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 re-coded 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.
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 re-coded 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.
Authors: S N Weingart; L I Iezzoni; R B Davis; R H Palmer; M Cahalane; M B Hamel; K Mukamal; R S Phillips; D T Davies; N J Banks Journal: Med Care Date: 2000-08 Impact factor: 2.983
Authors: William R Best; Shukri F Khuri; Maureen Phelan; Kwan Hur; William G Henderson; John G Demakis; Jennifer Daley Journal: J Am Coll Surg Date: 2002-03 Impact factor: 6.113
Authors: Yana Gurevich; Anne McFarlane; Kathleen Morris; Aleksandra Jokovic; Gail M Peterson; Gregory K Webster Journal: Can J Cardiol Date: 2010 Aug-Sep Impact factor: 5.223
Authors: Morgan E Grams; Laura C Plantinga; Elizabeth Hedgeman; Rajiv Saran; Gary L Myers; Desmond E Williams; Neil R Powe Journal: Am J Kidney Dis Date: 2010-08-06 Impact factor: 8.860
Authors: Anthony D Harris; Alyssa N Sbarra; Surbhi Leekha; Sarah S Jackson; J Kristie Johnson; Lisa Pineles; Kerri A Thom Journal: Infect Control Hosp Epidemiol Date: 2018-02-05 Impact factor: 3.254
Authors: Matthew J O'Brien; Susan L Karam; Amisha Wallia; Raymond H Kang; Andrew J Cooper; Nicola Lancki; Margaret R Moran; David T Liss; Theodore A Prospect; Ronald T Ackermann Journal: JAMA Netw Open Date: 2018-12-07
Authors: Kevin Antoine Brown; Bradley Langford; Kevin L Schwartz; Christina Diong; Gary Garber; Nick Daneman Journal: Clin Infect Dis Date: 2021-03-01 Impact factor: 9.079
Authors: Jacob K Greenberg; Travis R Ladner; Margaret A Olsen; Chevis N Shannon; Jingxia Liu; Chester K Yarbrough; Jay F Piccirillo; John C Wellons; Matthew D Smyth; Tae Sung Park; David D Limbrick Journal: Neurosurgery Date: 2015-08 Impact factor: 4.654
Authors: Lauren D Garfield; Jeffrey F Scherrer; Paul J Hauptman; Kenneth E Freedland; Tim Chrusciel; Sumitra Balasubramanian; Robert M Carney; John W Newcomer; Richard Owen; Kathleen K Bucholz; Patrick J Lustman Journal: Psychosom Med Date: 2014-01-16 Impact factor: 4.312