scholarly journals Measuring and Improving the Quality of Data Used for Syndromic Surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Brian E. Dixon ◽  
Jon Duke ◽  
Shaun Grannis
Keyword(s):  
Public Health ◽  
Open Source ◽  
Health Professionals ◽  
Syndromic Surveillance ◽  
Health Data ◽  
Quality Of Data ◽  
Public Health Professionals ◽  
Health Agencies ◽  
Electronic Health

ObjectiveTo extend an open source platform for measuring the qualityof electronic health data by adding functions useful for syndromicsurveillance.IntroductionNearly all of the myriad activities (or use cases) in clinical andpublic health (e.g., patient care, surveillance, community healthassessment, policy) involve generating, collecting, storing, analyzing,or sharing data about individual patients or populations. Effectiveclinical and public health practice in the twenty-first century requiresaccess to data from an increasing array of information systems,including but not limited to electronic health records. However, thequality of data in electronic health record systems can be poor or“unfit for use.” Therefore measuring and monitoring data quality isan essential activity for clinical and public health professionals aswell as researchers.MethodsUsing the Health Data Stewardship Framework1, we will extendAutomated Characterization of Health Information at Large-scaleLongitudinal Evidence Systems (ACHILLES), a software packagepublished open-source by the Observational Health Data Sciencesand Informatics collaborative (OHDSI; www.ohdsi.org) to measurethe quality of data electronically reported from disparate informationsystems. Our extensions will focus on analysis of data reportedelectronically to public health agencies for disease surveillance. Nextwe will apply the ACHILLES extensions to explore the quality ofdata captured from multiple real-world health systems, hospitals,laboratories, and clinics. We will further demonstrate the extendedsoftware to public health professionals, gathering feedback on theability of the methods and software tool to support public healthagencies’ efforts to routinely monitor the quality of data received forsurveillance of disease prevalence and burden.ResultsTo date we have mapped key surveillance data fields into theOHDSI common data model, and we have transformed 111 millionsyndromic surveillance message segments pertaining to 16.4 millionemergency department encounters representing 6 million patientsfor importation into ACHILLES. Using these data, we are exploringthe existing 167 metrics across 16 categories available withinACHILLES, including a person (e.g., number of unique persons);and observation period (e.g., Distribution of age at first observationperiod). Syndromic surveillance (SS), however, is driven largelyby monitoring patient stated chief complaints (non-standard freetext clinical data) in addition to coded diagnoses. Consequently,ACHILLES must be extended to maximally support use in analyzingSS datasets.ConclusionsThis work remains a work-in-progress. Over the coming year, wewill not only explore existing ACHILLES constructs using real-worldpublic health data but also introduce new functionality to explore1) patient demographics; 2) facility and location (e.g., emergencydepartment where care was delivered); and 3) clinical observations(e.g., chief complaint). The design and methods for examining theseaspects of surveillance data will be included on the poster, and theywill be made freely available for distribution with a future instance ofthe ACHILLES software. We ultimately envision these tools beingavailable for use on platforms such as the CDC’s Biosense – open toall local and state health agencies as a one-stop portal for surveillancedata analysis – or research environments where they can be used toexamine and improve the quality of data output from informaticssystems.

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.

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.

scholarly journals Health Data for Research Through a Nationwide Privacy-Proof System in Belgium: Design and Implementation (Preprint)

2018 ◽  
Author(s):  
Nicolas Delvaux ◽  
Bert Aertgeerts ◽  
Johan CH van Bussel ◽  
Geert Goderis ◽  
Bert Vaes ◽  
...  
Keyword(s):  
Data Collection ◽  
Primary Source ◽  
Data Retrieval ◽  
Health Data ◽  
Scientific Data ◽  
Primary Data ◽  
Scientific Integrity ◽  
Quality Of Data ◽  
Electronic Health

BACKGROUND Health data collected during routine care have important potential for reuse for other purposes, especially as part of a learning health system to advance the quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures and, thus, ensure privacy. OBJECTIVE This study aims to address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring the accuracy, validity, safety, and privacy. In addition, the study demonstrates how these processes can be used in various research designs relevant for learning health systems. METHODS The Healthdata.be platform urges uniformity of the data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer are organized through end-to-end encrypted electronic health channels, and data are encoded using token keys. In addition, patient identifiers are pseudonymized so that health data from the same patient collected across various sources can still be linked without compromising the deidentification. RESULTS The Healthdata.be platform currently collects data for >150 clinical registries in Belgium. We demonstrated how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial. CONCLUSIONS Collecting health data in various sources and linking these data to a single patient is a promising feature that can potentially address important concerns on the validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to a research facility. More research is required to demonstrate that these methods improve the quality of data collection, allowing researchers to rely on electronic health records as a valid source for scientific data.

Oral health diseases among the older people: a general health perspective

2020 ◽  
Keyword(s):  
Public Health ◽  
Health Care ◽  
Oral Health ◽  
Older People ◽  
Health Professionals ◽  
General Health ◽  
Oral Health Care ◽  
Oral Diseases ◽  
Public Health Professionals ◽  
The Relationship

Background: The relationship between oral health and general health is gaining interest in geriatric research; however, a lack of studies dealing with this issue from a general perspective makes it somewhat inaccessible to non-clinical public health professionals. Purpose: The purpose of this review is to describe the relationship between oral health and general health of the elderly on the basis of literature review, and to give non-clinical medical professionals and public health professionals an overview of this discipline. Methods: This study was based on an in-depth review of the literature pertaining to the relationship between oral health and general health among the older people. The tools commonly used to evaluate dental health and the academic researches of male elderly people were also reviewed. And future research directions were summarized. Results: Dental caries, periodontal disease, edentulism, and xerostomia are common oral diseases among the older people. Dental caries and periodontal diseases are the leading causes of missing teeth and edentulism. Xerostomia, similar to dry mouth, is another common oral health disease in the older people. No clear correlation exists between the subjective feeling of dryness and an objective decrease of saliva. Rather, both conditions can be explained by changes in saliva. The General Oral Health Assessment Index (GOHAI) and the Oral Health Impact Profile (OHIP) are the main assessment tools used to examine oral health and quality of life in the older people. The GOHAI tends to be more sensitive to objective values pertaining to oral function. In addition, oral health studies in male elderly people are population-based cohort or cross-sectional studies, involving masticatory function, oral prevention, frailty problems, cardiovascular disease risk, and cognitive status. Conclusion: It is possible to reduce the incidence of certain oral diseases, even among individuals who take oral health care seriously. Oral health care should be based on the viewpoint of comprehensive treatment, including adequate nutrition, good life and psychology, and correct oral health care methods. In the future, researchers could combine the results of meta-analysis with the clinical experience of doctors to provide a more in-depth and broader discussion on oral health research topics concerning the older people.

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.

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