scholarly journals Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Sripriya Rajamani ◽  
Ann Kayser ◽  
Emily Emerson ◽  
Sarah Solarz
Keyword(s):  
Public Health ◽  
Data Quality ◽  
Disease Surveillance ◽  
Data Exchange ◽  
Health Program ◽  
Exchange Process ◽  
Quality Of Data ◽  
The Public ◽  
Electronic Laboratory Reporting

Background: Past and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. The critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (ELR). Many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts.Objectives: To understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. The study was conducted in context of Minnesota Electronic Disease Surveillance System (MEDSS), the public health information system for supporting infectious disease surveillance in Minnesota. Data Quality (DQ) dimensions by Strong et al., was chosen as the guiding framework for evaluation.Methods: The process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the MEDSS system. Interviews were conducted in an open-ended format from November 2017 through February 2018. Based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. This in turn comprised of eight critical steps and its impact on quality of data was identified through expert input. This was followed by analysis of data in MEDSS by various criteria identified by the informatics team.Results: All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ) were impacted in the data exchange process with varying influence on DQ dimensions. Some errors such as improper mapping in electronic health records (EHRs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. The study revealed that data quality is dynamic and on-going oversight is a combined effort by MEDSS Operations Team and Review by Technical and Public Health Program Professionals.Conclusion: With increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. This study focused on electronic laboratory reporting to public health and analyzed both on-boarding and internal data exchange processes. Insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future.

scholarly journals Advanced Visualization and Analysis of Data Quality for Syndromic Surveillance Systems

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Brian E. Dixon ◽  
Chen Wen ◽  
Tony French ◽  
Jennifer Williams ◽  
Shaun J. Grannis
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Information Systems ◽  
Data Quality ◽  
Open Source ◽  
Syndromic Surveillance ◽  
Health Data ◽  
Quality Of Data ◽  
Electronic Health

ObjectiveTo extend an open source analytics and visualization platform for measuring the quality of electronic health data transmitted to syndromic surveillance systems.IntroductionEffective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems. However, the quality of data in these systems can be poor or “unfit for use.” Therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers1. Current methods for examining data quality largely rely on manual queries and processes conducted by epidemiologists. Better, automated tools for examining data quality are desired by the surveillance community.MethodsUsing the existing, open-source platform Atlas developed by the Observational Health Data Sciences and Informatics collaborative (OHDSI; www.ohdsi.org), we added new functionality to measure and visualize the quality of data electronically reported from disparate information systems. Our extensions focused on analysis of data reported electronically to public health agencies for disease surveillance. Specifically, we created methods for examining the completeness and timeliness of data reported as well as the information entropy of the data within syndromic surveillance messages sent from emergency department information systems.ResultsTo date we transformed 111 million syndromic surveillance message segments pertaining to 16.4 million emergency department encounters representing 6 million patients into the OHDSI common data model. We further measured completeness, timeliness and entropy of the syndromic surveillance data. In Figure-1, the OHDSI tool Atlas summarizes the analysis of data completeness for key fields in over one million syndromic surveillance messages sent to Indiana’s health department in 2014. Completeness is reported by age category (e.g., 0-10, 20-30, 60+). Gender is generally complete, but both race and ethnicity fields are often complete for less than half of the patients in the cohort. These results suggest areas for improvement with respect to data quality that could be actionable by the syndromic surveillance coordinator at the state health department.ConclusionsOur project remains a work-in-progress. While functions that assess completeness, timeliness and entropy are complete, there may be other functions important to public health that need to be developed. We are currently soliciting feedback from syndromic surveillance stakeholders to gather ideas for what other functions would be useful to epidemiologists. Suggestions could be developed into functions over the next year. We are further working with the OHDSI collaborative to distribute the Atlas enhancements to other platforms, including the National Syndromic Surveillance Platform (NSSP). Our goal is to enable epidemiologists to quickly analyze data quality at scale.References1. Dixon BE, Rosenman M, Xia Y, Grannis SJ. A vision for the systematic monitoring and improvement of the quality of electronic health data. Studies in health technology and informatics. 2013;192:884-8.

Digital Data Exchange Between Health Care and Public Health: Lessening the Burden

2019 ◽  
pp. 177-190
Author(s):  
Jeffrey P. Engel ◽  
W. Edward Hammond
Keyword(s):  
Public Health ◽  
Health Care ◽  
Data Exchange ◽  
Health Data ◽  
Digital Data ◽  
Technology Infrastructure ◽  
Information Technology Infrastructure ◽  
The Public ◽  
Infrastructure Maintenance ◽  
Electronic Laboratory Reporting

This chapter seeks to provide an overview of automated data exchange between public health and health care, highlighting cases which use electronic laboratory reporting (ELR) and electronic case reporting (ECR) and how health data is standardized and messaged. It also provides the example of the West Nile Virus (WNV). This example demonstrates how health data can be used to lessen the burden of mandated disease reporting for health care and improve timeliness, completeness, and accuracy of surveillance for public health. The Digital Bridge project is also introduced, as a national single and scalable solution emphasizing the importance of the public-private partnership. Challenges facing the Digital Bridge approach include ensuring ongoing investments in all sectors for automation and data exchange, especially information technology infrastructure maintenance and a prepared workforce of engineers, epidemiologists, and data scientists.

Assessing the quality of public health in the Russian Federation and Israel: Main problems and solutions

2020 ◽  
Vol 19 (4) ◽  
pp. 618-632
Author(s):  
A.S. Panchenko
Keyword(s):  
Public Health ◽  
Russian Federation ◽  
Population Based ◽  
The Public ◽  
Disability Adjusted Life ◽  
Problems And Solutions ◽  
The Russian Federation ◽  
Disability Adjusted Life Year ◽  
Practical Recommendations

Subject. The article addresses the public health in the Russian Federation and Israel. Objectives. The focus is on researching the state of public health in Russia and Israel, using the Global Burden of Disease (GBD) project methodology, identifying problem areas and searching for possible ways to improve the quality of health of the Russian population based on the experience of Israel. Methods. The study draws on the ideology of the GBD project, which is based on the Disability-Adjusted Life-Year (DALY) metric. Results. The paper reveals the main causes of DALY losses and important risk factors for cancer for Russia and Israel. The findings show that the total DALY losses for Russia exceed Israeli values. The same is true for cancer diseases. Conclusions. Activities in Israel aimed at improving the quality of public health, the effectiveness of which has been proven, can serve as practical recommendations for Russia. The method of analysis, using the ideology of the GBD project, can be used as a tool for quantitative and comparative assessment of the public health.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

scholarly journals Response Behavior and Quality of Survey Data: Comparing Elderly Respondents in Institutions and Private Households

2021 ◽  
pp. 004912412199553
Author(s):  
Jan-Lucas Schanze
Keyword(s):  
Data Quality ◽  
The Elderly ◽  
Response Behavior ◽  
Quality Of Data ◽  
Social Surveys ◽  
Private Households ◽  
Confounding Variables ◽  
Health Related ◽  
Survey Interviews

An increasing age of respondents and cognitive impairment are usual suspects for increasing difficulties in survey interviews and a decreasing data quality. This is why survey researchers tend to label residents in retirement and nursing homes as hard-to-interview and exclude them from most social surveys. In this article, I examine to what extent this label is justified and whether quality of data collected among residents in institutions for the elderly really differs from data collected within private households. For this purpose, I analyze the response behavior and quality indicators in three waves of Survey of Health, Ageing and Retirement in Europe. To control for confounding variables, I use propensity score matching to identify respondents in private households who share similar characteristics with institutionalized residents. My results confirm that most indicators of response behavior and data quality are worse in institutions compared to private households. However, when controlling for sociodemographic and health-related variables, differences get very small. These results suggest the importance of health for the data quality irrespective of the housing situation.

scholarly journals In the pursuit of equity: COVID-19, data and Aboriginal and Torres Strait Islander people in Australia

2021 ◽  
Vol 37 (1) ◽  
pp. 37-45
Author(s):  
Kalinda Griffiths ◽  
Ian Ring ◽  
Richard Madden ◽  
Lisa Jackson Pulver
Keyword(s):  
Public Health ◽  
Torres Strait Islander ◽  
Self Determination ◽  
Quality Of Data ◽  
Success Story ◽  
Torres Strait Islander People ◽  
Pandemic Planning ◽  
The Future ◽  
Torres Strait

Since March 2020 in Australia, there has been decisive national, and state and territory policy as well as community led action involving Aboriginal and Torres Strait Islander people as information about COVID-19 arose. This has resulted in, what could only be framed as a success story in self-determination. However, there continues to be issues with the quality of data used for the surveillance and reporting of Aboriginal and Torres Strait Islander people during the pandemic. This article discusses some of the important events in pandemic planning regarding Aboriginal and Torres Strait Islander people and how this relates to surveillance and monitoring in the emerging and ongoing threat of COVID-19 within Aboriginal and Torres Strait Islander communities. The authors also identify some of the data considerations required in the future to monitor and address public health.

scholarly journals Evaluation of Education for Public Health: A Practical Foundation for Solidarity

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
M Perkiö ◽  
R Harrison ◽  
M Grivna ◽  
D Tao ◽  
C Evashwich
Keyword(s):  
Public Health ◽  
Health Education ◽  
Health Workforce ◽  
Health Professionals ◽  
Public Health Education ◽  
Public Health Workforce ◽  
Rigorous Evaluation ◽  
The Public ◽  
Public Health Professionals

Abstract Education is a key to creating solidary among the professionals who advance public health’s interdisciplinary mission. Our assumption is that if all those who work in public health shared core knowledge and the skills for interdisciplinary interaction, collaboration across disciplines, venues, and countries would be facilitated. Evaluation of education is an essential element of pedagogy to ensure quality and consistency across boundaries, as articulated by the UNESCO education standards. Our study examined the evaluation studies done by programs that educate public health professionals. We searched the peer reviewed literature published in English between 2000-2017 pertaining to the education of the public health workforce at a degree-granting level. The 2442 articles found covered ten health professions disciplines and had lead authors representing all continents. Only 86 articles focused on evaluation. The majority of the papers examined either a single course, a discipline-specific curriculum or a teaching method. No consistent methodologies could be discerned. Methods ranged from sophisticated regression analyses and trends tracked over time to descriptions of focus groups and interviews of small samples. We found that evaluations were primarily discipline-specific, lacked rigorous methodology in many instances, and that relatively few examined competencies or career expectations. The public health workforce enjoys a diversity of disciplines but must be able to come together to share diverse knowledge and skills. Evaluation is critical to achieving a workforce that is well trained in the competencies pertinent to collaboration. This study informs the pedagogical challenges that must be confronted going forward, starting with a commitment to shared core competencies and to consistent and rigorous evaluation of the education related to training public health professionals. Key messages Rigorous evaluation is not sufficiently used to enhance the quality of public health education. More frequent use of rigorous evaluation in public health education would enhance the quality of public health workforce, and enable cross-disciplinary and international collaboration for solidarity.

Running the Titan_Illumina_PE Workflow on Terra.bio v1

2021 ◽  
Author(s):  
Jill V Hagey ◽  
Kevin Libuit ◽  
Frank J Ambrosio ◽  
Technical Outreach and Assistance for States Team
Keyword(s):  
Public Health ◽  
Technical Assistance ◽  
File Format ◽  
Technical Documentation ◽  
Video Instruction ◽  
The Public ◽  
Viral Genomics ◽  
Genomic Characterization ◽  
Primer Sequence

The Titan_Illumina_PE workflow is a part of the Public Health Viral Genomics Titan series for SARS-CoV-2 genomic characterization. Titan_Illumina_PE was written specifically to process Illumina paired-end (PE) read data. Input reads are assumed to be the product of sequencing tiled PCR-amplicons designed for the SARS-CoV-2 genome. The most common read data analyzed by the Titan_Illumina_PE workflow are generated with the ARTIC V3 protocol. However, alternative primer schemes such as the QIAseq Primer Panel are also suitable for this workflow. The primer sequence coordinates of the PCR scheme utilized must be provided in BED format along with the raw Illumina read data. Upon initiating a Titan_Illumina_PE job, the input primer scheme coordinates and raw paired-end Illumina read data provided for each sample will be processed to perform consensus genome assembly, infer the quality of both raw read data and the generated consensus genome, and assign lineage or clade designations as outlined in the Titan_Illumina_PE data workflow diagram below. Additional technical documentation for the Titan_Illumina_PE workflow is available at: https://public-health-viral-genomics-theiagen.readthedocs.io/en/latest/titan_workflows.html#titan-workflows-for-genomic-characterization Required input data for Titan Illumina PE: Illumina paired-end read data (forward and reverse FASTQ files per sample) Primer sequence coordinates of the PCR scheme utilized in BED file format Video Instruction: Theiagen Genomics: Titan Genomic Characterization https://www.youtube.com/watch?v=zP9I1r6TNrw Theiagen Genomics: Titan Outputs QC https://www.youtube.com/watch?v=Amb-8M71umw For technical assistance please contact us at: [email protected]

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