scholarly journals 4.C. Round table: Joining forces: frameworks for international and multi-sectoral collaborations in health information

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  

Abstract Countries have a wide range of lifestyles, environmental exposures and different health(care) systems providing a large natural experiment to be investigated. Through pan-European comparative studies, underlying determinants of population health can be explored and provide rich new insights into the dynamics of population health and care such as the safety, quality, effectiveness and costs of interventions. Additionally, in the big data era, secondary use of data has become one of the major cornerstones of digital transformation for health systems improvement. Several countries are reviewing governance models and regulatory framework for data reuse. Precision medicine and public health intelligence share the same population-based approach, as such, aligning secondary use of data initiatives will increase cost-efficiency of the data conversion value chain by ensuring that different stakeholders needs are accounted for since the beginning. At EU level, the European Commission has been raising awareness of the need to create adequate data ecosystems for innovative use of big data for health, specially ensuring responsible development and deployment of data science and artificial intelligence technologies in the medical and public health sectors. To this end, the Joint Action on Health Information (InfAct) is setting up the Distributed Infrastructure on Population Health (DIPoH). DIPoH provides a framework for international and multi-sectoral collaborations in health information. More specifically, DIPoH facilitates the sharing of research methods, data and results through participation of countries and already existing research networks. DIPoH's efforts include harmonization and interoperability, strengthening of the research capacity in MSs and providing European and worldwide perspectives to national data. In order to be embedded in the health information landscape, DIPoH aims to interact with existing (inter)national initiatives to identify common interfaces, to avoid duplication of the work and establish a sustainable long-term health information research infrastructure. In this workshop, InfAct lays down DIPoH's core elements in coherence with national and European initiatives and actors i.e. To-Reach, eHAction, the French Health Data Hub and ECHO. Pitch presentations on DIPoH and its national nodes will set the scene. In the format of a round table, possible collaborations with existing initiatives at (inter)national level will be debated with the audience. Synergies will be sought, reflections on community needs will be made and expectations on services will be discussed. The workshop will increase the knowledge of delegates around the latest health information infrastructure and initiatives that strive for better public health and health systems in countries. The workshop also serves as a capacity building activity to promote cooperation between initiatives and actors in the field. Key messages DIPoH an infrastructure aiming to interact with existing (inter)national initiatives to identify common interfaces, avoid duplication and enable a long-term health information research infrastructure. National nodes can improve coordination, communication and cooperation between health information stakeholders in a country, potentially reducing overlap and duplication of research and field-work.

2020 ◽  
Vol 27 (7) ◽  
pp. 1072-1083
Author(s):  
Stacey Marovich ◽  
Genevieve Barkocy Luensman ◽  
Barbara Wallace ◽  
Eileen Storey

Abstract Objective The study sought to develop an information model of data describing a person’s work for use by health information technology (IT) systems to support clinical care, population health, and public health. Materials and Methods Researchers from the National Institute for Occupational Safety and Health worked with stakeholders to define relationships and structure, vocabulary, and interoperability standards that would be useful and collectable in health IT systems. Results The Occupational Data for Health (ODH) information model illustrates relationships and attributes for a person’s employment status, retirement dates, past and present jobs, usual work, and combat zone periods. Key data about the work of a household member that could be relevant to the health of a minor were also modeled. Existing occupation and industry classification systems were extended to create more detailed value sets that enable self-reporting and support patient care. An ODH code system, available in the Public Health Information Network Vocabulary Access and Distribution System, was established to identify the remaining value sets. ODH templates were prepared in all 3 Health Level 7 Internationalinteroperability standard formats. Discussion The ODH information model suggests data elements ready for use by health IT systems in the United States. As new data elements and values are better defined and refined by stakeholders and feedback is obtained through experience using ODH in clinical settings, the model will be updated. Conclusion The ODH information model suggests standardized work information for trial use in health IT systems to support patient care, population health, and public health.


2020 ◽  
Author(s):  
Romana Haneef ◽  
Marie Delnord ◽  
Michel Vernay ◽  
Emmanuelle Bauchet ◽  
Rita Gaidelyte ◽  
...  

Abstract Background The availability of data generated from different sources is increasing with the possibility to link these data sources together. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and the artificial intelligence (AI) in routine public health activities, and to identify the related health outcome and intervention indicators and determinants of health for non-communicable diseases. Method We performed a survey across European countries to explore the current practices applied by national institutes of public health and health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or the AI). Results The use of data linkage and the AI at national institutes of public health and health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health and health information and statistics. Using linked data, 46 health outcome indicators related to seven health conditions, 34 indicators related to determinants and 23 to health interventions were estimated in routine. Complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to link different data sources in routine for public health surveillance and research. Conclusions Our results highlight that the majority of European countries have integrated data linkage in routine public health activities but a few use the AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development process. Building analytical capacity and awareness of the added value of data linkage in national institutes is necessary for improving the utilization of linked data in order to improve the monitoring of public health activities.


2014 ◽  
Vol 23 (01) ◽  
pp. 42-47 ◽  
Author(s):  
J. H. Holmes ◽  
J. Sun ◽  
N. Peek

Summary Objectives: To review technical and methodological challenges for big data research in biomedicine and health. Methods: We discuss sources of big datasets, survey infrastructures for big data storage and big data processing, and describe the main challenges that arise when analyzing big data. Results: The life and biomedical sciences are massively contributing to the big data revolution through secondary use of data that were collected during routine care and through new data sources such as social media. Efficient processing of big datasets is typically achieved by distributing computation over a cluster of computers. Data analysts should be aware of pitfalls related to big data such as bias in routine care data and the risk of false-positive findings in high-dimensional datasets. Conclusions: The major challenge for the near future is to transform analytical methods that are used in the biomedical and health domain, to fit the distributed storage and processing model that is required to handle big data, while ensuring confidentiality of the data being analyzed.


2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110628
Author(s):  
Rachel Rowe

Amidst the climate of crisis surrounding the rise in opioid-related overdose in the USA, early in 2019, Google and Deloitte launched ‘Opioid360’. Here came a platform combining browser histories, credit, insurance, social media, and traditional survey data to sell the service of risk calculation in population health. Opioid360's approach to automating risk calculation not only promised to identify persons ‘at risk’ of opioid dependence, but also paved the way for broader applications anticipating common chronic diseases and coordinating logistical operations involved in pandemic response. Beginning with this experimental platform, this paper develops an analysis of the Big Data mode of risk calculation - an epistemological and political shift that involves technology companies, investors, insurers, governments, and public health institutions. The analysis focuses on the re-emergence of ‘social determinants of health’ (SDOH) in the rhetoric accompanying novel analytic platforms that estimate, calculate, and compute individual health risks. While the treatment of SDOH has always been a site of political contestation within the discipline of public health, powerful interests are crystallising around the concept and instrumentalising it in platforms that sell algorithmic prediction. Silicon Valley's breed of asset-oriented technoscience appears not only to be amplifying the behaviouralist elements of public health. Among the stakes of the Big Data mode is the paradoxical retreat from changing social conditions that contribute to the prevalence of health and illness in populations; and instead, the promotion of an apparatus for pricing and exchanging individual risk or excluding from services those who bear risk most acutely.


Author(s):  
Usman Iqbal ◽  
Phung Anh Nguyen ◽  
Shabbir Syed-Abdul ◽  
Wen-Shan Jian ◽  
Yu-Chuan Jack Li

ABSTRACTObjectiveRapid change in health information technology system had dramatically increased health data accumulated. We aimed to develop an online informatics tool in order to evaluate the risk of drugs for cancer by utilizing medical big data. Data SourceWe use the Taiwan’s National Health Insurance Database that has provided a huge data which covered all health information including characteristics and all drug information i.e. prescriptions, etc. of 23 million Taiwanese population. Front-end development: Web-based interface was developed by using PHP package and Javascript. In addition, we included the guidelines of evidence based medicine (EBM) level 3 for observational study such as cohort, case-control, and/or case serial self-control in order to support users interact with system. Back-end development: A package of Apache, MySQL & PHP was used to build the serve-side of the system. We integrated the Elasticsearch API5 to our system in order to search and analyze data immediately. The example of data transform to person-level from Taiwan NHI database is shown in Box 1. After then, we also integrated the analytics package (ie. R package) to perform the statistical analysis to a given study. This online analytical tool has capability to massively explore and visualize big data for long term use drugs and cancers through OMOSC system which will help to do mass online studies for long term use drugs and cancer risk. It would help to direct the health care professionals with lack of datamining skills to lead the study. The constructed online system would generate automatically case and controls by utilizing large databases for long term drug exposures and cancer risk. ResultsThe results are shown in odds ratio (OR) and if selected some confounding factors then could also get adjusted odds ratio (AOR) for risk estimation with 95% Confidence Intervals (CI). We used SAS statistical software on the same dataset to validate the OMOSC system results. It could help to do massive online studies which will saves time and cost effective. ConclusionSince the clinical trials are impossible to conduct due to cultural, cost, ethical, political or social obstacles. Therefore, this kind of research model would play an important role in health care industry by providing an excellent opportunities for solving the technological, informatics, and organizational issues towards other broad domains of drugs evaluation by utilizing large-scale databases.


2018 ◽  
Vol 27 (01) ◽  
pp. 199-206 ◽  
Author(s):  
Roland Gamache ◽  
Hadi Kharrazi ◽  
Jonathan Weiner

Objective: To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations. Methods: The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords “public health informatics” and “social determinants of health”. The “social determinants of health” search was refined to include articles that contained the keywords “public health”, “population health” or “surveillance”. Results: Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health. Conclusions: Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this “bridging” is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.


2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
R Haneef ◽  
H Van Oyen ◽  
R Gaidelyte ◽  
O Zeynep ◽  
B Pérez-Gomez ◽  
...  

Abstract Background Health information systems both at the national and international level play a key role in ensuring that timely and reliable evidence is used for operational and strategic decision making inside and outside the health sector. The availability of data generated from different sources is increasing with the possibility to link these data sources together. However, more efficient data generation processes are required to use data collected for different purposes initially, as well as advanced statistical techniques to generate comparable and timely health information. The main objective is to explore the innovative use of health information for better public health policy across the Member States. Methods As part of InfAct, we have conducted as survey among EU-MS to describe the innovative use of data sources. We are collecting inspiring examples on the innovative use of health information based on national or European data networks involved with health policy-making at national, regional or local level. We are further developing generic methods to estimate health indicators using machine learning techniques and mathematical modelling. Results These approaches will generate a roadmap on the innovative use of health information across Member States, enlarge the existing list of health indicators estimated from linked data and/or advanced statistical techniques, inform on the implications of these indicators in health policy with inspiring examples from Member States, and provide methodological guidelines for using linked data and advanced statistics to estimate health indicators, and composite outcome measures. Conclusions This work will highlight the gaps in the innovative use of data sources, and improve the comparability of health indicators and the capacity of EU-Member states to apply innovation for increased relevance and timeliness of health information for public health policy-making.


Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 102 ◽  
Author(s):  
Fernando López-Martínez ◽  
Edward Rolando Núñez-Valdez ◽  
Vicente García-Díaz ◽  
Zoran Bursac

Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population health include better healthcare outcomes, improvement of clinical operations, reducing costs of care, and generation of accurate medical information. Several machine learning algorithms implemented by the authors can use the large standardized datasets integrated into the platform to improve the effectiveness of public health interventions, improving diagnosis, and clinical decision support. The data integrated into the platform come from Electronic Health Records (EHR), Hospital Information Systems (HIS), Radiology Information Systems (RIS), and Laboratory Information Systems (LIS), as well as data generated by public health platforms, mobile data, social media, and clinical web portals. This massive volume of data is integrated using big data techniques for storage, retrieval, processing, and transformation. This paper presents the design of a digital health platform in a healthcare organization in Colombia to integrate operational, clinical, and business data repositories with advanced analytics to improve the decision-making process for population health management.


2020 ◽  
Author(s):  
Romana Haneef ◽  
Marie Delnord ◽  
Michel Vernay ◽  
Emmanuelle Bauchet ◽  
Rita Gaidelyte ◽  
...  

Abstract Background: The availability of data generated from different sources is increasing with the possibility to link these data sources with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of non-communicable diseases and the obstacles to linking different data sources. Method: We performed a survey across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI). Results: The use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research. Conclusions: Our results highlight that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development. Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities.


Author(s):  
Sundeep Sahay ◽  
T Sundararaman ◽  
Jørn Braa

Rapid and unpredictable developments in health policies, technologies, disease profiles, institutional environments, and their inter-connections have significant implications on how we design, develop, implement, and use health information systems (HIS) in low and middle-income countries (LMICs). Our current systems have heightened expectations but have proven largely incapable of meeting these new challenges. Nor have they been able to effectively leverage upon the new opportunities that are emerging, such as through the cloud, big data, the proliferation of mobile devices and the Internet of Things, and also the increasing array of new open source software solutions being made available through global development communities. What is required to try and address these challenges and opportunities? This book proposes the ‘Expanded PHI’ (public health informatics) perspective as a way forward, and through the various chapters first seeks to define it, and then apply it to analyse the following key problematics facing public health informatics in the domains of research, practice, and policy: use of information; integration of systems; leveraging cloud computing and big data; design and building of institutions that facilitate; managing complexity; evolving governance mechanisms and standards; responding to the new challenges thrown up by universal health coverage and Sustainable Development Goals; and building synergies between health systems strengthening and health information strengthening efforts. In defining the scope of Expanded PHI, the field of public health informatics is first situated within an informatics context, and then within public health and finally within the context of changing global health policies. Drawing from these contextualizations, the design principles for Expanded PHI are elucidated, based primarily on a social systems perspective, where the health of populations is kept as the central purpose and a participatory and incremental nature of change as the primary strategy.


Sign in / Sign up

Export Citation Format

Share Document