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With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. This study investigated public preferences for digital health data sharing. A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes (\"data collector,\" \"data user,\" \"reason for data use,\" \"information on data sharing and consent,\" and \"availability of review process\"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets. A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for \"data collector,\" \"data user,\" \"reason,\" \"type of consent,\" and \"review\" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Combining patient-generated health data and digital health platforms may improve patient experience and population health, mitigate rising health care costs, reduce clinician burnout, and enable health equity. However, lack of trust may be a notable barrier to the data-sharing required by such platforms. Understanding sociodemographic, health, and personal characteristics will enable developers and implementers of such technologies to consider these in their technical design requirements. This study aims to understand relationships between sociodemographic characteristics of caregivers of children or adolescents and trust in and willingness to use digital platforms to store and share personal health information for clinical care and research. This study used a mixed methods approach, including surveys of caregivers of youth aged <18 years living in Canada or the United States and youth aged 16 to 17 years living in Canada, as well as web-based bulletin board discussions to further explore topics of trust in data sharing. Sociodemographic and survey data were tabulated and explored using proportional odds ordinal regression models. Comments from web-based group discussions were analyzed thematically using a coding approach to identify issues important to the participants. Survey data from 1128 caregivers (female participants: n=549, 48.7%; 36-50 years old: n=660, 58.5%; Canadian: n=603, 53.5%; urban population: n=494, 43.8%) were collected, of which 685 (60.7%) completed all questions. Data from 173 youth (female participants: n=73, 42.2%; urban population: n=94, 54.3%) were collected, of which 129 (74.6%) completed all questions, and data were available for analysis. Furthermore, among 40 participants, 23 (58%) caregivers contributed to the web-based discussion boards. Related to trust, living in a rural area (vs urban; odds ratio [OR] 0.66, 95% CI 0.46-0.95) resulted in lower concern for data privacy and security, while having an undergraduate (OR 1.82, 95% CI 1.30-2.55) or graduate degree (vs secondary or trade school; OR 2.50, 95% CI 1.68-3.73) resulted in higher levels of concern. Living with a chronic disease (OR 1.81, 95% CI 1.35-2.44) increased levels of concern regarding data privacy and security. Interestingly, those with chronic disease were more willing to use digital platforms for clinical care and share personal health information for not-for-profit research. Caregivers were most concerned about data breaches involving data from their children but also highlighted that digital platforms would allow for better coordination of care for their children. Our research confirms the willingness of caregivers and youth to use digital platforms for both clinical care delivery and research and suggests that the value of a digital platform may outweigh the risks of its use. Engagement of end users in co-designing such platforms has the potential to enhance digital trust. However, digital trust varies across sociodemographic groups; therefore, diverse end user engagement is necessary when designing digital applications.

Background Most people receive most of their health care in in Australia in primary care, yet researchers and policymakers have limited access to resulting clinical data. Widening access to primary care data and linking it with hospital or other data can contribute to research informing policy and provision of services and care; however, limitations of primary care data and barriers to access curtail its use. The Australian Health Research Alliance (AHRA) is seeking to build capacity in data-driven healthcare improvement; this study formed part of its workplan. Methods The study aimed to build capacity for data driven healthcare improvement through identifying primary care datasets in Australia available for secondary use and understand data quality frameworks being applied to them, and factors affecting national capacity for secondary use of primary care data from the perspectives of data custodians and users. Purposive and snowball sampling were used to disseminate a questionnaire and respondents were invited to contribute additional information via semi-structured interviews. Results Sixty-two respondents collectively named 106 datasets from eclectic sources, indicating a broad conceptualisation of what a primary care dataset available for secondary use is. The datasets were generated from multiple clinical software systems, using different data extraction tools, resulting in non-standardised data structures. Use of non-standard data quality frameworks were described by two-thirds of data custodians. Building trust between citizens, clinicians, third party data custodians and data end-users was considered by many to be a key enabler to improve primary care data quality and efficiencies related to secondary use. Trust building qualities included meaningful stakeholder engagement, transparency, strong leadership, shared vision, robust data security and data privacy protection. Resources to improve capacity for primary care data access and use were sought for data collection tool improvements, workforce upskilling and education, incentivising data collection and making data access more affordable. Conclusions The large number of identified Australian primary care related datasets suggests duplication of labour related to data collection, preparation and utilisation. Benefits of secondary use of primary care data were many, and strong national leadership is required to reach consensus on how to address limitations and barriers, for example accreditation of EMR clinical software systems and the adoption of agreed data and quality standards at all stages of the clinical and research data-use lifecycle. The study informed the workplan of AHRA’s Transformational Data Collaboration to improve partner engagement and use of clinical data for research.

The secondary use of health data is associated with enormous opportunities to improve healthcare efficiency and the creation of novel data-driven value propositions. Information Systems research has recognized this potential and focuses on that topic from various perspectives, such as data-driven value creation through health analytics or data platform ecosystems to facilitate data sharing. However, with emerging regulatory initiatives, such as the European Health Data Space, rules for the secondary use are changing, with implications for researchers, innovators, and healthcare organizations. The outcomes of data value creation efforts are mainly dependent on available data, with the assurance of high data quality remaining a challenge. To discuss how value can be created from the secondary use of health data, what challenges may arise, and how they can be addressed, a workshop on this topic was organized in November 2023, in which 19 academic researchers and 18 practitioners from 27 organizations participated. This workshop report highlights the challenges of and potential solutions for the secondary use of health data. While current Information Systems research often focuses on utilizing Electronic Health Records, the field could be enriched by understanding the secondary use of health data on an individual, organizational, and societal level.

Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aims to utilize machine learning models to detect high-risk groups of excessive health and social services utilization at early stage, facilitating the implementation of preventive interventions. We used pseudonymized data covering a four-year period and including information on a total of 33,374 senior citizens from Southern Finland. The endpoint was defined based on the occurrence of unplanned healthcare visits and the total number of different services used. Input features included individual's basic demographics, health status and past usage of healthcare resources. Logistic regression and eXtreme Gradient Boosting (XGBoost) methods were used for binary classification, with the dataset split into 70% training and 30% testing sets. Subgroup-based results mirrored trends observed in the full cohort, with age and certain health issues, e.g. mental health, emerging as positive predictors for high service utilization. Conversely, hospital stay and urban residence were associated with decreased risk. The models achieved a classification performance (AUC) of 0.61 for the full cohort and varying in the range of 0.55-0.62 for the subgroups. Predictive models offer potential for predicting future high service utilization in the older adult population. Achieving high classification performance remains challenging due to diverse contributing factors. We anticipate that classification performance could be increased by including features based on additional data categories such as socio-economic data.

Background The reuse of health data is critical for advancing health research, yet it raises complex ethical, legal, and societal challenges. In the European Union, the recently adopted European Health Data Space (EHDS) aims to harmonize access to and reuse of health data for research and innovation, while safeguarding individual rights. However, questions remain about what patients value in data reuse and how their values can be embedded in governance frameworks. Belgium, with its strong research tradition and central role in EU policymaking, offers an important testbed for these questions. Methods We conducted a qualitative study combining semi-structured interviews with 39 professionals (policy and governance representatives, researchers, physicians, industry, law and ethics experts, and supporting actors) and three focus groups with ten oncology patients and patient representatives. The interviews explored professional perspectives on data reuse and informed the design of the focus groups. Data were analyzed using the framework method, integrating inductive and deductive coding. Patient-researchers were involved throughout the study design, data collection, and interpretation. Results Participants unanimously recognized the value of health data reuse at both individual (improved healthcare) and collective (advancing scientific research) levels. Patients emphasized the importance of transparency, feedback about research results, and opportunities for meaningful involvement in data governance. While patients were generally willing to share their data, they highlighted conditions such as trust in the system, strong data protection, and safeguards against misuse. Both professionals and patients considered the EHDS a positive step for facilitating research but expressed concerns about its practical implementation. All participants recognized the value of both individual and collective control over health data, though challenges of representation and resource constraints were noted when it came to patient involvement. Conclusions This study provides the first in-depth investigation in Belgium of what patients and professionals value in relation to health data reuse, particularly in the context of the EHDS. Findings underscore that the legitimacy of the EHDS will depend on embedding patient values into its implementation through transparency, participatory data governance, and legal and technical safeguards. The results provide actionable insights for policymakers, researchers, and practitioners to build a value-oriented and socially legitimate health data ecosystem at national and EU levels.

The General Data Protection Regulation (\"GDPR\") legal basis for obtaining consent for the processing of personal data for research purposes, where those purposes cannot be fully specified in advance, is provided for in Articles 6, 7 and Recital 33. However, GDPR's requirements for obtaining consent, as to the secondary use and sharing of data in research, have been argued to have generated confusion, whilst the conflicts between the Regulation itself, its practical application and research ethics are well-documented (1). The requirements for \"informed consent\", as defined within the GDPR, have not been well defined in the context of genome research or clinical trials (2), which has in turn led to the implementation and interpretation of the lawful basis to span into different idiosyncratic models. This naturally has fed into the uncertainty of how the legal basis can be applied in practice and calls for an investigation into the requirements for consent to be \"informed\" in the context of health research. This work aims to provide a scoping review and analysis of relevant publications with ultimate purpose to examine whether the concept of 'data altruism', as stipulated within Article 2 (10) of the Data Governance Act (\"DGA\"), addresses the gaps left behind by the application of the legal basis of 'consent', under the GDPR (Art. 6 (1) and 7), in so far as the secondary uses of data for research are concerned. In this light the article, by exploring available solutions found in relevant literature and used in practice in national and European projects, examines how 'data altruism' can add any value and work as a cohesive solution that the research community can use. The article, through its research, intends to answer the following questions:What gaps has the GDPR left when it comes to the interpretation and practical application of \"consent\" towards the secondary use of health data;Can the DGA, through the mechanism of 'data altruism', address these issues and provide a solution;What solutions have been used so far in practice to address this issue. To address the above-mentioned questions, the Arskey and O'Malley scoping review methodology and best practice, as outlined in the Joanna Briggs scoping review guidelines, have been applied. The research questions have been identified through an extensive literature review and consultation with subject matter experts. The search was conducted using six search engines and utilising a tailored search strategy, with the application of both MESH and non-MESH based search terms. From the identified relevant publications, 148 abstracts were kept to be read and 60 of those publications were kept as relevant. A PRISMA chart showcases the process in which the publications were reviewed and the process which led to the final papers kept as relevant. The title-abstract and full text screening and charting the data were concluded independently by two reviewers. Discrepancies were then resolved by a third reviewer. Results are summarised in both chart and narrative form below. The final 60 publications were then split into three subcategories: (i) GDPR critique (23 publications listed); (ii) iterations of consent and data altruism (21 publications listed); and (iii) proposed solutions and current practices (31 publications). Certain of the publications fell into more than one of the above subcategories, given the interdisciplinary element of the subject and theme of each paper. Throughout the research, 5 of the publications discuss the Data Governance Act and data altruism, with 4 of those providing a critique over the text used in the DGA and the concept of 'data altruism' in relation to 'consent' as defined within the GDPR and the overall legislative framework for the secondary uses of data.

The aim of this study was to evaluate the effectiveness of the health screening procedure for home-dwelling older adults in reducing emergency department visits and associated costs. Data were derived from health screenings from 2020 to 2021 for 75-year-old home-dwelling residents of Western Finland. The study compared emergency department visits and associated costs between older adults who participated in the health screening (intervention group) and those who did not (non-intervention group). For each older adult, three non-intervention controls were matched according to age, sex, health screening year and wellbeing service county. Emergency department visits and International Classification of Diseases (ICD)-10 codes from one year before to two years after health screening were analyzed. In the non-intervention group, a 19% increase in emergency visit rates was seen (457-564 per 1000 person-years), while the intervention group showed a 67% decrease (165-23). Annual costs for the non-intervention group increased from 148 euros (€) to €183, a mean ratio increase of 1.24 per person-year (range 1.08-1.40). In contrast, the intervention group's costs decreased from €53 to €8, a mean reduction ratio of 0.15 per person-year (range 0.10-0.71). The intervention group had lower frequency of visits for respiratory and circulatory diseases but higher for digestive and metabolic diseases, unlike the non-intervention group. The implementation of the health screening is an effective strategy for reducing both the frequency of emergency department visits and associated costs in home-dwelling older adults in good condition.

Objective The quantity of patient data in healthcare is exponentially increasing. While big data and artificial intelligence have emerged across the fields, in healthcare, such rapid development is hindered by numerous factors. Predominantly, health-care software developed decades ago cannot foresee the demands of modern data processing and analysis. We present the challenges, remedies, and steps of efficient patient data integration that have been co-developed with clinicians at Lenval Children's University Hospital in Nice, France. Methods In collaboration with pediatricians, we created an integration framework that integrated a patient's germane historical data (from the past 10 years) for research purposes. The clinical data presented in this study were collected between 2012 and 2021 in the Lenval Children's University Hospital Pediatric Emergency Department. Results We present the architecture of a clinical data warehouse (CDW) and demonstrate its use. CDW can also host doctoral notes, which is the key element for creating large language models that can help predict patient outcomes and provide critical information to health-care professionals. We also conducted several tests on the utilization of this new CDW, recorded multiple challenges on data integration, and gave three suggestions on software design. The CDW we created represents a solid foundation for future machine learning models of patient flow, hospital economics, and studies on rare diseases at CHU-Lenval. Conclusion Although the integration framework is grounded in pediatrics, the challenges discussed, and the proposed remedies are relevant for software development across medical specializations. Our recommendations for software design can help with future secondary usage of Electronic Health Record.

Clinical documentation plays a crucial role in providing and coordinating care. Despite the widespread adoption of electronic health record (EHR) systems, many end users still document clinical data in a manner similar to traditional paper-based records. To fully leverage the benefits of EHR systems, it is necessary to adopt new documentation approaches that facilitate easy access to information at the point of care and seamless exchange of information across health care facilities. We aimed to evaluate how the transition from an older EHR system to a cross-institutional EHR system impacts physicians' documentation practices and gain a deeper understanding of the factors influencing their choice between free text and structured and standardized documentation methods. A qualitative study was conducted between September 2023 and January 2024. It involved participant observations and individual semistructured interviews with physicians at a university hospital in Norway. Data were analyzed using reflexive thematic analysis. The analysis revealed 3 main themes. First, physicians encountered challenges during the implementation phase of the new EHR system due to its complexity and their unfamiliarity with its use. However, with time, physicians gradually adopted new documentation processes. This integration or adoption primarily occurred by learning through practical experience and collaborative knowledge exchange with their peers. Second, although the implementation of the new EHR system had increased structured and standardized clinical documentation, free text remained the preferred method, with some exceptions. In addition, the fact that many physicians still relied on free-text documentation created a sense of distrust among them toward some of the standardized clinical data. Finally, the informants had mixed perceptions of Systematized Nomenclature of Medicine-Clinical Terms. Some viewed it as a more nuanced terminology system, while others found it more complex. Most informants found using templates for routine procedures beneficial as it saved time in the documentation process and ensured that all necessary parameters and documentation requirements were met. The study findings revealed that physicians' acceptance of new documentation processes is influenced by various social and technological factors. These factors include previous documentation experiences, perceived benefits, familiarity with the EHR system, time constraints, and user-friendliness of the system. While physicians generally have a positive attitude toward using templates for routine procedures, they often create their own templates, and data within these templates are documented in a free-text format. To address this, health care organizations should consider implementing common standardized or semistandardized templates to reduce disparities in documentation, enhance data recording, and ensure adherence to guidelines. Furthermore, to facilitate the transition to the new documentation processes, we recommend providing physicians with customized training programs and platforms for tacit knowledge exchange.