these new diagnoses are the discovery of novel gene-disease and variants-disease relationships; however, most decisions to trigger re-analysis are based on the passage of time rather than the accumulation of new knowledge. To explore how our understanding of a specific condition changes and how this impacts re-analysis of genomic data from epilepsy patients, we developed Vigelint. This approach combines the information from PanelApp and ClinVar to characterise how the clinically relevant genes and causative variants available to laboratories change over time, and this approach to five clinical-grade epilepsy panels. Applying the Vigelint pipeline to these panels revealed highly variable patterns in new, clinically relevant knowledge becoming publicly available. This variability indicates that a more dynamic approach to re-analysis may benefit the diagnosis and treatment of epilepsy patients. Moreover, this work suggests that Vigelint can provide empirical data to guide more nuanced, condition-specific approaches to re-analysis., (© 2024. The Author(s).)

Background: Clinical pharmacy quality indicators are often non-uniform and measure individual activities not linked to outcomes., Aim: To define a consensus agreed pharmaceutical care bundle and patient outcome measures across an entire state health service., Method: A four-round modified-Delphi approach with state Directors of Pharmacy was performed (n = 25). They were asked to rate on a 5-point Likert scale the relevance and measurability of 32 inpatient clinical pharmacy quality indicators and outcome measures. They also ranked clinical pharmacy activities in order from perceived most to least beneficial. Based upon these results, pharmaceutical care bundles consisting of multiple clinical pharmacy activities were formed, and relevance and measurability assessed., Results: Response rate ranged from 40 to 60%. Twenty-six individual clinical pharmacy quality indicators reached consensus. The top ranked clinical pharmacy quality indicator was 'proportion of patients where a pharmacist documents an accurate list of medicines during admission'. There were nine pharmaceutical care bundles formed consisting between 3 and 7 activities. Only one pharmaceutical care bundle reached consensus: medication history, adverse drug reaction/allergy documentation, admission and discharge medication reconciliation, medication review, provision of medicines education and provision of a medication list on discharge. Sixteen outcome measures reached consensus. The top ranked were hospital acquired complications, readmission due to medication misadventure and unplanned readmission within 10 days., Conclusion: Consensus has been reached on one pharmaceutical care bundle and sixteen outcomes to monitor clinical pharmacy service delivery. The next step is to measure the extent of pharmaceutical care bundle delivery and the link to patient outcomes., (© 2024. Crown.)

Globally, there is a growing acknowledgment of Indigenous Peoples' rights to control data related to their communities. This is seen in the development of Indigenous Data Governance standards. As health data collection increases, it's crucial to apply these standards in research involving Indigenous communities. Our study, therefore, aims to systematically review research using routinely collected health data of Indigenous Peoples, understanding the Indigenous Data Governance approaches and the associated advantages and challenges. We searched electronic databases for studies from 2013 to 2022, resulting in 85 selected articles. Of these, 65 (77%) involved Indigenous Peoples in the research, and 60 (71%) were authored by Indigenous individuals or organisations. While most studies (93%) provided ethical approval details, only 18 (21%) described Indigenous guiding principles, 35 (41%) reported on data sovereignty, and 28 (33%) addressed consent. This highlights the increasing focus on Indigenous Data Governance in utilising health data. Leveraging existing data sources in line with Indigenous data governance principles is vital for better understanding Indigenous health outcomes., (© 2024. The Author(s).)

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., (©Oliver J Canfell, Leanna Woods, Yasaman Meshkat, Jenna Krivit, Brinda Gunashanhar, Christine Slade, Andrew Burton-Jones, Clair Sullivan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.03.2024.)

Background: Globally, emergency departments (EDs) are overcrowded and unable to meet an ever-increasing demand for care. The aim of this study is to comprehensively review and synthesise literature on potential solutions and challenges throughout the entire health system, focusing on ED patient flow., Methods: An umbrella review was conducted to comprehensively summarise and synthesise the available evidence from multiple research syntheses. A comprehensive search strategy was employed in four databases alongside government or organisational websites in March 2023. Gray literature and reports were also searched. Quality was assessed using the JBI critical appraisal checklist for systematic reviews and research syntheses. We summarised and classified findings using qualitative synthesis, the Population-Capacity-Process (PCP) model, and the input/throughput/output (I/T/O) model of ED patient flow and synthesised intervention outcomes based on the Quadruple Aim framework., Results: The search strategy yielded 1263 articles, of which 39 were included in the umbrella review. Patient flow interventions were categorised into human factors, management-organisation interventions, and infrastructure and mapped to the relevant component of the patient journey from pre-ED to post-ED interventions. Most interventions had mixed or quadruple nonsignificant outcomes. The majority of interventions for enhancing ED patient flow were primarily related to the 'within-ED' phase of the patient journey. Fewer interventions were identified for the 'post-ED' phase (acute inpatient transfer, subacute inpatient transfer, hospital at home, discharge home, or residential care) and the 'pre-ED' phase. The intervention outcomes were aligned with the aim (QAIM), which aims to improve patient care experience, enhance population health, optimise efficiency, and enhance staff satisfaction., Conclusions: This study found that there was a wide range of interventions used to address patient flow, but the effectiveness of these interventions varied, and most interventions were focused on the ED. Interventions for the remainder of the patient journey were largely neglected. The metrics reported were mainly focused on efficiency measures rather than addressing all quadrants of the quadruple aim. Further research is needed to investigate and enhance the effectiveness of interventions outside the ED in improving ED patient flow. It is essential to develop interventions that relate to all three phases of patient flow: pre-ED, within-ED, and post-ED., (© 2024. The Author(s).)

Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers. Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site. Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting. Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data. Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Digital health technologies are a proposed solution to improve healthcare delivery and reduce pressures on the healthcare system, but these technologies are new to much of the health workforce. This perspective paper highlights lessons learned from the global experience of rapid digital transformation of health workforces, including fostering a culture of learning, ensuring accreditation and recognition, and adopting a transdisciplinary approach. Evidence-based actions are proposed to address recommendations to (1) ensure foundational workforce digital health capability and (2) build specialist digital health career pathways. Australia must take a national approach and strategically leverage strong collaborations across sectors including healthcare, education and government to ensure a consistent, regulated and sustainable digital workforce capability.

Digital transformation in healthcare improves the safety of health systems. Within our health service, a new digital hospital has been established and two wards from a neighbouring paper-based hospital transitioned into the new digital hospital. This created an opportunity to evaluate the impact of complete digital transformation on medication safety. Here we discuss the impact of transition from a paper-based to digital hospital on voluntarily reported medication incidents and prescribing errors. This study utilises an interrupted time-series design and takes place across two wards as they transition from a paper to a digital hospital. Two data sources are used to assess impacts on medication incidents and prescribing errors: (1) voluntarily reported medication incidents and 2) a chart audit of medications prescribed on the study wards. The chart audit collects data on procedural, dosing and therapeutic prescribing errors. There are 588 errors extracted from incident reporting software during the study period. The average monthly number of errors reduces from 12.5 pre- to 7.5 post-transition (p < 0.001). In the chart audit, 5072 medication orders are reviewed pre-transition and 3699 reviewed post-transition. The rates of orders with one or more error reduces significantly after transition (52.8% pre- vs. 15.7% post-, p < 0.001). There are significant reductions in procedural (32.1% pre- vs. 1.3% post-, p < 0.001), and dosing errors (32.3% pre- vs. 14% post-, p < 0.001), but not therapeutic errors (0.6% pre- vs. 0.7% post-, p = 0.478). Transition to a digital hospital is associated with reductions in voluntarily reported medication incidents and prescribing errors., (© 2023. The Author(s).)

Background: Health care organizations understand the importance of new technology implementations; however, the best strategy for implementing successful digital transformations is often unclear. Digital health maturity assessments allow providers to understand the progress made toward technology-enhanced health service delivery. Existing models have been criticized for their lack of depth and breadth because of their technology focus and neglect of meaningful outcomes., Objective: We aimed to examine the perceived impacts of digital health reported by health care staff employed in health care organizations across a spectrum of digital health maturity., Methods: A mixed methods case study was conducted. The digital health maturity of public health care systems (n=16) in Queensland, Australia, was examined using the quantitative Digital Health Indicator (DHI) self-assessment survey. The lower and upper quartiles of DHI scores were calculated and used to stratify sites into 3 groups. Using qualitative methods, health care staff (n=154) participated in interviews and focus groups. Transcripts were analyzed assisted by automated text-mining software. Impacts were grouped according to the digital maturity of the health care worker's facility and mapped to the quadruple aims of health care: improved patient experience, improved population health, reduced health care cost, and enhanced provider experience., Results: DHI scores ranged between 78 and 193 for the 16 health care systems. Health care systems in the high-maturity category (n=4, 25%) had a DHI score of ≥166.75 (the upper quartile); low-maturity sites (n=4, 25%) had a DHI score of ≤116.75 (the lower quartile); and intermediate-maturity sites (n=8, 50%) had a DHI score ranging from 116.75 to 166.75 (IQR). Overall, 18 perceived impacts were identified. Generally, a greater number of positive impacts were reported in health care systems of higher digital health maturity. For patient experiences, higher maturity was associated with maintaining a patient health record and tracking patient experience data, while telehealth enabled access and flexibility across all digital health maturity categories. For population health, patient journey tracking and clinical risk mitigation were reported as positive impacts at higher-maturity sites, and telehealth enabled health care access and efficiencies across all maturity categories. Limited interoperability and organizational factors (eg, strategy, policy, and vision) were universally negative impacts affecting health service delivery. For health care costs, the resource burden of ongoing investments in digital health and a sustainable skilled workforce was reported. For provider experiences, the negative impacts of poor usability and change fatigue were universal, while network and infrastructure issues were negative impacts at low-maturity sites., Conclusions: This is one of the first studies to show differences in the perceived impacts of digital maturity of health care systems at scale. Higher digital health maturity was associated with more positive reported impacts, most notably in achieving outcomes for the population health aim., (©Leanna Woods, Ronald Dendere, Rebekah Eden, Brittany Grantham, Jenna Krivit, Andrew Pearce, Keith McNeil, Damian Green, Clair Sullivan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.07.2023.)

As health care continues to change and evolve in a digital society, there is an escalating need for physicians who are skilled and enabled to deliver care using digital health technologies, while remaining able to successfully broker the triadic relationship among patients, computers and themselves. The focus needs to remain firmly on how technology can be leveraged and used to support good medical practice and quality health care, particularly around resolution of longstanding challenges in health care delivery, including equitable access in rural and remote areas, closing the gap on health outcomes and experiences for First Nations peoples and better support in aged care and those living with chronic disease and disability. We propose a set of requisite digital health competencies and recommend that the acquisition and evaluation of these competencies become embedded in physician training curricula and continuing professional development programmes., (© 2023 The Authors. Internal Medicine Journal published by John Wiley & Sons Australia, Ltd on behalf of Royal Australasian College of Physicians.)

Introduction: Traditional face-to-face family member visits in the intensive care unit (ICU) are challenged during the coronavirus disease pandemic with time-critical visiting of the ICU patient being impossible. Objective: This study aimed to explore reported experiences and satisfaction surrounding the use of technology for virtual visits and virtual family meetings in the ICU setting. Two groups were surveyed: (1) family members of critically ill patients in the ICU and (2) health care workers caring for these patients. Design: The study, conducted in the 36-bed ICU of a speciality metropolitan acute care facility in Australia, used a pragmatic post-test survey design. Data were analyzed descriptively. Results: Of health care worker subjects, 106 completed the survey and the majority of communication episodes favored virtual visits (79.2%, n  = 84). Of family member subjects, 69 completed the survey, with the majority participating in virtual family meetings (40.6%, n  = 28). Both groups indicated satisfaction with virtual communication. Conclusions: We found virtual communication was positively received.

Objective: This systematic literature review aimed to identify factors that influence the implementation of electronic patient-reported outcome measures (ePROMs) and patient-reported experience measures (ePREMs) in healthcare settings., Introduction: Improvements in health care through increased patient engagement have gained traction in recent years. Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are tools used to improve the quality of care from the patient perspective. The influence of implementing PROMs and PREMs using electronic information systems (ePROMs and ePREMs) is not well understood., Inclusion Criteria: Studies with information related to the implementation of ePROMs and/or ePREMs with a focus on health-related services, irrespective of provider type, were included., Methods: A literature search of peer-reviewed databases was conducted on the 24th of January 2022 for articles about barriers and facilitators of the implementation of ePROMs/ePREMs in healthcare settings. Two reviewers independently extracted relevant findings from the included studies and performed a descriptive code-based synthesis before collaboratively creating a final consensus set of code categories, which were then mapped to the consolidated framework of implementation research (CFIR). Study quality was appraised using a mixed-methods appraisal tool (MMAT)., Results: 24 studies were eligible for inclusion in the screening of 626 nonduplicate studies. Quality assessment using the MMAT revealed that 20/24 studies met at least 60% of the MMAT criteria. Ninety-six code categories were identified and mapped to the constructs across all CFIR domains., Conclusion: To guide the effective implementation of ePROMs/ePREMs in healthcare settings, factors shown to influence their implementation have been summarised as an implementation checklist for adoption and use by clinicians, organisations, and policymakers., (© 2023. The Author(s).)

Introduction: In Australia, the available published literature demonstrated a spike in dispensed prescription medicines after the onset of the COVID-19 pandemic that subsequently returned to expected levels. Smoking cessation medicines may not follow this pattern because quit attempts are influenced by a range of factors. Knowledge of whether dispensing of these medicines has changed since the pandemic is lacking. We explored the change in dispensing of publicly subsidised smoking cessation medicines since the pandemic., Methods: Australia's universal health-care system provides access to government-subsidised medicines via the Pharmaceutical Benefits Scheme and records of dispensed medicines are publicly available on a nationally aggregated level. We retrieved Pharmaceutical Benefits Scheme data from January 2016 to January 2021. We used interrupted time series modelling to quantify the impact of COVID-19 on dispensing of nicotine replacement therapy (NRT) patches, varenicline and all smoking cessation treatments combined separately., Results: After an initial spike in medicines at the onset of the pandemic, the monthly rate of prescriptions dispensed for varenicline was predominantly within predicted ranges, while that of NRT patches was predominantly below predicted ranges., Discussion and Conclusions: There has been a differential change in the number of subsidised smoking cessation medicines supplied in Australia since the COVID-19 pandemic, with varenicline prescriptions largely within, and NRT patches largely lower than, expected ranges. The reasons for the apparent change in dispensing of subsidised smoking cessation medicines are unclear., (© 2022 The Authors. Drug and Alcohol Review published by John Wiley & Sons Australia, Ltd on behalf of Australasian Professional Society on Alcohol and other Drugs.)

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., (© 2022. The Author(s).)

Background: Digital health maturity models allow healthcare organizations to evaluate digital health capability and to develop roadmaps for improving patient care through technology. There are many models available commercially for healthcare providers to use to assess their digital health maturity. Currently, there are limited evidence-based methods to assess the quality, utility, and efficacy of maturity models to select the most appropriate model for the given context., Objective: To develop a framework to assess digital maturity models and facilitate recommendations for digital maturity model selection., Methods: A systematic, consultative, and iterative process was used. Literature analyses and a stakeholder needs analysis ( n  = 23) was conducted to develop content and design considerations. These considerations were incorporated into the initial version of the framework developed by researchers in a design workshop. External stakeholder review ( n  = 20) and improvements strengthened and finalized the framework., Results: The criteria of the framework include assessment of healthcare context, feasibility, integrity, completeness and actionability. Users can compare model performance in order to select the most appropriate model for their context., Conclusion: The framework provides healthcare stakeholders with a consistent and objective methodology to compare digital health maturity models, informing approaches to choosing a suitable model. This is a critical step as healthcare evolves towards a digital health system focused on improving the quality of care, reducing costs and improving the provider and consumer experience., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Woods, Eden, Duncan, Kodiyattu, Macklin and Sullivan.)

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).

Background: Health service providers must understand their digital health capability if they are to drive digital transformation in a strategic and informed manner. Little is known about the assessment and benchmarking of digital maturity or capability at scale across an entire jurisdiction. The public health care system across the state of Queensland, Australia has an ambitious 10-year digital transformation strategy., Objective: The aim of this research was to evaluate the digital health capability in Queensland to inform digital health strategy and investment., Methods: The Healthcare Information and Management Systems Society Digital Health Indicator (DHI) was used via a cross-sectional survey design to assess four core dimensions of digital health transformation: governance and workforce; interoperability; person-enabled health; and predictive analytics across an entire jurisdiction simultaneously. The DHI questionnaire was completed by each health care system ( n  = 16) within Queensland in February to July 2021. DHI is scored 0 to 400 and dimension score is 0 to 100., Results: The results reveal a variation in DHI scores reflecting the diverse stages of health care digitization across the state. The average DHI score across sites was 143 (range 78-193; SD35.3) which is similar to other systems in the Oceania region and global public systems but below the global private average. Governance and workforce was on average the highest scoring dimension (x̅= 54), followed by interoperability (x̅ = 46), person-enabled health (x̅ = 36), and predictive analytics (x̅ = 30)., Conclusion: The findings were incorporated into the new digital health strategy for the jurisdiction. As one of the largest single simultaneous assessments of digital health capability globally, the findings and lessons learnt offer insights for policy makers and organizational managers., Competing Interests: A.P. and L.J. are employees of HIMSS, the organization who developed the DHI. Remaining authors declare that they have no conflict of interest in the research., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

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.", Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Effective consumer centred healthcare incorporates consumer and clinician perspectives into decision making, in addition to traditional quantitative measures. This information is usually captured in qualitative data that requires manual analysis. Healthcare systems often lack resources to systematically incorporate qualitative feedback into decision making. Semi-automated content analysis tools, such as Leximancer, provide an efficient and objective alternative to time consuming manual content analysis (MCA). Literature on the validity of Leximancer in healthcare is sparse. This study seeks to validate Leximancer against MCA on a broad emotive conversational dataset gathered in a healthcare setting. At the outset of the COVID-19 pandemic, a large Australian hospital and health service conducted interactive webcasts with staff to provide updates and answer questions. A manual thematic analysis and a Leximancer content analysis were conducted independently on 20 webcast transcripts. The findings were compared, along with the time required to the complete each analysis. The Leximancer analysis identified nine concepts, while the manual analysis identified 12 concepts. The Leximancer concepts mapped to five of the concepts identified in the manual analysis, which accounted for 74% of mentions tagged in the text through the manual analysis. Leximancer missed concepts which required an emotional or contextual interpretation. The Leximancer analysis took 21 hours (excluding time to learn the program), compared to 73 hours for the manual analysis. Semi-automated content analysis provides an efficient alternative to manual qualitative data analysis, shifting it from a small-scale research activity to a more routine operational activity, albeit with some limitations. This is critical to be able to utilise at scale the rich narratives from consumers and clinicians in healthcare decision making., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Background and Significance: Intravascular (IV) catheters are the most invasive medical device in healthcare. Localized priority-setting related to IV catheter quality surveillance is a key objective of recent healthcare reform in Australia. We sought to determine the plausibility of using electronic health record (EHR) data for catheter surveillance by mapping currently available data across state-wide platforms. This work has identified barriers and facilitators to a state-wide EHR surveillance initiative., Materials and Methods: Data variables were generated and mapped from routinely used EHR sources across Queensland, Australia through a systematic search of gray literature and expert consultation with clinical information specialists. EHR systems were eligible for inclusion if they collected data related to IV catheter insertion, care, or outcomes of hospitalized patients. Generated variables were mapped against international recommendations for IV catheter surveillance, with data linkage and data export capacity narratively summarized., Results: We identified five EHR systems, namely, iEMR, MetaVision ICU ® , Multiprac, RiskMan, and the Nephrology Registry. Systems were used across jurisdictions and hospital wards. Data linkage was not evident across systems. Extraction processes for catheter data were not standardized, lacking clear and reliable extraction techniques. In combination, EHR systems collected 43/50 international variables recommended for catheter surveillance, however, individual systems collected a median of 24/50 (IQR 22, 30) variables. We did not identify integrated clinical analytic systems (incorporating machine learning) to support clinical decision making or for risk stratification (e.g., catheter-related infection)., Conclusion: Current data linkage across EHR systems limits the development of an IV catheter quality surveillance system to provide timely data related to catheter complications and harm. To facilitate reliable and timely surveillance of catheter outcomes using clinical informatics, substantial work is needed to overcome existing barriers and transform health surveillance., Competing Interests: JS has received grant funding from Griffith University, the Children’s Hospital Foundation, and investigator-initiated research and educational grants provided to Griffith University by vascular access product manufacturers (Becton Dickinson), unrelated to this project. GR-B previous employer, Griffith University has received on her behalf speaker fees and consultancy payments from Ausmed, 3M, Becton Dickinson, and Wolters Kluwer unrelated to this project. RW reports investigator-initiated research grants provided to Griffith University by vascular access product manufacturer Becton Dickinson, unrelated to this project. BS has received funding from CRC Blue Economy, Sustainable Built Environment National Research Centre, SEQ Water, The National Environmental Science Programme, and North Coast Primary Health Network, all unrelated to this project. CR reports investigator-initiated research grants and speaker fees provided to Griffith University by vascular access product manufacturers (3M, BD-Bard, Cardinal Health, Eloquest), unrelated to this project. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Schults, Ball, Sullivan, Rossow, Ray-Barruel, Walker, Stantic and Rickard.)

Background: Anticoagulants are high-risk medications and are a common cause of adverse events of hospitalized inpatients. The incidence of adverse events involving anticoagulants has remained relatively unchanged over the past two decades, suggesting that novel approaches are required to address this persistent issue. Electronic medication management systems (eMMSs) offer strategies to help reduce medication incidents and adverse drug events, yet poor system design can introduce new error types., Objective: Our objective was to evaluate the effect of the introduction of an electronic medical record (EMR) on the quality and safety of therapeutic anticoagulation management., Methods: A retrospective, observational pre-/poststudy was conducted, analyzing real-world data across five hospital sites in a single health service. Four metrics were compared 1-year pre- and 1-year post-EMR implementation. They included clinician-reported medication incidents, toxic pathology results, hospital-acquired bleeding complications (HACs), and rate of heparin-induced thrombocytopenia. Further subanalyses of patients experiencing HACs in the post-EMR period identified key opportunities for intervention to maximize safety and quality of anticoagulation within an eMMS., Results: A significant reduction in HACs was observed in the post-EMR implementation period (mean [standard deviation [SD]] =12.1 [4.4]/month vs. mean [SD] = 7.8 [3.5]/month; p  = 0.01). The categorization of potential EMR design enhancements found that new automated clinical decision support or improved pathology result integration would be suitable to mitigate future HACs in an eMMS. There was no significant difference in the mean monthly clinician-reported incident rates for anticoagulants or the rate of toxic pathology results in the pre- versus post-EMR implementation period. A 62.5% reduction in the cases of heparin-induced thrombocytopenia was observed in the post-EMR implementation period., Conclusion: The implementation of an EMR improves clinical care outcomes for patients receiving anticoagulation. System design plays a significant role in mitigating the risks associated with anticoagulants and consideration must be given to optimizing eMMSs., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).)

Background: Chronic diseases contribute to high rates of disability and mortality. Patient engagement in chronic disease self-management is an essential component of chronic disease models of health care. Wearables provide patient-centered health data in real time, which can help inform self-management decision-making. Despite the perceived benefits of wearables in improving chronic disease self-management, their influence on health care outcomes remains poorly understood., Objective: This review aimed to examine the influence of wearables on health care outcomes in individuals with chronic diseases through a systematic review of the literature., Methods: A narrative systematic review was conducted by searching 6 databases for randomized and observational studies published between January 1, 2016, and July 1, 2021, that included the use of a wearable intervention in a chronic disease group to assess its impact on a predefined outcome measure. These outcomes were defined as any influence on the patient or clinician experience, cost-effectiveness, or health care outcomes as a result of the wearable intervention. Data from the included studies were extracted based on 6 key themes, which formed the basis for a narrative qualitative synthesis. All outcomes were mapped against each component of the Quadruple Aim of health care. The guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement were followed in this study., Results: A total of 30 articles were included; studies reported 2446 participants (mean age: range 10.1-74.4 years), and the influence of 14 types of wearables on 18 chronic diseases was presented. The most studied chronic diseases were type 2 diabetes (4/30, 13%), Parkinson disease (3/30, 10%), and chronic lower back pain (3/30, 10%). The results were mixed when assessing the impact on a predefined primary outcome, with 50% (15/30) of studies finding a positive influence on the studied outcome and 50% (15/30) demonstrating a nil effect. There was a positive effect of 3D virtual reality systems on chronic pain in 7% (2/30) of studies that evaluated 2 distinct chronic pain syndromes. Mixed results were observed in influencing exercise capacity; weight; and biomarkers of disease, such as hemoglobin A 1c , in diabetes. In total, 155 outcomes were studied. Most (139/155, 89.7%) addressed the health care outcomes component. This included pain (11/155, 7.5%), quality of life (7/155, 4.8%), and physical function (5/155, 3.4%). Approximately 7.7% (12/155) of outcome measures represented the patient experience component, with 1.3% (2/155) addressing the clinician experience and cost., Conclusions: Given their popularity and capability, wearables may play an integral role in chronic disease management. However, further research is required to generate a strong evidence base for safe and effective implementation., Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021244562; https://www.crd.york.ac.uk/prospero/display&#95;record.php?RecordID=244562., (©Graeme Mattison, Oliver Canfell, Doug Forrester, Chelsea Dobbins, Daniel Smith, Juha Töyräs, Clair Sullivan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.07.2022.)

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.

Background and Objective: An epidemic of silicosis has emerged due to a failure to control risks associated with exposure to high-silica content respirable dust generated while working with artificial stone products. Methods for quantification of alveolar crystal burden are needed to advance our understanding of the pathobiology of silica-related lung injury as well as assisting in the diagnosis, clinical management and prognostication of affected workers. The objective of this study was to develop and validate novel methods to quantify alveolar crystal burden in bronchoalveolar lavage (BAL) fluid from patients with artificial stone silicosis., Methods: New methods to quantify and analyse alveolar crystal in BAL from patients with artificial stone silicosis were developed. Crystals were isolated and counted by microscopy and alveolar crystal burden was calculated using a standard curve generated by titration of respirable α-Quartz. The utility of the assay was then assessed in 23 patients with artificial stone silicosis., Results: Alveolar crystal burden was greater in patients with silicosis (0.44 picograms [pg]/cell [0.08-3.49]) compared to patients with other respiratory diagnoses (0.057 pg/cell [0.01-0.34]; p < 0.001). Alveolar crystal burden was positively correlated with years of silica exposure (ρ = 0.49, p = 0.02) and with decline in diffusing capacity of the lungs for carbon monoxide (ρ = -0.50, p = 0.02)., Conclusion: Alveolar crystal burden quantification differentiates patients with silicosis from patients with other respiratory disorders. Furthermore, crystal burden is correlated with the rate of decline in lung function in patients with artificial stone silicosis., (© 2022 The Authors. Respirology published by John Wiley & Sons Australia, Ltd on behalf of Asian Pacific Society of Respirology.)

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., Competing Interests: BZ was employed by Commonwealth Informatics Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Canfell, Davidson, Woods, Sullivan, Cocoros, Klompas, Zambarano, Eakin, Littlewood and Burton-Jones.)

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., (Copyright © 2022. Published by Elsevier B.V.)

Re-analyzing genomic information from a patient suspected of having an underlying genetic condition can improve the diagnostic yield of sequencing tests, potentially providing significant benefits to the patient and to the health care system. Although a significant number of studies have shown the clinical potential of re-analysis, less work has been performed to characterize the mechanisms responsible for driving the increases in diagnostic yield. Complexities surrounding re-analysis have also emerged. The terminology itself represents a challenge because "re-analysis" can refer to a range of different concepts. Other challenges include the increased workload that re-analysis demands of curators, adequate reimbursement pathways for clinical and diagnostic services, and the development of systems to handle large volumes of data. Re-analysis also raises ethical implications for patients and families, most notably when re-classification of a variant alters diagnosis, treatment, and prognosis. This review highlights the possibilities and complexities associated with the re-analysis of existing clinical genomic data. We propose a terminology that builds on the foundation presented in a recent statement from the American College of Medical Genetics and Genomics and describes each re-analysis process. We identify mechanisms for increasing diagnostic yield and provide perspectives on the range of challenges that must be addressed by health care systems and individual patients., Competing Interests: Conflict of Interest A.J.R. is the founder and director of ClearSKY Genomics. N.W. is a founder and board member of genomiQa., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Digital health in hospital settings is viewed as a panacea for achieving the "quadruple aim" of health care, yet the outcomes have been largely inconclusive. To optimize digital health outcomes, a strategic approach is necessary, requiring digital maturity assessments. However, current approaches to assessing digital maturity have been largely insufficient, with uncertainty surrounding the dimensions to assess., Objective: The aim of this study was to identify the current dimensions used to assess the digital maturity of hospitals., Methods: A systematic literature review was conducted of peer-reviewed literature (published before December 2020) investigating maturity models used to assess the digital maturity of hospitals. A total of 29 relevant articles were retrieved, representing 27 distinct maturity models. The articles were inductively analyzed, and the maturity model dimensions were extracted and consolidated into a maturity model framework., Results: The consolidated maturity model framework consisted of 7 dimensions: strategy; information technology capability; interoperability; governance and management; patient-centered care; people, skills, and behavior; and data analytics. These 7 dimensions can be evaluated based on 24 respective indicators., Conclusions: The maturity model framework developed for this study can be used to assess digital maturity and identify areas for improvement., (©Rhona Duncan, Rebekah Eden, Leanna Woods, Ides Wong, Clair Sullivan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.03.2022.)

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., (© 2022. The Author(s).)

Digital transformation is expensive and rarely smooth, often leading to higher costs than anticipated. It is challenging to demonstrate the contribution of digital health investment in achieving the healthcare aims of population health and workforce sustainability. We conducted a scoping review to understand how electronic medical record (EMR) implementations in the hospital setting have been evaluated using cost-benefit analysis (CBA) approaches. The review search resulted in 1184 unique articles, a final list of 28 was collated of which 20 were US-based studies. All studies were published in 2010-2019, with fewer studies published in more recent years. The data used to estimate benefits and costs were dated from 1996 to 2016, with most data from 2000 to 2010. Only three studies were qualified as using cost-benefit analysis approaches. While studies indicated that there is a positive impact from the EMR implementation, the impacts measured varied greatly. We concluded that the current literature demonstrates a lack of appropriate and comprehensive economic frameworks to understand the value of digital hospital implementations. Additionally, most studies failed to align fully to the quadruple aims of healthcare: they focused either on cost savings and/or improved patient outcomes and population health, none investigated healthcare-workforce sustainability., (© 2022. The Author(s).)

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., Competing Interests: None declared., (Thieme. All rights reserved.)

Objectives The aim of this study was to examine patient perceptions regarding vascular access quality measurement. Methods A web-based, cross-sectional survey was performed using a convenience sample of healthcare consumers with vascular access experience, recruited from September 2019 to June 2020. Survey respondents were asked to rate the perceived importance of 50 vascular access data items, including patient demographics, clinical and device characteristics, and insertion, management and complication data. Data were ranked using a five-point Likert scale (1, least important; 5, most important), and are reported as median values. Respondents proposed additional items and explored broader perspectives using free-text responses, which were analysed using inductive thematic analysis. Results In all, 68 consumers completed the survey. Participants were primarily female (82%), aged 40-49 years (29%) and living in Australia or New Zealand (84%). All respondents indicated that measuring the quality of vascular access care was important. Of the 50 items, 37 (74%) were perceived as 'most important' (median score 5), with measures of quality (i.e. outcomes and complications) rated highly (e.g. thrombosis and primary blood stream infection). Participants proposed 16 additional items. 'Gender' received the lowest perceived importance score (median score 3). Two themes emerged from the qualitative analysis of broader perspectives: (1) measurement of vascular access device complication severity and associated factors; and (2) patient experience. Conclusion Measuring vascular access quality and safety is important to consumers. Outcome and complication measures were rated 'most important', with respondents identifying a need for increased monitoring of their overall vascular access journey through the health system. What is known about the topic? The use of vascular access devices is common among hospitalised patients. Quality surveillance is not standardised, with no incorporation of patient preference. What does this paper add? We identify the data items consumers perceive as valuable to measure related to their vascular access journey; most importantly, consumers perceived the collecting of vascular access data as important. What are the implications for practitioners? Health services can use these data to develop platforms to monitor the quality and safety of vascular access care.

Importance: Diabetes is common amongst hospitalised patients and contributes to increased length of stay and poorer outcomes. Digital transformation, particularly the implementation of electronic medical records (EMRs), is rapidly occurring across the healthcare sector and provides an opportunity to improve the safety and quality of inpatient diabetes care. Alongside this revolution has been a considerable and ongoing evolution of digital interventions to optimise care of inpatients with diabetes including optimisation of EMRs, digital clinical decision support systems (CDSS) and solutions utilising data visibility to allow targeted patient review., Objective: To systematically appraise the recent literature to determine which digitally-enabled interventions including EMR, CDSS and data visibility solutions improve the safety and quality of non-critical care inpatient diabetes management., Methods: Pubmed, Embase and Cochrane databases were searched for suitable articles. Selected articles underwent quality assessment and analysis with results grouped by intervention type., Results: 1202 articles were identified with 42 meeting inclusion criteria. Four key interventions were identified; computerised physician order entry (n = 4), clinician decision support systems (n = 21), EMR driven active case finding (data visibility solutions) and targeted patient review (n = 10) and multicomponent system interventions (n = 7). Studies reported on glucometric outcomes, evidence-based medication ordering including medication errors, and patient and user outcomes. An improvement in glucometric measures particularly mean blood glucose and proportion of target range blood glucose levels and rates of evidence-based insulin prescribing were consistently demonstrated., Conclusion: Digitally-enabled interventions utilised to improve quality and safety of inpatient diabetes care were heterogenous in design. The majority of studies across all intervention types reported positive effects for evidence-based prescribing and glucometric outcomes. There was less evidence for digital interventions reducing diabetes medication administration errors or impacting patient outcomes (length of stay)., (Crown Copyright © 2021. Published by Elsevier B.V. All rights reserved.)

Successful implementation of digital health programs is imperative as it is becoming increasingly clear that digital solutions will underpin modern health care. These projects are often supported by large budgets and if not implemented successfully, the quality, safety, and efficiency of patient care may be compromised. Failure rates for the implementation of large, complex healthcare software platforms in digital health programs have been persistently high. Although several factors may contribute to the failure of such projects, the majority have been reported to fail largely due to poor project management. Nevertheless, little is known about the optimal project management approaches for digital health projects, with many health services reliant on external advisory companies and contractors for advice. Although publication bias makes it difficult to reliably study and understand global trends for the failure of digital health projects, examination of media reports and published literature indicates that this is a global phenomenon affecting digital health projects in North America, Europe and Australasia. In this article, our aim is to examine the literature for evidence underpinning current project management approaches used when implementing commercial, off-the-shelf healthcare information technology solutions, including complex healthcare software in large digital health programs in hospitals or across health systems, and evaluate the suitability of current project management approaches to deliver these projects. This starts to build an important evidence base for hospitals and health services considering digital transformation projects.

Background: The use of electronic medical records (EMRs)/electronic health records (EHRs) provides potential to reduce unwarranted clinical variation and thereby improve patient health care outcomes. Minimization of unwarranted clinical variation may raise and refine the standard of patient care provided and satisfy the quadruple aim of health care., Objective: A systematic review of the impact of EMRs and specific subcomponents (PowerPlans/SmartSets) on variation in clinical care processes in hospital settings was undertaken to summarize the existing literature on the effects of EMRs on clinical variation and patient outcomes., Methods: Articles from January 2000 to November 2020 were identified through a comprehensive search that examined EMRs/EHRs and clinical variation or PowerPlans/SmartSets. Thirty-six articles met the inclusion criteria. Articles were examined for evidence for EMR-induced changes in variation and effects on health care outcomes and mapped to the quadruple aim of health care., Results: Most of the studies reported positive effects of EMR-related interventions (30/36, 83%). All of the 36 included studies discussed clinical variation, but only half measured it (18/36, 50%). Those studies that measured variation generally examined how changes to variation affected individual patient care (11/36, 31%) or costs (9/36, 25%), while other outcomes (population health and clinician experience) were seldom studied. High-quality study designs were rare., Conclusions: The literature provides some evidence that EMRs can help reduce unwarranted clinical variation and thereby improve health care outcomes. However, the evidence is surprisingly thin because of insufficient attention to the measurement of clinical variation, and to the chain of evidence from EMRs to variation in clinical practices to health care outcomes., (©Tobias Hodgson, Andrew Burton-Jones, Raelene Donovan, Clair Sullivan. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 17.11.2021.)

Background: The COVID-19 pandemic has forced rapid digital transformation of many health systems. These innovations are now entering the literature, but there is little focus on the resulting disruption., Objective: We describe the implementation of digital innovations during the COVID-19 response of Australia's largest health service, Metro North (in Brisbane, Queensland), the challenges of the subsequent digital disruption, how these were managed, and lessons learned., Methods: Prior to the COVID-19 pandemic, the Australian state of Queensland created the Queensland Digital Clinical Charter, which provides guidance for the development of digital health programs. The guidelines utilize three horizons: digitizing workflows, leveraging digital data to transform clinical care, and reimagining new and innovative models of care. The technical response to COVID-19 in Metro North is described across these horizons. The rapid digital response caused significant disruption to health care delivery; management of the disruption and the outcomes are detailed. This is a participatory action research project, with members of the research team assisting with leading the implementation project informing the case report content., Results: Several digital innovations were introduced across Metro North during the COVID-19 response. This resulted in significant disruption creating digital hypervigilance, digital deceleration, data discordance, and postdigital "depression." Successful management of the digital disruption minimized the negative effects of rapid digital transformation, and contributed to the effective management of the pandemic in Queensland., Conclusion: The rapid digital transformation in Metro North during COVID-19 was successful in several aspects; however, ongoing challenges remain. These include the need to improve data sharing and increase interoperability. Importantly, the innovations need to be evaluated to ensure that Metro North can capitalize on these changes and incorporate them into long-term routine practice. Moving forward, it will be essential to manage not only the pandemic, but increasingly, the resultant digital disruption., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates., Objective: This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals., Methods: An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded., Results: A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools., Conclusions: Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration., (©Kay D Mann, Norm M Good, Farhad Fatehi, Sankalp Khanna, Victoria Campbell, Roger Conway, Clair Sullivan, Andrew Staib, Christopher Joyce, David Cook. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.09.2021.)