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OBJECTIVES: This manuscript presents a comprehensive framework for the assessment of the value of real-world evidence (RWE) in healthcare decision-making. While RWE has been proposed to overcome some limitations of traditional, one-off studies, no systematic framework exists to measure if RWE actually lowers the burden. This framework aims to fill that gap by providing conceptual approaches for evaluating the time and cost efficiencies of RWE, thus guiding strategic investments in RWE infrastructure. METHODS: The framework consists of four components: (114th Congress. 21st Century Cures Act.; 2015. https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf .) identification of stakeholders using and producing RWE, (National Health Council. Glossary of Patient Engagement Terms. Published 2019. Accessed May 18. 2021. https://nationalhealthcouncil.org/glossary-of-patient-engagement-terms/ .) understanding value propositions on how RWE can benefit stakeholders, (Center for Drug Evaluation and Research. CDER Patient-Focused Drug Development. U.S. Food & Drug Administration.) defining key performance indicators (KPIs), and (U.S. Department of Health and Human Services - Food and Drug Administration: Center for Devices and Radiological Health and Center for Biologics Evaluation and Research. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices - Guidance for Industry and Food and Drug Administration Staff. 2017. http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guida .) establishing metrics and case studies to assess value. KPIs are categorized as 'better, faster, or cheaper" as an indicator of value: better focusing on high-quality actionable evidence; 'faster,' denoting time-saving in evidence generation, and 'cheaper,' emphasizing cost-efficiency decision compared to methodologies that do not involve data routinely collected in clinical practice. Metrics and relevant case studies are tailored based on stakeholder value propositions and selected KPIs that can be used to assess what value has been created by using RWE compared to traditional evidence-generation approaches and comparing different RWE sources. RESULTS: Operationalized through metrics and case studies drawn from the literature, the value of RWE is documented as improving treatment effect heterogeneity evaluation, expanding medical product labels, and expediting post-market compliance. RWE is also shown to reduce the cost and time required to produce evidence compared to traditional one-off approaches. An original example of a metric that measures the time saved by RWE methods to detect a signal of a product failure was presented based on analysis of the National Cardiovascular Disease Registry. CONCLUSIONS: The framework presented in this manuscript offers a comprehensive approach for evaluating the value of RWE, applicable to all stakeholders engaged in leveraging RWE for healthcare decision-making. Through the proposed metrics and illustrated case studies, valuable insights are provided into the heightened efficiency, cost-effectiveness, and improved decision-making within clinical and regulatory domains facilitated by RWE. While this framework is primarily focused on medical devices, it could potentially inform the determination of RWE value in other medical products. By discerning the variations in cost, time, and data utility among various evidence-generation methods, stakeholders are empowered to invest strategically in RWE infrastructure and shape future research endeavors.

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The United Nations Sustainable Development Goals call for innovative proposals to ensure access to clean water and sanitation. While significant strides have been made in enhancing drinking water purification technologies, the role of drinking water distribution systems (DWDS) in maintaining water quality safety has increasingly become a focal point of concern. The presence of scale within DWDS can impede the secure and efficient functioning of the drinking water supply system, posing risks to the safety of drinking water quality. Previous research has identified that the primary constituents of scale in DWDS are insoluble minerals, such as calcium and magnesium carbonate. Elevated levels of hardness and alkalinity in the water can exacerbate scale formation. To address the scaling issue, softening technologies like induced crystallization, nanofiltration/reverse osmosis, and ion exchange are currently in widespread use. These methods effectively mitigate the scaling in DWDS by reducing the water's hardness and alkalinity. However, the application of softening technologies not only alters the hardness and alkalinity but also induces changes in the fundamental characteristics of water quality, leading to transition effects within the DWDS. This article reviews the impact of various softening technologies on the intrinsic properties of water quality and highlights the merits of electrochemical characteristic indicators in the assessment of water quality stability. Additionally, the paper delves into the factors that influence the transition effects in DWDS. It concludes with a forward-looking proposal to leverage artificial intelligence, specifically machine learning and neural networks, to develop an evaluation and predictive framework for the stability of drinking water quality and the transition effects observed in DWDS. This approach aims to provide a more accurate and proactive method for managing and predicting the impacts of water treatment processes on distribution system integrity and water quality over time.

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The Icelandic Prevention Model (IPM) follows a systematic but flexible process of community capacity building, data collection, analysis, dissemination, and community-engaged decision-making to guide the data-informed selection, prioritization, and implementation of intervention strategies in preventing adolescent substance use. This paper describes two new evaluation tools intended to assess the: 1) integrity of IPM implementation, and 2) unique aspects of IPM implementation in different community contexts. These evaluation tools include a: 1) five-phase IPM Evaluation Framework for Assessing Value Across Communities, Cultures, and Outcomes (IPM-EF); and 2) 10-Step IPM Implementation Integrity and Consistency Assessment (IPM-IICA) that utilizes both quantitative (scored) and qualitative (narrative) data elements to characterize implementation integrity and consistency at both community coalition and school community levels. The IPM-EF includes five phases. Phase 1: Describe the Intervention Context; Phase 2a: Document the Extent to Which the 10 Steps of the IPM were Implemented (using the IPM-IICA scored); Phase 2b: Document the Unique Community-Specific Methods Used within the 10 Steps of the IPM to Tailor Local Intervention Delivery (using the IPM-IICA narrative); Phase 3: Measure Changes in Community Risk and Protective Factors; Phase 4: Measure the Outcomes Associated with the IPM; and Phase 5: Investigate Multiple Full Cycles Over Time.

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This study comprehensively explores the factors that lead to low performers in an organization. A thorough literature review was conducted to construct an interview guide and obtain classification criteria for the factors that lead to low performers. Managers and low performers at multiple firms were interviewed individually to understand the various phenomena related to low performers in organizations. Based on the content of these interviews, 12 factors, classified into individual, leader, work, and organizational dimensions, were identified after three rounds of revisions by business administration experts. Next, a case study of Korea's S Life Insurance Company was conducted to examine the practical implications of the factors that contribute to creating low performers. In this case study, the analytic hierarchy process (AHP), involving eight departmental heads S Life Insurance Company's HR division, was utilized to identify the main factors that must be considered when evaluating low performers. While previous studies have examined low performers either at the individual, organizational, or institutional levels, this study presents a comprehensive and integrated evaluation framework of the factors that cause low performers. The proposed framework facilitates the identification and evaluation of low performers in various organizations and industries, and thus has practical implications in terms of establishing strategies to manage low performers more efficiently and improve organizational performance.

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Despite the existence of many interventions to mitigate or adapt to the health effects of climate change, their effectiveness remains unclear. Here, we introduce the Comprehensive Evaluation Framework for Intervention on Health Effects of Ambient Temperature to evaluate study designs and effects of intervention studies. The framework comprises three types of interventions: proactive, indirect, and direct, and four categories of indicators: classification, methods, scope, and effects. We trialed the framework by an evaluation of existing intervention studies. The evaluation revealed that each intervention has its own applicable characteristics in terms of effectiveness, feasibility, and generalizability scores. We expanded the framework's potential by offering a list of intervention recommendations in different scenarios. Future applications are then explored to establish models of the relationship between study designs and intervention effects, facilitating effective interventions to address the health effects of ambient temperature under climate change.

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BACKGROUND: Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient data for training, testing and validation. Synthetic data has been suggested in response to privacy concerns and regulatory requirements and can be created by training a generator on real data to produce a dataset with similar statistical properties. Competing metrics with differing taxonomies for quality evaluation have been proposed, resulting in a complex landscape. Optimising quality entails balancing considerations that make the data fit for use, yet relevant dimensions are left out of existing frameworks. METHOD: We performed a comprehensive literature review on the use of quality evaluation metrics on synthetic data within the scope of synthetic tabular healthcare data using deep generative methods. Based on this and the collective team experiences, we developed a conceptual framework for quality assurance. The applicability was benchmarked against a practical case from the Dutch National Cancer Registry. CONCLUSION: We present a conceptual framework for quality assuranceof synthetic data for AI applications in healthcare that aligns diverging taxonomies, expands on common quality dimensions to include the dimensions of Fairness and Carbon footprint, and proposes stages necessary to support real-life applications. Building trust in synthetic data by increasing transparency and reducing the safety risk will accelerate the development and uptake of trustworthy AI tools for the benefit of patients. DISCUSSION: Despite the growing emphasis on algorithmic fairness and carbon footprint, these metrics were scarce in the literature review. The overwhelming focus was on statistical similarity using distance metrics while sequential logic detection was scarce. A consensus-backed framework that includes all relevant quality dimensions can provide assurance for safe and responsible real-life applications of synthetic data. As the choice of appropriate metrics are highly context dependent, further research is needed on validation studies to guide metric choices and support the development of technical standards.

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Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.

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INTRODUCTION: The uptake of complex technologies and platforms has resulted in several challenges in the pricing and reimbursement of innovative pharmaceuticals. To address these challenges, plenty of concepts have already been described in the scientific literature about innovative value judgment or payment models, which are either (1) remaining theoretical; or (2) applied only in pilots with limited impact on patient access; or (3) applied so heterogeneously in many different countries that it prevents the health care industry from meeting expectations of HTA bodies and health care payers in the evidence requirements or offerings in different jurisdictions. AREAS COVERED: This paper provides perspectives on how to reduce the heterogeneity of pharmaceutical payment models across European countries in five areas, including 1) extended evaluation frameworks, 2) performance-based risk-sharing agreements, 3) pooled procurement for low volume or urgent technologies, 4) alternative access schemes, and 5) delayed payment models for technologies with high upfront costs. EXPERT OPINION: Whilst pricing and reimbursement decisions will remain a competence of EU member states, there is a need for alignment of European pharmaceutical payment model components in critical areas with the ultimate objective of improving the equitable access of European patients to increasingly complex pharmaceutical technologies.

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Coronary artery disease is one of the leading causes of death worldwide, and medical imaging methods such as coronary artery computed tomography are vitally important in its detection. More recently, various computational approaches have been proposed to automatically extract important artery coronary features (e.g., vessel centerlines, cross-sectional areas along vessel branches, etc.) that may ultimately be able to assist with more accurate and timely diagnoses. The current study therefore validated and benchmarked a recently developed automated 3D centerline extraction method for coronary artery centerline tracking using synthetically segmented coronary artery models based on the widely used Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) training dataset. Based on standard accuracy metrics and the ground truth centerlines of all 32 coronary vessel branches in the RCAAEF training dataset, this 3D divide and conquer Voronoi diagram method performed exceptionally well, achieving an average overlap accuracy (OV) of 99.97%, overlap until first error (OF) of 100%, overlap of the clinically relevant portion of the vessel (OT) of 99.98%, and an average error distance inside the vessels (AI) of only 0.13 mm. Accuracy was also found to be exceptionally for all four coronary artery sub-types, with average OV values of 99.99% for right coronary arteries, 100% for left anterior descending arteries, 99.96% for left circumflex arteries, and 100% for large side-branch vessels. These results validate that the proposed method can be employed to quickly, accurately, and automatically extract 3D centerlines from segmented coronary arteries, and indicate that it is likely worthy of further exploration given the importance of this topic.

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BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation. RESULTS: We systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks. CONCLUSIONS: We highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types.

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Introduction: The United States Veterans Health Administration (VHA) Office of Rural Health funds Enterprise-Wide Initiatives (system-wide initiatives) to spread promising practices to rural Veterans. The Office requires that evaluations of Enterprise-Wide Initiatives use the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. This presents a unique opportunity to understand the experience of using RE-AIM across a series of evaluations. The authors conducted a study to document the benefits and pitfalls of using RE-AIM, capture the variety of ways that the team captured the elements of RE-AIM, and develop recommendations for the future use of RE-AIM in evaluation. Materials and methods: The authors first conducted a document review to capture pre-existing information about how RE-AIM was used. They subsequently facilitated two focus groups to gather more detailed information from team members who had used RE-AIM. Finally, they used member-checking throughout the writing process to ensure accurate data representation and interpretation and to gather additional feedback. Results: Four themes emerged from the document review, focus groups, and member checking. RE-AIM: provides parameters and controls the evaluation scope, "buckets" are logical, plays well with other frameworks, and can foster collaboration or silo within a team. Challenges and attributes for each RE-AIM dimension were also described. Discussion: Overall, participants reported both strengths and challenges to using RE-AIM as an evaluation framework. The overarching theme around the challenges with RE-AIM dimensions was the importance of context. Many of these benefits and challenges of using RE-AIM may not be unique to RE-AIM and would likely occur when using any prescribed framework. The participants reported on the RE-AIM domains in a variety of ways in their evaluation reports and were not always able capture data as originally planned. Recommendations included: start with an evaluation framework (or frameworks) and revisit it throughout the evaluation, consider applying RE-AIM PRISM (Practical Robust Implementation Framework) to gain a broader perspective, and intentionally integrate quantitative and qualitative team members, regardless of the framework used.

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The evaluation of Responsible Research and Innovation (RRI) actions presents important challenges for the key stakeholders engaged in the process of RRI implementation, such as policy makers, programme managers, and researchers. While there is a considerable body of literature on the conceptualization of responsibility in research and a plethora of attempts to practice RRI, there is a need for increased attention to the monitoring and evaluation of case studies of RRI implementations in research organisations, in particular regarding their structural change effects. This paper aims to discuss a contextualised developmental framework for evaluating RRI implementation in research organisations, with a specific focus on achieving structural change through tailor-made action plans. The framework, developed through RRI evaluation work in the field of biosciences, adopts a systemic and process-oriented perspective, encompassing participatory, anticipatory, reflexive, and responsive dimensions. Concrete empirical examples from bioscience organizations are provided to illustrate how the framework relates to specific conditions, experiences, and solutions, demonstrating how conceptual insights have emerged from real-life practices and data analysis. While the framework was initially customized for the specific contexts of six bioscience research organizations, it holds potential for broader relevance and applicability in addressing challenges related to RRI design, implementation, and evaluation.

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Background: Low-and middle-income countries mostly have ageing populations with many unmet economic, social, or health-related needs, Vietnam being an example. Community-based support in Vietnam, organized as Intergenerational Self-Help Clubs (ISHCs) based on the Older People Associations (OPA) model, can help to meet these needs by the provision of services for various aspects of life. This study aims to assess the implementation of the ISHCs and whether successful implementation is associated with more member-reported positive health. Methods: We used the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework to evaluate the implementation using multiple data sources: ISHC board surveys (n = 97), ISHC member surveys (n = 5,080 in 2019 and n = 5,555 in 2020), focus group discussions (6; n = 44), and interviews with members and board leaders (n = 4). Results: Reach ranged between 46 and 83% of ISHCs reaching target groups, with a majority of women and older people participating. Regarding Effectiveness, members indicated high satisfaction with the ISHCs. Adoption scores were high, with 74%-99% for healthcare and community support activities, and in 2019, higher adoption scores were associated with more members reporting good positive health. In 2020, reported positive health slightly decreased, probably due to the influence of the COVID-19 pandemic. A total of 61 ISHCs had consistent or improving Implementation from 2019 to 2020, and confidence in Maintenance was high. Conclusion: The implementation of the OPA model in Vietnam is promising regarding its promotion of health and may help to tackle the needs of an ageing population. This study further shows that the RE-AIM framework helps to assess community health promotion approaches.

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Introduction: The purpose of this study is to evaluate two recruitment strategies on schools and participant participation rates and representativeness (reach) within a pediatric obesity treatment trial tailored for families who live in rural areas. Methods: Recruitment of schools was evaluated based on their progress toward enrolling participants. Recruitment and reach of participants were evaluated using (1) participation rates and (2) representativeness of demographics and weight status of participants compared to eligible participants (who did not consent and enroll) and all students (regardless of eligibility). School recruitment, as well as participant recruitment and reach, were evaluated across recruitment methods comparing opt-in (i.e., caregivers agreed to allow their child to be screened for eligibility) vs. screen-first (i.e., all children screened for eligibility). Results: Of the 395 schools contacted, 34 schools (8.6%) expressed initial interest; of these, 27 (79%) proceeded to recruit participants, and 18 (53%) ultimately participated in the program. Of schools who initiated recruitment, 75% of schools using the opt-in method and 60% of schools using the screen-first method continued participation and were able to recruit a sufficient number of participants. The average participation rate (number of enrolled individuals divided by those who were eligible) from all 18 schools was 21.6%. This percentage was higher in schools using the screen-first method (average of 29.7%) compared to schools using the opt-in method (13.5%). Study participants were representative of the student population based on sex (female), race (White), and eligibility for free and reduced-price lunch. Study participants had higher body mass index (BMI) metrics (BMI, BMIz, and BMI%) than eligible non-participants. Conclusions: Schools using the opt-in recruitment were more likely to enroll at least 5 families and administer the intervention. However, the participation rate was higher in screen-first schools. The overall study sample was representative of the school demographics.

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BACKGROUND: The ability to compare RNA secondary structures is important in understanding their biological function and for grouping similar organisms into families by looking at evolutionarily conserved sequences such as 16S rRNA. Most comparison methods and benchmarks in the literature focus on pseudoknot-free structures due to the difficulty of mapping pseudoknots in classical tree representations. Some approaches exist that permit to cluster pseudoknotted RNAs but there is not a general framework for evaluating their performance. RESULTS: We introduce an evaluation framework based on a similarity/dissimilarity measure obtained by a comparison method and agglomerative clustering. Their combination automatically partition a set of molecules into groups. To illustrate the framework we define and make available a benchmark of pseudoknotted (16S and 23S) and pseudoknot-free (5S) rRNA secondary structures belonging to Archaea, Bacteria and Eukaryota. We also consider five different comparison methods from the literature that are able to manage pseudoknots. For each method we clusterize the molecules in the benchmark to obtain the taxa at the rank phylum according to the European Nucleotide Archive curated taxonomy. We compute appropriate metrics for each method and we compare their suitability to reconstruct the taxa.

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BACKGROUND: The COVID-19 pandemic has resulted in profound and far-reaching impacts on maternal and newborn care and outcomes. As part of the ASPIRE COVID-19 project, we describe processes and outcome measures relating to safe and personalised maternity care in England which we map against a pre-developed ASPIRE framework to establish the potential impact of the COVID-19 pandemic for two UK trusts. METHODS: We undertook a mixed-methods system-wide case study using quantitative routinely collected data and qualitative data from two Trusts and their service users from 2019 to 2021 (start and completion dates varied by available data). We mapped findings to our prior ASPIRE conceptual framework that explains pathways for the impact of COVID-19 on safe and personalised care. RESULTS: The ASPIRE framework enabled us to develop a comprehensive, systems-level understanding of the impact of the pandemic on service delivery, user experience and staff wellbeing, and place it within the context of pre-existing challenges. Maternity services experienced some impacts on core service coverage, though not on Trust level clinical health outcomes (with the possible exception of readmissions in one Trust). Both users and staff found some pandemic-driven changes challenging such as remote or reduced antenatal and community postnatal contacts, and restrictions on companionship. Other key changes included an increased need for mental health support, changes in the availability and uptake of home birth services and changes in induction procedures. Many emergency adaptations persisted at the end of data collection. Differences between the trusts indicate complex change pathways. Staff reported some removal of bureaucracy, which allowed greater flexibility. During the first wave of COVID-19 staffing numbers increased, resolving some pre-pandemic shortages: however, by October 2021 they declined markedly. Trying to maintain the quality and availability of services had marked negative consequences for personnel. Timely routine clinical and staffing data were not always available and personalised care and user and staff experiences were poorly captured. CONCLUSIONS: The COVID-19 crisis magnified pre-pandemic problems and in particular, poor staffing levels. Maintaining services took a significant toll on staff wellbeing. There is some evidence that these pressures are continuing. There was marked variation in Trust responses. Lack of accessible and timely data at Trust and national levels hampered rapid insights. The ASPIRE COVID-19 framework could be useful for modelling the impact of future crises on routine care.

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Background: Rigorous evaluations of health system interventions to strengthen hypertension and cardiovascular disease (CVD) care remain scarce in sub-Saharan Africa. This study aims to evaluate the reach, effectiveness, adoption / acceptability, implementation fidelity, cost, and sustainability of the Ghana Heart Initiative (GHI), a multicomponent supply-side intervention to improve cardiovascular health in Ghana. Methods: This study adopts a mixed- and multi-methods design comparing the effects of the GHI in 42 intervention health facilities (i.e. primary, secondary and tertiary) in the Greater Accra Region versus 56 control health facilities in the Central and Western Regions. The evaluation design is guided by the RE-AIM framework underpinned by the WHO health systems building blocks framework, integrated by the Institute of Medicine's six dimensions of health care quality: safe, effective, patient-centered, timely, effi cient, equitable. The assessment tools include: (i) a health facility survey, (ii) a healthcare provider survey assessing the knowledge, attitudes, and practices on hypertension and CVD management, (iii) a patient exit survey, (iv) an outpatient and in-patient medical record review and (v) qualitative interviews with patients and various health system stakeholders to understand the barriers and facilitators around the implementation of the GHI. In addition to primary data collection, the study also relies on secondary routine health system data, i.e., the District Health Information Management System to conduct an interrupted time series analysis using monthly counts for relevant hypertension and CVD specific indicators as outcomes. The primary outcome measures are performance of health service delivery indicators, input, process and outcome of care indicators (including screening of hypertension, newly diagnosed hypertension, prescription of guideline directed medical therapy, and satisfaction with service received and acceptability) between the intervention and control facilities. Lastly, an economic evaluation and budget impact analysis is planned to inform the nationwide scale-up of the GHI. Discussion: This study will generate policy-relevant data on the reach, effectiveness, implementation fidelity, adoption / acceptability, and sustainability of the GHI, and provide insights on the costs and budget-impacts to inform nation-wide scale-up to expand the GHI to other regions across Ghana and offer lessons to other low- and middle-income countries settings as well. RIDIE Registration Number: RIDIE-STUDY-ID-6375e5614fd49 (https://ridie.3ieimpact.org/index.php).

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The digital transformation of our health care system will require not only digitization of existing tools but also a redesign of our care delivery system and collaboration with digital partners. Traditional patient journeys are reactive to symptom presentation and delayed by health care system-centric scheduling, leading to poor experience and avoidable adverse outcomes. Patient journeys will be reimagined to a digital health pathway that seamlessly integrates various care experiences from telemedicine, remote monitoring, to in-person clinic visits. Through centering the care delivery around the patients, they can have more delightful experiences and enjoy the quality of standardized condition pathways and outcomes. To design and implement digital health pathways at scale, enterprise health care systems need to develop capabilities and partnerships in human-centered design, operational workflow, clinical content management, communication channels and mechanisms, reporting and analytics, standards-based integration, security and data management, and scalability. Using a human-centered design methodology, care pathways will be built upon an understanding of the unmet needs of the patients to have a more enjoyable experience of care with improved clinical outcomes. To power this digital care pathway, enterprises will choose to build or partner for clinical content management to operationalize up-to-date, best-in-class pathways. With this clinical engine, this digital solution will engage with patients through multimodal communication modalities, including written, audio, photo, or video, throughout the patient journey. Leadership teams will review reporting and analytics functions to track that the digital care pathways will be iterated to improve patient experience, clinical metrics, and operational efficiency. On the backend, standards-based integration will allow this system to be built in conjunction with the electronic medical record and other data systems to provide safe and efficient use of the digital care solution. For protecting patient information and compliance, a security and data management strategy is critical to derisking breeches and preserving privacy. Finally, a framework of technical scalability will allow digital care pathways to proliferate throughout the enterprise and support the entire patient population. This framework empowers enterprise health care systems to avoid collecting a fragmented series of one-off solutions but develop a sustainable concerted roadmap to the future of proactive intelligent patient care.

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Background: Health information communication technology (ICT) has rapidly evolved in contemporary pharmacy practice worldwide. The Australian healthcare system is experiencing a paradigm shift to real-time interconnectivity for practitioners and consumers and interoperable digital health. With these developments come a need to evaluate use of technologies specifically in pharmacy practice to optimize their clinical functionality. There are no published frameworks for evaluating ICT needs or implementation in pharmacy practice. Objective: This paper proposes a theoretical framework for evaluating health ICT in pharmacy. Methods: Development of the evaluation framework was informed by a systematic scoping review and health informatics literature. Specifically, the framework drew upon critical appraisal and concept mapping of the TAM, ISS and HOT-fit validated models, with respect to health ICT in contemporary pharmacy practice. Results: The proposed model was named the Technology Evaluation Key (TEK). The TEK comprises of 10 domains; healthcare system, organization, practitioner, user interface, ICT, use, operational outcomes, system outcomes, clinical outcomes and timely access to care. Conclusions: This is the first published proposed evaluation framework developed for health ICT specifically in contemporary pharmacy practice. TEK represents a pragmatic way to ensure the development, refinement and implementation of new and existing technologies in contemporary pharmacy practice to keep pace with the clinical and professional requirements of community pharmacists. Operational, clinical and system outcomes should be evaluated as coexisting factors that may impact implementation. Validation research utilizing Design Science Research Methodology will enhance utility for end users and ensure the relevance and application of the TEK to contemporary pharmacy practice.

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BACKGROUND: Data-driven research is a very important component of One Health. As the core part of the global One Health index (GOHI), the global One Health Intrinsic Drivers index (IDI) is a framework for evaluating the baseline conditions of human-animal-environment health. This study aims to assess the global performance in terms of GOH-IDI, compare it across different World Bank regions, and analyze the relationships between GOH-IDI and national economic levels. METHODS: The raw data among 146 countries were collected from authoritative databases and official reports in November 2021. Descriptive statistical analysis, data visualization and manipulation, Shapiro normality test and ridge maps were used to evaluate and identify the spatial and classificatory distribution of GOH-IDI. This paper uses the World Bank regional classification and the World Bank income groups to analyse the relationship between GOH-IDI and regional economic levels, and completes the case studies of representative countries. RESULTS: The performance of One Health Intrinsic Driver in 146 countries was evaluated. The mean (standard deviation, SD) score of GOH-IDI is 54.05 (4.95). The values (mean SD) of different regions are North America (60.44, 2.36), Europe and Central Asia (57.73, 3.29), Middle East and North Africa (57.02, 2.56), East Asia and Pacific (53.87, 5.22), Latin America and the Caribbean (53.75, 2.20), South Asia (52.45, 2.61) and sub-Saharan Africa (48.27, 2.48). Gross national income per capita was moderately correlated with GOH-IDI (R2 = 0.651, Deviance explained = 66.6%, P < 0.005). Low income countries have the best performance in some secondary indicators, including Non-communicable Diseases and Mental Health and Health risks. Five indicators are not statistically different at each economic level, including Animal Epidemic Disease, Animal Biodiversity, Air Quality and Climate Change, Land Resources and Environmental Biodiversity. CONCLUSIONS: The GOH-IDI is a crucial tool to evaluate the situation of One Health. There are inter-regional differences in GOH-IDI significantly at the worldwide level. The best performing region for GOH-IDI was North America and the worst was sub-Saharan Africa. There is a positive correlation between the GOH-IDI and country economic status, with high-income countries performing well in most indicators. GOH-IDI facilitates researchers' understanding of the multidimensional situation in each country and invests more attention in scientific questions that need to be addressed urgently.

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