A practical strategy to use the ICD-11 for morbidity coding in the United States without a clinical modification

OBJECTIVE

The aim of this study was to derive and evaluate a practical strategy of replacing ICD-10-CM codes by ICD-11 for morbidity coding in the United States, without the creation of a Clinical Modification.

MATERIALS AND METHODS

A stepwise strategy is described, using first the ICD-11 stem codes from the Mortality and Morbidity Statistics (MMS) linearization, followed by exposing Foundation entities, then adding postcoordination (with existing codes and adding new stem codes if necessary), with creating new stem codes as the last resort. The strategy was evaluated by recoding 2 samples of ICD-10-CM codes comprised of frequently used codes and all codes from the digestive diseases chapter.

RESULTS

Among the 1725 ICD-10-CM codes examined, the cumulative coverage at the stem code, Foundation, and postcoordination levels are 35.2%, 46.5% and 89.4% respectively. 7.1% of codes require new extension codes and 3.5% require new stem codes. Among the new extension codes, severity scale values and anatomy are the most common categories. 5.5% of codes are not one-to-one matches (1 ICD-10-CM code matched to 1 ICD-11 stem code or Foundation entity) which could be potentially challenging.

CONCLUSION

Existing ICD-11 content can achieve full representation of almost 90% of ICD-10-CM codes, provided that postcoordination can be used and the coding guidelines and hierarchical structures of ICD-10-CM and ICD-11 can be harmonized. The various options examined in this study should be carefully considered before embarking on the traditional approach of a full-fledged ICD-11-CM.

Mapping 3 procedure coding systems to the International Classification of Health Interventions (ICHI): coverage and challenges

OBJECTIVE

To study the coverage and challenges in mapping 3 national and international procedure coding systems to the International Classification of Health Interventions (ICHI).

MATERIALS AND METHODS

We identified 300 commonly used codes each from SNOMED CT, ICD-10-PCS, and CCI (Canadian Classification of Health Interventions) and mapped them to ICHI. We evaluated the level of match at the ICHI stem code and Foundation Component levels. We used postcoordination (modification of existing codes by adding other codes) to improve matching. Failure analysis was done for cases where full representation was not achieved. We noted and categorized potential problems that we encountered in ICHI, which could affect the accuracy and consistency of mapping.

RESULTS

Overall, among the 900 codes from the 3 sources, 286 (31.8%) had full match with ICHI stem codes, 222 (24.7%) had full match with Foundation entities, and 231 (25.7%) had full match with postcoordination. 143 codes (15.9%) could only be partially represented even with postcoordination. A small number of SNOMED CT and ICD-10-PCS codes (18 codes, 2% of total), could not be mapped because the source codes were underspecified. We noted 4 categories of problems in ICHI-redundancy, missing elements, modeling issues, and naming issues.

CONCLUSION

Using the full range of mapping options, at least three-quarters of the commonly used codes in each source system achieved a full match. For the purpose of international statistical reporting, full matching may not be an essential requirement. However, problems in ICHI that could result in suboptimal maps should be addressed.

Evaluation of the International Classification of Health Interventions (ICHI) in the coding of common surgical procedures

OBJECTIVE

: To evaluate the International Classification of Health Interventions (ICHI) in the clinical and statistical use cases.

MATERIALS AND METHODS

: We identified 300 most-performed surgical procedures as represented by their display names in an electronic health record. For comparison with existing coding systems, we coded the procedures in ICHI, SNOMED CT, International Classification of Diseases (ICD)-10-PCS, and CCI (Canadian Classification of Health Interventions), using postcoordination (modification of existing codes by adding other codes), when applicable. Failure analysis was done for cases where full representation was not achieved. The ICHI encoding was further evaluated for adequacy to support statistical reporting by the Organisation for Economic Co-operation and Development (OECD) and European Union (EU) categories of surgical procedures.

RESULTS

: After deduplication, 229 distinct procedures remained. Without postcoordination, ICHI achieved full representation in 52.8%. A further 19.2% could be fully represented with postcoordination. SNOMED CT was the best performing overall, with 94.3% full representation without postcoordination, and 99.6% with postcoordination. Failure analysis showed that "method" and "target" constituted most of the missing information for ICHI encoding. For all OECD/EU surgical categories, ICHI coding was adequate to support statistical reporting. One OECD/EU category ("Hip replacement, secondary") required postcoordination for correct assignment.

CONCLUSION

: In the clinical use case of capturing information in the electronic health record, ICHI was outperformed by the clinically oriented procedure coding systems (SNOMED CT and CCI), but was comparable to ICD-10-PCS. Postcoordination could be an effective and efficient means of improving coverage. ICHI is generally adequate for the collection of international statistics.

Feasibility of replacing the ICD-10-CM with the ICD-11 for morbidity coding: A content analysis

OBJECTIVE

The study sought to assess the feasibility of replacing the International Classification of Diseases-Tenth Revision-Clinical Modification (ICD-10-CM) with the International Classification of Diseases-11th Revision (ICD-11) for morbidity coding based on content analysis.

MATERIALS AND METHODS

The most frequently used ICD-10-CM codes from each chapter covering 60% of patients were identified from Medicare claims and hospital data. Each ICD-10-CM code was recoded in the ICD-11, using postcoordination (combination of codes) if necessary. Recoding was performed by 2 terminologists independently. Failure analysis was done for cases where full representation was not achieved even with postcoordination. After recoding, the coding guidance (inclusions, exclusions, and index) of the ICD-10-CM and ICD-11 codes were reviewed for conflict.

RESULTS

Overall, 23.5% of 943 codes could be fully represented by the ICD-11 without postcoordination. Postcoordination is the potential game changer. It supports the full representation of 8.6% of 943 codes. Moreover, with the addition of only 9 extension codes, postcoordination supports the full representation of 35.2% of 943 codes. Coding guidance review identified potential conflicts in 10% of codes, but mostly not affecting recoding. The majority of the conflicts resulted from differences in granularity and default coding assumptions between the ICD-11 and ICD-10-CM.

CONCLUSIONS

With some minor enhancements to postcoordination, the ICD-11 can fully represent almost 60% of the most frequently used ICD-10-CM codes. Even without postcoordination, 23.5% full representation is comparable to the 24.3% of ICD-9-CM codes with exact match in the ICD-10-CM, so migrating from the ICD-10-CM to the ICD-11 is not necessarily more disruptive than from the International Classification of Diseases-Ninth Revision-Clinical Modification to the ICD-10-CM. Therefore, the ICD-11 (without a CM) should be considered as a candidate to replace the ICD-10-CM for morbidity coding.

Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts

Objective

The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts.

Materials and Methods

Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts’ embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus.

Results

Sentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec.

Conclusions

Word and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.

The new International Classification of Diseases 11th edition: a comparative analysis with ICD-10 and ICD-10-CM

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.

Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases

The Internet and social media is an enormous source of information. Health social networks and online collaborative environments enable users to create shared content that afterwards can be discussed. The aim of this paper is to present a novel methodology designed for quantifying relevant information provided by different participants in clinical online discussions. The main goal of the methodology is to facilitate the comparison of participant interactions in clinical conversations. A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. Particularly, three new indicators have been proposed to make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. Proposed indicators have been applied to one discussion extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a health social network. This work can be applied to assess collaborative diagnoses, discussions among patients, and the participation of students in clinical case discussions. It permits moderators and educators to obtain a quantitatively measure of the contribution of each participant.