Comparison of Knowledge Levels Required for SNOMED CT Coding of Diagnosis and Operation Names in Clinical Records

Comparison of terminology mapping methods for nursing wound care knowledge representation

BACKGROUND

Standardized clinical terminologies are increasingly used to design and support advanced information systems. In order to examine the representativeness of these terminologies for different professional groups or clinical areas, researchers may perform different methods of terminology mapping.

OBJECTIVE

The purpose of this study was to evaluate the ability of four mapping methods to identify concepts related to wound care in SNOMED CT.

METHODS

A class diagram of 107 concepts was developed to represent the nursing context of wound assessment, wound diagnosis, and goal of care for wound management. All concepts were mapped to SNOMED CT and identified as a direct match, a one-to-many match, or no match using four mapping methods (manual, automated, comparison, and concordance). The manual, automated and comparison methods produced candidate lists of SNOMED CT concepts, which were then used by two nursing wound care experts. The experts completed concordance mapping, which produced the final list. The SNOMED CT concepts from the manual, automated and comparison mappings were compared to the concordance mapping to generate a proportion of representation by each mapping method.

RESULTS

The manual, automated and comparison mappings produced partial lists of unique candidate concept matches not found in the other mapping methods. The concordance mapping produced a final list which included: 43 terms (40%) that had direct matches, 2 terms (2%) that had one-to-many matches, and 62 terms (58%) that had no matches to SNOMED CT. All mapping methods were necessary to achieve the representativeness captured in the final list.

CONCLUSION

To increase the representativeness of candidate mapping lists, multiple mapping methods and considerations may be necessary.

Mapping frailty concepts to SNOMED CT

BACKGROUND

Frailty is considered an emerging syndrome characterized by a decrease in physiological ability to respond to stressors, leading to increased morbidity and mortality rates. Frailty is distinguished from normal age-associated decline because it is a sharp and often rapid decline rather than a gradual slowing down of general functioning. The comprehensive geriatric assessment is currently considered the gold standard for identifying frailty in older adults. The electronic version of this tool is called the eCGA and is commonly included in electronic medical records (EMR) in primary care settings.

OBJECTIVES

We explored the adequacy of SNOMED CT to represent frailty concepts by addressing three research questions: 1) What are the defining characteristics of frailty most commonly used in frailty assessment tools? 2) Are these characteristics captured within one or many frailty assessment tools? 3) Which data elements from existing tool(s) can be reliably mapped to existing SNOMED CT terms?

METHODS

We conducted a literature search to explore the defining characteristics of frailty and the most commonly used assessment tools. We compared these findings to the components of frailty captured within the eCGA. We then used a descriptive study design to manually map concepts from the eCGA to SNOMED CT.

RESULTS

Our literature review demonstrated that the eCGA contains all common defining characteristics of frailty. Unique assessment questions from the eCGA (n = 133) were manually mapped to SNOMED CT, using expert consensus. Of these, 72 % were direct matches, 17 % were one-to-many matches, and the remaining 11 % were non-matches. Two rounds of expert clinician mapping occurred; inter-rater reliability between the two clinicians was 0.75 (kappa).

CONCLUSIONS/IMPLICATIONS

The resulting list of mapped eCGA elements to SNOMED CT terms can inform revisions to existing chronic disease databases to include frailty monitoring and surveillance.

SNOMEDtxt: Natural Language Generation from SNOMED Ontology

SNOMED Clinical Terms (SNOMED CT) defines over 70,000 diseases, including many rare ones. Meanwhile, descriptions of rare conditions are missing from online educational resources. SNOMEDtxt converts ontological concept definitions and relations contained in SNOMED CT into narrative disease descriptions using Natural Language Generation techniques. Generated text is evaluated using both computational methods and clinician and lay user feedback. User evaluations indicate that lay people prefer generated text to the original SNOMED content, find it more informative, and understand it significantly better. This method promises to improve access to clinical knowledge for patients and the medical community and to assist in ontology auditing through natural language descriptions.

Clinical coding and data quality in oculoplastic procedures

INTRODUCTION

Hospitals in England are reimbursed via national tariffs set out by NHS England. The tariffs payable to hospitals are determined by the activity coded for each patient's hospital visit. There are no national standards or publications within oculoplastics for coding accuracy. Our audit aimed to determine the accuracy of coding oculoplastic procedures carried out in theatres and to assess the financial implications of any discrepancies.

METHODS

We carried out a prospective audit of consecutive oculoplastic procedures performed at one hospital site over a 6-week period. We subsequently created a coding proforma and performed a re-audit using the same methods.

RESULTS

In the first cycle, clinical coding was 'correct' in 30.7% of cases, 'incomplete' for 12.9% and 'incorrect' for 56.5%. Of the 'incorrect' codes, 54.3% were coded as non-oculoplastic procedures (e.g. extraocular muscle surgery). We discussed our findings with the coding team in order to address the sources of error. We also created a 'tick box' coding proforma, for completion by surgeons. Our re-audit results showed an improvement of 'correct' coding to 85.7%.

CONCLUSION

Clinical coding is complex and vulnerable to inaccuracy. Our audit showed a high rate of coding error, which improved following collaboration with our coding team to address the sources of error and by creating a coding proforma to improve accuracy. Accurate clinical coding has financial implications for hospital trusts and consequently Clinical Commissioning Groups. In times of severe financial pressures, this could be a valuable tool, if rolled out over all specialities, to make much needed savings.

Qualitative analysis of manual annotations of clinical text with SNOMED CT

SNOMED CT provides about 300,000 codes with fine-grained concept definitions to support interoperability of health data. Coding clinical texts with medical terminologies it is not a trivial task and is prone to disagreements between coders. We conducted a qualitative analysis to identify sources of disagreements on an annotation experiment which used a subset of SNOMED CT with some restrictions. A corpus of 20 English clinical text fragments from diverse origins and languages was annotated independently by two domain medically trained annotators following a specific annotation guideline. By following this guideline, the annotators had to assign sets of SNOMED CT codes to noun phrases, together with concept and term coverage ratings. Then, the annotations were manually examined against a reference standard to determine sources of disagreements. Five categories were identified. In our results, the most frequent cause of inter-annotator disagreement was related to human issues. In several cases disagreements revealed gaps in the annotation guidelines and lack of training of annotators. The reminder issues can be influenced by some SNOMED CT features.

Automated mapping of clinical terms into SNOMED-CT. An application to codify procedures in pathology

Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-to-concept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0% precision and 51.4% recall, providing a substantial improvement of recall (28% and 60%) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.