Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study.

BACKGROUND: Few studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration. OBJECTIVE: This study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via standardized nursing records. METHODS: In this study, 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to the intensive care unit within 7 days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International Edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on their meaning by 2 experts with more than 5 years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure-related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19. RESULTS: All nursing statements were semantically mapped to SNOMED CT concepts for "clinical finding," "situation with explicit context," and "procedure" hierarchies. The interrater reliability of the mapping results was 87.7%. The most important features calculated by random forest were "oxygen saturation below reference range," "dyspnea," "tachypnea," and "cough" in "clinical finding," and "oxygen therapy," "pulse oximetry monitoring," "temperature taking," "notification of physician," and "education about isolation for infection control" in "procedure." Among these, "dyspnea" and "inadequate food diet" in "clinical finding" increased clinical deterioration risk (dyspnea: odds ratio [OR] 5.99, 95% CI 2.25-20.29; inadequate food diet: OR 10.0, 95% CI 2.71-40.84), and "oxygen therapy" and "notification of physician" in "procedure" also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97). CONCLUSIONS: The study used SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.

Exploring Nursing Care for Patients With COVID-19 Using International Classification for Nursing Practice-Based Nursing Records.

This study explored nursing care topics for patients with the coronavirus disease 2019 admitted to the wards and intensive care units using International Classification for Nursing Practice-based nursing narratives. A total of 256630 nursing statements from 555 adult patients admitted from December 2019 to June 2022 were extracted from the clinical data warehouse. The International Classification for Nursing Practice concepts mapped to 301 unique nursing statements that accounted for the top 90% of all cumulative nursing narratives were used for analysis. The standardized number of nursing statements for each concept was calculated according to the types of nursing care and compared between the two groups. The most documented topics were related to infection; physical symptoms such as sputum, cough, dyspnea, and shivering; and vital signs including blood oxygen saturation and body temperature. Nurses in the intensive care units frequently documented concepts related to the directly monitored and assessed physical signs such as consciousness, pupil reflex, and skin integrity, whereas nurses in wards documented more concepts related to symptoms patients complained. This study showed that the International Classification for Nursing Practice-based nursing records can be used as source of information to identify nursing care for patients with coronavirus disease 19.

Improving Prediction of Fall Risk Using Electronic Health Record Data With Various Types and Sources at Multiple Times.

Inpatient falls are among the most common adverse events threatening patient safety. Although many studies have developed predictive models for fall risk, there are some drawbacks. First, most previous studies have relied on an incident-reporting system alone to identify fall events. Thus, it has been found that falls are more likely to be underreported. Second, there has been a controversy on how to select accurate representative values for patient status data across multiple times and various data sources in electronic health records. Given this background, this study used nurses' progress notes as a complementary data source to detect fall events. In addition, we developed criteria including coverage, currency, and granularity in order to integrate electronic health records data documented at multiple times in various data types and sources. Based on this methodology, we developed three models, logistic regression, Cox proportional hazard regression, and decision tree, to predict risk of patient falls and evaluate the predictive performance of these models by comparing the results to results from the Hendrich II Fall Risk Model. The findings of this study will be used in a clinical decision support system to predict risk of falling and provide evidence-based tailored recommendations in the future.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

BACKGROUND: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. OBJECTIVE: The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. METHODS: The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. RESULTS: We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, "academic stresses" and "suicide" contributed negatively to the sentiment of adolescent depression. CONCLUSIONS: The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.

Dementia Ontology Development to Facilitate Collection of High-Quality Dementia Data.

The population of dementia patients is on the rise, as society undergoes rapid aging. This led to an expansion of dementia-related data. This study aims to develop a comprehensive dementia ontology to facilitate the collection and analysis of high-quality dementia data. We followed an ontology building process from Ontology Development 101 and the content of the dementia ontology was validated by experts in dementia care and dementia-related research.

A SNOMED CT Mapping Guideline for the Local Terms Used to Document Clinical Findings and Procedures in Electronic Medical Records in South Korea: Methodological Study.

BACKGROUND: South Korea joined SNOMED International as the 39th member country. To ensure semantic interoperability, South Korea introduced SNOMED CT (Systemized Nomenclature of Medicine-Clinical Terms) in 2020. However, there is no methodology to map local Korean terms to SNOMED CT. Instead, this is performed sporadically and independently at each local medical institution. The quality of the mapping, therefore, cannot be guaranteed. OBJECTIVE: This study aimed to develop and introduce a guideline to map local Korean terms to the SNOMED CT used to document clinical findings and procedures in electronic health records at health care institutions in South Korea. METHODS: The guidelines were developed from December 2020 to December 2022. An extensive literature review was conducted. The overall structures and contents of the guidelines with diverse use cases were developed by referencing the existing SNOMED CT mapping guidelines, previous studies related to SNOMED CT mapping, and the experiences of the committee members. The developed guidelines were validated by a guideline review panel. RESULTS: The SNOMED CT mapping guidelines developed in this study recommended the following 9 steps: define the purpose and scope of the map, extract terms, preprocess source terms, preprocess source terms using clinical context, select a search term, use search strategies to find SNOMED CT concepts using a browser, classify mapping correlations, validate the map, and build the final map format. CONCLUSIONS: The guidelines developed in this study can support the standardized mapping of local Korean terms into SNOMED CT. Mapping specialists can use this guideline to improve the mapping quality performed at individual local medical institutions.

Impact of a Decision Support System on Fall-Prevention Nursing Practices.

OBJECTIVES: The aim of this study was to develop a computerized decision support system (CDSS) that could automatically calculate the risk of falls using electronic medical record data and provide evidence-based fall-prevention recommendations based on risk factors. Furthermore, we analyzed the usability and effect of the system on fall-prevention nursing practices. METHODS: A computerized fall-prevention system was developed according to the system development life cycle, and implemented between March and August 2019 in a single medical unit with a high prevalence of falls. The usability was evaluated 1 month after CDSS implementation. In terms of time and frequency, changes in fall-prevention nursing practices were analyzed using survey data and nursing documentation, respectively. Finally, the incidence of falls before and after system implementation was compared to examine the clinical effectiveness of the CDSS. RESULTS: According to the usability test, the average ease of learning score (5.083 of 7) was the highest among 4 dimensions. The time spent engaged in fall-prevention nursing care per patient per shift increased, particularly for nursing diagnoses and planning. Moreover, the mean frequency of daily documented fall-prevention interventions per patient also increased. Particularly, nursing statements related to nonspecific interventions such as environmental modifications increased. However, the incidence of falls did not decrease after implementation of the CDSS. CONCLUSIONS: Although adoption of the computerized system increased the time spent and number of records created in terms of fall-prevention practices in nurses, no improvement in clinical outcomes was observed, particularly in terms of fall rate reduction.

Mapping Korean National Health Insurance Claim Codes for Laboratory Test to SNOMED CT.

The aim of this study was to map Korean national health insurance claims codes for laboratory tests to SNOMED CT. The mapping source codes were 4,111 claims codes for laboratory test and mapping target codes were the International Edition of SNOMED CT released on July 31, 2020. We used rule-based automated and manual mapping methods. The mapping results were validated by two experts. Out of 4,111 codes, 90.5% were mapped to the concepts of procedure hierarchy in SNOMED CT. Of them, 51.4% of the codes were exactly mapped to SNOMED CT concepts, and 34.8% of the codes were mapped to SNOMED CT concepts as one-to-one mapping.

Nurses' Burden of Elimination Care: Sequential Explanatory Mixed-Methods Design.

Background: Inpatients commonly experience problems with elimination due to incontinence, urinary retentions, and complications with indwelling catheters. Although elimination care (EC) is an important nursing area, few studies explore the burden of EC on nurses. Aim: To identify the burden on EC by analyzing nurses' opinions using sequential explanatory mixed method. Methods: This research was conducted using a sequential explanatory mixed-methods design. A total of 59 nurses at a tertiary hospital in South Korea participated in the study from January 1 to March 31, 2022. For quantitative analysis, information about number of delays of work due to EC, required time for serving bedpan or diaper changes, percentage of EC per shift, and percentage of patients who need EC was collected through a survey. For qualitative analysis, focus group interviews were conducted to identify factors that put a burden on EC. Important themes were derived by analyzing nurses' opinions on EC. Results: For nurses in intensive care units, general wards, and integrated nursing care wards, the number of work delays due to EC was 3.6 ± 1.5, 2.3 ± 1.2, and 4.8 ± 2.4 (p<0.01), respectively. The mean percentage of EC work out of total nursing tasks per shift was 36.2 ± 19.0, 29.3 ± 14.4, and 43.8 ± 14.1 (p=0.02), respectively. The mean percentage of patients requiring EC out of patients a nurse cares was 85.4 ± 16.6, 41.3 ± 26.1, and 58.8 ± 21.9 (p<0.01), respectively. Following qualitative analysis, four themes related to nurses' EC burden were derived: physical burden, frequent care needs, delay of other jobs due to EC, and complications. Among them, frequent care needs were found to be the primary factor requiring consideration to reduce nurses' burden. Conclusion: This research found that EC is one of the most burdensome tasks that nurses want to avoid. To alleviate their burden, effective EC protocol or smart medical devices assisting with EC should be developed.

Comparisons of Fall Prevention Activities Using Electronic Nursing Records: A Case-Control Study.

OBJECTIVE: The aim of this study was to compare the current fall prevention nursing practices with the evidence-based practices recommended in clinical practice guidelines according to the risk of falling and specific risk factors. METHODS: The standardized nursing statements of 12,277 patients were extracted from electronic nursing records and classified into groups according to the risk of falling and individual patients' specific risk factors. The mean frequencies of the fall prevention practices in 10 categories derived from clinical practice guidelines were compared among the groups. We additionally analyzed the differences in the mean frequencies of tailored fall prevention practices according to individual patients' specific risk factors. RESULTS: The nurses documented more fall prevention practices for patients at a high risk of falling and nonfallers than for patients at a low risk of falling and fallers. Specifically, the difference in nursing practices related to environmental modifications was largest between patients at a high risk of falling and those at a low risk of falling. There were also large differences in the nursing practices related to mental status, dizziness/vertigo, and mobility limitations between fallers and nonfallers. There was more documentation of tailored fall prevention practices related to mobility limitations for patient with mild lower limb weakness than for those with good power and balance. In contrast, patients with severe lower limb weakness had received fewer fall prevention practices related to mobility limitations. CONCLUSIONS: The present findings emphasize that individual risk-specific nursing interventions in addition to universal precautions are crucial for preventing falls among patients.

Effectiveness of the Use of Standardized Vocabularies on Epilepsy Patient Cohort Generation.

OBJECTIVES: This study investigated the effectiveness of using standardized vocabularies to generate epilepsy patient cohorts with local medical codes, SNOMED Clinical Terms (SNOMED CT), and International Classification of Diseases tenth revision (ICD-10)/Korean Classification of Diseases-7 (KCD-7). METHODS: We compared the granularity between SNOMED CT and ICD-10 for epilepsy by counting the number of SNOMED CT concepts mapped to one ICD-10 code. Next, we created epilepsy patient cohorts by selecting all patients who had at least one code included in the concept sets defined using each vocabulary. We set patient cohorts generated by local codes as the reference to evaluate the patient cohorts generated using SNOMED CT and ICD-10/KCD-7. We compared the number of patients, the prevalence of epilepsy, and the age distribution between patient cohorts by year. RESULTS: In terms of the cohort size, the match rate with the reference cohort was approximately 99.2% for SNOMED CT and 94.0% for ICD-10/KDC7. From 2010 to 2019, the mean prevalence of epilepsy defined using the local codes, SNOMED CT, and ICD-10/KCD-7 was 0.889%, 0.891% and 0.923%, respectively. The age distribution of epilepsy patients showed no significant difference between the cohorts defined using local codes or SNOMED CT, but the ICD-9/KCD-7-generated cohort showed a substantial gap in the age distribution of patients with epilepsy compared to the cohort generated using the local codes. CONCLUSIONS: The number and age distribution of patients were substantially different from the reference when we used ICD-10/KCD-7 codes, but not when we used SNOMED CT concepts. Therefore, SNOMED CT is more suitable for representing clinical ideas and conducting clinical studies than ICD-10/KCD-7.

Mapping Korean National Health Insurance Pharmaceutical Claim Codes to SNOMED CT.

The objective of this study was to map pharmaceutical claim codes to SNOMED CT and thereby facilitate multicenter collaborative research and improve semantic interoperability. The claim codes were mapped to SNOMED CT using rule-based automated and manual methods. The maps were internally validated by terminologists and a pharmacist. Finally, 80% of all claim codes were mapped to the concepts of Pharmaceutical/biologic product hierarchy in SNOMED CT. Of them, 50.6% of the codes were exactly mapped to one clinical drug branch concept.

Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's Common Data Model: Pilot Feasibility Study.

BACKGROUND: Falls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication. OBJECTIVE: The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods. METHODS: As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest). RESULTS: In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models. CONCLUSIONS: To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.

Strategies for Adopting and Implementing SNOMED CT in Korea.

OBJECTIVES: The objective of this study was to introduce the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), to describe use cases of SNOMED CT with the barriers and facilitators, and finally, to propose strategies for adopting and implementing SNOMED CT in Korea as a member of SNOMED International. METHODS: We reviewed a collection of SNOMED CT documents, such as introductory materials, practical guides, technical specifications, and reference materials provided by SNOMED International and the literature on SNOMED CT published by researchers to identify use cases of SNOMED CT with barriers and facilitators. We also surveyed the attendees of SNOMED CT education and training series offered by the Korea Human Resource Development Institute for Health and Welfare to identify perceived barriers to adopting SNOMED CT in Korea. RESULTS: We identified the barriers and facilitators to adopt SNOMED CT experienced by international terminology experts and prospective Korean users. They were related to governance and infrastructure, support services for use, education and training programs, use cases, and vendor capability to implement SNOMED CT. Based on these findings, we identified strategies for adopting and implementing SNOMED CT in Korea. They included the establishment of SNOMED CT management infrastructure, the development of SNOMED CT education and training programs for various user groups, the provision of support services for SNOMED CT use, and the development of SNOMED CT use cases. CONCLUSIONS: These strategies for the adoption and implementation of SNOMED CT need to be executed step by step.

Development and Evaluation of a Prototype CDSS for Fall Prevention.

We developed a prototype CDSS that 1) provides tailored recommendations by combining a fall-risk prediction model, patients data, and evidence from CPGs, and 2) helps nurses to plan nursing care and document their activities for fall prevention. The accuracy of rules in knowledge base and inference engine was verified using ten scenarios and heuristics of user interface evaluated by four experts. We are currently evaluating the effects of the system on nurses' workflow and patient outcomes.

Testing the Predictive Validity of the Hendrich II Fall Risk Model.

Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies.

OBJECTIVES: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. METHODS: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. RESULTS: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. CONCLUSIONS: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

Use of EHR Data to Identify Factors Affecting the Time to Fall.

Although there are many studies of falls occurring in a hospital setting, research on factors affecting time to fall after admission is scarce. It is important for nurses to identify factors contributing to an early fall so that they can pay particular attention to patients with such factors. In this study, patients who sustained a fall were extracted from an adverse event reporting system and narrative nursing records of those hospitalized between January 2015 and May 2016. We used the electronic health records of ten different data sources to extract fall-related variables; the data were integrated according to normalization criteria. Univariate and multiple linear regression analyses were used to identify factors influencing the time to fall from admission. About 49% of fallers fell within the first week after admission. A walking disorder, comorbid disease, intravenous therapy, and arterial lines were related to early falls.

2016 Year-in-Review of Clinical and Consumer Informatics: Analysis and Visualization of Keywords and Topics.

OBJECTIVES: The objective of this study was to review and visualize the medical informatics field over the previous 12 months according to the frequencies of keywords and topics in papers published in the top four journals in the field and in Healthcare Informatics Research (HIR), an official journal of the Korean Society of Medical Informatics. METHODS: A six-person team conducted an extensive review of the literature on clinical and consumer informatics. The literature was searched using keywords employed in the American Medical Informatics Association year-in-review process and organized into 14 topics used in that process. Data were analyzed using word clouds, social network analysis, and association rules. RESULTS: The literature search yielded 370 references and 1,123 unique keywords. 'Electronic Health Record' (EHR) (78.6%) was the most frequently appearing keyword in the articles published in the five studied journals, followed by 'telemedicine' (2.1%). EHR (37.6%) was also the most frequently studied topic area, followed by clinical informatics (12.0%). However, 'telemedicine' (17.0%) was the most frequently appearing keyword in articles published in HIR, followed by 'telecommunications' (4.5%). Telemedicine (47.1%) was the most frequently studied topic area, followed by EHR (14.7%). CONCLUSIONS: The study findings reflect the Korean government's efforts to introduce telemedicine into the Korean healthcare system and reactions to this from the stakeholders associated with telemedicine.

Development and Evaluation of an Adolescents' Depression Ontology for Analyzing Social Data.

This study aims to develop and evaluate an ontology for adolescents' depression to be used for collecting and analyzing social data. The ontology was developed according to the 'ontology development 101' methodology. Concepts were extracted from clinical practice guidelines and related literatures. The ontology is composed of five sub-ontologies which represent risk factors, sign and symptoms, measurement, diagnostic result and management care. The ontology was evaluated in four different ways: First, we examined the frequency of ontology concept appeared in social data; Second, the content coverage of ontology was evaluated by comparing ontology concepts with concepts extracted from the youth depression counseling records; Third, the structural and representational layer of the ontology were evaluated by 5 ontology and psychiatric nursing experts; Fourth, the scope of the ontology was examined by answering 59 competency questions. The ontology was improved by adding new concepts and synonyms and revising the level of structure.