Description of COVID-19 patients and mapping nursing data to ICNP 2021 reference set in SNOMED CT.

AIM: To describe nursing care of COVID-19 patients with International Classification for Nursing Practice (ICNP) 2019, ICNP 2021 reference set, and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). BACKGROUND: From the beginning of the COVID-19 pandemic, nurses have realised the importance of documenting nursing care. INTRODUCTION: It is important to recognise how real nursing data match the ICNP reference set in SNOMED CT as that is the terminology to be used in Iceland. METHODS: A descriptive study with two methods: (a) statistical analysis of demographic and coded clinical data identified and retrieved from Electronic Health Record (EHR) and (b) mapping of documented nursing diagnoses and interventions in EHRs into ICNP 2019, ICNP 2021 and SNOMED CT 2021. RESULTS: The sample consisted of all (n = 91) adult COVID-19 patients admitted to the National University Hospital between 28 February and 30 June 2020. Nurses used 62 different diagnoses and 79 interventions to document nursing care. Diagnoses and interventions were best represented by SNOMED CT (85.4%; 100%), then by ICNP 2019 version (79.2%; 85%) and least by the ICNP 2021 reference set (70.8; 83.3%). Ten nursing diagnoses did not have a match in the ICNP 2021 reference set. DISCUSSION: Nurses need to keep up with the development of ICNP and submit to ICN new terms and concepts deemed necessary for nursing practice for inclusion in ICNP and SNOMED CT. CONCLUSION: Not all concepts in ICNP 2019 for COVID-19 patients were found to have equivalence in ICNP 2021. SNOMED CT-preferred terms cover the description of COVID-19 patients better than the ICNP 2021 reference set in SNOMED CT. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Through the use of ICNP, nurses can articulate the unique contribution made by the profession and make visible the specific role of nursing worldwide.

Implementing SNOMED CT in Open Software Solutions to Enhance the Findability of COVID-19 Questionnaires.

SNOMED CT fosters interoperability in healthcare and research. This use case implemented SNOMED CT for browsing COVID-19 questionnaires in the open-software solutions OPAL/MICA. We implemented a test server requiring files in a given YAML format for implementation of taxonomies with only two levels of hierarchy. Within this format, neither the implementation of SNOMED CT hierarchies and post-coordination nor the use of release files were possible. To solve this, Python scripts were written to integrate the required SNOMED CT concepts (Fully Specified Name, FSN and SNOMED CT Identifier, SCTID) into the YAML format (YAML Mode). Mappings of SNOMED CT to data items of the questionnaires had to be provided as Excel files for implementation into Opal/MICA and further Python scripts were established within the Excel Mode. Finally, a total of eight questionnaires containing 1.178 data items were successfully mapped to SNOMED CT and implemented in OPAL/MICA. This use case showed that implementing SNOMED CT for browsing COVID-19 questionnaires is feasible despite software solutions not supporting SNOMED CT. However, limitations of not being able to implement SNOMED CT release files and its provided hierarchy and post-coordination still have to be overcome.

Deep SNOMED CT Enabled Large Clinical Database About COVID-19.

In spring 2020, as the COVID-19 pandemic is in its first wave in Europe, the University hospitals of Geneva (HUG) is tasked to take care of all Covid inpatients of the Geneva canton. It is a crisis with very little tools to support decision-taking authorities, and very little is known about the Covid disease. The need to know more, and fast, highlighted numerous challenges in the whole data pipeline processes. This paper describes the decisions taken and processes developed to build a unified database to support several secondary usages of clinical data, including governance and research. HUG had to answer to 5 major waves of COVID-19 patients since the beginning of 2020. In this context, a database for COVID-19 related data has been created to support the governance of the hospital in their answer to this crisis. The principles about this database were a) a clearly defined cohort; b) a clearly defined dataset and c) a clearly defined semantics. This approach resulted in more than 28 000 variables encoded in SNOMED CT and 1 540 human readable labels. It covers more than 216 000 patients and 590 000 inpatient stays. This database is used daily since the beginning of the pandemic to feed the "Predict" dashboards of HUG and prediction reports as well as several research projects.

Decluttering the problem list in electronic health records.

PURPOSE: The purpose of this study was to evaluate the current state of problem list maintenance at an academic medical center. SUMMARY: We included problem list data for patients who had at least 2 face-to-face encounters at Vanderbilt University Medical Center or its clinics between January 1, 2018, and December 31, 2019. We used the frequency of problem list additions, resolutions, deletions, duplicate problems (exact and SNOMED CT duplicates), inconsistencies (contradicting stages of disease state), and items that could be documented elsewhere in the electronic health record as surrogate markers of problem list maintenance. Descriptive statistics were used to summarize the results. A total of 546,510 patients met inclusion criteria. There were 3,762 (0.7%) patients who had the exact same active problem listed more than once. SNOMED CT code duplications occurred in the records for 56,399 (10.5%) patients. Of the patients with asthma, 2.5% (223/8,779) had contradicting asthma stages active on their problem list, and 6.4% (950/14,950) of patients with chronic kidney disease (CKD) had contradicting CKD stages. In addition, 17,205 (3.1%) patients had 20,365 active family history problems and 39,464 (7.2%) patients had an allergy documented on their problem list. On average, there were 43.7 (95% confidence interval [CI], 14-73.4) additions, 8.7 (95% CI, 0.1-17.4) resolutions, and 2.1 (95% CI, 0-4.6) deletions of problems per 100 face-to-face encounters, inpatient or outpatient. CONCLUSION: Our study suggests areas for improvement for problem list maintenance. Further studies into semantic duplication and clinical decision support tools to encourage problem list maintenance and deduplication are needed.

Evaluating Suitability of SNOMED CT in Structured Searches for COVID-19 Studies.

Studies investigating the suitability of SNOMED CT in COVID-19 datasets are still scarce. The purpose of this study was to evaluate the suitability of SNOMED CT for structured searches of COVID-19 studies, using the German Corona Consensus Dataset (GECCO) as example. Suitability of the international standard SNOMED CT was measured with the scoring system ISO/TS 21564, and intercoder reliability of two independent mapping specialists was evaluated. The resulting analysis showed that the majority of data items had either a complete or partial equivalent in SNOMED CT (complete equivalent: 141 items; partial equivalent: 63 items; no equivalent: 1 item). Intercoder reliability was moderate, possibly due to non-establishment of mapping rules and high percentage (74%) of different but similar concepts among the 86 non-equal chosen concepts. The study shows that SNOMED CT can be utilized for COVID-19 cohort browsing. However, further studies investigating mapping rules and further international terminologies are necessary.

Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models.

BACKGROUND: COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization. OBJECTIVE: This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time. MATERIAL AND METHODS: The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020. RESULTS: An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts. CONCLUSION: This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs.

COVID-19 detection in radiological text reports integrating entity recognition.

COVID-19 diagnosis is usually based on PCR test using radiological images, mainly chest Computed Tomography (CT) for the assessment of lung involvement by COVID-19. However, textual radiological reports also contain relevant information for determining the likelihood of presenting radiological signs of COVID-19 involving lungs. The development of COVID-19 automatic detection systems based on Natural Language Processing (NLP) techniques could provide a great help in supporting clinicians and detecting COVID-19 related disorders within radiological reports. In this paper we propose a text classification system based on the integration of different information sources. The system can be used to automatically predict whether or not a patient has radiological findings consistent with COVID-19 on the basis of radiological reports of chest CT. To carry out our experiments we use 295 radiological reports from chest CT studies provided by the ''HT médica" clinic. All of them are radiological requests with suspicions of chest involvement by COVID-19. In order to train our text classification system we apply Machine Learning approaches and Named Entity Recognition. The system takes two sources of information as input: the text of the radiological report and COVID-19 related disorders extracted from SNOMED-CT. The best system is trained using SVM and the baseline results achieve 85% accuracy predicting lung involvement by COVID-19, which already offers competitive values that are difficult to overcome. Moreover, we apply mutual information in order to integrate the best quality information extracted from SNOMED-CT. In this way, we achieve around 90% accuracy improving the baseline results by 5 points.