Patient-generated health data analytics on ambulatory behaviours of people living with T2D: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Meghan Shyama Nagpal ◽  
Antonia Barbaric ◽  
Diana Sherifali ◽  
Plinio P Morita ◽  
Joseph A Cafazzo
Keyword(s):  
Scoping Review ◽  
Health Behaviours ◽  
Health Data ◽  
Clinical Findings ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Critical Examination ◽  
Online Forums ◽  
Behavioural Patterns ◽  
Health Data Analytics

BACKGROUND Complications due to Type 2 Diabetes (T2D) can be mitigated through proper self-management which can positively change health behaviours. Technological tools are available to help people living with T2D manage their condition and such tools provide a large repository for patient-generated health data (PGHD). Analytics can provide insights about the ambulatory behaviours of people living with T2D. OBJECTIVE The objective of this review was to investigate analytical insights can be derived through PGHD with respect to ambulatory behaviours of people living with T2D. METHODS A scoping review using the Arksey & O’Malley framework was conducted in which a comprehensive search of the literature was conducted by two reviewers. Three electronic databases (PubMed, IEEE, ACM) were searched using keywords associated with diabetes, behaviours, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted and studies were selected. Critical examination took place through a descriptive-analytical narrative method and data extracted from the studies was classified into thematic categories. These categories reflect the findings of this study as per our objective. RESULTS We identified 43 studies that met the inclusion criteria for this review. While 70% of the studies examined PGHD independently, 30% of the studies combined PGHD with other data sources. The majority of these studies used machine learning algorithms to perform their analysis. Themes identified through this review include 1) predicting diabetes / obesity, 2) factors that contribute to diabetes / obesity, 3) insights from social media & online forums, 4) predicting glycemia, 5) improved adherence / outcomes, 6) analysis of sedentary behaviours, 7) deriving behavioural patterns, 8) discovering clinical findings, and 9) developing design principles. CONCLUSIONS The increased volume and availability of PGHD has the potential to derive analytical insights regarding the ambulatory behaviours of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavioural patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, that would not be available through other data sources.

scholarly journals A scoping review of ‘big data’, ‘informatics’, and ‘bioinformatics’ in the animal health and veterinary medical literature

2019 ◽  
Vol 20 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Zenhwa Ouyang ◽  
Jan Sargeant ◽  
Alison Thomas ◽  
Kate Wycherley ◽  
Rebecca Ma ◽  
...  
Keyword(s):  
Big Data ◽  
Veterinary Medicine ◽  
Scoping Review ◽  
Full Text ◽  
Medical Literature ◽  
Animal Health ◽  
Genetic Data ◽  
Health Data ◽  
Data Sources ◽  
Steady Increase

AbstractResearch in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193–1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how ‘big data’, ‘informatics’, and ‘bioinformatics’ have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms ‘big data’, ‘informatics’, and ‘bioinformatics’ was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words ‘big data’, ‘informatics’, or ‘bioinformatics’ in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in ‘bioinformatics’ articles between 1995 and the end of the study period, ‘informatics’ articles reached their peak in 2012, then declined. The first ‘big data’ publication in animal health and veterinary medicine was in 2012. While few articles used the term ‘big data’ (n = 14), recent growth in ‘big data’ articles was observed. All geographic regions produced publications in ‘informatics’ and ‘bioinformatics’ while only North America, Europe, Asia, and Australia/Oceania produced publications about ‘big data’. ‘Bioinformatics’ primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, ‘informatics’ primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of ‘big data’ may lead to avoidance of the term.

Sharing of military Veterans’ mental health data across Canada: A scoping review

2022 ◽  
Author(s):  
Abraham Rudnick ◽  
Dougal Nolan ◽  
Patrick Daigle
Keyword(s):  
Mental Health ◽  
Information Sharing ◽  
Scoping Review ◽  
Information Sources ◽  
Military Veterans ◽  
Health Data ◽  
Data Sources ◽  
Data Sets ◽  
Comprehensive Review ◽  
Canadian Military

LAY SUMMARY Information on Canadian military Veterans’ mental health is needed to develop and improve mental health services. It is not clear to what extent such information is available and connected across its sources. A comprehensive review of scientific and other authorized publications was conducted to identify information sources related to Canadian Veteran mental health, connections between them, and related policies or guidelines. Ten data sources related to military Veterans’ mental health in Canada were found, but no policies or guidelines specifically addressing information sharing across these data sets were discovered. Secure, Accessible, eFfective, and Efficient (SAFE) information sharing across these sources was implied but not confirmed. The authors recommend consideration be given to establishing a repository of relevant data sets and policies and guidelines for information sharing and standardization across all relevant data sets.

scholarly journals Imagining the postantibiotic future: the visual culture of a global health threat

2020 ◽  
pp. medhum-2020-011884
Author(s):  
Rachel Irwin
Keyword(s):  
Global Health ◽  
Visual Culture ◽  
Bacterial Infections ◽  
Health Data ◽  
Data Sources ◽  
Story Telling ◽  
Specific Data ◽  
Public Health Data ◽  
Collective Imagination ◽  
Global Data

This article is concerned with the visual culture of global health data using antimicrobial resistance (AMR) as an example. I explore how public health data and knowledge are repackaged into visualisations and presented in four contemporary genres: the animation, the TED Talk, the documentary and the satire programme. I focus on how different actors describe a world in which there are no or few antibiotics that are effective against bacterial infections. I examine the form, content and style of the visual cultural of AMR, examining how these genres tell a story of impending apocalypse while also trying to advert it. This is a form of story-telling based around the if/then structure: we are told that if we do not take certain actions today, then we will face a postantibiotic future with certain, often catastrophic, consequences. Within this if/then structure, there are various aims and objectives: the goal may be preventing further spread of AMR, building awareness or pushing for certain policy or funding decisions. These stories also serve to place or deflect blame, on animals, occupations, patients, industries and others and to highlight risks and consequences. These examples share similarities in the forms of story-telling and narrative, and in the use of specific data sources and other images. By using several Swedish examples, I demonstrate how global data are reinterpreted for a national audience. Overall, I argue that while the convergence of a dominant narrative indicates scientific consensus, this consensus also stifles our collective imagination in finding new solutions to the problem. Finally, I also use the example of AMR to discuss the need for a broader social science and humanities engagement with the visual culture of global health data.

Regulations and Norms for Reuse of Residual Clinical Biospecimens and Health Data

2021 ◽  
pp. 019394592110292
Author(s):  
Elizabeth E. Umberfield ◽  
Sharon L. R. Kardia ◽  
Yun Jiang ◽  
Andrea K. Thomer ◽  
Marcelline R. Harris
Keyword(s):  
Informed Consent ◽  
Scoping Review ◽  
Real World ◽  
Genetic Information ◽  
Final Analysis ◽  
Health Data ◽  
Federal Regulations ◽  
Secondary Research ◽  
Domain Experts ◽  
Key Issues

Nurse scientists are increasingly interested in conducting secondary research using real world collections of biospecimens and health data. The purposes of this scoping review are to (a) identify federal regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data, (b) summarize domain experts’ interpretations of permissions of such reuse, and (c) summarize key issues for interpreting regulations and norms. Final analysis included 25 manuscripts and 23 regulations and norms. This review illustrates contextual complexity for reusing residual clinical biospecimens and health data, and explores issues such as privacy, confidentiality, and deriving genetic information from biospecimens. Inconsistencies make it difficult to interpret, which regulations or norms apply, or if applicable regulations or norms are congruent. Tools are necessary to support consistent, expert-informed consent processes and downstream reuse of residual clinical biospecimens and health data by nurse scientists.

scholarly journals Using routinely collected primary care records to identify and investigate severe asthma: a scoping review

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Jonathan Stewart ◽  
Frank Kee ◽  
Nigel Hart
Keyword(s):  
Primary Care ◽  
High Risk ◽  
Severe Asthma ◽  
Scoping Review ◽  
Asthma Severity ◽  
Data Sources ◽  
Full Potential ◽  
High Risk Disease ◽  
Asthma Outcomes ◽  
Disease Variant

AbstractShielding during the coronavirus pandemic has highlighted the potential of routinely collected primary care records to identify patients with ‘high-risk’ conditions, including severe asthma. We aimed to determine how previous studies have used primary care records to identify and investigate severe asthma and whether linkage to other data sources is required to fully investigate this ‘high-risk’ disease variant. A scoping review was conducted based on the Arksey and O’Malley framework. Twelve studies met all criteria for inclusion. We identified variation in how studies defined the background asthma cohort, asthma severity, control and clinical outcomes. Certain asthma outcomes could only be investigated through linkage to secondary care records. The ability of primary care records to represent the entire known asthma population is unique. However, a number of challenges need to be overcome if their full potential to accurately identify and investigate severe asthma is to be realised.

scholarly journals DERIVATION OF A MODEL THAT ACCURATELY PREDICTS CARDIOVASCULAR FROM NON-CARDIOVASCULAR CAUSE OF DEATH USING ADMINISTRATIVE HEALTH DATA SOURCES

2019 ◽  
Vol 35 (10) ◽  
pp. S17
Author(s):  
S. Patel ◽  
A. Khan ◽  
A. Sivaswamy ◽  
L. Ferreira-Legere ◽  
P. Austin ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Health Data ◽  
Data Sources ◽  
Administrative Health Data

scholarly journals Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review.

2020 ◽  
Author(s):  
Michael Moor ◽  
Bastian Rieck ◽  
Max Horn ◽  
Catherine Jutzeler ◽  
Karsten Borgwardt
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Quality Assessment ◽  
Biomarker Discovery ◽  
Learning Algorithms ◽  
Quality Criteria ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Synthesis Methods ◽  
Early Prediction

Background: Sepsis is among the leading causes of death in intensive care units (ICU) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study eligibility criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study appraisal and synthesis methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from "poor" (satisfying less than 40% of the quality criteria) to "very good" (satisfying more than 90% of the quality criteria). The majority of the studies (n= 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n= 2,9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only 2 studies provided publicly-accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and key findings: A growing number of studies employs machine learning to31optimise the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic review registration number: CRD42020200133

scholarly journals Community Attitudes Towards Sharing Government Health Data with Private Companies: A Scoping Review

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Jackie Street ◽  
Belinda Fabrianesi ◽  
Rebecca Bosward ◽  
Stacy Carter ◽  
Annette Braunack-Mayer
Keyword(s):  
Data Sharing ◽  
Health Care Providers ◽  
Scoping Review ◽  
Public Attitudes ◽  
Health Data ◽  
Public Confidence ◽  
Care Providers ◽  
Private Industry ◽  
Good Communication ◽  
Government Health

IntroductionLarge volumes of health data are generated through the interaction of individuals with hospitals, government agencies and health care providers. There is potential in the linkage and sharing of administrative data with private industry to support improved drug and device provision but data sharing is highly contentious. Objectives and ApproachWe conducted a scoping review of quantitative and qualitative studies examining public attitudes towards the sharing of health data, held by government, with private industry for research and development. We searched four data bases, PubMed, Scopus, Cinahl and Web of Science as well as Google Scholar and Google Advanced. The search was confined to English-only publications since January 2014 but was not geographically limited. We thematically coded included papers. ResultsWe screened 6788 articles. Thirty-six studies were included primarily from UK and North America. No Australian studies were identified. Across studies, willingness to share non-identified data was generally high with the participant’s own health provider (84-91%) and academic researchers (64-93%) but fell if the data was to be shared with private industry (14-53%). There was widespread misunderstanding of the benefits of sharing data for health research. Publics expressed concern about a range of issues including data security, misuse of data and use of data to generate profit. Conditions which would increase public confidence in sharing of data included: strict safeguards on data collection and use including secure storage, opt-in or opt-out consent mechanisms, and good communication through trusted agents. Conclusion / ImplicationsWe identified a research gap: Australian views on sharing government health data with private industry. The international experience suggests that public scepticism about data sharing with private industry will need to be addressed by good communication about public benefit of data sharing, a strong program of public engagement and information sharing conducted through trusted entities.

A health data analytics maturity model for hospitals information systems

2019 ◽  
Vol 46 ◽  
pp. 278-285 ◽  
Author(s):  
João Vidal Carvalho ◽  
Álvaro Rocha ◽  
José Vasconcelos ◽  
António Abreu
Keyword(s):  
Information Systems ◽  
Data Analytics ◽  
Health Data ◽  
Maturity Model ◽  
Health Data Analytics
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