Enriching a primary health care version of ICD-10 using SNOMED CT mapping

<p>Abstract</p> <p>Background</p> <p>In order to satisfy different needs, medical terminology systems must have richer structures. This study examines whether a Swedish primary health care version of the mono-hierarchical ICD-10 (KSH97-P) may obtain a richer structure using category and chapter mappings from KSH97-P to SNOMED CT and SNOMED CT's structure. Manually-built mappings from KSH97-P's categories and chapters to SNOMED CT's concepts are used as a starting point.</p> <p>Results</p> <p>The mappings are manually evaluated using computer-produced information and a small number of mappings are updated. A new and poly-hierarchical chapter division of KSH97-P's categories has been created using the category and chapter mappings and SNOMED CT's generic structure. In the new chapter division, most categories are included in their original chapters. A considerable number of concepts are included in other chapters than their original chapters. Most of these inclusions can be explained by ICD-10's design. KSH97-P's categories are also extended with attributes using the category mappings and SNOMED CT's defining attribute relationships. About three-fourths of all concepts receive an attribute of type <it>Finding site </it>and about half of all concepts receive an attribute of type <it>Associated morphology</it>. Other types of attributes are less common.</p> <p>Conclusions</p> <p>It is possible to use mappings from KSH97-P to SNOMED CT and SNOMED CT's structure to enrich KSH97-P's mono-hierarchical structure with a poly-hierarchical chapter division and attributes of type <it>Finding site </it>and <it>Associated morphology</it>. The final mappings are available as additional files for this paper.</p

Coding of procedures documented by general practitioners in Swedish primary care-an explorative study using two procedure coding systems

<p>Abstract</p> <p>Background</p> <p>Procedures documented by general practitioners in primary care have not been studied in relation to procedure coding systems. We aimed to describe procedures documented by Swedish general practitioners in electronic patient records and to compare them to the Swedish Classification of Health Interventions (KVÅ) and SNOMED CT.</p> <p>Methods</p> <p>Procedures in 200 record entries were identified, coded, assessed in relation to two procedure coding systems and analysed.</p> <p>Results</p> <p>417 procedures found in the 200 electronic patient record entries were coded with 36 different Classification of Health Interventions categories and 148 different SNOMED CT concepts. 22.8% of the procedures could not be coded with any Classification of Health Interventions category and 4.3% could not be coded with any SNOMED CT concept. 206 procedure-concept/category pairs were assessed as a complete match in SNOMED CT compared to 10 in the Classification of Health Interventions.</p> <p>Conclusions</p> <p>Procedures documented by general practitioners were present in nearly all electronic patient record entries. Almost all procedures could be coded using SNOMED CT.</p> <p>Classification of Health Interventions covered the procedures to a lesser extent and with a much lower degree of concordance. SNOMED CT is a more flexible terminology system that can be used for different purposes for procedure coding in primary care.</p

Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT

Background Primary care (PC) in Sweden provides ambulatory and home health care outside hospitals. Within the County Council of Stockholm, coding of diagnoses in PC is mandatory and is done by general practitioners (GPs) using a Swedish primary care version of the International Statistical Classification of Diseases, version 10 (ICD-10). ICD-10 has amono-hierarchical structure. SNOMED CT is poly-hierarchical and belongs to a new generation of terminology systems with attributes (characteristics) that connect concepts in SNOMED CT and build relationships. Mapping terminologies and classifications has been pointed out as a way to attain additional advantages in describing and documenting healthcare data. A poly-hierarchical system supports the representation and aggregation of healthcare data on the basis of specific medical aspects and various levels of clinical detail. Objective To describe and compare diagnoses and health problems in KSH97-P/ICD-10 and SNOMED CT using primary care diagnostic data, and to explore and exemplify complementary aggregations of diagnoses and health problems generated from a mapping to SNOMED CT. Methods We used diagnostic data collected throughout 2006 and coded in electronic patient records (EPRs), and a mapping from KSH97-P/ ICD-10 to SNOMED CT, to aggregate the diagnostic data with SNOMED CT defining hierarchical relationship Is a and selected attribute relationships. Results The chapter level comparison between ICD-10 and SNOMED CT showed minor differences except for infectious and digestive system disorders. The relationships chosen aggregated the diagnostic data to 2861 concepts, showing a multidimensional view on different medical and specific levels and also including clinically relevant characteristics through attribute relationships. Conclusions SNOMED CT provides a different view of diagnoses and health problems on a chapter level, and adds significant new views of the clinical data with aggregations generated fromSNOMED CT Is a and attribute relationships. A broader use of SNOMED CT is therefore of importance when describing and developing primary care

Applying a common data model to Asian databases for multinational pharmacoepidemiologic studies: opportunities and challenges

Objective: The goal of the Asian Pharmacoepidemiology Network (AsPEN) is to study the effectiveness and safety of medications commonly used in Asia using databases from individual Asian countries. An efficient infrastructure to support multinational pharmacoepidemiologic studies is critical to this effort. Study Design and Setting: We converted data from the Japan Medical Data Center (JMDC) database, Taiwan’s National Health Insurance Research Database (NHIRD), Hong Kong’s Clinical Data Analysis and Reporting System (CDARS), South Korea’s Ajou University School of Medicine (AUSOM) database, and the US Medicare 5% sample to the Observational Medical Outcome Partnership (OMOP) Common Data Model. Results: We completed and documented the process for the Common Data Model (CDM) conversion. The coordinating center and participating sites reviewed the documents and refined the conversions based on the comments. The time required to convert data to the CDM varied widely across sites and included conversion to standard terminology codes and refinements of the conversion based on reviews. We mapped 97.2%, 86.7%, 92.6%, and 80.1% of domestic drug codes from the United States, Taiwan, Hong Kong, and Korea to RxNorm, respectively. The mapping rate from Japanese domestic drug codes to RxNorm (70.7%) was lower than from other countries, and we mapped remaining unmapped drugs to Anatomical Therapeutic Chemical Classification System codes. Because the native databases used international procedure codingsystems for which mapping tables have been established, we were able to map more than 90% of diagnosis and procedure codes to standard terminology codes. Conclusion: The Common Data Model established the foundation and reinforced collaboration for multinational pharmacoepidemiologic studies in Asia. Mapping of terminology codes was the greatest challenge, because of differences in health systems, cultures, and coding systems

Automatic medical term generation for a low-resource language: translation of SNOMED CT into Basque

211 p. (eusk.) 148 p. (eng.)Tesi-lan honetan, terminoak automatikoki euskaratzeko sistemak garatu eta ebaluatu ditugu. Horretarako,SNOMED CT, terminologia kliniko zabala barnebiltzen duen ontologia hartu dugu abiapuntutzat, etaEuSnomed deritzon sistema garatu dugu horren euskaratzea kudeatzeko. EuSnomedek lau urratsekoalgoritmoa inplementatzen du terminoen euskarazko ordainak lortzeko: Lehenengo urratsak baliabidelexikalak erabiltzen ditu SNOMED CTren terminoei euskarazko ordainak zuzenean esleitzeko. Besteakbeste, Euskalterm banku terminologikoa, Zientzia eta Teknologiaren Hiztegi Entziklopedikoa, eta GizaAnatomiako Atlasa erabili ditugu. Bigarren urratserako, ingelesezko termino neoklasikoak euskaratzekoNeoTerm sistema garatu dugu. Sistema horrek, afixu neoklasikoen baliokidetzak eta transliterazio erregelakerabiltzen ditu euskarazko ordainak sortzeko. Hirugarrenerako, ingelesezko termino konplexuak euskaratzendituen KabiTerm sistema garatu dugu. KabiTermek termino konplexuetan agertzen diren habiaratutakoterminoen egiturak erabiltzen ditu euskarazko egiturak sortzeko, eta horrela termino konplexuakosatzeko. Azken urratsean, erregeletan oinarritzen den Matxin itzultzaile automatikoa osasun-zientziendomeinura egokitu dugu, MatxinMed sortuz. Horretarako Matxin domeinura egokitzeko prestatu dugu,eta besteak beste, hiztegia zabaldu diogu osasun-zientzietako testuak itzuli ahal izateko. Garatutako lauurratsak ebaluatuak izan dira metodo ezberdinak erabiliz. Alde batetik, aditu talde txiki batekin egin dugulehenengo bi urratsen ebaluazioa, eta bestetik, osasun-zientzietako euskal komunitateari esker egin dugunMedbaluatoia kanpainaren baitan azkeneko bi urratsetako sistemen ebaluazioa egin da

Mapping of electronic health records in Spanish to the unified medical language system metathesaurus

[EN] This work presents a preliminary approach to annotate Spanish electronic health records with concepts of the Unified Medical Language System Metathesaurus. The prototype uses Apache Lucene R to index the Metathesaurus and generate mapping candidates from input text. In addition, it relies on UKB to resolve ambiguities. The tool has been evaluated by measuring its agreement with MetaMap in two English-Spanish parallel corpora, one consisting of titles and abstracts of papers in the clinical domain, and the other of real electronic health record excerpts.[EU] Lan honetan, espainieraz idatzitako mediku-txosten elektronikoak Unified Medical Languge System Metathesaurus deituriko terminologia biomedikoarekin etiketatzeko lehen urratsak eman dira. Prototipoak Apache Lucene R erabiltzen du Metathesaurus-a indexatu eta mapatze hautagaiak sortzeko. Horrez gain, anbiguotasunak UKB bidez ebazten ditu. Ebaluazioari dagokionez, prototipoaren eta MetaMap-en arteko adostasuna neurtu da bi ingelera-gaztelania corpus paralelotan. Corpusetako bat artikulu zientifikoetako izenburu eta laburpenez osatutako dago. Beste corpusa mediku-txosten pasarte batzuez dago osatuta

Domain-aware ontology matching

During the last years, technological advances have created new ways of communication, which have motivated governments, companies and institutions to digitalise the data they have in order to make it accessible and transferable to other people. Despite the millions of digital resources that are currently available, their diversity and heterogeneous knowledge representation make complex the process of exchanging information automatically. Nowadays, the way of tackling this heterogeneity is by applying ontology matching techniques with the aim of finding correspondences between the elements represented in different resources. These approaches work well in some cases, but in scenarios when there are resources from many different areas of expertise (e.g. emergency response) or when the knowledge represented is very specialised (e.g. medical domain), their performance drops because matchers cannot find correspondences or find incorrect ones. In our research, we have focused on tackling these problems by allowing matchers to take advantage of domain-knowledge. Firstly, we present an innovative perspective for dealing with domain-knowledge by considering three different dimensions (specificity - degree of specialisation -, linguistic structure - the role of lexicon and grammar -, and type of knowledge resource - regarding generation methodologies). Secondly, domain-resources are classified according to the combination of these three dimensions. Finally, there are proposed several approaches that exploit each dimension of domain-knowledge for enhancing matchers’ performance. The proposals have been evaluated by matching two of the most used classifications of diseases (ICD-10 and DSM-5), and the results show that matchers considerably improve their performance in terms of f-measure. The research detailed in this thesis can be used as a starting point to delve into the area of domain-knowledge matching. For this reason, we have also included several research lines that can be followed in the future to enhance the proposed approaches

An Evaluation of the ICD-10-CM System: Documentation Specificity, Reimbursement, and Methods for Improvement (International Classification of Diseases; 10th Revision; Clinical Modification)

The research project consists of three studies to identify the documentation specificity, reimbursement and documentation improvement for the upcoming International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) coding system. A descriptive research study using quantitative methods was conducted for the first study, which focused on coding electronic documents across each major diagnostic chapter for ICD-10-CM. The coding was ranked according to the Watzlaf et al (2007) study where a ranking score was provided if the diagnosis was fully captured by the ICD-10-CM code sets. The ICD-10-CM codes were then compared to the current ICD-9-CM codes to evaluate the details on the descriptions of the codes. The rankings were determined by comparing the ICD-10-CM systems for the number of codes, the level of specificity and the ability of the code description to fully capture the diagnostic term based on the resources available at the time of coding. A descriptive research study using quantitative methods was conducted for the second study, which focused on evaluating the reimbursement differences in coding with ICD-10- CM with and without the supporting documentation. Reimbursement amounts or the MS-DRG (Medicare Severity Diagnosis Related Groups) weight differences were examined to demonstrate the amount of dollars lost due to incomplete documentation. Reimbursement amounts were calculated by running the code set on the CMS ICD-10 grouper. An exploratory descriptive research study using qualitative methods was conducted for the third study which focused on developing a documentation improvement toolkit for providers and technology experts to guide them towards an accurate selection of codes. Furthermore a quick reference checklist geared towards the physician, coders and the information technology development team was developed based on their feedback and documentation needs. The results of the studies highlighted the clinical areas which needed the most documentation attention in order to accurately code in ICD-10-CM and the associated potential loss of revenue due to absent documentation. Further, the results from the educational tool kit could be used in the development of a better inpatient Computer Assisted Coding (CAC) product