Can SNOMED CT replace ICD-coding?

Can SNOMED CT replace ICD-coding? B. Van Bruwaene, K. Mies +, J. Schots, R. Van de Velde ± *Medical Registration university hospital AZ V.U.B., Brussels, Belgium + Medical Registration, H. Hart Hospital Tienen, Belgium Medical director, university hospital AZ V.U.B., Brussels, Belgium ± ICT director university hospital AZ V.U.B., Brussels Correspondence Dr. B. Van Bruwaene AZ VUB Medische Registratie Laarbeeklaan 101, B1090 Brussels, Belgium benny.vanbruwaene@az.vub.ac.be Tel +32 472 99 01 16 Fax: +32 25 68 01 32 1

Summary Objective: For on line coding, at the point of care, clinical vocabularies are needed which are close to the natural language of the physician. These vocabularies should be connected to a multiaxial medical coding system which enables artificial intelligence within the Electronic Health Record (EHR). When clinical information is coded within the EHR for clinical reasons, the administrative codes for billing could be produced automatically saving lots of coding work and reducing the financial bias in registration. SNOMED CT provides cross maps from SNOMED CT codes to ICD-9-CM and ICD-10. This means that the same SNOMED CT data set could generate ICD-9-CM and ICD-10 tables which offers the possibility of comparisons between a SNOMED CT data set and previous ICD-9-CM data or ICD-10 data in other countries. Methods: We studied the content coverage (granularity) of SNOMED CT in comparison with ICD. Then we checked to which extent an automatic conversion from SNOMED CT registration to ICD-9-CM would change the DRG s (APR-DRG version 15) as calculated on manually ICD- 9-CM coded information. Results and conclusion: We concluded that SNOMED CT is a better candidate for the integration in an Electronic Health Record than ICD being much more granular (87 % of concept coverage for SNOMED CT vs. 45 % for ICD-9-CM) and having a multi-axial ontology. DRG s as calculated for hospital financing were identical to manual ICD-coding as with automatic cross mapping from SNOMED CT. However more detailed research on a larger sample is needed. A lot of research and development needs also to be done concerning technology for on line clinical coding. Medical vocabularies in the local language, connected to SNOMED CT and with powerful search engines for the appropriate terms are needed. Keywords: SNOMED CT, ICD, Controlled Vocabulary, Medical Records, Problem-Oriented 2

1. Introduction In Belgium, hospital financing has been based for more than 10 years on Diagnosis Related Groups (DRG). Diagnoses and procedures are coded for all hospitalisations with ICD-9-CM, the US clinical modification of the ninth version of the International Classification of Diseases (ICD) and procedures. Based on the principle diagnosis and procedure of the admission, patients are categorised in homogeneous groups. Since 1999 grouping is done with the 3M grouping software APR-DRG version 15. For any DRG the severity of illness and risk of mortality is calculated based on the secondary diagnoses. Many European countries, Canada and Australia moved to a registration with various versions of ICD-10 (ICD-10, ICD-10-CA, ICD-10-AM, ICD-10-GM) for diagnoses and use national nomenclatures for procedures. Grouping software was developed to classify patients on ICD-10 codes combined with the local coding systems for procedures. Dr. Martti Virtanen, head of the Nordic collaborating centre of the WHO, estimated the development cost of the Nordic DRG grouping logic at 200.000 with a running cost of 100.000 per year [personal communication]. The US plans the transition from ICD-9-CM to ICD-10-CM in 2007. The President s Information Technology Advisory Committee recommended in June 2004 [15]: SNOMED CT is a dynamic, scientifically validated clinical health care terminology and infrastructure that provides a common language that enables a consistent way of capturing, sharing and aggregating health data across specialties and sites of care. A migration strategy must be adopted for Federal health program reimbursements to be based on the reporting of diagnoses and procedures coded in SNOMED CT for clinical purposes. In the proposed rulemaking process of replacing ICD-9-CM with ICD-10-CM, the US department of Health & Human Services (HHS) must avoid the potential for that migration to retard the adoption and implementation of SNOMED CT in Electronic Health Records (EHR) systems. Coding in the EHR is beneficial for medical practice because it empowers clinical decision support like alerts and reminders, pharmaco-surveillance and clinical protocols. For on line coding, at the point of care, clinical vocabularies are needed which are close to the natural language of the physician. These vocabularies should be connected to a multi-axial medical coding system connected to an ontology which enables artificial intelligence within the EHR. 3

When clinical information is coded within the EHR for clinical reasons, the administrative codes for billing could be produced automatically saving lots of coding work and reducing the financial bias in registration. SNOMED CT provides cross maps from SNOMED CT codes to ICD-9-CM and ICD-10. This means that the same SNOMED CT data set could generate ICD-9- CM and ICD-10 tables which offers the possibility of comparisons between SNOMED CT data and previous ICD-9-CM data or ICD-10 data in other countries. We studied the content coverage (granularity) of SNOMED CT in comparison with ICD-9-CM and ICD-10. Then we checked to what extent an automatic conversion from SNOMED CT data to ICD-9-CM would change the calculated DRG s (APR-DRG version 15) based on manual coding. 2. Methods Sixty-three patient records within the Academic Hospital of the Free University of Brussels (AZ VUB) were selected from the hospital Minimum Basis Data Set of 2003 with a stratification of approximately two records per MDC (Major Diagnostic Category) resulting in a broad coverage of pathology. Two hundred seventy three medical terms for diagnoses and procedures as typed by the physician in a kind of problem list were selected for evaluation. The International Classification of Diseases 9th Revision, Clinical Modification Ninth Edition (ICD-9-CM) was used. The coding of SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) was done with the CLUE-browser version 5 (kind of code finder) on the version January 2004. SNOMED CT proposes a multi-axial coding system, which allows a further specification of a given concept through formally defined qualifiers and attributes. E.g. appendicitis is characterised by qualifiers like onset (gradual or sudden), severity (6 grades), A mass on the other hand is characterised by volume, consistency, pathology SNOMED CT kept in his system the compounded codes like used in ICD so the same concept can be coded as a pre-coordinated concept: E.g. Acute Appendicitis 4

D5-46210 Acute appendicitis, NOS or as a post-coordinated concept. The concept is know after the combination of the code with the attributes: G-A231 Acute M-40000 Inflammation, NOS G-C006 In T-59200 Appendix, NOS A consistent coding system should define rules for pre- and post-coordination which is not the case for SNOMED CT. Because rules for pre- and post-coordination of terms are not specified in SNOMED CT the closest pre-coordinated concept was chosen. Every term used by the physician was translated to one or more concepts of the classification system by an experienced coder (physician with Master degree in Medical Data Management and with regular ICD-9-CM coding experience). The following scores reflected the degree of similarity between the code/concept and the given term: 1 Term was more specific than the code (examples in table 1) 2 Term as specific as code 3 Combination code (code is based on the combination of two concepts) (examples in table 2). Every term directly coded in ICD-9-CM and ICD-10 was compared with the ICD-9-CM/ICD-10 code derived from the cross mapping between SNOMED CT and ICD-9-CM and ICD-10. The SNOMED CT ICD-9-CM mapping is a one-one cross map table whereas the SNOMED CT ICD-10 mapping consists in cross maps between the SNOMED CT concept and all ICD-10 codes possible (another code can be used for the same concept in function of the context, e.g. other code for the same disease during pregnancy). If there is more than one cross map provided for 5

one SNOMED CT code, the different maps are ranked in function of their probability. In this study, only the cross map with the highest priority was used. The following scores reflected the degree of correspondence between the code from direct coding and the code resulting from the cross map: 1 Perfect mapping: the codes are identical 2 No mapping was possible 3 Good mapping: codes are different but the mapping covers the same concept 4 Bad mapping: codes and concepts were different after mapping In a second step the APR-DRG was calculated for every patient using ICD-9-CM codes from manual coding and from cross mapping with SNOMED CT. When the APR-DRG and the severity were the same the mapping was scored identical. In case APR-DRG or severity was different or not calculable due to lack of mapping the case mix was not identical. 3. Results The coverage of Dutch medical terms by SNOMED CT concepts/codes (87 %) was far (p < 0,0001; cf. table 3) better than for ICD-9-CM (45 %). This confirms previous findings [1, 2, 3, 4]. SNOMED CT ICD-9-CM mapping resulted in 81 % of cases in identical codes as when manually coded. In 8 % of cases, the ICD-9-CM codes were context dependent. Therefore, the automatic mapping lead to the same medical concepts, but not to the same ICD-9-CM codes (cf. table 4). In 11 % of cases no mapping was available (especially V-codes and non specific codes). The study enclosed also rather vague terms which correspond to SNOMED CT concepts called status limited and for which concepts no cross maps are provided. As there is no SNOMED CT mapping to ICD-9-CM for procedures, only the mapping of 247 SNOMED CT codes for clinical diagnoses were tested. For the calculation of the DRG s the manually coded procedure-codes were used. In this study, the change in codes or lack of mapping didn t result in different APR-DRG s. 6

The quality of the SNOMED CT ICD-10 mapping is comparable to the ICD-9-CM mapping. In general the ICD-10-codes resulting from automatic conversion reflected the pathology well (85 % Perfect mapping ). 4. Discussion In Belgium, like in many other countries, a lot of money and effort is spent on the coding of clinical information for financial purposes: in Belgium, procedures are coded with the RIZIV/INAMI-nomenclature and the case mix for hospitalised patients is based on ICD-9-CM codes for diagnoses and procedures. Unfortunately these efforts do not contribute to a more comprehensive and intelligent medical record. Clinical coding requires a highly specific and multi-axial coding system based on a medical ontology. We evaluated the concept coverage of ICD-9-CM and SNOMED CT and concluded that SNOMED CT is, concerning this character, by far superior to ICD-9-CM. SNOMED CT is a real multi axial-coding (cf. supra) system based on a multidimensional hierarchy (ontology). ICD-9-CM is a classification system with a limited and mono-dimensional hierarchy. E.g. Acute myocardial infarction of the anterior wall in ICD-9-CM 410.1 is an acute myocardial infarction (code 410) because the ICD-9-CM code is hierarchical from the third digit on (all infarctions start with code 410). However it is impossible to determine both angina pectoris (code 413) and acute myocardial infarction (code 410) as coronary diseases because there is no relationship implying so. Another limitation of ICD is the mono-axial structure. E.g. a lung cancer (code 162.9) is defined as a Neoplasm but not as a lung disease. SNOMED CT has a multidimensional hierarchy. Lung cancer is described as a cancer and as a lung disease. The medical ontology of SNOMED CT could empower clinical decision support because it allows multiple relationships between concepts. E.g. the warning that Antiinflammatory drugs are contraindicated to diseases due to excessive secretion of gastric acid. In the way that concepts oesophagitis, gastritis, gastric ulcus and duodenal ulcus are connected to the concept diseases due to excessive secretion of gastric acid one single rule will include all conditions (and the linked terms) in a warning scheme for pharmaco-surveillance. Although SNOMED CT is not perfect and still much work is to be done, the system is probably the best one can get at this moment. When clinical information is coded in SNOMED CT, ICD-9-CM and ICD-10 data sets can be generated, based on the cross mapping tables between ICD-9-CM and ICD-10 with SNOMED 7

CT. We could confirm that the automatic cross mapping leads to the same DRG s for hospital financing as with the classic, manual coding. However a study on a larger sample is needed. The quality of the cross mapping is promising in the context of the transition from ICD-9-CM to ICD- 10. Coding in SNOMED CT could generate 2 data sets allowing historical comparisons between data before and after the transition and between countries using ICD-9 or ICD-10. Although SNOMED CT appears to be the best coding system, a lot of research and development is also to be done about technology of on line clinical coding at the point of care. Medical vocabularies in the local language, connected to SNOMED CT and with powerful search engines for the appropriate terms are needed. References 1 Chute. C., Cohn S., Campbell K., Oliver D., Campbell J. The Content coverage of Clinical Classifications. J Am Med Inform Assoc. 1996;3:224-233 2 Chute. C., Clinical Classification and Terminology: Some History and Current Observations. J Am Med Inform Assoc. 2000;7:298-303 3 Chute C., MD, Cohn S.P., Campbell J.R. A Framework for Comprehensive Health Terminology Systems in the United States: Development Guidelines, Criteria for Selection, and Public Policy Implications. J Am Med Inform Assoc. 1998;5-503-510 4 Campbell J., Carpenter P., Sneiderman C., Cohn S., MD, Chute C., Warren J. Phase II Evaluation of Clinical Coding Schemes: Completeness, Taxonomy, Mapping, Definitions, and Clarity. J Am Med Inform Assoc. 1997;4:238-251 5 Schultz S.,Zaiss A.,Brunner R.,Spinner D.,Klar R., Conversion Problems Concerning Automated Mapping from ICD-10 to ICD-9, Department of Medical Informatics, University of Freiburg, Freiburg, Germany: http://www.coling.uni-freiburg.de/pub/schulz/ref/schulz.mim98 6 de Keizer N.F., Abu-Hannu, Understanding Terminological Systems II: Experience with Conceptual and Formal Representation of Structure. Method Inform Med 2000;39:22-29: 7 DRG and NordDRG Inpatient Classification DRG: http://norddrg.kuntaliitto.fi/indexen.html 8

8 Structure, content and weaknesses of the International Statistical Classification of Diseases and Related Health Problems 10 (ICD-10) 9 Roger France F., Case mix use in 25 countries: a migration success but international comparisons failure. International Journal of Medical Informatics 2003 Jul; 70(2-3):215-219 10 Rose J. S., Fisch B.J., Hogan W.R., Levy, Marshall P., Thomas D.R., Kirkley D. Common Medical Terminology Comes of Age: Standard Language Improves Healthcare Quality. Journal of Healthcare Information Management, vol. 15 no. 3 Fall 2001; 307-330 11 Zanstra P., Van der Haring E., Cornet*, R. Depts. Medical Informatics, UMC St Radboud Nijmegen, *AMC Amsterdam. Introduction of a Clinical Terminology in the Netherlands; Needs, Contraints, Opportunities. NICTIZ. September 2003: http://www.nictiz.nl/kr_nictiz/uploaddb/downl_object.asp?atoom=2128&volgnr=1 12 AHIMA Press Release: AHIMA APPLAUDS NCVHS VOTE TO RECOMMEND ADOPTION OF ICD-10-CM, ICD-10-PCS HIM professionals preparing for implementation:http://www.ahima.org/press/press_releases/03.1106.cfm 13 Meeting of WHO Collaborating centres for the Family of International Classifications. Cologne, Germany. 19-25 October 2003. Mapping between SNOMED and ICD. Rosemary Roberts, Kerry Innes, Margo Imel, James Campbell. WHO/HFS/CAS/C/03.52: http://www.rivm.nl/whofic/colognepapers/cologne52.rtf 14 Wasserman H, Wang J. An Applied Evaluation of SNOMED CT as a Clinical Vocabulary for the Computerized Diagnosis and Problem List. Proc AMIA Symp 2003;:699-703: http://www.amia.org/pubs/proceedings/symposia/2003/142.pdf 15 President s Information Technology Advisory Committee: Revolutionizing Health Care Through Information Technology, june 2004. http://www.nitrd.gov 9

Term Code ICD-9-CM Description ICD-9-CM Pneumonia right lobe 481 pneumonia, organism nos inferior sterile pyuria 791.9 abnormal urine findings nec Term SNOMED CT code Preferred term SNOMED CT definitif tracheacanullation 397740001 cannulation of trachea with speaking valve Table 1: Examples of 1 Term was more specific than the code 10

Term Diabetes mellitus type II Retinopathy ICD-9-CM code 250.50 362.01 Description ICD-9-CM Diabetes with ophthalmic manifestations type II or unspecified type, not stated as uncontrolled Background diabetic retinopathy Table 2: Example of 3 Combination code 11

ICD-9-CM SNOMED CT Number Percentage Number Percentage 1 Term was more specific than the code 128 47% 30 11% 2 Term as specific as code 123 45% 238 87% 3 Combination code 22 8% 5 2% Table 3: Correspondence between free text terms in a medical problem list and ICD-9-CM and SNOMED CT 12

SNOMED CT Mapping ICD-9-CM ICD-10 Number Percentage Number Percentage 1 Perfect mapping 201 81% 211 85% 2 No mapping 27 11% 27 11% 3 Good mapping 20 8% 10 4% 4 Bad mapping 0 0 0 0% Table 4: Similarity between ICD-9-CM and ICD-10 codes from manual coding and from automatic conversion from SNOMED CT 13