» Articles » PMID: 28815121

Enhancing Electronic Health Record Data with Geospatial Information

Overview
Specialty Biology
Date 2017 Aug 18
PMID 28815121
Citations 23
Authors
Affiliations
Soon will be listed here.
Abstract

Electronic Health Record (EHR)-derived data is a valuable resource for research, and efforts are underway to overcome some of its limitations by using data from external sources to gain a fuller picture of patient characteristics, symptoms, and exposures. Our goal was to assess the utility of augmenting EHR data with geocoded patient addresses to identify geospatial variation of disease that is not explained by EHR-derived demographic factors. Using 2011-2014 encounter data from 27,604 University of Pennsylvania Hospital System asthma patients, we identified factors associated with asthma exacerbations: risk was higher in female, black, middle aged to elderly, and obese patients, as well as those with positive smoking history and with Medicare or Medicaid vs. private insurance. Significant geospatial variability of asthma exacerbations was found using generalized additive models, even after adjusting for demographic factors. Our work shows that geospatial data can be used to cost-effectively enhance EHR data.

Citing Articles

The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team-based approach to clinically validated, research-ready electronic health record data.

Schneider A, Ginestra J, Kerlin M, Shashaty M, Miano T, Herman D Learn Health Syst. 2025; 9(1):e10439.

PMID: 39822919 PMC: 11733450. DOI: 10.1002/lrh2.10439.


Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review.

Mollalo A, Hamidi B, Lenert L, Alekseyenko A JMIR Med Inform. 2024; 12:e56343.

PMID: 39405525 PMC: 11522649. DOI: 10.2196/56343.


Model-based estimation of individual-level social determinants of health and its applications in All of Us.

Kim B, Anthopolos R, Do H, Zhong J J Am Med Inform Assoc. 2024; 31(12):2880-2889.

PMID: 39003521 PMC: 11631124. DOI: 10.1093/jamia/ocae168.


Implementation of High-Value Care for Physical Therapy Residents Through Systems-Based Practice Curriculum Development: Case Report.

Pak S, Scheid A, Hoang C, Fitzsimmons A, Topp K J Phys Ther Educ. 2024; 39(1):80-90.

PMID: 38978183 PMC: 11827684. DOI: 10.1097/JTE.0000000000000355.


Trends in publication and levels of social determinants of health reporting in from 2017 to 2023.

Levites Strekalova Y, Wang X, Sanchez O, Midence S J Clin Transl Sci. 2024; 8(1):e58.

PMID: 38655458 PMC: 11036436. DOI: 10.1017/cts.2024.508.


References
1.
Vieira V, Webster T, Weinberg J, Aschengrau A, Ozonoff D . Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: an application of generalized additive models to case-control data. Environ Health. 2005; 4:11. PMC: 1183231. DOI: 10.1186/1476-069X-4-11. View

2.
Gainer V, Cagan A, Castro V, Duey S, Ghosh B, Goodson A . The Biobank Portal for Partners Personalized Medicine: A Query Tool for Working with Consented Biobank Samples, Genotypes, and Phenotypes Using i2b2. J Pers Med. 2016; 6(1). PMC: 4810390. DOI: 10.3390/jpm6010011. View

3.
Himes B, Klanderman B, Kohane I, Weiss S . Assessing the reproducibility of asthma genome-wide association studies in a general clinical population. J Allergy Clin Immunol. 2011; 127(4):1067-9. DOI: 10.1016/j.jaci.2010.12.007. View

4.
Torgerson D, Ampleford E, Chiu G, Gauderman W, Gignoux C, Graves P . Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011; 43(9):887-92. PMC: 3445408. DOI: 10.1038/ng.888. View

5.
Akinbami L, Moorman J, Bailey C, Zahran H, King M, Johnson C . Trends in asthma prevalence, health care use, and mortality in the United States, 2001-2010. NCHS Data Brief. 2012; (94):1-8. View