scholarly journals Methodological research priorities for data sciences: Report from The International Methodology Consortium for Coded Health Information (IMECCHI)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Rachel Jolley ◽  
Danielle Southern ◽  
Hude Quan ◽  
William Ghali ◽  
Bernard Burnand
Keyword(s):  
Big Data ◽  
Health Information ◽  
Disease Surveillance ◽  
Health Management ◽  
International Comparisons ◽  
Research Priorities ◽  
Clinical Decision ◽  
Methodological Research ◽  
Wide Range

ABSTRACT ObjectivesThe vast amount of data produced by healthcare systems both structured and unstructured, termed ‘Big Data’ have the potential to improve the quality of healthcare through supporting a wide range of medical and healthcare functions, including clinical decision support, disease surveillance, and population health management. As the field of big data in healthcare is rapidly expanding, methodology to understand and analyze thereby enhancing and optimizing the use of this data is needed. We present priorities determined for future work in this area. ApproachAn international collaboration of health services researchers who aim to promote the methodological development and use of coded health information to promote quality of care and quality health policy decisions known as IMECCHI –proposes areas of development and future priorities for use of big data in healthcare. Thematic areas were determined through discussion of potential projects related to the use and evaluation of both structured /codeable and unstructured health information, during a recent meeting in October 2015 ResultsSeveral themes were identified. The top priorities included: 1) electronic medical record data exploration and utilization; 2) developing common data models and multimodal /multi-source databases from disparate sources development; 3) data quality assessment including developing indicators, automated logic checks and international comparisons; 4) the translation of ICD-10 to ICD-11 through field-testing 5) Exploration of non-physician produced/coded data; and 6) Patient safety and quality measure development. ConclusionsA list of expert views on critical international priorities for future methodological research relating to big data in healthcare were determined. The consortium's members welcome contacts from investigators involved in research using health data, especially in cross-jurisdictional collaborative studies.

scholarly journals Student Physical Health Information Management Model under Big Data Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Wang ◽  
Ning Wang ◽  
MeiJie Li ◽  
Simeng Mi ◽  
YaYa Shi
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Physical Health ◽  
Health Information ◽  
Information Management ◽  
Health Management ◽  
Health Information Management ◽  
Management Model ◽  
Student Health ◽  
Management Service

Health is considered an important foundation for students’ success. However, with the accelerated pace of life, rising pressure from various parties, weak health awareness, lack of exercise time, and other reasons, students’ physical quality is generally declining, the incidence of health diseases is increasing, and the onset age tends to be younger. With the development of the concept of “health first,” health management continues to expand and extend and students’ health management has attracted more attention from many aspects. Due to the late and low starting point of health management research and the lack of professional theoretical support, a complete, mature, and effective health management service system has not been established to deal with the students’ health. In order to make student health management more scientific, normative, and effective, this article has proposed big data technology to build the student health information management model. The first step of the approach is to store and analyze the data of students’ physical health. It is necessary to combine the data collection, supervision, data analysis, and data application of students’ physical health and gradually improve the national monitoring and evaluation system of students’ physical health. Student health check-up management platform is mainly used in realizing the school student information management and student health information relationship between system, science, standardization, and automation, and its main task is to use a computer to perform daily management of all previous medical information of students, such as query, modify, add, delete, and enhance the physical health of students information management ability given the large data analysis of useful information. In addition, we have built a doctor recommendation model based on online questions and answers to give specific health recommendations for students of different physiques.

scholarly journals An international, Delphi consensus study to identify priorities for methodological research in behavioural trials: A study protocol

2018 ◽  
Vol 1 ◽  
pp. 11 ◽  
Author(s):  
Molly Byrne ◽  
Jenny Mc Sharry ◽  
Oonagh Meade ◽  
Kim L. Lavoie ◽  
Simon L. Bacon
Keyword(s):  
Research Priorities ◽  
Future Research ◽  
Behavioural Intervention ◽  
Point Scale ◽  
Delphi Consensus ◽  
Global Improvement ◽  
Behavioural Interventions ◽  
Methodological Research ◽  
Important Health

Background: Effective behaviour change interventions are needed to impact important health outcomes, including morbidity and mortality. However, the uptake and impact of behavioural interventions have been limited by methodological challenges. The International Behavioural Trials Network (IBTN) was established in 2013 to facilitate global improvement in methodological quality of behavioural trials. There has been no formal process, within the network or in the broader literature, to define the most important research priorities to achieve this aim. In this project, we will conduct an international, Delphi consensus study to identify and achieve consensus on priorities for methodological research in behavioural trials among IBTN members. Methods: Fifteen core members of IBTN, who are experts in the field of behavioural intervention research, will be invited to generate a list of all items they consider priority areas for methodological research in trials of behavioural interventions. The IBTN Research Prioritisation team (the authors) will review all items generated, removing duplicates and merging similar topics, and generate a ‘long-list’ of items. This long-list will be sent to the 15 IBTN core members for approval. We will then administer two online Delphi surveys to all IBTN members. In the first survey, respondents will be asked to rate the importance of each item on a nine-point scale and rank their top five priorities. In the second survey, respondents will receive feedback on others’ responses and a reminder of their own responses in survey 1, and will be asked to re-rate items and re-select their ‘top five’. Discussion: Findings from the project will be used to inform the research agenda of the IBTN and to make recommendations for future research.

POSITIONING OF THE REGION ON THE PRINCIPLES OF BRAND FORMATION

2017 ◽  
Author(s):  
Liudmyla Danyluk ◽  
◽  
Keyword(s):  
Local Governments ◽  
Health Management ◽  
Geographical Location ◽  
Strategic Plan ◽  
Rapid Decline ◽  
Competitive Advantages ◽  
Wide Range ◽  
Brand Promotion

The process of positioning a region is one of the most important tools for attracting investment to a territory. In turn, the methods of work of the human health management system, contributing to the improvement of the quality of life of the population. However, effective tools for positioning the region, which take into account the geographical location, recreational conditions of the territory, the available opportunities, its cultural, national and ethnic identification, as well as differences, have not yet been proposed. This is evidenced by the low level of investment in the regions, the weak development of the real sector of the territory's economy, the rapid decline in the living standards of most of the region's residents, and, as a consequence, the growth in the rate of population migration. Thus, the formation of tools for positioning and identification of the region, which allows you to determine their competitive advantages, is a basic prerequisite for ensuring the economic development of the territory. In modern conditions of the transfer of powers and budgets of state bodies to local governments, the positioning of the region becomes a possible condition for their competitiveness. The article discusses the interpretation of the concept of "positioning", in particular, two of its basic components are highlighted: positioning as a strategy, positioning as a set of actions. Approaches to the positioning of the brand of the region are analyzed, where the region is considered from the position of a unique phenomenon and a complex product with a wide range of properties. The signs of a successful brand are determined, the main goals of branding, assessment, classification of models and stages of positioning the region are highlighted. and also: diagnosis of the situation, formulation of the essence of the brand, a strategic plan for brand promotion. implementation of the strategic plan, assessment of the effectiveness of the formed brand.

scholarly journals Beyond simple charts: Design of visualizations for big health data

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Oluwakemi Ola ◽  
Kamran Sedig
Keyword(s):  
Big Data ◽  
Disease Surveillance ◽  
Large Scale ◽  
Health Management ◽  
High Volume ◽  
Health Data ◽  
Making Sense ◽  
Interactive Visualizations ◽  
Scatter Plots ◽  
At Risk Populations

Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data’s utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data. 

scholarly journals Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis

2019 ◽  
Vol 26 (1) ◽  
pp. 642-651
Author(s):  
Laura Schubel ◽  
Danielle L Mosby ◽  
Joseph Blumenthal ◽  
Muge Capan ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Information Technology ◽  
Decision Support ◽  
Human Factors ◽  
Organ Failure ◽  
Health Information Technology ◽  
Health Information ◽  
Clinical Decision Support ◽  
Clinical Care ◽  
Clinical Decision ◽  
Wide Range

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance. We developed graphical model displays of two popular sepsis scoring systems, quick Sepsis Related Organ Failure Assessment and Predisposition, Infection, Response, Organ Failure, using human factors principles grounded in user-centered and interaction design. Models will be evaluated in a larger research effort to optimize alert design to improve the collective awareness of high-risk populations and develop a relevant point-of-care clinical decision support system for sepsis.

scholarly journals Effects of health information technology on patient outcomes: a systematic review

2015 ◽  
Vol 23 (5) ◽  
pp. 1016-1036 ◽  
Author(s):  
Samantha K Brenner ◽  
Rainu Kaushal ◽  
Zachary Grinspan ◽  
Christine Joyce ◽  
Inho Kim ◽  
...  
Keyword(s):  
Information Technology ◽  
Patient Safety ◽  
Health Information Technology ◽  
Health Information ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Health It ◽  
Term Care ◽  
Safety Outcomes

Abstract Objective To systematically review studies assessing the effects of health information technology (health IT) on patient safety outcomes. Materials and Methods The authors employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement methods. MEDLINE, Cumulative Index to Nursing Allied Health (CINAHL), EMBASE, and Cochrane Library databases, from 2001 to June 2012, were searched. Descriptive and comparative studies were included that involved use of health IT in a clinical setting and measured effects on patient safety outcomes. Results Data on setting, subjects, information technology implemented, and type of patient safety outcomes were all abstracted. The quality of the studies was evaluated by 2 independent reviewers (scored from 0 to 10). A total of 69 studies met inclusion criteria. Quality scores ranged from 1 to 9. There were 25 (36%) studies that found benefit of health IT on direct patient safety outcomes for the primary outcome measured, 43 (62%) studies that either had non-significant or mixed findings, and 1 (1%) study for which health IT had a detrimental effect. Neither the quality of the studies nor the rate of randomized control trials performed changed over time. Most studies that demonstrated a positive benefit of health IT on direct patient safety outcomes were inpatient, single-center, and either cohort or observational trials studying clinical decision support or computerized provider order entry. Discussion and Conclusion Many areas of health IT application remain understudied and the majority of studies have non-significant or mixed findings. Our study suggests that larger, higher quality studies need to be conducted, particularly in the long-term care and ambulatory care settings.

Deep Learning in IoT

2021 ◽  
pp. 1-54
Author(s):  
Devika G. ◽  
Asha Gowda Karegowda
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Big Data Analytics ◽  
Future Research ◽  
Great Success ◽  
Sensory Data ◽  
Digital Era ◽  
New Information ◽  
Wide Range

The internet of things (IoT), big data analytics, and deep learning (DL) applications in the mechanical internet are expanding. The current digital era has various sensory devices for a wide range of fields and applications, which all generate various sensory data. DL is being applied for handling big data and has achieved great success in the IoT and other fields. The applications for data streams to discover new information, predict future insights, and make control decisions are crucial processes that make the IoT a worthy paradigm for businesses and a quality-of-life improving technology. This chapter provides a detailed account of the IoT domain, machine learning, and DL techniques and applications. The IoT that consists of DL with intelligence backgrounds is also discussed. Recent research on DL in the IoT within the big data domain is also discussed. Current challenges and potential areas for future research are discussed.

scholarly journals Consumer Evaluation of the Quality of Online Health Information: Systematic Literature Review of Relevant Criteria and Indicators (Preprint)

2018 ◽  
Author(s):  
Yalin Sun ◽  
Yan Zhang ◽  
Jacek Gwizdka ◽  
Ciaran B. Trace
Keyword(s):  
Health Information ◽  
Information Quality ◽  
Quality Evaluation ◽  
Qualitative Content Analysis ◽  
Online Health Information ◽  
Related Factors ◽  
Consumer Evaluation ◽  
Wide Range ◽  
User Groups

BACKGROUND As the quality of online health information remains questionable, there is a pressing need to understand how consumers evaluate this information. Past reviews identified content-, source-, and individual-related factors that influence consumer judgment in this area. However, systematic knowledge concerning the evaluation process, that is, why and how these factors influence the evaluation behavior, is lacking. OBJECTIVE This review aims (1) to identify criteria (rules that reflect notions of value and worth) that consumers use to evaluate the quality of online health information and the indicators (properties of information objects to which criteria are applied to form judgments) they use to support the evaluation in order to achieve a better understanding of the process of information quality evaluation and (2) to explicate the relationship between indicators and criteria to provide clear guidelines for designers of consumer health information systems. METHODS A systematic literature search was performed in seven digital reference databases including Medicine, Psychology, Communication, and Library and Information Science to identify empirical studies that report how consumers directly and explicitly describe their evaluation of online health information quality. Thirty-seven articles met the inclusion criteria. A qualitative content analysis was performed to identify quality evaluation criteria, indicators, and their relationships. RESULTS We identified 25 criteria and 165 indicators. The most widely reported criteria used by consumers were trustworthiness, expertise, and objectivity. The indicators were related to source, content, and design. Among them, 114 were positive indicators (entailing positive quality judgments), 35 were negative indicators (entailing negative judgments), and 16 indicators had both positive and negative quality influence, depending on contextual factors (eg, source and individual differences) and criteria applied. The most widely reported indicators were site owners/sponsors; consensus among multiple sources; characteristics of writing and language; advertisements; content authorship; and interface design. CONCLUSIONS Consumer evaluation of online health information is a complex cost-benefit analysis process that involves the use of a wide range of criteria and a much wider range of quality indicators. There are commonalities in the use of criteria across user groups and source types, but the differences are hard to ignore. Evidently, consumers’ health information evaluation can be characterized as highly subjective and contextualized, and sometimes, misinformed. These findings invite more research into how different user groups evaluate different types of online sources and a personalized approach to educate users about evaluating online health information quality.

scholarly journals An international, Delphi consensus study to identify priorities for methodological research in behavioural trials: A study protocol

2018 ◽  
Vol 1 ◽  
pp. 11 ◽  
Author(s):  
Molly Byrne ◽  
Jenny Mc Sharry ◽  
Oonagh Meade ◽  
Kim L. Lavoie ◽  
Simon L. Bacon
Keyword(s):  
Research Priorities ◽  
Future Research ◽  
Behavioural Intervention ◽  
Point Scale ◽  
Delphi Consensus ◽  
Global Improvement ◽  
Behavioural Interventions ◽  
Methodological Research ◽  
Important Health

Background: Effective behaviour change interventions are needed to impact important health outcomes, including morbidity and mortality. However, the uptake and impact of behavioural interventions have been limited by methodological challenges. The International Behavioural Trials Network (IBTN) was established in 2013 to facilitate global improvement in methodological quality of behavioural trials. There has been no formal process, within the network or in the broader literature, to define the most important research priorities to achieve this aim. In this project, we will conduct an international, Delphi consensus study to identify and achieve consensus on priorities for methodological research in behavioural trials among IBTN members. Methods: Fifteen core members of IBTN, who are experts in the field of behavioural intervention research, will be invited to brainstorm a complete list of all items they consider priority areas for methodological research in trials of behavioural interventions. The IBTN Research Prioritisation team (the authors) will review all items generated, removing duplicates and merging similar topics, and generate a ‘long-list’ of items. This long-list will be sent to the 15 IBTN core members for approval. We will then administer two online Delphi surveys to all IBTN members. In the first survey, respondents will be asked to rate the importance of each item on a nine-point scale and rank their top five priorities. In the second survey, respondents will receive feedback on others’ responses and a reminder of their own responses in survey 1, and will be asked to re-rate items and re-select their ‘top five’. Discussion: Findings from the project will be used to develop the research agenda of the IBTN and to make recommendations for future research.

WHICH QUALITY OF LIFE MEASURES FIT YOUR RELATIVE EFFECTIVENESS ASSESSMENT?

2015 ◽  
Vol 31 (3) ◽  
pp. 147-153 ◽  
Author(s):  
Irina Cleemput ◽  
Mattias Neyt
Keyword(s):  
Quality Of Life ◽  
Clinical Decision Making ◽  
Relative Effectiveness ◽  
Clinical Decision ◽  
Cost Utility ◽  
Public Consultation ◽  
Hrqol Data ◽  
Wide Range ◽  
Effectiveness Assessment

Background: Health-related quality of life (HRQoL) is an important endpoint of many healthcare interventions. This study develops guidance on how to select appropriate HRQoL measures for inclusion in a clinical trial, given the purposes of the HRQoL measurement.Methods: The guidance is based on a systematic literature review, discussions with members of the European Network for Health Technology Assessment (EUnetHTA) and two rounds of public consultation.Results: A set of twelve recommendations was developed, addressing the requirements for HRQoL data for relative effectiveness assessment, for cost-utility analyses and for informing clinical decision making. Recommendations relate to the choice of the type of measure as well as to aspects such as measurement frequency, target population and presentation.Conclusions: The purpose and context of HRQoL measurement is crucial for the relevance of the data obtained with a specific HRQoL measure. It is recommended to always include a generic HRQoL instrument in clinical trials to cover a wide range of possible future uses of the HRQoL data.

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