The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Assessing Validity of ICD‐9‐CM and ICD‐10 Administrative Data in Recording Clinical Conditions in a Unique Dually Coded Database
Open accessAbstract
Objective. The goal of this study was to assess the validity of the International Classification of Disease, 10th Version (ICD‐10) administrative hospital discharge data and to determine whether there were improvements in the validity of coding for clinical conditions compared with ICD‐9 Clinical Modification (ICD‐9‐CM) data. Methods. We reviewed 4,008 randomly selected charts for patients admitted from January 1 to June 30, 2003 at four teaching hospitals in Alberta, Canada to determine the presence or absence of 32 clinical conditions and to assess the agreement between ICD‐10 data and chart data. We then recoded the same charts using ICD‐9‐CM and determined the agreement between the ICD‐9‐CM data and chart data for recording those same conditions. The accuracy of ICD‐10 data relative to chart data was compared with the accuracy of ICD‐9‐CM data relative to chart data. Results. Sensitivity values ranged from 9.3 to 83.1 percent for ICD‐9‐CM and from 12.7 to 80.8 percent for ICD‐10 data. Positive predictive values ranged from 23.1 to 100 percent for ICD‐9‐CM and from 32.0 to 100 percent for ICD‐10 data. Specificity and negative predictive values were consistently high for both ICD‐9‐CM and ICD‐10 databases. Of the 32 conditions assessed, ICD‐10 data had significantly higher sensitivity for one condition and lower sensitivity for seven conditions relative to ICD‐9‐CM data. The two databases had similar sensitivity values for the remaining 24 conditions. Conclusions. The validity of ICD‐9‐CM and ICD‐10 administrative data in recording clinical conditions was generally similar though validity differed between coding versions for some conditions. The implementation of ICD‐10 coding has not significantly improved the quality of administrative data relative to ICD‐9‐CM. Future assessments like this one are needed because the validity of ICD‐10 data may get better as coders gain experience with the new coding system.
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The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90171-8 · Cited by 49,742
Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data
Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., Saunders, L. D., Beck, C. A., Feasby, T. E., & Ghali, W. A. (2005) · Medical Care · doi:10.1097/01.mlr.0000182534.19832.83 · Cited by 10,669
Self-citationComorbidity measures for use with administrative data.
Elixhauser, A., Steiner, C., Harris, D. R., & Coffey, R. M. (1998) · Medical Care · doi:10.1097/00005650-199801000-00004 · Cited by 9,921
New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality
Sundararajan, V., Henderson, T., Perry, C., Muggivan, A., Quan, H., & Ghali, W. A. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.03.012 · Cited by 2,439
Self-citationMeasuring Diagnoses: ICD Code Accuracy
O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005) · Health Services Research · doi:10.1111/j.1475-6773.2005.00444.x · Cited by 1,116
Kokotailo, R. A., & Hill, M. D. (2005) · Stroke · doi:10.1161/01.str.0000174293.17959.a1 · Cited by 709
Jollis, J. G., Ancukiewicz, M., DeLong, E. R., Pryor, D. B., Muhlbaier, L. H., & Mark, D. B. (1993) · Annals of Internal Medicine · doi:10.7326/0003-4819-119-8-199310150-00011 · Cited by 625
Validity of Information on Comorbidity Derived From ICD-9-CCM Administrative Data
Quan, H., Parsons, G. A., & Ghali, W. A. (2002) · Medical Care · doi:10.1097/00005650-200208000-00007 · Cited by 579
Self-citationSouthern, D. A., Quan, H., & Ghali, W. A. (2004) · Medical Care · doi:10.1097/01.mlr.0000118861.56848.ee · Cited by 573
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van Walraven, C., Austin, P. C., Jennings, A., Quan, H., & Forster, A. J. (2009) · Medical Care · doi:10.1097/mlr.0b013e31819432e5 · Cited by 2,200
Self-citationGroenewegen, A., Rutten, F. H., Mosterd, A., & Hoes, A. W. (2020) · European Journal of Heart Failure · doi:10.1002/ejhf.1858 · Cited by 2,043
Jernberg, T., Hasvold, P., Henriksson, M., Hjelm, H., Thuresson, M., & Janzon, M. (2015) · European Heart Journal · doi:10.1093/eurheartj/ehu505 · Cited by 875
Dementia Risk After Traumatic Brain Injury vs Nonbrain Trauma: The Role of Age and Severity
Gardner, R. C., Burke, J. F., Nettiksimmons, J., Kaup, A., Barnes, D. E., & Yaffe, K. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2014.2668 · Cited by 451
Methods for identifying 30 chronic conditions: application to administrative data
Tonelli, M., Wiebe, N., Fortin, M., Guthrie, B., Hemmelgarn, B. R., James, M. T., Klarenbach, S. W., Lewanczuk, R., Manns, B. J., Ronksley, P., et al. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-015-0155-5 · Cited by 400
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Metadata
- Authors
- Quan, H., Li, B., Duncan Saunders, L., Parsons, G. A., Nilsson, C. I., Alibhai, A., Ghali, W. A., & for the IMECCHI Investigators
- Venue
- Health Services Research
- Publisher
- Wiley
- Published
- 2008-07-16
- Volume
- 43
- Issue
- 4
- Pages
- 1424-1441
- ISSN
- 0017-9124 (ISSN-L)
- 1475-6773
- OpenAlex
- W1582456500
- Cited by
- 892
- References
- 25
- FWCI
- 35.46
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Quan, H., Li, B., Duncan Saunders, L., Parsons, G. A., Nilsson, C. I., Alibhai, A., Ghali, W. A., & for the IMECCHI Investigators. (2008). Assessing Validity of ICD‐9‐CM and ICD‐10 Administrative Data in Recording Clinical Conditions in a Unique Dually Coded Database. Health Services Research. https://doi.org/10.1111/j.1475-6773.2007.00822.x
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