Causation in Population Health Informatics and Data Science

2019 ◽  
Author(s):  
Olaf Dammann ◽  
Benjamin Smart
Keyword(s):  
Population Health ◽  
Health Informatics ◽  
Data Science

scholarly journals Evaluation Considerations for Secondary Uses of Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis

2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  
Keyword(s):  
Clinical Data ◽  
Health Informatics ◽  
Best Practice ◽  
Data Science ◽  
Clinical Performance ◽  
Secondary Analysis ◽  
Health Data ◽  
Evidence Based ◽  
Great Expectations ◽  
Scientific Context

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.

Data Science in Health Informatics

2021 ◽  
pp. 299-340
Author(s):  
Behzad Soleimani Neysiani ◽  
Nasim Soltani ◽  
Saeed Doostali ◽  
Mohammad Shiralizadeh Dezfoli ◽  
Zahra Aminoroaya ◽  
...  
Keyword(s):  
Health Informatics ◽  
Data Science

scholarly journals EpiGraphDB: a database and data mining platform for health data science

2020 ◽  
Author(s):  
Yi Liu ◽  
Benjamin Elsworth ◽  
Pau Erola ◽  
Valeriia Haberland ◽  
Gibran Hemani ◽  
...  
Keyword(s):  
Population Health ◽  
Data Science ◽  
Health Data ◽  
Biomedical Literature ◽  
Therapeutic Interventions ◽  
Supplementary Information ◽  
Reproducible Research ◽  
Protein Interaction Data ◽  
Study Results

Abstract Motivation The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein–protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to ‘triangulate’ evidence from different sources. Availability and implementation The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for replicating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Evaluation Considerations for Secondary Uses of Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis

2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  
Keyword(s):  
Clinical Data ◽  
Health Informatics ◽  
Best Practice ◽  
Data Science ◽  
Clinical Performance ◽  
Secondary Analysis ◽  
Health Data ◽  
Evidence Based ◽  
Great Expectations ◽  
Scientific Context

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.

scholarly journals Public Health, Population Health, and Epidemiology Informatics: Recent Research and Trends in the United States

2017 ◽  
Vol 26 (01) ◽  
pp. 241-247 ◽  
Author(s):  
B. L. Massoudi ◽  
K. G. Chester
Keyword(s):  
Public Health ◽  
Population Health ◽  
Workforce Development ◽  
Health Informatics ◽  
English Language ◽  
The United States ◽  
Public Health Workforce ◽  
Future Research ◽  
Public Health Informatics ◽  
Research Activities

Summary Objectives: To survey advances in public and population health and epidemiology informatics over the past 18 months. Methods: We conducted a review of English-language research works conducted in the domain of public and population health informatics and published in MEDLINE or Web of Science between January 2015 and June 2016 where information technology or informatics was a primary subject or main component of the study methodology. Selected articles were presented using a thematic analysis based on the 2011 American Medical Informatics Association (AMIA) Public Health Informatics Agenda tracks as a typology. Results: Results are given within the context developed by Dixon et al., (2015) and key themes from the 2011 AMIA Public Health Informatics Agenda. Advances are presented within a socio-technical infrastructure undergirded by a trained, competent public health workforce, systems development to meet the business needs of the practice field, and research that evaluates whether those needs are adequately met. The ability to support and grow the infrastructure depends on financial sustainability. Conclusions: The fields of public health and population health informatics continue to grow, with the most notable developments focused on surveillance, workforce development, and linking to or providing clinical services, which encompassed population health informatics advances. Very few advances addressed the need to improve communication, coordination, and consistency with the field of informatics itself, as identified in the AMIA agenda. This will likely result in the persistence of the silos of public health information systems that currently exist. Future research activities need to aim toward a holistic approach of informatics across the enterprise.

scholarly journals The Delirium and Population Health Informatics Cohort study protocol: ascertaining the determinants and outcomes from delirium in a whole population

2018 ◽  
Author(s):  
Daniel Davis ◽  
Sarah Richardson ◽  
Joanne Hornby ◽  
Helen Bowden ◽  
Katrin Hoffmann ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cognitive Impairment ◽  
Population Health ◽  
Health Informatics ◽  
Social Care ◽  
Population Sample ◽  
Cognitive Status ◽  
Health And Social Care ◽  
The Impact

AbstractBackgroundDelirium affects 25% of older inpatients and is associated with long-term cognitive impairment and future dementia. However, no population studies have systematically ascertained cognitive function before, cognitive deficits during, and cognitive impairment after delirium. Therefore, there is a need to address the following question: does delirium, and its features (including severity, duration, and presumed aetiologies), predict long-term cognitive impairment, independent of cognitive impairment at baseline?MethodsThe Delirium and Population Health Informatics Cohort (DELPHIC) study is an observational population-based cohort study based in the London Borough of Camden. It is recruiting 2000 individuals aged ≥70 years and prospectively following them for two years, including daily ascertainment of all inpatient episodes for delirium. Daily inpatient assessments include the Memorial Delirium Assessment Scale, the Observational Scale for Level of Arousal, and the Hierarchical Assessment of Balance and Mobility. Data on delirium aetiology is also collected. The primary outcome is the change in the modified Telephone Interview for Cognitive Status at two years.DiscussionDELPHIC is the first population sample to assess older persons before, during and after hospitalisation. The cumulative incidence of delirium in the general population aged ≥70 will be described. DELPHIC offers the opportunity to quantify the impact of delirium on cognitive and functional outcomes. Overall, DELPHIC will provide a real-time public health observatory whereby information from primary, secondary, intermediate and social care can be integrated to understand how acute illness is linked to health and social care outcomes.

scholarly journals Informatics for Health 2017: Advancing both science and practice

2017 ◽  
Vol 24 (1) ◽  
pp. 1 ◽  
Author(s):  
Philip J. Scott ◽  
Ronald Cornet ◽  
Colin McCowan ◽  
Niels Peek ◽  
Paolo Fraccaro ◽  
...  
Keyword(s):  
Big Data ◽  
Medical Informatics ◽  
Health Informatics ◽  
Data Science ◽  
Digital Health ◽  
Health It ◽  
Special Issue ◽  
Organizational Intervention ◽  
Policy Makers ◽  
Multi Level

Introduction: The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress.Discussion: The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes.Conclusions: Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.

Digital continuous healthcare and disruptive medical technologies: m-Health and telemedicine skills training for data-driven healthcare

2018 ◽  
Vol 25 (10) ◽  
pp. 623-635 ◽  
Author(s):  
A Hasan Sapci ◽  
H Aylin Sapci
Keyword(s):  
Health Informatics ◽  
Data Science ◽  
Medical Information ◽  
Training Session ◽  
Skills Training ◽  
Application Development ◽  
Healthcare Applications ◽  
Wireless Monitoring ◽  
Medical Technologies ◽  
Monitoring Devices

Introduction Disruptive medical technologies, wearable devices and new diagnostic solutions have been shaping the future of healthcare, and the health informatics skills gap has become a major problem for technology-centric healthcare applications. This study evaluated the relationships between a specific practical skills training method and students' confidence in using wireless monitoring devices along with the attitude towards technology adoption. Methods Six practical exercises were developed to provide health informatics technical skills to transfer medical information and display multi-channel biological signals. Two hundred and six undergraduate nursing students received a telemedicine and homecare training course. Their familiarity with various data formats and likelihood to recommend telemedicine and remote monitoring applications were measured. Results The skills training session changed students' attitudes towards remote patient monitoring, and the majority of students provided positive feedback about their confidence in using wireless monitoring devices after the training session. Students stated their plans to use the technology when they start practising and to educate their patients to promote the use of telemedicine. Conclusion We propose a skills training framework that covers (a) telemedicine, (b) m-Health and connected health, (c) health informatics application development, (d) health informatics device innovation, and (e) data science.

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