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.

The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices

Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient's feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them.

Exploring Online Crowdfunding for Cancer-Related Costs Among LGBTQ+ (Lesbian, Gay, Bisexual, Transgender, Queer, Plus) Cancer Survivors: Integration of Community-Engaged and Technology-Based Methodologies

BACKGROUND

Cancer survivors frequently experience cancer-related financial burdens. The extent to which Lesbian, Gay, Bisexual, Transgender, Queer, Plus (LGBTQ+) populations experience cancer-related cost-coping behaviors such as crowdfunding is largely unknown, owing to a lack of sexual orientation and gender identity data collection and social stigma. Web-scraping has previously been used to evaluate inequities in online crowdfunding, but these methods alone do not adequately engage populations facing inequities.

OBJECTIVE

We describe the methodological process of integrating technology-based and community-engaged methods to explore the financial burden of cancer among LGBTQ+ individuals via online crowdfunding.

METHODS

To center the LGBTQ+ community, we followed community engagement guidelines by forming a study advisory board (SAB) of LGBTQ+ cancer survivors, caregivers, and professionals who were involved in every step of the research. SAB member engagement was tracked through quarterly SAB meeting attendance and an engagement survey. We then used web-scraping methods to extract a data set of online crowdfunding campaigns. The study team followed an integrated technology-based and community-engaged process to develop and refine term dictionaries for analyses. Term dictionaries were developed and refined in order to identify crowdfunding campaigns that were cancer- and LGBTQ+-related.

RESULTS

Advisory board engagement was high according to metrics of meeting attendance, meeting participation, and anonymous board feedback. In collaboration with the SAB, the term dictionaries were iteratively edited and refined. The LGBTQ+ term dictionary was developed by the study team, while the cancer term dictionary was refined from an existing dictionary. The advisory board and analytic team members manually coded against the term dictionary and performed quality checks until high confidence in correct classification was achieved using pairwise agreement. Through each phase of manual coding and quality checks, the advisory board identified more misclassified campaigns than the analytic team alone. When refining the LGBTQ+ term dictionary, the analytic team identified 11.8% misclassification while the SAB identified 20.7% misclassification. Once each term dictionary was finalized, the LGBTQ+ term dictionary resulted in a 95% pairwise agreement, while the cancer term dictionary resulted in an 89.2% pairwise agreement.

CONCLUSIONS

The classification tools developed by integrating community-engaged and technology-based methods were more accurate because of the equity-based approach of centering LGBTQ+ voices and their lived experiences. This exemplar suggests integrating community-engaged and technology-based methods to study inequities is highly feasible and has applications beyond LGBTQ+ financial burden research.

Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions

BACKGROUND

There is a flora of health care information models but no consensus on which to use. This leads to poor information sharing and duplicate modelling work. The amount and type of differences between models has, to our knowledge, not been evaluated.

OBJECTIVE

This work aims to explore how information structured with various information models differ in practice. Our hypothesis is that differences between information models are overestimated. This work will also assess the usability of competency questions as a method for evaluation of information models within health care.

METHODS

In this study, 4 information standards, 2 standards for secondary use, and 2 electronic health record systems were included as material. Competency questions were developed for a random selection of recommendations from a clinical guideline. The information needed to answer the competency questions was modelled according to each included information model, and the results were analyzed. Differences in structure and terminology were quantified for each combination of standards.

RESULTS

In this study, 36 competency questions were developed and answered. In general, similarities between the included information models were larger than the differences. The demarcation between information model and terminology was overall similar; on average, 45% of the included structures were identical between models. Choices of terminology differed within and between models; on average, 11% was usable in interaction with each other. The information models included in this study were able to represent most information required for answering the competency questions.

CONCLUSIONS

Different but same same; in practice, different information models structure much information in a similar fashion. To increase interoperability within and between systems, it is more important to move toward structuring information with any information model rather than finding or developing a perfect information model. Competency questions are a feasible way of evaluating how information models perform in practice.

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.

Systematized Nomenclature of Medicine-Clinical Terminology (SNOMED CT) Clinical Use Cases in the Context of Electronic Health Record Systems: Systematic Literature Review

BACKGROUND

The Systematized Medical Nomenclature for Medicine-Clinical Terminology (SNOMED CT) is a clinical terminology system that provides a standardized and scientifically validated way of representing clinical information captured by clinicians. It can be integrated into electronic health records (EHRs) to increase the possibilities for effective data use and ensure a better quality of documentation that supports continuity of care, thus enabling better quality in the care process. Even though SNOMED CT consists of extensively studied clinical terminology, previous research has repeatedly documented a lack of scientific evidence for SNOMED CT in the form of reported clinical use cases in electronic health record systems.

OBJECTIVE

The aim of this study was to explore evidence in previous literature reviews of clinical use cases of SNOMED CT integrated into EHR systems or other clinical applications during the last 5 years of continued development. The study sought to identify the main clinical use purposes, use phases, and key clinical benefits documented in SNOMED CT use cases.

METHODS

The Cochrane review protocol was applied for the study design. The application of the protocol was modified step-by-step to fit the research problem by first defining the search strategy, identifying the articles for the review by isolating the exclusion and inclusion criteria for assessing the search results, and lastly, evaluating and summarizing the review results.

RESULTS

In total, 17 research articles illustrating SNOMED CT clinical use cases were reviewed. The use purpose of SNOMED CT was documented in all the articles, with the terminology as a standard in EHR being the most common (8/17). The clinical use phase was documented in all the articles. The most common category of use phases was SNOMED CT in development (6/17). Core benefits achieved by applying SNOMED CT in a clinical context were identified by the researchers. These were related to terminology use outcomes, that is, to data quality in general or to enabling a consistent way of indexing, storing, retrieving, and aggregating clinical data (8/17). Additional benefits were linked to the productivity of coding or to advances in the quality and continuity of care.

CONCLUSIONS

While the SNOMED CT use categories were well supported by previous research, this review demonstrates that further systematic research on clinical use cases is needed to promote the scalability of the review results. To achieve the best out-of-use case reports, more emphasis is suggested on describing the contextual factors, such as the electronic health care system and the use of previous frameworks to enable comparability of results. A lesson to be drawn from our study is that SNOMED CT is essential for structuring clinical data; however, research is needed to gather more evidence of how SNOMED CT benefits clinical care and patient safety.

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

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.

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.

Rigor in electronic health record knowledge representation: Lessons learned from a SNOMED CT clinical content encoding exercise

Rigor in clinical knowledge representation is necessary foundation for meaningful interoperability, exchange and reuse of electronic health record (EHR) data. It is critical for clinicians to understand principles and implications of using clinical standards for knowledge representation within EHRs.

PURPOSE

To educate clinicians and students about knowledge representation and to evaluate their success of applying the manual lookups method for assigning Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concept identifiers using formally mapped concepts from the Omaha System interface terminology.

METHODS

Clinicians who were students in a doctoral nursing program conducted 21 lookups for Omaha System terms in publicly available SNOMED CT browsers. Lookups were deemed successful if results matched exactly with the corresponding code from the January 2013 SNOMED CT-Omaha System terminology cross-map.

RESULTS

Of the 21 manual lookups attempted, 12 (57.1%) were successful. Errors were due to semantic gaps differences in granularity and synonymy or partial term matching.

CONCLUSIONS

Achieving rigor in clinical knowledge representation across settings, vendors and health systems is a globally recognized challenge. Cross-maps have potential to improve rigor in SNOMED CT encoding of clinical data. Further research is needed to evaluate outcomes of using of terminology cross-maps to encode clinical terms with SNOMED CT concept identifiers based on interface terminologies.

Leveraging terminological resources for mapping between rare disease information sources

BACKGROUND

Rare disease information sources are incompletely and inconsistently cross-referenced to one another, making it difficult for information seekers to navigate across them. The development of such cross-references established manually by experts is generally labor intensive and costly.

OBJECTIVES

To develop an automatic mapping between two of the major rare diseases information sources, GARD and Orphanet, by leveraging terminological resources, especially the UMLS.

METHODS

We map the rare disease terms from Orphanet and ORDR to the UMLS. We use the UMLS as a pivot to bridge between the rare disease terminologies. We compare our results to a mapping obtained through manually established cross-references to OMIM.

RESULTS

Our mapping has a precision of 94%, a recall of 63% and an F1-score of 76%. Our automatic mapping should help facilitate the development of more complete and consistent cross-references between GARD and Orphanet, and is applicable to other rare disease information sources as well.

Maintaining a consistent big picture: meaningful use of a Web-based POC EHR system

OBJECTIVE

  To test the hypothesis that Hands-on Automated Nursing Data System (HANDS) "big picture summary" can be implemented uniformly across diverse settings, and result in positive registered nurse (RN) and plan of care (POC) data outcomes across time.

DESIGN

  In a longitudinal, multisite, full test study, a representative convenience sample of eight medical-surgical units from four hospitals (one university, two large community, and one small community) in one Midwestern state implemented the HANDS intervention for 24 (four units) or 12 (four units) months.

MEASUREMENTS

  (a) RN outcomes-percentage completing training, satisfaction with standardized terminologies, perception of HANDS usefulness, POC submission compliance rate. (b) POC data outcomes-validity (rate of optional changes/episode); reliability of terms and ratings; and volume of standardized data generated.

RESULTS

  One hundred percent of the RNs who worked on the eight study units successfully completed the required standardized training; all units selected participated for the entire 12- or 24-month designated period; compliance rates for POC entry at every patient hand-off were 78-92%; reliability coefficients for use of the standardized terms and ratings were moderately strong; the pattern of optional POC changes per episode declined but remained reasonable across time; and the nurses generated a database of 40,747 episodes of care.

LIMITATIONS

  Only RNs and medical-surgical units participated.

CONCLUSION

  It is possible to effectively standardize the capture and visualization of useful "big picture" healthcare information across diverse settings. Findings offer a viable alternative to the current practice of introducing new health information layers that ultimately increase the complexity and inconsistency of information for frontline users.

Standards for scalable clinical decision support: need, current and emerging standards, gaps, and proposal for progress

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS.

Policy agenda for the next decade: creating a path for graceful evolution and harmonized classifications and terminologies used for encoding health information in electronic environments

Health information management (HIM) professionals' involvement with disease classification and nomenclature in the United States can be traced back to the early 20th century. In 1914, Grace Whiting Myers, the founder of the association known today as the American Health Information Management Association (AHIMA), served on the Committee on Uniform Nomenclature, which developed a disease classification system based upon etiological groupings. The profession's expertise and leadership in the collection, classification, and reporting of health data has continued since then. For example, in the early 1960s, another HIM professional (a medical record librarian) served as the associate editor of the fifth edition of the Standard Nomenclature of Disease (SNDO), a forerunner of the widely used clinical terminology, Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT). During the same period in history, the medical record professionals working in hospitals throughout the country were responsible for manually collecting and reporting disease and procedure information from medical records using SNDO. Because coded data have played a pivotal role in the ability to record and share health information through the years, creating the appropriate policy framework for the graceful evolution and harmonization of classification systems and clinical terminologies is essential.

An ontology-based methodology for the migration of biomedical terminologies to electronic health records

Biomedical terminologies are focused on what is general, Electronic Health Records (EHRs) on what is particular, and it is commonly assumed that the step from the one to the other is unproblematic. We argue that this is not so, and that, if the EHR of the future is to fulfill its promise, then the foundations of both EHR architectures and biomedical terminologies need to be reconceived. We accordingly describe a new framework for the treatment of both generals and particulars in biomedical information systems that is designed: 1) to provide new opportunities for the sharing and management of data within and between healthcare institutions, 2) to facilitate interoperability among different terminology and record systems, and thereby 3) to allow new kinds of reasoning with biomedical data.

Automating terminological networks to link heterogeneous biomedical databases

As cross-disciplinary research escalates, researchers are facing the challenge of linking disparate biomedical databases that have been developed without common indexes. Manually indexing these large-scale databases is laborious and often impractical. Solutions involving mediating terminologies have been proposed, but coordination of terms from the databases of interest to these mediating terminologies is also laborious, and regular synchronization between indexes is an additional problem. In this study we describe a novel method of linking heterogeneous databases using terminology networks constructed with automated mapping methods. Linkage was established between two disparate biomedical databases (SNOMED-CT and HDG), using two relevant intermediating databases (UMLS and OMIM). One gold standard of 514 distinct matches is used as proof-of-principle. In conclusion, as hypothesized, 1) Manually curated pathways provide high precision, but offer low recall, 2) the automated terminology pathways can significantly increase recall at acceptable precision. Taken together, our conclusion may suggest the combined manual and automated terminology networks could offer recall and precision in an incremental manner