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.

Methods for development and application of data standards in an ontology-driven information model for measuring, managing, and computing social determinants of health for individuals, households, and communities evaluated through an example of asthma.

OBJECTIVE: To describe methods to approach application of data standards to integrate social determinants of health (SDoH) into EHRs through evaluation of a case of clinical decision support for pediatric asthma. MATERIALS AND METHODS: We identified a list of environmental factors important for managing pediatric asthma. We identified and integrated data from local outdoor air quality monitors with elements available from the clinic's EHR and self-reported indoor air quality questionnaire data. We assessed existing SDoH frameworks, assessment tools, and terminologies to identify representative data standards for these environmental SDoH measures. RESULTS: We found many-to-many relationships between the multiple framework domains, the environmental exposure measures collected, and existing standards. The majority of concepts did not accurately align with environmental exposure measurements. We propose an ontology-driven information framework methodology to apply standards for SDoH measurements to support measuring, managing, and computing SDoH data. DISCUSSION: To support methods of integrating SDoH data in the EHR via an ontology-driven information framework, a common SDoH ecosystem should be developed descriptively and prescriptively integrating framework domains, assessment tools, and standard ontologies to support future data sharing, aggregation, and interoperability. A hierarchical object-oriented information model should be adopted to manage SDoH to extend beyond patient-centered orientation of EHRs to orient to households and communities. CONCLUSION: SDoH data pose unique challenges and opportunities in collecting, measuring, and managing health information. Future work is needed to define data standards for implementing SDoH in a hierarchical, object-oriented information model representing multiple units of orientation including individuals, households, and communities.

The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology.

Biomedical research data reuse and sharing is essential for fostering research progress. To this aim, data producers need to master data management and reporting through standard and rich metadata, as encouraged by open data initiatives such as the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines. This helps data re-users to understand and reuse the shared data with confidence. Therefore, dedicated frameworks are required. The provenance reporting throughout a biomedical study lifecycle has been proposed as a way to increase confidence in data while reusing it. The Biomedical Study - Lifecycle Management (BMS-LM) data model has implemented provenance and lifecycle traceability for several multimodal-imaging techniques but this is not enough for data understanding while reusing it. Actually, in the large scope of biomedical research, a multitude of metadata sources, also called Knowledge Organization Systems (KOSs), are available for data annotation. In addition, data producers uses local terminologies or KOSs, containing vernacular terms for data reporting. The result is a set of heterogeneous KOSs (local and published) with different formats and levels of granularity. To manage the inherent heterogeneity, semantic interoperability is encouraged by the Research Data Management (RDM) community. Ontologies, and more specifically top ontologies such as BFO and DOLCE, make explicit the metadata semantics and enhance semantic interoperability. Based on the BMS-LM data model and the BFO top ontology, the BioMedical Study - Lifecycle Management (BMS-LM) core ontology is proposed together with an associated framework for semantic interoperability between heterogeneous KOSs. It is made of four ontological levels: top/core/domain/local and aims to build bridges between local and published KOSs. In this paper, the conversion of the BMS-LM data model to a core ontology is detailed. The implementation of its semantic interoperability in a specific domain context is explained and illustrated with examples from small animal preclinical research.

Kinmaking, progeneration, and ethnography.

Philosophers of biology and biologists themselves for the most part assume that the concept of kin is progenerative: what makes two individuals kin is a direct or indirect function of reproduction. Derivatively, kinship might likewise be presumed to be progenerative in nature. Yet a prominent view of kinship in contemporary cultural anthropology is a kind of constructivism or performativism that rejects such progenerativist views. This paper critically examines an influential line of thinking used to critique progenerativism and support performativism that cites cross-cultural diversity in what I will call kinmaking. I challenge several key assumptions made in moving from this appeal to ethnography to conclusions about kinship and progeneration, arguing that closer scrutiny of both the ethnographic record and inferences that draw on it in fact support progenerative views of kinmaking.

Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort.

From the start of the coronavirus disease 2019 (COVID-19) pandemic, researchers have looked to electronic health record (EHR) data as a way to study possible risk factors and outcomes. To ensure the validity and accuracy of research using these data, investigators need to be confident that the phenotypes they construct are reliable and accurate, reflecting the healthcare settings from which they are ascertained. We developed a COVID-19 registry at a single academic medical center and used data from March 1 to June 5, 2020 to assess differences in population-level characteristics in pandemic and non-pandemic years respectively. Median EHR length, previously shown to impact phenotype performance in type 2 diabetes, was significantly shorter in the SARS-CoV-2 positive group relative to a 2019 influenza tested group (median 3.1 years vs 8.7; Wilcoxon rank sum P = 1.3e-52). Using three phenotyping methods of increasing complexity (billing codes alone and domain-specific algorithms provided by an EHR vendor and clinical experts), common medical comorbidities were abstracted from COVID-19 EHRs, defined by the presence of a positive laboratory test (positive predictive value 100%, recall 93%). After combining performance data across phenotyping methods, we observed significantly lower false negative rates for those records billed for a comprehensive care visit (p = 4e-11) and those with complete demographics data recorded (p = 7e-5). In an early COVID-19 cohort, we found that phenotyping performance of nine common comorbidities was influenced by median EHR length, consistent with previous studies, as well as by data density, which can be measured using portable metrics including CPT codes. Here we present those challenges and potential solutions to creating deeply phenotyped, acute COVID-19 cohorts.

French FastContext: A publicly accessible system for detecting negation, temporality and experiencer in French clinical notes.

The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives: (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator: a publicly accessible Web service to annotate French biomedical text data (http://bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes.

Extending import detection algorithms for concept import from two to three biomedical terminologies.

BACKGROUND: While enrichment of terminologies can be achieved in different ways, filling gaps in the IS-A hierarchy backbone of a terminology appears especially promising. To avoid difficult manual inspection, we started a research program in 2014, investigating terminology densities, where the comparison of terminologies leads to the algorithmic discovery of potentially missing concepts in a target terminology. While candidate concepts have to be approved for import by an expert, the human effort is greatly reduced by algorithmic generation of candidates. In previous studies, a single source terminology was used with one target terminology. METHODS: In this paper, we are extending the algorithmic detection of "candidate concepts for import" from one source terminology to two source terminologies used in tandem. We show that the combination of two source terminologies relative to one target terminology leads to the discovery of candidate concepts for import that could not be found with the same "reliability" when comparing one source terminology alone to the target terminology. We investigate which triples of UMLS terminologies can be gainfully used for the described purpose and how many candidate concepts can be found for each individual triple of terminologies. RESULTS: The analysis revealed a specific configuration of concepts, overlapping two source and one target terminology, for which we coined the name "fire ladder" pattern. The three terminologies in this pattern are tied together by a kind of "transitivity." We provide a quantitative analysis of the discovered fire ladder patterns and we report on the inter-rater agreement concerning the decision of importing candidate concepts from source terminologies into the target terminology. We algorithmically identified 55 instances of the fire ladder pattern and two domain experts agreed on import for 39 instances. In total, 48 concepts were approved by at least one expert. In addition, 105 import candidate concepts from a single source terminology into the target terminology were also detected, as a "beneficial side-effect" of this method, increasing the cardinality of the result. CONCLUSION: We showed that pairs of biomedical source terminologies can be transitively chained to suggest possible imports of concepts into a target terminology.

Ontologies for Liver Diseases Representation: A Systematic Literature Review.

Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.

Alternative classification of identical concepts in different terminologies: Different ways to view the world.

In previous research, we have studied concepts that occur in pairs of medical terminologies and are known to be identical, because they have the same ID number in the Unified Medical Language System (UMLS). We observed that such concepts rarely have exactly the same sets of children (=subconcepts) in the two terminologies. The number of common children was found to vary widely. A special situation was identified where the children in one terminology relate to the common parent in a very different way than the children in the other terminology. For example, children in one terminology might subdivide a parent concept by anatomical location in one terminology and by disease kind in the other terminology. We coined the term "alternative classification" (of the same parent concept) for such situations. In previous work, only human experts could recognize alternative classifications. In this paper, we present a mathematically expressed criterion for likely cases of alternative classifications. We compare the recommendations of this criterion, expressed by a mathematical quantity called "EFI" becoming zero, with the decisions of a human expert. It is found that the human expert agreed with the criterion in 72% of all cases, which is a big improvement over having no computable criterion at all. Besides alternative classifications, common parent concepts in a pair of terminologies might also indicate a possible import of a child concept missing in one terminology, different granularities, or errors in either one of the two terminologies. In this paper, we further investigate different kinds of alternative classifications.

Quality criteria, instruments, and requirements for nursing documentation: A systematic review of systematic reviews.

AIM: To obtain an overview of existing evidence on quality criteria, instruments, and requirements for nursing documentation. DESIGN: Systematic review of systematic reviews. DATA SOURCES: We systematically searched the databases PubMed and CINAHL for the period 2007-April 2017. We also performed additional searches. REVIEW METHODS: Two reviewers independently selected the reviews using a stepwise procedure, assessed the methodological quality of the selected reviews, and extracted the data using a predefined extraction format. We performed descriptive synthesis. RESULTS: Eleven systematic reviews were included. Several quality criteria were described referring to the importance of following the nursing process and using standardized nursing terminologies. In addition, some evidence-based instruments were described for assessing the quality of nursing documentation, such as the D-Catch. Furthermore, several requirements for formats and systems of electronic nursing documentation were found that refer to the importance of user-friendliness and development in consultation with nursing staff. CONCLUSION: Aligning documentation with the nursing process, using standard terminologies, and using user-friendly formats and systems appear to be important for high-quality nursing documentation. The lack of evidence-based quality indicators presents a challenge in the pursuit of high-quality nursing documentation. IMPACT: There is uncertainty in nursing practice about which criteria have to be met to achieve high-quality documentation. Aligning documentation with the nursing process, using standard terminologies, and using user-friendly formats and systems appear to be important. These findings can help nursing staff and care organizations enhance the quality of nursing documentation.

Terminology, units and reporting - how harmonized do we need to be?

Harmonization initiatives in laboratory medicine seek to eliminate or reduce illogical variations in service to patients, clinicians and other healthcare professionals. Significant effort will be required to achieve consistent application of terminology, units and reporting across laboratory testing providers. Current variations in practice for nomenclature, reference intervals, flagging, units, standardization and traceability between analytical methods, and presentation of cumulative result data are inefficient and inconvenient, or worse yet, patient safety risks. All aspects of laboratory service across the "total testing process" ultimately depend on concise, reliable communication. Clinical terminologies (e.g. SNOMED-CT, LOINC, IFCC/IUPAC NPU) provide a mechanism to correctly identify an analyte or panel of tests within a request for testing and communicate the results back to the clinician or electronic health record (EHR). Electronic systems for requesting and reporting laboratory testing are said to be interoperable when reliable connection and communication of content occur. Modern electronic reports and EHRs will provide greater flexibility and functionality, but also require effective guidelines or standards to ensure consistent representation of laboratory data. Programs to harmonize service in these areas require ongoing local, national and international efforts and should incorporate stakeholders from laboratories, medical staff, information technology and informatics specialists, patient representatives and government. The process of identifying harmonized best practice, then ensuring uptake across many laboratory testing providers, is generally iterative rather than "one off". New opportunities for additional harmonization will be generated as analytical performance, standardization and traceability, and diagnosis and treatment continue to evolve.

The International Glossary on Infertility and Fertility Care, 2017.

STUDY QUESTION: Can a consensus and evidence-driven set of terms and definitions be generated to be used globally in order to ensure consistency when reporting on infertility issues and fertility care interventions, as well as to harmonize communication among the medical and scientific communities, policy-makers, and lay public including individuals and couples experiencing fertility problems? SUMMARY ANSWER: A set of 283 consensus-based and evidence-driven terminologies used in infertility and fertility care has been generated through an inclusive consensus-based process with multiple stakeholders. WHAT IS KNOWN ALREADY: In 2006 the International Committee for Monitoring Assisted Reproductive Technologies (ICMART) published a first glossary of 53 terms and definitions. In 2009 ICMART together with WHO published a revised version expanded to 87 terms, which defined infertility as a disease of the reproductive system, and increased standardization of fertility treatment terminology. Since 2009, limitations were identified in several areas and enhancements were suggested for the glossary, especially concerning male factor, demography, epidemiology and public health issues. STUDY DESIGN, SIZE, DURATION: Twenty-five professionals, from all parts of the world and representing their expertise in a variety of sub-specialties, were organized into five working groups: clinical definitions; outcome measurements; embryology laboratory; clinical and laboratory andrology; and epidemiology and public health. Assessment for revisions, as well as expansion on topics not covered by the previous glossary, were undertaken. A larger group of independent experts and representatives from collaborating organizations further discussed and assisted in refining all terms and definitions. PARTICIPANTS/MATERIALS, SETTING, METHODS: Members of the working groups and glossary co-ordinators interacted through electronic mail and face-to-face in international/regional conferences. Two formal meetings were held in Geneva, Switzerland, with a final consensus meeting including independent experts as well as observers and representatives of international/regional scientific and patient organizations. MAIN RESULTS AND THE ROLE OF CHANCE: A consensus-based and evidence-driven set of 283 terminologies used in infertility and fertility care was generated to harmonize communication among health professionals and scientists as well as the lay public, patients and policy makers. Definitions such as 'fertility care' and 'fertility awareness' together with terminologies used in embryology and andrology have been introduced in the glossary for the first time. Furthermore, the definition of 'infertility' has been expanded in order to cover a wider spectrum of conditions affecting the capacity of individuals and couples to reproduce. The definition of infertility remains as a disease characterized by the failure to establish a clinical pregnancy; however, it also acknowledges that the failure to become pregnant does not always result from a disease, and therefore introduces the concept of an impairment of function which can lead to a disability. Additionally, subfertility is now redundant, being replaced by the term infertility so as to standardize the definition and avoid confusion. LIMITATIONS, REASONS FOR CAUTION: All stakeholders agreed to the vast majority of terminologies included in this glossary. In cases where disagreements were not resolved, the final decision was reached after a vote, defined before the meeting as consensus if passed with 75%. Over the following months, an external expert group, which included representatives from non-governmental organizations, reviewed and provided final feedback on the glossary. WIDER IMPLICATIONS OF THE FINDINGS: Some terminologies have different definitions, depending on the area of medicine, for example demographic or clinical as well as geographic differences. These differences were taken into account and this glossary represents a multinational effort to harmonize terminologies that should be used worldwide. STUDY FUNDING/COMPETING INTERESTS: None. TRIAL REGISTRATION NUMBER: N/A.

Integrating cancer diagnosis terminologies based on logical definitions of SNOMED CT concepts.

In oncology, the reuse of data is confronted with the heterogeneity of terminologies. It is necessary to semantically integrate these distinct terminologies. The semantic integration by using a third terminology as a support is a conventional approach for the integration of two terminologies that are not very structured. The aim of our study was to use SNOMED CT for integrating ICD-10 and ICD-O3. We used two complementary resources, mapping tables provided by SNOMED CT and the NCI Metathesaurus, in order to find mappings between ICD-10 or ICD-O3 concepts and SNOMED CT concepts. We used the SNOMED CT structure to filter inconsistent mappings, as well as to disambiguate multiple mappings. Based on the remaining mappings, we used semantic relations from SNOMED CT to establish links between ICD-10 and ICD-O3. Overall, the coverage of ICD-O3 and ICD10 codes was over 88%. Finally, we obtained an integration of 24% (203/852) of ICD-10 concepts with 86% (888/1032) of ICD-O3 morphology concepts combined to 39% (127/330) of ICD-O3 topography concepts. Comparing our results with the 23,684 ICD-O3 pairs mapped to ICD-10 concepts in the SEER conversion file, we found 17,447 pairs of ICD-O3 concepts in common among which 11,932 pairs were integrated with the same ICD-10 concept as the SEER conversion file. The automated process leverages logical definitions of SNOMED CT concepts. While the low quality of some of these definitions impacted negatively the integration process, the identification of such situations made it possible to indirectly audit the structure of SNOMED CT.

Describing pediatric hospital discharge planning care processes using the Omaha System.

PURPOSE: Although discharge planning (DP) is recognized as a critical component of hospital care, national initiatives have focused on older adults, with limited focus on pediatric patients. We aimed to describe patient problems and targeted interventions as documented by social workers or DP nurses providing specialized DP services in a children's hospital. METHODS: Text from 67 clinical notes for 28 patients was mapped to a standardized terminology (Omaha System). Data were deductively analyzed. RESULTS: A total of 517 phrases were mapped. Eleven of the 42 Omaha System problems were identified. The most frequent problem was health care supervision (297/517; 57.4%). Three Omaha System intervention categories were used (teaching, guidance, and counseling; case management; and surveillance). Intervention targets are varied by role. CONCLUSION: The findings provide a rich description of the nature of DP for complex pediatric patients and increase our understanding of the work of DP staff and the influence of the DP practice model.

Resilience: synthesis of concept analyses and contribution to nursing classifications.

AIM: To synthetize elements of research on concept analyses of resilience; and to propose a definition of resilience to NANDA International (NANDA-I) and International Classification for Nursing Practice (ICNP(®) ). BACKGROUND: Nursing classifications represent nursing knowledge that is clinically relevant and in continuous development. INTRODUCTION: Nursing is considered holistic and there is always the need to consider human responses to health and life processes. Continuously there are new and emerging concepts concerning humans and health that should be considered within the organization and delivery of health care as resilience. METHODS: In March 2015, the authors performed a synthesis of the findings of research concept analysis research derived from a systematic literature search of electronic databases (PsycINFO, CINAHL and PubMed). Search terms used in the title were: 'resilience' AND 'analysis' AND 'concept'. Papers written in Portuguese or English were included. FINDINGS: A total of 27 papers were identified and eight papers were included. Of these, seven papers used Walker & Avant's () model and one used Rodger's () model. Resilience emerged as a fundamental concept across the lifespan that is closely related to health and well-being. DISCUSSION: Resilience is a phenomenon of interest for nurses. Similar attributes, antecedents, consequents and definitions were synthesized into a new proposal of a definition of resilience. While some diagnoses related to resilience are classified in NANDA-I, the concept is not included in ICNP(®) . CONCLUSION AND IMPLICATIONS FOR NURSING AND HEALTH POLICY: The inclusion of the concept into ICNP(®) and the update in NANDA-I is a contribution for nursing knowledge through education and for clinical practice, as this could promote the effectiveness of interventions in several contexts.

Evolution of natural disaster terminologies, with a case study of the covid-19 pandemic.

Disaster, catastrophe, and cataclysm are some English terminologies that describe the severity of adverse events. Civilians, reporters, and professionals often use these terminologies to communicate and report any event's severity. This linguistic method is the most practical way to rapidly reach all levels of local/regional/national, and international stakeholders during disasters. Therefore, disaster terminologies play a significant role in disaster management. However, attaining the actual magnitude of a disaster's severity cannot be comprehended simply by using these terminologies because they are used interchangeably. Unfortunately, there is no consistent method to differentiate disaster terminologies from one another. Additionally, no globally accepted standard technique exists to communicate the severity level when disasters strike; one observer's 'disaster' can be another's 'catastrophe'. Hence, a nation's ability to manage extreme events is difficult when there are no agreed terminologies among emergency management systems. A standard severity classification system is required to understand, communicate, report, and educate stakeholders. This paper presents perceptions of people about disaster terminologies in different geographical regions, rankings and differences in disaster lexical and lexicon. It explores how people perceive major events (e.g., the Covid-19 pandemic), and proposes a ranking of disaster terminologies to create a severity classification system suitable for global use.

An example of leveraging AI for documentation: ChatGPT-generated nursing care plan for an older adult with lung cancer.

OBJECTIVE: Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer. METHOD: This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT. RESULTS: While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs. CONCLUSION: Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.

Integrating a Categorial Structure for Clinical Practice into EHRs.

A continuing global desire to be using clinical systems within a digital health ecosystem, able to facilitate data flows and information exchange as required to support person-centred, predictive, preventative, participatory and precision (5p) health and medical care can best be supported through the use of the standard categorial structure able to represent not only the clinical nursing practice domain but also other clinical disciplines by the generic labelling of some high-level categories. It is hypothesised that adoption of this generic clinical categorial structure within any electronic health/medical record within a well connected digital health ecosystem, supported by a cloud based openEHR platform, will enable the 5p support to be realized. This presentation provides the results of the latest update of this technical standard based on the 20+ year nursing practice categorial structure development process adopted to achieve this aim and a summary about linking this categorial structure to standard terminologies and to standard EHR/EMR system architectures.

Promoting interoperability between SNOMED CT and ICD-11: lessons learned from the pilot project mapping between SNOMED CT and the ICD-11 Foundation.

OBJECTIVE: To explore the feasibility and challenges of mapping between SNOMED CT and the ICD-11 Foundation in both directions, SNOMED International and the World Health Organization conducted a pilot mapping project between September 2021 and August 2022. MATERIALS AND METHODS: Phase 1 mapped ICD-11 Foundation entities from the endocrine diseases chapter, excluding malignant neoplasms, to SNOMED CT. In phase 2, SNOMED CT concepts equivalent to those covered by the ICD-11 entities in phase 1 were mapped to the ICD-11 Foundation. The goal was to identify equivalence between an ICD-11 Foundation entity and a SNOMED CT concept. Postcoordination was used for mapping to ICD-11. Each map was done twice independently, the results were compared, and discrepancies were reconciled. RESULTS: In phase 1, 59% of 637 ICD-11 Foundation entities had an exact match in SNOMED CT. In phase 2, 32% of 1893 SNOMED CT concepts had an exact match in the ICD-11 Foundation, and postcoordination added 15% of exact match. Challenges encountered included non-synonymous synonyms, mismatch in granularity, composite conditions, and residual categories. CONCLUSION: This pilot project shed light on the tremendous amount of effort required to create a map between the 2 coding systems and uncovered some common challenges. Future collaborative work between SNOMED International and WHO will likely benefit from its findings. It is recommended that the 2 organizations should clarify goals and use cases of mapping, provide adequate resources, set up a road map, and reconsider their original proposal of incorporating SNOMED CT into the ICD-11 Foundation ontology.

Exploring the nexus between the standardized nursing terminologies and the unfinished nursing care phenomenon: An empty systematic review.

PURPOSE: To identify and synthesize evidence regarding the documented relationship between the standardized nursing terminologies and the unfinished nursing care phenomenon. DATA SOURCES: A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Scopus, and Cumulative Index to Nursing and Allied Health Literature Complete databases were last consulted on November 27, 2023. The review included primary quantitative studies that reported an association between recognized standardized nursing terminologies and unfinished nursing care. Two researchers completedtitle and abstract and full-text screening. DATA SYNTHESIS: Our search identified 149 citations. A full-text review of one paper was undertaken. No studies met our inclusion criteria. We report an empty review. CONCLUSIONS: Standardized nursing terminologies and Unfinished Care are two sides of the same coin: despite their potential commonalities, no studies have documented their potential links. Digital systems, such as electronic health records and decision support systems, could foster this linkage. IMPLICATIONS FOR NURSING PRACTICE: This review suggests that linking the conceptual frameworks can promote the diffusion of standardized nursing terminologies in clinical practice and increase accuracy in the measurement of Unfinished Care. This synergy could promote the contribution of nursing knowledge to patient care, nursing visibility, and be beneficial to clinical nurses, managers, and healthcare systems to international level.