ReviewActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2019
The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.
Review in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues.International journal of clinical practice · 2022Pooled it
- The Advanced Confidentiality Engine as a Scalable Tool for the Pseudonymization of Biomedical Data in Translational Settings: Development and Usability Study.Journal of medical Internet research · 2025Article
- SQL on FHIR - Tabular views of FHIR data using FHIRPath.NPJ digital medicine · 2025Article
- Developing a standardized framework for evaluating health apps using natural language processing.Scientific reports · 2025Article
- A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis.Scientific data · 2024Article
- The application of artificial intelligence in health policy: a scoping review.BMC health services research · 2023Article
- Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework.BioData mining · 2023Article
- Artificial Intelligence Program for Predicting Wrestlers' Sports Performances.Sports (Basel, Switzerland) · 2023Article
- Article
- Integrated personal health record (PHR) security: requirements and mechanisms.BMC medical informatics and decision making · 2023Review
- InfoSec Practices - a Survey Conducted in Greek Hospitals.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023Article
- Successful Aging for Community-Dwelling Older Adults: An Experimental Study with a Tablet App.International journal of environmental research and public health · 2022Article
- Health Professionals' Perception about Big Data Technology in Greece.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2020Article
- Modelling and Evaluation of Policies.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2020Article
- Advanced Interoperability Techniques: Structure Mapping Service in CrowdHEALTH Project.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2020Article
- Health in All Policy Making Utilizing Big Data.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2020Article
- Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2020Article
- Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
30 authors at 15 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWith the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet.
aimThe aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms.
methodsHHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed.
resultsCrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies.
conclusionsA holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.