Transcript CLICK TO EDIT MASTER STYLE - Public Health Informatics

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Leveraging
EHR data to estimate cost of illness and visualize
burden of Diabetes in Chicago
Onyinyechi U. Enyia Daniel, MA CCRP
Acknowledgements
Special thanks to Dr. Abel Kho, Executive Director at Chicago Health Information
Regional Technology Center and PI Chicago Health Atlas
John Cashy, PhD, data analyst at CHITREC
www.chitrec.org
Chicago Health Atlas team (under the leadership of Dr. Kho and Dan O’Neil)
www.chicagohealthatlas.org
Dr. Edward Mensah -Director of the Public Health Informatics program at UIC
School of Public Health Department of Health Policy and Information, and primary
advisor.
There is TO
limitedEDIT
literatureMASTER
on the use of EHR
data in cost of illness studies
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STYLE
• Public Health Informatics and public health surveillance has a far reaching
economic impact.
• EHRs are increasingly an essential function not only in clinical care, but
public health surveillance.
• EHR data can provide real-time chronic disease surveillance
• This data can also be used to estimate the cost of illness for chronic diseases
such as diabetes, hypertension, cardiovascular disease, and other comorbidities.
Research Objectives
Diabetes has reached epidemic proportions in some parts of Chicago.
Type 2 diabetes is at 19.3% on the South Side.
Levels on parts of the North side are approximately 7%.
Studies have demonstrated a correlation between racial/ethnic minority status
and diabetes risk
• Studies have also correlated built environmental factors that may contribute to
prevalence and incidence of chronic disease
• Cost of treatment for diabetes varies across healthcare institutions, and across
regions.
•
•
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National Studies
• Huang et al conducted a population level model to determine the cost of diabetes
at the national level.
• They estimate that between 2009 and 2034, number of people diagnosed with
diabetes will increase from 23.7 to 44.1 million.
• They estimate diabetes related spending to increase from $113 billion to $336
billion.
• For Medicare-eligible population, they estimate that spending will increase from
$45 billion to $75 billion.
What sets this study apart?
Previous studies differ significantly from this study on several fronts:
• They rely on national survey data from the Behavioral Risk Factor Surveillance Survey
(BRFSS) Administered by the CDC.
• They rely on the Medical Expenditure Panel Survey sponsored by AHRQ.
• These studies differ significantly from this study in that they rely on self-report, and are thus
subject to re-call bias.
• The method of response collection for BRFFS includes the use of landline telephones. This
method has implications for the demographic submitting responses.
Benefits of EHR
• The use of EHR data has also created a platform for visualization of diabetes
burden in various communities throughout Chicago.
• EHR data is not susceptible to recall and/or reporting bias
• EHR data is collected at routine clinical encounters, thus ensuring that data is up
to date.
• The widespread of EHR data has implications for health policies impacting the
use of information for clinical decision-making as well as public health
surveillance and research.
Study Design
• EHR Data was obtained using the Chicago Health Atlas Database.
• CHA is a shared resource with IRB approval to extract data such as diagnoses, medications,
and laboratory tests for patients seen at six healthcare institutions.
• EHR data was accessed through Structured Query Language queries, using local data
extraction and IBM SPSS modeler 10.1 for analysis.
• Patients are assigned a unique cluster ID, and were geocoded based on zip code. All data was
de-identified, and a hashing algorithm was used to match patients who may have visited more
than one institution.
Study population
• Diabetic and pre-diabetic patients were identified based on the standards
outlined in the American Diabetic Association Standards for diagnosis and
treatment of diabetes.
• Total of 11,188 diabetic and pre-diabetic patients included in the study.
• All patients were over age 18
• Pregnant women were excluded from the study
Cost/Charge Data
• Charge data was taken from the Department of Health and Human Services Inpatient
Prospective Payment System (IPPS) Provider Summary for the top 100 Diagnosis-Related
Groups (DRG) for 2011.
• This data provides a summary of IPPS discharges, average charges, and average Medicare and
Medicaid payments for the top 100 Diagnosis-Related Groups.
Average Cost Model
[Average Total Cost * [Average rate of inflation for healthcare
2011-2021 (2.3%)] * [Average annual increase in health care
costs between 2011-2021 (5.7%)] * [# Diabetic/Prediabetic
Patients]
Diabetes Economic Burden
$300,000,000.00
$250,000,000.00
$200,000,000.00
$150,000,000.00
$100,000,000.00
$50,000,000.00
$0.00
Series1
Projected Cost of Diabetes in Chicago 2011-2021
$400,000,000.00
$350,000,000.00
$300,000,000.00
$250,000,000.00
$200,000,000.00
$150,000,000.00
$100,000,000.00
$50,000,000.00
$0.00
Series1
Built Environment Data
• Studies have demonstrated the effects of built environment on disease. Geomedicine is a
growing field of interest, which highlights the impact of built environment on health outcomes.
• Built environmental factors that could impact diabetes prevalence, and thus impact economic
burden include access to farmer’s markets for fresh produce, parks, recreational facilities,
hospitals, and high quality grocery stores.
• This data was gleaned from the City of Chicago Data Portal, and is publicly available.
Visualization
Significance
• Based on economic data, diabetes clearly presents a major financial burden not only to
healthcare institutions, but to individuals as well.
• EHR data is crucial in joining health information with environmental information to inform
health policy.
• In the future, when all care documentations are recorded in EHRs, we will be able to use data
mining techniques and GIS to carry out extensive work such as that carried out in this study.
This has major implications for health care costs in the long term, as well as more efficient
allocation of resources to target diseases where they are most rampant.
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