1 / 18

Click to edit subtitle

Leveraging EHR data to estimate cost of illness and visualize burden of Diabetes in Chicago Onyinyechi U. Enyia Daniel, MA CCRP. Click to edit subtitle. Acknowledgements.

anevay
Download Presentation

Click to edit subtitle

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Leveraging EHR data to estimate cost of illness and visualize burden of Diabetes in ChicagoOnyinyechi U. Enyia Daniel, MA CCRP Click to edit subtitle

  2. 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.

  3. There is limited literature on the use of EHR data in cost of illness studies CLICK TO EDIT MASTER 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 co-morbidities.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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]

  12. Diabetes Economic Burden

  13. Projected Cost of Diabetes in Chicago 2011-2021

  14. 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.

  15. Visualization

  16. 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.

  17. References Andreachhia, S., Shine, K. (2013).. Combat ready: Fighting diabetes on Chicago’s South Side Jan 30, 2013 Buntin, M. B., M. F. Burke, and M. C. Hoaglin. "The Benefits of Health Information Technology: A Review of the Recent Literature Shows Predominantly Positive Results." Health Affairs 30 (2011): 64-71. Print. Carter, E., Nunlee-Bland, G., & Callender, C. (2011). A patient-centric, provider-assisted diabetes telehealth self-management intervention for urban minorities. Perspectives in Health Information Management. Cebul, R. D., & Jain, A. K. (2011). Electronic health records and quality of diabetees care. New England Journal of Medicine, 365, 825-833. Centers for Disease Control and Prevention. (2013, March 19). Retrieved from http://www.cdc.gov/brfss/ Centers for Disease Control and Prevention (2012, April 20) Retrieved from http://www.cdc/org/mmwr. Centers for Medicare Medicaid Services. (n.d.). Centers for Medicare Medicaid Services. Retrieved April 15, 2013, from http://www.slideserve.com/netis/centers-for-medicare-medicaid-services Chicago Health Atlas. (n.d.). CHITREC Chicago Health Atlas Comments. Retrieved from http://chitrec.org/research/chicago-health-atlas/ Medical Expenditure Panel Survey Background. (2013, June 20). Retrieved from Medical Expenditure Panel Survey: http://meps.ahrq.gov/mepsweb/about_meps/surve?back.jsp Dubowitz, T. W. (2011). Using geographic information systems to match local health needs with public health services and programs. American Journal of Public Health, 1664-1665. Finkelstein E.A, F. I. (2003). National Medical Expenditures Attributable to Overweight and Obesity: How Much and Who's Paying? Health Affairs, 219-296. Honeycutt, A. S. (2009). Comparing cost-of illness eestimates from alternative approaches: an application to diabetes. Health Services Research, 303-320. Hripcsak, G. S. (2009). Syndromic Surveillance using ambulatory electronic health records. Journal of the American Medical Informatics Association, 354-361. Huang, E. B. (2009). Projecting the future diabetes population size and related costs for the U.S. Diabetes Care, 2225-2229. Keehan, S. C. (2012). National Health Expenditure Projections: modest annual growth until coverage expands and economic growth accelerates. Health Affairs, 1600-1612. Kho, A. H. (2012). Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. Journal of the American Medical Informatics Association, 212-218. LaVeist, T. (2003). Racial segregation and longevity among African Americans: an individual-level analysis. Health Services Research, 1719-1734. Lavrac, N. B. (2007). Data mining and visualization for decision support and modeling of public health-care resources. Journal of Biomedical Informatics, 438-447. Liang, L. (2010). Connected for Health: Using electronic health records to transform care delivery. San Francisco: Jossey-Bass. Matthews, S. M. (2009). Work Group II: using geographic information systems for enhancing research relevant to policy on diet, physical activity, and weight. American Journal of Preventive Medicine, 171-176. Middleton, B. B. (2013). Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. Journal of the American Medical Informatics Association, 2-8. n.d. (2012). Standards of medical care in diabetes-2012. Diabetes Care. n.d. (2011) Congressional Budget Office Economic Projections for Calendar Years 2011-2021.Retrieved from http://www.cbo.gov/sites/default/files/cbofiles/attachments/EconomicTables.pdf June 27, 2013. n.d. (2013, June 20). American Recovery and Reinvestment Act of 2009, Title XII- Health Information Technology Subitle B- Incentives for the Use of Health Infomration Technology, Section 3031, State Grants to Promote Health Information Technology. State Health Information Exch. Retrieved from grants.gov: http://www.grants.gov/search/search.do?oppID=58990&mode=VIEW Olin, G. M. (2008, May). Estimating the cost of illiness: the case of diabetes. Retrieved from Agency for Healthcare Research and Quality: http://gold.ahrq.gov Accessed May 29, 2013 Onsurd, H. P. (2005). The future of the spatial information infrastructure. In R. U. McMaster, A research agenda for geographic information science. Boca Raton: CRC Press. Patel, C. B. (n.d.). An environment-wide association study (EWAS) on type 2 diabetes mellitus. Plos One. Payne, T. B. (2013). Healthcare information technology and economics. Journal of the American Medical Informatics Association, 212-217. Robins, K. C. (2010). Health Geography: A Companion to health and medical geography. Blackwell Publishing. Wen, M. K.-J. (2012). The built environment and risk of obesity in the United States: racial-ethnic disparities. Health and Place, 1314-1322.

More Related