Disparities in patient record completeness with respect to the health care utilization project
Abstract
Patient data completeness is an important characteristic in maintaining accurate health records and providing the highest standard of care. Furthermore, finding discrepancies in care based on different subpopulation parameters is important to identify areas of underlying systemic issues in order to address concerns and alleviate those discrepancies. In this project, the investigators use the Data Completeness Analysis Package to find trends in patient record completeness using Healthcare Cost and Utilization Project’s State Inpatient Database for the state of Florida, specifically focusing on finding discrepancies among subpopulations along the variables of age, race, and gender. The results from testing Data Completeness Analysis Package with State Inpatient Database show a variety of patterns that provides insights to the health care delivery in Florida.
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Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduce bias.
“Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduced bias.12,34,43–50”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [12]
Some studies tested data quality assessment tools such as the Data Quality Assessment Tool and the Data Completeness Analysis Package.
“Some studies tested data quality assessment tools such as the Data Quality Assessment Tool, created to evaluate patient records at Community Health Centers, and the Data Completeness Analysis Package.12,77”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [12]
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The quality of social determinants data in the electronic health record: a systematic review
Cook, L. A., Sachs, J., & Weiskopf, N. G. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab199 · Cited by 153 · see citing works
“Articles about geocoding patient address data, particularly, evaluated the ability of specific geocoding tools, such as ArcGIS, to increase data quality by improving match rates or positional accuracy.54,73–75 For researchers seeking to increase the quality of occupation data prior to analysis, the National Institute…”
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Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare
Gurupur, V. P., Hooshmand, S., Prabhu, D. F., Trader, E. A., & Salvi, S. (2025) · Healthcare · doi:10.3390/healthcare13222900 · Cited by 22 · see citing works
“This measure was later extended by Nasir et al. [21] to examine disparities across patient subgroups, demonstrating that incompleteness often reflects underlying inequities in healthcare access and utilization.”
Self-citationGurupur, V. P., Kulkarni, S. A., Liu, X., Desai, U., & Nasir, A. (2018) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2018.1518999 · Cited by 22 · see citing works
Self-citationA Framework for Developing Health Equity Initiatives in Radiology
Bucknor, M., Narayan, A., & Spalluto, L. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.12.018 · Cited by 13 · see citing works
“Of note, in some cases, incomplete data may reflect an underlying disparity among patient subpopulations [11].”
BackgroundKey observations in terms of management of electronic health records from a mHealth perspective.
Gurupur, V. (2021) · mHealth · doi:10.21037/mhealth-21-39 · Cited by 4 · see citing works
“(34,35) happens to be generic in nature and will lead to more sophisticated algorithms depending on the scenario, data lifecycle, data transmission process, or specialization under consideration.”
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Balakrishnan, A., Nguyen, H. G., Shinohara, K., Carroll, P. R., & Odisho, A. (2022) · Urologic oncology · doi:10.1016/j.urolonc.2022.06.001 · Cited by 3 · see citing works
Gurupur, V., Wan, T. T. H., Rudraraju, R., & Kulkarni, S. (2023) · Journal of Integrated Design & Process Science · doi:10.3233/jid-230041 · Cited by 1 · see citing works
“While data processing (Gurupur et al., 2018) is a key component of a healthcare decision support system Nasir (Nasir et al., 2017) presented the idea of data incompleteness in electronic health records.”
Self-citationPC-LSTM: Ontology-based Long Short-Term Memory State Model for Data Incompleteness Prediction
Shelleh, M., & Gurupur, V. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc48229.2022.9871867 · Cited by 1 · see citing works
Self-citationA Review of Data Engineering in United States Healthcare Infrastructure
Trader, E., Hooshmand, S., Abedin, P., Park, J., & Gurupur, V. (2026) · Healthcare · doi:10.3390/healthcare14101401
“This work was further elaborated upon by Nasir et al. [73], where data from the 2014 Florida Healthcare Cost and Utilization Project was used to identify areas of incompleteness in terms of education and other demographic variables.”
Self-citationGnanvi, J., Freire, G. C., Yanchar, N. L., Beaulieu, É., Tardif, P., Adadja, J., Berube, M., Macpherson, A., Pike, I., Zemek, R. L., et al. (2025) · Academic Pediatrics · doi:10.1016/j.acap.2025.103185 · Cited by 1 · see citing works
THNN - A Neural Network Model for Telehealth Data Incompleteness Prediction
Gurupur, V., Shelleh, M., Leone, C., Schupp-Omid, D., Azevedo, R., & Dubey, S. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc40787.2023.10340989
Self-citation
- Authors
- Nasir, A., Liu, X., Gurupur, V., & Qureshi, Z.
- Affiliations
- University of Central Florida (US)
- Nova Southeastern University (US)
- Corresponding author
- Gurupur, Varadraj
- Venue
- Health Informatics Journal
- Publisher
- SAGE Publications
- Published
- 2017-08-08
- Volume
- 25
- Issue
- 2
- Pages
- 401-416
- ISSN
- 1355-4743 (ISSN-L)
- 1460-4582
- 1741-2811
- OpenAlex
- W2743352809
- Cited by
- 12
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 3 · 2022: 4 · 2021: 1 · 2018: 1
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Health Informatics Journal (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Keywords
- health records, health care utilization, racial disparities, age disparities, gender disparities, inpatient data, Healthcare Cost and Utilization Project, Florida
- MeSH terms
- Data Accuracy, Analysis of Variance, Florida, Humans, Patient Acceptance of Health Care, Healthcare Disparities, Electronic Health Records
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