2014.ISDS.Abstracts.Final.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
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ISDS 2014 Conference Abstracts

Automated Collection of Electronic Health Record 
Healthy Weight Data for Surveillance
Sean P. Mikles1, Jennifer L. Foltz2, Ian Painter1 and William B. Lober*1

1University of Washington Schools of Medicine, Nursing and Public Health, Seattle, WA, USA; 2Centers for Disease Control and 
Prevention, Atlanta, GA, USA

Objective
To demonstrate the feasibility of using healthy weight (HW) IT 

standards in public health surveillance through the collection and 
visualization of patient height, weight and behavioral data.

Introduction
Clinical data captured in electronic health records (EHR) for patient 

health care could be used for chronic disease surveillance, helping 
to inform and prioritize interventions at a state or community level. 
While there has been significant progress in the collection of clinical 
information such as immunizations for public health purposes, greater 
attention could be paid to the collection of data on chronic illness. 
Obesity is a chronic disease that affects over a third of the US adult 
population1, making it an important public health concern. Both HL7 
v.2.5.12 and Clinical Document Architecture (CDA) messages3 can
be used to facilitate the collection of HW EHR data. These standards 
include anthropometric and demographic information along with the 
option to transmit behavioral, continuity of care, community resource 
identification and care plan information. We worked with vendors 
participating in the Integrating the Healthcare Enterprise initiative 
(IHE) in developing, testing and showcasing scenarios to facilitate 
system development, increase the visibility of HW standards and 
demonstrate potential usages of obesity-related information.

Methods
The IHE initiative showcases integration of health information 

systems within and across institutions. Integration profiles, which 
describe the use of standards to implement data transactions that 
satisfy specific use cases, are developed by committees, implemented 
and tested in a multi-vendor Connectathon, and then demonstrated 
at national and international meetings. This process was followed to 
illustrate the feasibility and potential uses of HW data. A system was 
developed to receive and manage data from v.2.5.1 and CDA HW 
messages sent by clinical systems. Potential uses of HW data were 
conceptualized through project team collaborations and discussions 
with clinical and public health stakeholders and used to create a HW 
dashboard. These uses included stratified displays of overall weight 
metrics by demographics and locations, displays of the distribution 
of behavioral risk factors such as diet and physical activity, and the 
display of the metrics of a hypothetical provider’s cohort of patients 
as compared to the entire community.

Results
The HW surveillance data collection and dashboard visualizations 

have been demonstrated at the 2014 Health Information and 
Management Systems Society (HIMSS, Orlando FL) and Public Health 
Informatics (PHIC, Atlanta GA) conferences. The visualizations were 
presented as part of an integrated walkthrough with vendor partners, 
which included the entering of clinical information in an EHR, the 
transmission of the information to the HW Information System, and 
the display of graphs and visualizations of a hypothetical data set 
based on metrics from the state of Washington. The dashboard and 

displays for HIMSS and PHIC may be viewed at http://ihe2014.cirg.
washington.edu (accessed Sept 7, 2014).

Conclusions
This project demonstrated HW IT standards based collection of 

weight-related data from EHRs and explored data visualizations 
derived from those data. HW surveillance could identify the need for, 
shape the design of, and support monitoring and evaluation of public 
health interventions. Future work can expand these demonstrations 
into limited pilot projects to investigate the efficacy of EHR-based 
HW data collection.

Visualizations, like this map of simulated BMI data by zip code, can help 
public health agencies target interventions.

Keywords
Healthy Weight; Obesity; Chronic Disease; Informatics; Surveillance

Acknowledgments

This work was supported by funding from CDC/National Center for 
Chronic Disease Prevention and Health Promotion and in part by NIH 
NLM Training Grant Nr. T15LM007442.

References

1. CDC. (2011). Obesity: Halting the Epidemic by Making Health
Easier At A Glance 2011. [Online]. Available: http://www.cdc.gov/
chronicdisease/resources/publications/AAG/obesity.htm. [6 Sep
2014].

2. HL7. HL7 Version 2.5.1 Implementation Guide: Height and Weight
Report, Release 1. [Online]. Available: http://www.hl7.org/implement/
standards/product_brief.cfm?product_id=315. [6 Sep 2014]

3. IHE. IHE Quality, Research and Public Health Technical Framework
Supplement for Healthy Weight. [Online]. Available: http://www.ihe.
net/uploadedFiles/Documents/QRPH/IHE_QRPH_Suppl_HW.pdf. [6 
Sep 2014]

*William B. Lober
E-mail: lober@uw.edu    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e88, 201