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Fung KW, Xu J, Bodenreider O. The new International Classification of Diseases 11th edition: a comparative analysis with ICD-10 and ICD-10-CM. J Am Med Inform Assoc 2021; 27:738-746. [PMID: 32364236 DOI: 10.1093/jamia/ocaa030] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 01/28/2020] [Accepted: 03/09/2020] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE To study the newly adopted International Classification of Diseases 11th revision (ICD-11) and compare it to the International Classification of Diseases 10th revision (ICD-10) and International Classification of Diseases 10th revision-Clinical Modification (ICD-10-CM). MATERIALS AND METHODS : Data files and maps were downloaded from the World Health Organization (WHO) website and through the application programming interfaces. A round trip method based on the WHO maps was used to identify equivalent codes between ICD-10 and ICD-11, which were validated by limited manual review. ICD-11 terms were mapped to ICD-10-CM through normalized lexical mapping. ICD-10-CM codes in 6 disease areas were also manually recoded in ICD-11. RESULTS Excluding the chapters for traditional medicine, functioning assessment, and extension codes for postcoordination, ICD-11 has 14 622 leaf codes (codes that can be used in coding) compared to ICD-10 and ICD-10-CM, which has 10 607 and 71 932 leaf codes, respectively. We identified 4037 pairs of ICD-10 and ICD-11 codes that were equivalent (estimated accuracy of 96%) by our round trip method. Lexical matching between ICD-11 and ICD-10-CM identified 4059 pairs of possibly equivalent codes. Manual recoding showed that 60% of a sample of 388 ICD-10-CM codes could be fully represented in ICD-11 by precoordinated codes or postcoordination. CONCLUSION In ICD-11, there is a moderate increase in the number of codes over ICD-10. With postcoordination, it is possible to fully represent the meaning of a high proportion of ICD-10-CM codes, especially with the addition of a limited number of extension codes.
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Affiliation(s)
- Kin Wah Fung
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Julia Xu
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Olivier Bodenreider
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
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2
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He M, Santiago Ortiz AJ, Marshall J, Mendelsohn AB, Curtis JR, Barr CE, Lockhart CM, Kim SC. Mapping from the International Classification of Diseases (ICD) 9th to 10th Revision for Research in Biologics and Biosimilars Using Administrative Healthcare Data. Pharmacoepidemiol Drug Saf 2019; 29:770-777. [PMID: 31854053 DOI: 10.1002/pds.4933] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 11/03/2019] [Accepted: 11/08/2019] [Indexed: 11/07/2022]
Abstract
PURPOSE The Centers for Medicare and Medicaid Services (CMS) mandated the transition from ICD-9 to ICD-10 codes on October 1, 2015. Postmarketing surveillance of newly marketed drugs, including novel biologics and biosimilars, requires a robust approach to convert ICD-9 to ICD-10 codes for study variables. We examined three mapping methods for health conditions (HCs) of interest to the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) and compared their prevalence. METHODS Using CMS General Equivalence Mappings, we applied forward-backward mapping (FBM) to 108 HCs and secondary mapping (SM) and tertiary mapping (TM) to seven preselected HCs. A physician reviewed the mapped ICD-10 codes. The prevalence of the 108 HCs defined by ICD-9 versus ICD-10 codes was examined in BBCIC's distributed research network (September 1, 2012 to March 31, 2018). We visually assessed prevalence trends of these HCs and applied a threshold of 20% level change in ICD-9 versus ICD-10 prevalence. RESULTS Nearly four times more ICD-10 codes were mapped by SM and TM than FBM, but most were irrelevant or nonspecific. For conditions like myocardial infarction, SM or TM did not generate additional ICD-10 codes. Through visual inspection, one-fifth of the HCs had inconsistent ICD-9 versus ICD-10 prevalence trends. 13% of HCs had a level change greater than +/-20%. CONCLUSION FBM is generally the most efficient way to convert ICD-9 to ICD-10 codes, yet manual review of converted ICD-10 codes is recommended even for FBM. The lack of existing guidance to compare the performance of ICD-9 with ICD-10 codes led to challenges in empirically determining the quality of conversions.
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Affiliation(s)
- Mengdong He
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Adrian J Santiago Ortiz
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - James Marshall
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts
| | - Aaron B Mendelsohn
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts
| | - Jeffrey R Curtis
- Division of Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Charles E Barr
- Biologics and Biosimilars Collective Intelligence Consortium, Academy of Managed Care Pharmacy, Alexandria, Virginia
| | - Catherine M Lockhart
- Biologics and Biosimilars Collective Intelligence Consortium, Academy of Managed Care Pharmacy, Alexandria, Virginia
| | - Seoyoung C Kim
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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3
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Rahman N, Wang DD, Ng SHX, Ramachandran S, Sridharan S, Khoo A, Tan CS, Goh WP, Tan XQ. Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation. JMIR Med Inform 2018; 6:e10933. [PMID: 30578188 PMCID: PMC6320424 DOI: 10.2196/10933] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 10/09/2018] [Accepted: 10/10/2018] [Indexed: 01/08/2023] Open
Abstract
Background Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress. Objective The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. Methods On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. Results Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. Conclusions The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.
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Affiliation(s)
- Nabilah Rahman
- Centre for Health Services and Policy Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Debby D Wang
- Centre for Health Services and Policy Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Sheryl Hui-Xian Ng
- Centre for Health Services and Policy Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Sravan Ramachandran
- Centre for Health Services and Policy Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Srinath Sridharan
- Centre for Health Services and Policy Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Astrid Khoo
- Regional Health System Planning Office, National University Health System, Singapore, Singapore
| | - Chuen Seng Tan
- Centre for Health Services and Policy Research, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Wei-Ping Goh
- University Medicine Cluster, National University Hospital, Singapore, Singapore
| | - Xin Quan Tan
- Regional Health System Planning Office, National University Health System, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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4
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Caskey RN, Abutahoun A, Polick A, Barnes M, Srivastava P, Boyd AD. Transition to international classification of disease version 10, clinical modification: the impact on internal medicine and internal medicine subspecialties. BMC Health Serv Res 2018; 18:328. [PMID: 29728145 PMCID: PMC5935982 DOI: 10.1186/s12913-018-3110-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 04/11/2018] [Indexed: 12/02/2022] Open
Abstract
Background The US health care system uses diagnostic codes for billing and reimbursement as well as quality assessment and measuring clinical outcomes. The US transitioned to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) on October, 2015. Little is known about the impact of ICD-10-CM on internal medicine and medicine subspecialists. Methods We used a state-wide data set from Illinois Medicaid specified for Internal Medicine providers and subspecialists. A total of 3191 ICD-9-CM codes were used for 51,078 patient encounters, for a total cost of US $26,022,022 for all internal medicine. We categorized all of the ICD-9-CM codes based on the complexity of mapping to ICD-10-CM as codes with complex mapping could result in billing or administrative errors during the transition. Codes found to have complex mapping and frequently used codes (n = 295) were analyzed for clinical accuracy of mapping to ICD-10-CM. Each subspecialty was analyzed for complexity of codes used and proportion of reimbursement associated with complex codes. Results Twenty-five percent of internal medicine codes have convoluted mapping to ICD-10-CM, which represent 22% of Illinois Medicaid patients, and 30% of reimbursements. Rheumatology and Endocrinology had the greatest proportion of visits and reimbursement associated with complex codes. We found 14.5% of ICD-9-CM codes used by internists, when mapped to ICD-10-CM, resulted in potential clinical inaccuracies. Conclusions We identified that 43% of diagnostic codes evaluated and used by internists and that account for 14% of internal medicine reimbursements are associated with codes which could result in administrative errors. Electronic supplementary material The online version of this article (10.1186/s12913-018-3110-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rachel N Caskey
- Department of Internal Medicine, University of Illinois at Chicago, 840 S. Wood St, Clinical Sciences North 440, MC 718, Chicago IL, Chicago, Illinois, 60612, USA. .,Department of Pediatrics, University of Illinois at Chicago, 840 S. Wood St Clinical Sciences North 440, MC 718, Chicago IL, Chicago, Illinois, 60612, USA.
| | - Angelos Abutahoun
- Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, 1919 W Taylor St (M/C 530), Chicago, IL, Chicago, Illinois, 60612, USA
| | - Anne Polick
- Department of Internal Medicine, University of Illinois at Chicago, 840 S. Wood St, Clinical Sciences North 440, MC 718, Chicago IL, Chicago, Illinois, 60612, USA
| | - Michelle Barnes
- Department of Internal Medicine, University of Illinois at Chicago, 840 S. Wood St, Clinical Sciences North 440, MC 718, Chicago IL, Chicago, Illinois, 60612, USA.,Department of Pediatrics, University of Illinois at Chicago, 840 S. Wood St Clinical Sciences North 440, MC 718, Chicago IL, Chicago, Illinois, 60612, USA
| | - Pavan Srivastava
- Department of Internal Medicine, University of Illinois at Chicago, 840 S. Wood St, Clinical Sciences North 440, MC 718, Chicago IL, Chicago, Illinois, 60612, USA.,Department of Pediatrics, University of Illinois at Chicago, 840 S. Wood St Clinical Sciences North 440, MC 718, Chicago IL, Chicago, Illinois, 60612, USA
| | - Andrew D Boyd
- Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, 1919 W Taylor St (M/C 530), Chicago, IL, Chicago, Illinois, 60612, USA.
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5
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Fung KW, Richesson R, Smerek M, Pereira KC, Green BB, Patkar A, Clowse M, Bauck A, Bodenreider O. Preparing for the ICD-10-CM Transition: Automated Methods for Translating ICD Codes in Clinical Phenotype Definitions. EGEMS (Wash DC) 2016; 4:1211. [PMID: 27195309 PMCID: PMC4862764 DOI: 10.13063/2327-9214.1211] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Background: The national mandate for health systems to transition from ICD-9-CM to ICD-10-CM in October 2015 has an impact on research activities. Clinical phenotypes defined by ICD-9-CM codes need to be converted to ICD-10-CM, which has nearly four times more codes and a very different structure than ICD-9-CM. Methods: We used the Centers for Medicare & Medicaid Services (CMS) General Equivalent Maps (GEMs) to translate, using four different methods, condition-specific ICD-9-CM code sets used for pragmatic trials (n=32) into ICD-10-CM. We calculated the recall, precision, and F score of each method. We also used the ICD-9-CM and ICD-10-CM value sets defined for electronic quality measure as an additional evaluation of the mapping methods. Results: The forward-backward mapping (FBM) method had higher precision, recall and F-score metrics than simple forward mapping (SFM). The more aggressive secondary (SM) and tertiary mapping (TM) methods resulted in higher recall but lower precision. For clinical phenotype definition, FBM was the best (F=0.67), but was close to SM (F=0.62) and TM (F=0.60), judging on the F-scores alone. The overall difference between the four methods was statistically significant (one-way ANOVA, F=5.749, p=0.001). However, pairwise comparisons between FBM, SM, and TM did not reach statistical significance. A similar trend was found for the quality measure value sets. Discussion: The optimal method for using the GEMs depends on the relative importance of recall versus precision for a given use case. It appears that for clinically distinct and homogenous conditions, the recall of FBM is sufficient. The performance of all mapping methods was lower for heterogeneous conditions. Since code sets used for phenotype definition and quality measurement can be very similar, there is a possibility of cross-fertilization between the two activities. Conclusion: Different mapping approaches yield different collections of ICD-10-CM codes. All methods require some level of human validation.
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Affiliation(s)
| | | | | | | | | | - Ashwin Patkar
- Duke Clinical Research Institute; Duke University School of Medicine
| | | | - Alan Bauck
- Center for Health Research, Kaiser Permanente Northwest
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6
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Grief SN, Patel J, Kochendorfer KM, Green LA, Lussier YA, Li J, Burton M, Boyd AD. Simulation of ICD-9 to ICD-10-CM Transition for Family Medicine: Simple or Convoluted? J Am Board Fam Med 2016; 29:29-36. [PMID: 26769875 DOI: 10.3122/jabfm.2016.01.150146] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE The objective of this study was to examine the impact of the transition from International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), to Interactional Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), on family medicine and to identify areas where additional training might be required. METHODS Family medicine ICD-9-CM codes were obtained from an Illinois Medicaid data set (113,000 patient visits and $5.5 million in claims). Using the science of networks, we evaluated each ICD-9-CM code used by family medicine physicians to determine whether the transition was simple or convoluted. A simple transition is defined as 1 ICD-9-CM code mapping to 1 ICD-10-CM code, or 1 ICD-9-CM code mapping to multiple ICD-10-CM codes. A convoluted transition is where the transitions between coding systems is nonreciprocal and complex, with multiple codes for which definitions become intertwined. Three family medicine physicians evaluated the most frequently encountered complex mappings for clinical accuracy. RESULTS Of the 1635 diagnosis codes used by family medicine physicians, 70% of the codes were categorized as simple, 27% of codes were convoluted, and 3% had no mapping. For the visits, 75%, 24%, and 1% corresponded with simple, convoluted, and no mapping, respectively. Payment for submitted claims was similarly aligned. Of the frequently encountered convoluted codes, 3 diagnosis codes were clinically incorrect, but they represent only <0.1% of the overall diagnosis codes. CONCLUSIONS The transition to ICD-10-CM is simple for 70% or more of diagnosis codes, visits, and reimbursement for a family medicine physician. However, some frequently used codes for disease management are convoluted and incorrect, and for which additional resources need to be invested to ensure a successful transition to ICD-10-CM.
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Affiliation(s)
- Sarah Gebauer
- Department of Anesthesiology, University of New Mexico , Albuquerque, New Mexico
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Krive J, Patel M, Gehm L, Mackey M, Kulstad E, Li JJ, Lussier YA, Boyd AD. The complexity and challenges of the International Classification of Diseases, Ninth Revision, Clinical Modification to International Classification of Diseases, 10th Revision, Clinical Modification transition in EDs. Am J Emerg Med 2015; 33:713-8. [PMID: 25863652 DOI: 10.1016/j.ajem.2015.03.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 03/03/2015] [Accepted: 03/03/2015] [Indexed: 11/21/2022] Open
Abstract
Beginning October 2015, the Center for Medicare and Medicaid Services will require medical providers to use the vastly expanded International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) system. Despite wide availability of information and mapping tools for the next generation of the ICD classification system, some of the challenges associated with transition from ICD-9-CM to ICD-10-CM are not well understood. To quantify the challenges faced by emergency physicians, we analyzed a subset of a 2010 Illinois Medicaid database of emergency department ICD-9-CM codes, seeking to determine the accuracy of existing mapping tools in order to better prepare emergency physicians for the change to the expanded ICD-10-CM system. We found that 27% of 1830 codes represented convoluted multidirectional mappings. We then analyzed the convoluted transitions and found that 8% of total visit encounters (23% of the convoluted transitions) were clinically incorrect. The ambiguity and inaccuracy of these mappings may impact the workflow associated with the translation process and affect the potential mapping between ICD codes and Current Procedural Codes, which determine physician reimbursement.
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9
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Boyd AD, Li JJ, Kenost C, Joese B, Yang YM, Kalagidis OA, Zenku I, Saner D, Bahroos N, Lussier YA. Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM. J Am Med Inform Assoc 2015; 22:730-7. [PMID: 25681260 PMCID: PMC4457110 DOI: 10.1093/jamia/ocu003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 10/19/2014] [Indexed: 11/18/2022] Open
Abstract
In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will transition to ICD-10-CM (the tenth revision) diagnosis codes. One challenge posed to clinical researchers and other analysts is conducting diagnosis-related queries across datasets containing both coding schemes. Further, healthcare administrators will manage growth, trends, and strategic planning with these dually-coded datasets. The majority of the ICD-9-CM to ICD-10-CM translations are complex and nonreciprocal, creating convoluted representations and meanings. Similarly, mapping back from ICD-10-CM to ICD-9-CM is equally complex, yet different from mapping forward, as relationships are likewise nonreciprocal. Indeed, 10 of the 21 top clinical categories are complex as 78% of their diagnosis codes are labeled as “convoluted” by our analyses. Analysis and research related to external causes of morbidity, injury, and poisoning will face the greatest challenges due to 41 745 (90%) convolutions and a decrease in the number of codes. We created a web portal tool and translation tables to list all ICD-9-CM diagnosis codes related to the specific input of ICD-10-CM diagnosis codes and their level of complexity: “identity” (reciprocal), “class-to-subclass,” “subclass-to-class,” “convoluted,” or “no mapping.” These tools provide guidance on ambiguous and complex translations to reveal where reports or analyses may be challenging to impossible. Web portal: http://www.lussierlab.org/transition-to-ICD9CM/ Tables annotated with levels of translation complexity: http://www.lussierlab.org/publications/ICD10to9
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Affiliation(s)
- Andrew D Boyd
- Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA University of Illinois Hospital and Health Science System, Chicago, IL, USA
| | - Jianrong John Li
- Department of Medicine, University of Arizona, Tucson, AZ, USA The University of Arizona Health Sciences Center, Tucson, AZ, USA
| | - Colleen Kenost
- Department of Medicine, University of Arizona, Tucson, AZ, USA The University of Arizona Health Sciences Center, Tucson, AZ, USA
| | - Binoy Joese
- University of Illinois Hospital and Health Science System, Chicago, IL, USA Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Young Min Yang
- Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Olympia A Kalagidis
- Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Ilir Zenku
- University of Illinois Hospital and Health Science System, Chicago, IL, USA
| | - Donald Saner
- The University of Arizona Health Sciences Center, Tucson, AZ, USA Biomedical Informatics Service Group, Arizona Health Science Center, University of Arizona, Tucson, AZ, USA
| | - Neil Bahroos
- University of Illinois Hospital and Health Science System, Chicago, IL, USA
| | - Yves A Lussier
- University of Illinois Hospital and Health Science System, Chicago, IL, USA Department of Medicine, University of Arizona, Tucson, AZ, USA The University of Arizona Health Sciences Center, Tucson, AZ, USA Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA Biomedical Informatics Service Group, Arizona Health Science Center, University of Arizona, Tucson, AZ, USA Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL, USA Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Cancer Center, Tucson, AZ, USA The work was completed in part at The University of Illinois
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10
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Venepalli NK, Shergill A, Dorestani P, Boyd AD. Conducting Retrospective Ontological Clinical Trials in ICD-9-CM in the Age of ICD-10-CM. Cancer Inform 2014; 13:81-8. [PMID: 25452683 PMCID: PMC4226400 DOI: 10.4137/cin.s14032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Revised: 06/30/2014] [Accepted: 07/01/2014] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE To quantify the impact of International Classification of Disease 10th Revision Clinical Modification (ICD-10-CM) transition in cancer clinical trials by comparing coding accuracy and data discontinuity in backward ICD-10-CM to ICD-9-CM mapping via two tools, and to develop a standard ICD-9-CM and ICD-10-CM bridging methodology for retrospective analyses. BACKGROUND While the transition to ICD-10-CM has been delayed until October 2015, its impact on cancer-related studies utilizing ICD-9-CM diagnoses has been inadequately explored. MATERIALS AND METHODS Three high impact journals with broad national and international readerships were reviewed for cancer-related studies utilizing ICD-9-CM diagnoses codes in study design, methods, or results. Forward ICD-9-CM to ICD-10-CM mapping was performing using a translational methodology with the Motif web portal ICD-9-CM conversion tool. Backward mapping from ICD-10-CM to ICD-9-CM was performed using both Centers for Medicare and Medicaid Services (CMS) general equivalence mappings (GEMs) files and the Motif web portal tool. Generated ICD-9-CM codes were compared with the original ICD-9-CM codes to assess data accuracy and discontinuity. RESULTS While both methods yielded additional ICD-9-CM codes, the CMS GEMs method provided incomplete coverage with 16 of the original ICD-9-CM codes missing, whereas the Motif web portal method provided complete coverage. Of these 16 codes, 12 ICD-9-CM codes were present in 2010 Illinois Medicaid data, and accounted for 0.52% of patient encounters and 0.35% of total Medicaid reimbursements. Extraneous ICD-9-CM codes from both methods (Centers for Medicare and Medicaid Services general equivalent mapping [CMS GEMs, n = 161; Motif web portal, n = 246]) in excess of original ICD-9-CM codes accounted for 2.1% and 2.3% of total patient encounters and 3.4% and 4.1% of total Medicaid reimbursements from the 2010 Illinois Medicare database. DISCUSSION Longitudinal data analyses post-ICD-10-CM transition will require backward ICD-10-CM to ICD-9-CM coding, and data comparison for accuracy. Researchers must be aware that all methods for backward coding are not comparable in yielding original ICD-9-CM codes. CONCLUSIONS The mandated delay is an opportunity for organizations to better understand areas of financial risk with regards to data management via backward coding. Our methodology is relevant for all healthcare-related coding data, and can be replicated by organizations as a strategy to mitigate financial risk.
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Affiliation(s)
- Neeta K Venepalli
- Department of Medicine, Section of Hematology/Oncology, University of Illinois at Chicago, Chicago, IL, USA
| | - Ardaman Shergill
- Department of Medicine, Section of Hematology/Oncology, University of Illinois at Chicago, Chicago, IL, USA
| | - Parvaneh Dorestani
- Departments of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Andrew D Boyd
- Departments of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA. ; Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, IL, USA. ; Department of Strategic Initiatives, University of Illinois Hospital and Health Science System, Chicago, IL, USA. ; Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
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11
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Caskey R, Zaman J, Nam H, Chae SR, Williams L, Mathew G, Burton M, Li J“J, Lussier YA, Boyd AD. The transition to ICD-10-CM: challenges for pediatric practice. Pediatrics 2014; 134:31-6. [PMID: 24918217 PMCID: PMC4531279 DOI: 10.1542/peds.2013-4147] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Diagnostic codes are used widely within health care for billing, quality assessment, and to measure clinical outcomes. The US health care system will transition to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), in October 2015. Little is known about how this transition will affect pediatric practices. The objective of this study was to examine how the transition to ICD-10-CM may result in ambiguity of clinical information and financial disruption for pediatricians. METHODS Using a statewide data set from Illinois Medicaid specified for pediatricians, 2708 International Classification of Diseases, Ninth Revision, Clinical Modification, diagnosis codes were identified. Diagnosis codes were categorized into 1 of 5 categories: identity, class-to-subclass, subclass-to-class, convoluted, and no translation. The convoluted and high-cost diagnostic codes (n = 636) were analyzed for accuracy and categorized into "information loss," "overlapping categories," "inconsistent," and "consistent." Finally, reimbursement by Medicaid was calculated for each category. RESULTS Twenty-six percent of pediatric diagnosis codes are convoluted, which represents 21% of Illinois Medicaid pediatric patient encounters and 16% of reimbursement. The diagnosis codes represented by information loss (3.6%), overlapping categories (3.2%), and inconsistent (1.2%) represent 8% of Medicaid pediatric reimbursement. CONCLUSIONS The potential for financial disruption and administrative errors from 8% of reimbursement diagnosis codes necessitates special attention to these codes in preparing for the transition to ICD-10-CM for pediatric practices.
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Affiliation(s)
| | - Jeffrey Zaman
- Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, Illinois
| | | | | | - Lauren Williams
- Department of Pediatrics, Medstar Southern Maryland Hospital Center, Clinton, Maryland
| | - Gina Mathew
- Alexian Brothers Health System, Chicago, Illinois; and
| | | | | | - Yves A. Lussier
- Department of Medicine, University of Arizona, Tucson, Arizona
| | - Andrew D. Boyd
- Internal Medicine, and,Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, Illinois
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