Evidence into action: population impact assessment and population health decision support

Author(s):  
Richard F. Heller
2018 ◽  
Vol 200 ◽  
pp. 259-268 ◽  
Author(s):  
Mikołaj Owsianiak ◽  
Gerard Cornelissen ◽  
Sarah E. Hale ◽  
Henrik Lindhjem ◽  
Magnus Sparrevik

2021 ◽  
Author(s):  
Jawad Chishtie ◽  
Iwona Anna Bielska ◽  
Aldo Barrera ◽  
Jean-Sebastien Marchand ◽  
Muhammad Imran ◽  
...  

BACKGROUND Simple visualizations in health research data, such as scatter plots, heat maps and bar charts typically present relationships between two variables. Interactive visualization methods allow for multiple related facets, such as multiple risk factors, to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big healthcare data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support. OBJECTIVE The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods and tools being employed in population health and HSR, and their sub-domains in the last 15 years, from 1 January 2005 to 30 March 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals and co-design of applications. METHODS We adapted standard scoping review guidelines, with a peer reviewed search strategy, two independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sector. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and health services research, and their sub-domains such as epidemiologic surveillance, health resource planning, access, utilization and costs, among diverse clinical and demographic populations. RESULTS As a companion review to our earlier systematic synthesis of literature on visual analytic applications, we present findings in six major themes of interactive visualization applications developed for eight major problem categories. We found a wide application of interactive visualization methods, the major being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality and studying medication use patterns. Data sources included mostly secondary administrative and electronic medical record data. Additionally, at least two-third applications involved participatory co-design approaches, while introducing a distinct category ‘embedded research’ within co-design initiatives. These applications were in response to an identified need for data-driven insights towards knowledge generation and decision support. We further discuss the opportunities from the use of interactive visualization methods towards studying global health, inequities including social determinants of health, and other related areas. We also allude to the challenges in the uptake of these methods. CONCLUSIONS Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and health services research. Such applications are being fast utilized by academic and health care agencies for knowledge discovery, hypotheses generation and decision support. CLINICALTRIAL Protocol registration: RR1-10.2196/14019 Related first review: RR2-10.2196/14019 INTERNATIONAL REGISTERED REPORT RR2-10.2196/14019


Author(s):  
Isabella Castiglioni ◽  
Maria Carla Gilardi ◽  
Francesca Gallivanone

The increase of incidence and prevalence of dementia diseases makes urgent the clinical community to be supported in the difficult diagnostic process of dementia patients. E-health decision support systems, based on innovative algorithms able to extract information from in vivo neuroimaging studies, can make a quite different way to perform neurological diagnosis and enlarge domains and actors involved in the diagnostic process. A number of image-processing methods that extract potential biomarkers from the in vivo neuroimaging studies have been proposed (e.g. volume segmentation, voxel-based statistical mapping). A number of new shape descriptors have also been developed (e.g. texture-based). Other approaches (e.g. machine learning, pattern recognition) have been proven effective, for both structural and functional data, in making automatic diagnoses. The integration of these sophisticated diagnostic tools into secure, efficient, and wide e-infrastructures is the prerequisite for the real implementation of e-health support services to the clinical and industrial communities managing dementia patients.


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