Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Health Services Research
Published in: BMC Health Services Research 1/2015

Open Access 01-06-2015 | Research article

A comparison of a multistate inpatient EHR database to the HCUP Nationwide Inpatient Sample

Authors: Jonathan P. DeShazo, Mark A. Hoffman

Published in: BMC Health Services Research | Issue 1/2015

Login to get access

Abstract

Background

The growing availability of electronic health records (EHRs) in the US could provide researchers with a more detailed and clinically relevant alternative to using claims-based data.

Methods

In this study we compared a very large EHR database (Health Facts©) to a well-established population estimate (Nationwide Inpatient Sample). Weighted comparisons were made using t-value and relative difference over diagnoses and procedures for the year 2010.

Results

The two databases have a similar distribution pattern across all data elements, with 24 of 50 data elements being statistically similar between the two data sources. In general, differences that were found are consistent across diagnosis and procedures categories and were specific to the psychiatric–behavioral and obstetrics–gynecology services areas.

Conclusions

Large EHR databases have the potential to be a useful addition to health services researchers, although they require different analytic techniques compared to administrative databases; more research is needed to understand the differences.
Appendix
Available only for authorised users
Literature
1.
Peabody JW, Luck J, Jain S, Bertenthal D, Glassman P. Assessing the accuracy of administrative data in health information systems. Med Care. 2004;42(11):1066.CrossRefPubMed
2.
Benin AL, Vitkauskas G, Thornquist E, Shapiro ED, Concato J, Aslan M, et al. Validity of using an electronic medical record for assessing quality of care in an outpatient setting. Med Care. 2005;43(7):691.CrossRefPubMed
3.
Fowles JB, Fowler EJ, Craft C. Validation of claims diagnoses and self-reported conditions compared with medical records for selected chronic diseases. J Ambul Care Manage. 1998;21(1):24.CrossRefPubMed
4.
Steinbrook R. Health care and the American recovery and reinvestment act. N Engl J Med. 2009;360(11):1057–60.CrossRefPubMed
5.
Klompas M, McVetta J, Eggleston E, Mendoza T, Daly P, Oppedisano P, et al. Automated surveillance and public health reporting for gestational diabetes incidence and care using electronic health record data, Using administrative databases to identify cases of chronic kidney disease: a systematic review. 2011. p. 23.
6.
Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011;12(6):417–28.CrossRefPubMed
7.
Kalra D, Schmidt A, Potts H, Dupont D, Sundgren M, De Moor G. Case report from the EHR4CR project―a european survey on electronic health records systems for clinical research. 2011.
8.
9.
Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev. 2010;67(5):503–27.CrossRefPubMed
10.
Weiner JP, Fowles JB, Chan KS. New paradigms for measuring clinical performance using electronic health records. Int J Qual Health Care. 2012;24(3):200–5.CrossRefPubMed
11.
Health Information Technology for Economic and Clinical Health (HITECH) Act. In Title XIII of Division A and Title IV of Division B of the American Recovery and Reinvestment Act of 2009 (ARRA). Pub. L. No. 111–5, 123 Stat. 226 (Feb. 17, 2009).
12.
Tang PC, Ralston M, Arrigotti MF, Qureshi L, Graham J. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. J Am Med Inform Assoc. 2007;14(1):10–5.CrossRefPubMedPubMedCentral
13.
Blobel B, Hvannberg ET, Gunnarsdóttir V. Informatics EFfM, conference EFfMIST, press I: seamless care, safe care: the challenges of interoperability and patient safety in health care: proceedings of the EFMI special topic conference, june 2–4, 2010. Reykjavik, Iceland: Ios Press; 2010.
14.
15.
16.
17.
Hynes DM, Perrin RA, Rappaport S, Stevens JM, Demakis JG. Informatics resources to support health care quality improvement in the veterans health administration. J Am Med Inform Assoc. 2004;11(5):344–50.CrossRefPubMedPubMedCentral
18.
Terdiman J, Gul J. PS1-39: the kaiser permanente Northern California oracle research database. Clin Med Res. 2011;9(3–4):168–9.CrossRefPubMedCentral
19.
Crawford AG, Cote C, Couto J, Daskiran M, Gunnarsson C, Haas K, et al. Comparison of GE centricity electronic medical record database and national ambulatory medical care survey findings on the prevalence of major conditions in the united states. Population Health Management. 2010;13(3):139–50.CrossRefPubMed
20.
HCUP Nationwide Inpatient Sample (NIS): Healthcare Cost and Utilization Project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality; 2007–2009.
21.
22.
Caine K, Hanania R. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc. 2013;20(1):7–15.CrossRefPubMed
23.
24.
Fisher ES, Whaley FS, Krushat WM, Malenka DJ, Fleming C, Baron JA, et al. The accuracy of Medicare’s hospital claims data: progress has been made, but problems remain. Am J Public Health. 1992;82(2):243–8.CrossRefPubMedPubMedCentral
25.
Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB. Discordance of databases designed for claims payment versus clinical information systems: implications for outcomes research. Ann Intern Med. 1993;119(8):844–50.CrossRefPubMed
26.
27.
28.
Gold R, Angier H, Mangione-Smith R, Gallia C, McIntire PJ, Cowburn S, et al. Feasibility of evaluating the CHIPRA care quality measures in electronic health record data. Pediatrics. 2012;130(1):139–49.CrossRefPubMedPubMedCentral
Metadata
Title
A comparison of a multistate inpatient EHR database to the HCUP Nationwide Inpatient Sample
Authors
Jonathan P. DeShazo
Mark A. Hoffman
Publication date
01-06-2015
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2015
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-015-1025-7

Other articles of this Issue 1/2015

BMC Health Services Research 1/2015 Go to the issue

Research article

Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry

Research article

Everybody wants it done but nobody wants to do it: an exploration of the barrier and enablers of critical components towards creating a clinical pathway for anxiety and depression in cancer

Research article

Inclusion of short-term care patients affects the perceived performance of specialists: a retrospective cohort study

Research article

Task shifting of HIV/AIDS case management to Community Health Service Centers in urban China: a qualitative policy analysis

Research article

Impact of referral templates on the quality of referrals from primary to secondary care: a cluster randomised trial

Research article

Cost-effectiveness of the non-pneumatic anti-shock garment (NASG): evidence from a cluster randomized controlled trial in Zambia and Zimbabwe