RiskScape: A Data Visualization and Aggregation Platform for Public Health Surveillance Using Routine Electronic Health Record Data

2020 ◽  
pp. e1-e8
Author(s):  
Noelle M. Cocoros ◽  
Chaim Kirby ◽  
Bob Zambarano ◽  
Aileen Ochoa ◽  
Karen Eberhardt ◽  
...  
Keyword(s):  
Public Health ◽  
Electronic Health Record ◽  
Public Health Surveillance ◽  
Data Communication ◽  
Automated Analysis ◽  
Health Surveillance ◽  
Health Record ◽  
Clinical Practices ◽  
Electronic Health Record Data ◽  
Electronic Health

Automated analysis of electronic health record (EHR) data is a complementary tool for public health surveillance. Analyzing and presenting these data, however, demands new methods of data communication optimized to the detail, flexibility, and timeliness of EHR data. RiskScape is an open-source, interactive, Web-based, user-friendly data aggregation and visualization platform for public health surveillance using EHR data. RiskScape displays near-real-time surveillance data and enables clinical practices and health departments to review, analyze, map, and trend aggregate data on chronic conditions and infectious diseases. Data presentations include heat maps of prevalence by zip code, time series with statistics for trends, and care cascades for conditions such as HIV and HCV. The platform’s flexibility enables it to be modified to incorporate new conditions quickly—such as COVID-19. The Massachusetts Department of Public Health (MDPH) uses RiskScape to monitor conditions of interest using data that are updated monthly from clinical practice groups that cover approximately 20% of the state population. RiskScape serves an essential role in demonstrating need and burden for MDPH’s applications for funding, particularly through the identification of inequitably burdened populations. (Am J Public Health. Published online ahead of print December 22, 2020: e1–e8. https://doi.org/10.2105/AJPH.2020.305963 )

Use of an electronic health record system for public health surveillance

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
James Cheek ◽  
Aneel Advani ◽  
Brigg Reilley ◽  
Frank Hack ◽  
Amy Groom ◽  
...  
Keyword(s):  
Public Health ◽  
Electronic Health Record ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Electronic Health Record System ◽  
Health Record ◽  
Record System ◽  
Electronic Health

scholarly journals Building a high quality medical data architecture for multiple uses in an integrated health care environment

2014 ◽  
Vol 3 (5) ◽  
pp. 55 ◽  
Author(s):  
Frank Boterenbrood ◽  
Irene Krediet ◽  
William Goossen
Keyword(s):  
Electronic Health Record ◽  
Data Exchange ◽  
Research Question ◽  
Data Communication ◽  
Medical Data ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Clinical Models ◽  
Multiple Uses ◽  
Electronic Health

Objective: The aim was to create a reliable information provisioning system in healthcare for both care and research processes, based on existing data standards and standardized electronic messages. The research question is: How can a Clinical Data Ware House (CDWH) be developed for standardized basic patient data, generic nursing data and data about oncology nursing, allowing management of Electronic Health Record data, electronic data exchange and data analytics? Materials and methods: The main instrument used was the Detailed Clinical Model (DCM) data standardization approach. Further, data communication utilized HealthLevel7v3 (HL7v3) messages, transported by Mirth Connect. In an incremental, design-oriented research project, CDWH-prototypes were constructed using Enterprise Architect, a HL7v3 generator plug-in, SQL Server technology and PostgreSQL-based CDWH-technology. Results: The project resulted in 16 existing DCMs selected and 6 new DCMs defined. From those DCMs, a HL7v3 message structure was generated and a CDWH created. Implementing specialized Data Marts (DM) a connection between the CDWH and one Electronic Health Record system was built. Discussion: Detailed Clinical Models improve data quality, yet building them consumes time and resources. Some required data codes could not be identified in time and dummy codes were used instead. The existence of unstructured medical data in legacy systems may proof to be an obstacle in the future. Conclusion: The project shows that using Detailed Clinical Models as the sole source for system development leads to a sound design for a CDWH and HL7v3 messages, supporting a standards based health information system, suitable for multiple uses.

scholarly journals Innovations in Population Health Surveillance Using Electronic Health Record Data

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Sharon E. Perlman ◽  
Katharine H. McVeigh ◽  
Remle Newton-Dame ◽  
Lorna E. Thorpe ◽  
Elisabeth F. Snell ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Population Health ◽  
Health Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Leveraging electronic health record data to inform hospital resource management

2021 ◽  
Author(s):  
José Carlos Ferrão ◽  
Mónica Duarte Oliveira ◽  
Daniel Gartner ◽  
Filipe Janela ◽  
Henrique M. G. Martins
Keyword(s):  
Resource Management ◽  
Electronic Health Record ◽  
Health Record ◽  
Hospital Resource ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health
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