scholarly journals A policy framework for public health uses of electronic health data

2012 ◽  
Vol 21 ◽  
pp. 18-22 ◽  
Author(s):  
Deven McGraw ◽  
Kristen Rosati ◽  
Barbara Evans
Keyword(s):  
Public Health ◽  
Health Data ◽  
Policy Framework ◽  
Electronic Health

scholarly journals Privacy, security, and the public health researcher in the era of electronic health record research

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Neal D Goldstein ◽  
Anand D Sarwate
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Large Scale ◽  
Health Data ◽  
Shared Responsibility ◽  
Privacy And Security ◽  
The Public ◽  
Health Researcher ◽  
Public Health Researcher ◽  
Electronic Health

Health data derived from electronic health records are increasingly utilized in large-scale population health analyses. Going hand in hand with this increase in data is an increasing number of data breaches. Ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. In this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. In order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools.

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 Processing of Novel Electronic Health Data to Support Public Health Surveillance

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Peter Hicks ◽  
Henry Rolka ◽  
Mark Wooster ◽  
Lynette Brammer
Keyword(s):  
Public Health ◽  
Public Health Surveillance ◽  
Health Data ◽  
Health Surveillance ◽  
Electronic Health

scholarly journals Exploring the ethics of the use and commercialisation of New Zealand public health system data

2015 ◽  
Vol 11 (4) ◽  
Author(s):  
Kirsten Lovelock ◽  
Robin Gauld ◽  
Greg Martin ◽  
Jayden McRae
Keyword(s):  
Public Health ◽  
New Zealand ◽  
Health System ◽  
Public Health System ◽  
Methodological Approach ◽  
Health Data ◽  
Policy Framework ◽  
The Public ◽  
System Data ◽  
Health System Data

This article canvasses the literature exploring issues related to the commercialisation of health data from the public health system. It examines whether commercialisation is a viable proposition in New Zealand, socially and ethically. In doing so, it provides a methodological approach to the development of an ethics and privacy policy framework for any potential commercialisation of public health data in New Zealand.

scholarly journals Estimating Wisconsin Asthma Prevalence Using Clinical Electronic Health Records and Public Health Data

2014 ◽  
Vol 104 (1) ◽  
pp. e65-e73 ◽  
Author(s):  
Carrie D. Tomasallo ◽  
Lawrence P. Hanrahan ◽  
Aman Tandias ◽  
Timothy S. Chang ◽  
Kelly J. Cowan ◽  
...  
Keyword(s):  
Public Health ◽  
Electronic Health Records ◽  
Health Data ◽  
Asthma Prevalence ◽  
Health Records ◽  
Public Health Data ◽  
Electronic Health

scholarly journals Factors Affecting Views On Electronic Health Data Sharing：A Survey of Chinese Residents

2021 ◽  
Author(s):  
Ying He ◽  
Lun Li ◽  
Weihong Wang
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Health Data ◽  
Hunan Province ◽  
Health Administration ◽  
Electronic Health Record Data ◽  
Factors Affecting ◽  
Potential Benefits ◽  
Electronic Health ◽  
Chinese Public

Abstract Background: China has been promoting electronic health data sharing for years. However, few studies have focused on Chinese residents' views on sharing personal health data or analyzed factors that affect them. This study targets this gap and investigates China's public attitude toward sharing EHRs and the influential factors, including awareness of privacy and security and potential benefits of sharing EHRs. Methods: The survey adopted multi-stage stratified sampling to select residents in Hunan province randomly and received 932 responses. The primary outcome measures were the responses to the 29-item questionnaire evaluating different views on privacy and safety of Electronic Health Record Data Sharing, using a five-point answering scale with the extremes labeled as "poor" and "excellent".Results: Most Chinese residents hold reservations about sharing them. 297（32.18%）were willing to share their healthcare data between institutions; 438(47.45%)supported sharing only for a better healthcare service. The Logistic Regression(αin=0.05,αout=0.10) was used to find out the factors affecting willingness. The results demonstrated that people who hold the following characteristics would be more likely to consent to share EHRs: (1) have EHRs, (2) value potential benefits of sharing EHRs, (3) believe sharing EHRs would improve quality of care, (4)disagree that sharing EHRs would increase the risks of privacy breach, (5)prefer benefits of sharing EHRs than protecting privacy, and (6)work in a healthcare-related position.(likelihood-ratio chi-squared test 274.058 ,P<0.05).Conclusions: The attitude of the Chinese public towards data sharing in electronic health records is influenced by several factors. Our study provides new and solid references for Chinese public opinion on EHRs. Those results may assist related research or benefit public health administration, like formulating policies to improve public acceptance of EHRs and promoting EHRs-based public health services.

Initiative to Release Medicare, Public Health Data

2010 ◽  
Vol 43 (8) ◽  
pp. 46
Author(s):  
JANE ANDERSON
Keyword(s):  
Public Health ◽  
Health Data ◽  
Public Health Data
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