The Role of HL7 FHIR in the European Project GATEKEEPER

The European Project GATEKEEPER aims to develop a platform and marketplace to ensure a healthier independent life for the aging population. In this platform the role of HL7 FHIR is to provide a shared logical data model to collect data in heterogeneous living, which can be used by AI Service and the Gatekeeper HL7 FHIR Implementation Guide was created for this purpose. Independent pilots used this IG and illustrate the impact of the approach, benefit, value, and scalability.

Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11

Purpose

Widespread electronic health record adoption has generated a large volume of data and emphasized the need for standardized terminology to describe clinical concepts. Here, we undertook a systematic concept coverage analysis to determine the representation of clinical concepts in ophthalmic infection and ophthalmic trauma among standardized medical terminologies, including the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), the International Classification of Diseases (ICD) version 10 with clinical modifications (ICD-10-CM), and ICD version 11 (ICD-11).

Design

Extraction of concepts related to ophthalmic infection and ophthalmic trauma and structured search in terminology browsers.

Data Sources

The American Academy of Ophthalmology Basic and Clinical Science Course (BCSC), SNOMED-CT, and ICD-10-CM terminologies from the Observational Health Data Sciences and Informatics Athena browser, and the ICD-11 terminology browser.

Methods

Concepts pertaining to ophthalmic infection and ophthalmic trauma were extracted from the 2022 BCSC free text and index terms. We searched terminology browsers to identify corresponding codes and classified the extent of semantic alignment as equal, wide, narrow, or unmatched in each terminology. The overlap of equal concepts in each terminology was represented in a Venn diagram.

Main Outcome Measures

Proportions of clinical concepts with corresponding codes at various levels of semantic alignment.

Results

A total of 443 concepts were identified: 304 concepts related to ophthalmic infection and 139 concepts related to ophthalmic trauma. The SNOMED-CT had the highest proportion of equal coverage, with 82.0% (249 of 304) among concepts related to ophthalmic infection and 82.0% (115 of 139) among concepts related to ophthalmic trauma. Across all concepts, 28% (124 of 443) were classified as equal in ICD-10-CM and 52.8% (234 of 443) were classified as equal in ICD-11.

Conclusions

The SNOMED-CT had significantly better semantic alignment than ICD-10-CM and ICD-11 for ophthalmic infections and ophthalmic trauma. This demonstrates opportunity for continuing advancement of representation of ophthalmic concepts in standardized medical terminologies.

Experience from the Development of HL7 FHIR IG for Gatekeeper Project

The GATEKEEPER (GK) Project was financed by the European Commission to develop a platform and marketplace to share and match ideas, technologies, user needs and processes to ensure a healthier independent life for the aging population connecting all the actors involved in the care circle. In this paper, the GK platform architecture is presented focusing on the role of HL7 FHIR to provide a shared logical data model to be explored in heterogeneous daily living environments. GK pilots are used to illustrate the impact of the approach, benefit value, and scalability, suggesting ways to further accelerate progress.

In support of interoperability: A laboratory perspective

Healthcare in the United States has become increasingly digital since the passage of the HITECH Act in 2009. As a result, there is a growing need to optimize healthcare IT to allow for the interoperable exchange of data. As a result, the Office of the National Coordinator for Health IT has implemented their Final Rule for the 21st Century Cures Act. This requires certified health IT systems to use modernized messaging standards for the safe and secure exchange of data within health information networks and also requires the use of terminology standards including LOINC, SNOMED CT, and UCUM for coding clinical and laboratory data. Given the critical importance of laboratory results in the delivery of healthcare, laboratorians must become familiar with these principles of interoperability. Their clinical laboratory expertise is needed to appropriately structure and code test results to safeguard against improper aggregation or misinterpretation by downstream users and systems.

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.

Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary

Objectives

The Charlson comorbidity index (CCI), the most ubiquitous comorbid risk score, predicts one-year mortality among hospitalized patients and provides a single aggregate measure of patient comorbidity. The Quan adaptation of the CCI revised the CCI coding algorithm for applications to administrative claims data using the International Classification of Diseases (ICD). The purpose of the current study is to adapt and validate a coding algorithm for the CCI using the SNOMED CT standardized vocabulary, one of the most commonly used vocabularies for data collection in healthcare databases in the U.S.

Methods

The SNOMED CT coding algorithm for the CCI was adapted through the direct translation of the Quan coding algorithms followed by manual curation by clinical experts. The performance of the SNOMED CT and Quan coding algorithms were compared in the context of a retrospective cohort study of inpatient visits occurring during the calendar years of 2013 and 2018 contained in two U.S. administrative claims databases. Differences in the CCI or frequency of individual comorbid conditions were assessed using standardized mean differences (SMD). Performance in predicting one-year mortality among hospitalized patients was measured based on the c-statistic of logistic regression models.

Results

For each database and calendar year combination, no significant differences in the CCI or frequency of individual comorbid conditions were observed between vocabularies (SMD ≤ 0.10). Specifically, the difference in CCI measured using the SNOMED CT vs. Quan coding algorithms was highest in MDCD in 2013 (3.75 vs. 3.6; SMD = 0.03) and lowest in DOD in 2018 (3.93 vs. 3.86; SMD = 0.02). Similarly, as indicated by the c-statistic, there was no evidence of a difference in the performance between coding algorithms in predicting one-year mortality (SNOMED CT vs. Quan coding algorithms, range: 0.725–0.789 vs. 0.723–0.787, respectively). A total of 700 of 5,348 (13.1%) ICD code mappings were inconsistent between coding algorithms. The most common cause of discrepant codes was multiple ICD codes mapping to a SNOMED CT code (n = 560) of which 213 were deemed clinically relevant thereby leading to information gain.

Conclusion

The current study repurposed an important tool for conducting observational research to use the SNOMED CT standardized vocabulary.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02006-1.

Building a Comprehensive Clinical Data Repository Using FHIR, LOINC and SNOMED

In 2018 the University Hospital of Giessen (UHG) moved its hospital information system from an in-house solution to commercial software. The introduction of MEONA and Synedra-AIM allowed for the successful migration of clinical documents. The large pool of structured clinical data has been addressed in a second step and is now consolidated in a HAPI-FHIR server and mapped to LOINC and SNOMED for semantic interoperability in multicenter research projects, especially the German Medical Informatics Initiative (MII) and the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium.

Responding to COVID-19 with real-time general practice data in Australia

INTRODUCTION

As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies.

METHODS

The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures.

RESULTS

Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement.

CONCLUSIONS

The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response.

Mapping of Wound Infection Concepts

Wound infection is a serious health care complication. Standardized clinical terminologies could be leveraged to support the early identification of wound infection. The purpose of this study was to evaluate the representation of wound infection assessment and diagnosis concepts (N=26) in SNOMED CT and ICNP, using a synthesized procedural framework. A total of 13/26 (50%) assessment and diagnosis concepts had exact matches in SNOMED CT and 2/7 (29%) diagnosis concepts had exact matches in ICNP. This study demonstrated that the source concepts were moderately well represented in SNOMED CT and ICNP; however, further work is necessary to increase the representation of diagnostic infection types. The use of the framework facilitated a systematic, transparent, and repeatable mapping process, with opportunity to extend.

Using Systematized Nomenclature of Medicine clinical term codes to assign histological findings for prostate biopsies in the Gauteng province, South Africa: Lessons learnt

Background

Prostate cancer (PCa) is a leading male neoplasm in South Africa.

Objective

The aim of our study was to describe PCa using Systemized Nomenclature of Medicine (SNOMED) clinical terms codes, which have the potential to generate more timely data.

Methods

The retrospective study design was used to analyse prostate biopsy data from our laboratories using SNOMED morphology (M) and topography (T) codes where the term ’prostate’ was captured in the narrative report. Using M code descriptions, the diagnosis, sub-diagnosis, sub-result and International Classification of Diseases for Oncology (ICD-O-3) codes were assigned using a lookup table. Topography code descriptions identified biopsies of prostatic origin. Lookup tables were prepared using Microsoft Excel and combined with the data extracts using Access. Contingency tables reported M and T codes, diagnosis and sub-diagnosis frequencies.

Results

An M and T code was reported for 88% (n = 22 009) of biopsies. Of these, 20 551 (93.37%) were of prostatic origin. A benign diagnosis (ICD-O-3:8000/0) was reported for 10 441 biopsies (50.81%) and 45.26% had a malignant diagnosis (n = 9302). An adenocarcinoma (8140/3) sub-diagnosis was reported for 88.16% of malignant biopsies (n = 8201). An atypia diagnosis was reported for 760 biopsies (3.7%). Inflammation (39.03%) and hyperplasia (20.82%) were the predominant benign sub-diagnoses.

Conclusion

Our study demonstrated the feasibility of generating PCa data using SNOMED codes from national laboratory data. This highlights the need for extending the results of our study to a national level to deliver timeous monitoring of PCa trends.

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT

An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM), the Radiological Society of North America's radiology lexicon (RadLex), and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RGO (version 0.7; Jan 2018) incorporated 16,918 terms (classes) for diseases, interventions, and imaging observations linked by 1782 subsumption (class-subclass) relations and 55,569 causal ("may cause") relations. RGO classes were mapped to RadLex (46,656 classes, version 3.15), SNOMED CT (347,358 classes, version 2018AA), and ICD-10-CM (94,645 classes, version 2018AA) using the National Center for Biomedical Ontology (NCBO) Annotator web service. We identified 1275 exact mappings from RGO to RadLex, 5302 to SNOMED CT, and 941 to ICD-10-CM. RGO terms mapped to one ontology (n = 3401), two ontologies (n = 1515), or all three ontologies (n = 198). The mapped ontologies provide additional terms to support data mining from textual information in the electronic health record. The current work builds on efforts to map RGO to ontologies of diseases and phenotypes. Mappings between ontologies can support automated knowledge discovery, diagnostic reasoning, and data mining.

Argentinian Digital Health Strategy

Digital Health is one of the three pillars for the effective implementation of Universal Health Coverage in Argentina. The Ministry of Health published the National Digital Health Strategy 2018-2024 in order to establish the conceptual guidelines for the design and development of interoperable health information systems as a state policy. The World Health Organization "National eHealth Strategy Toolkit", "Global Strategy on Digital Health" and other international and local evidence and expert recommendations were taken into account. The path to better healthcare involves adopting systems at the point of care, allowing for the primary recording of information and enabling information exchange through real interoperability. In that way, people, technology and processes will synergize to enhance integrated health service networks. In this paper, we describe the plan and the first two years of implementation of the strategy.

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.

Linking Health Records with Knowledge Sources Using OWL and RDF

This paper describes a method by which the Web Ontology Language (OWL) can be used to specify a highly structured health record, following internationally recognised standards such as ISO 13606 and HL7 CDA. The structured record is coded using schemes such as SNOMED, ICD or LOINC, with the coding applied statically, on the basis of the predefined structure, or dynamically, on the basis of data values entered in the health record. The highly structured, coded record can then be linked with external knowledge sources which are themselves coded using the Resource Description Framework. These methods have been used to implement dynamic decision support in the open source cityEHR health records system. The effectiveness of the decision support depends on the scope and quality of the clinical coding and the sophistication of the algorithm used to match the structured record with knowledge sources.

How well can familial hypercholesterolemia be identified in an electronic health record database?

Background

Familial hypercholesterolemia (FH) is a condition characterized by high cholesterol levels and increased risk for coronary heart disease (CHD) that often goes undiagnosed. The Dutch Lipid Network Criteria (DLNC) are used to identify FH in clinical settings via physical examination, personal and family history of CHD, in addition to the presence of deleterious mutations of the LDLR, ApoB, and PCSK9 genes. Agreement between clinical and genetic diagnosis of FH varies. While an ICD diagnosis code was not available for coding FH until 2016, Systematized Nomenclature of Medicine (SNOMED) clinical concept codes, including genetic diagnoses, for FH have been utilized in electronic health records (EHRs).

Objective

To evaluate the concordance of identifying FH via SNOMED and ICD-10 CM codes vs the DLNC in an EHR database.

Methods

Using the Practice Fusion EHR database, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated comparing an FH cohort identified via SNOMED and ICD-10 CM codes to one identified via the DLNC.

Results

Among 907,616 patients with hypercholesterolemia, 2,180 were identified as FH via SNOMED code (zero were identified via ICD-10 CM), 259 had a DLNC score 6–8 (probable FH), and 45 had a DLNC score >8 (definite FH). Compared to DLNC score >8, the sensitivity, specificity, and PPV of the FH SNOMED code were 84.4%, 99.4%, and 6.4%, respectively. Compared to DLNC score ≥6, the sensitivity was 36.8% and the specificity was 99.5% with a PPV of 18.7%.

Conclusion

Compared to the clinical criteria for FH, identification of FH patients via SNOMED diagnosis codes had high sensitivity and specificity, but low PPV. The discordance of these two techniques in identifying FH patients speaks to the challenges in identifying FH patients in large electronic databases such as administrative claims and EHR.

Pheochromocytoma in Denmark during 1977-2016: validating diagnosis codes and creating a national cohort using patterns of health registrations

Background

Pheochromocytoma and catecholamine-secreting paraganglioma (PPGL) are rare but potentially life-threatening tumors. We aimed to validate diagnosis codes for PPGL in the Danish National Patient Registry, the Danish National Pathology Registry, and the Danish Registry of Causes of Death and to create a national cohort of incident PPGL patients by linking these three registries.

Patients and methods

We obtained data from the three abovementioned registries for all individuals registered with pheochromocytoma or catecholamine hypersecretion in Denmark during 1977–2016 (average population 5.30 million). We then reviewed health records for all individuals living in the North Denmark Region and Central Denmark Region (average population 1.75 million) to validate the diagnosis of PPGL. We tested a number of algorithms for accurately identifying true cases of PPGL to maximize positive predictive values (PPVs) and completeness. The best algorithm was subsequently validated in an external sample.

Results

We identified 2626 individuals with a PPGL diagnosis code in Denmark, including 787 (30.0%) in the North Denmark Region and Central Denmark Region. In this subsample, we retrieved the health records of 771/787 (98.0%) individuals and confirmed 198 incident PPGL patients (25.3%). The PPV of PPGL diagnosis codes was 21.7% in the Danish National Patient Registry, 50.0% in the Danish Registry of Causes of Death, and 79.5% in the Danish National Pathology Registry. By combining patterns of registrations in the three registries, we could increase the PPV to 93.1% (95% confidence interval [CI]: 88.5–96.3) and completeness to 88.9% (95% CI: 83.7–92.9), thus creating a national PPGL cohort of 588 patients. PPV for the optimal algorithm was 95.3% (95% CI: 88.5–98.7) in the external validation sample.

Conclusion

Diagnosis codes for pheochromocytoma had low PPV in several individual health registries. However, with a combination of registries we were able to identify a near-complete national cohort of PPGL patients in Denmark, as a valuable source for epidemiological research.

A review of medical terminology standards and structured reporting

Much effort has been invested in standardizing medical terminology for representation of medical knowledge, storage in electronic medical records, retrieval, reuse for evidence-based decision making, and for efficient messaging between users. We only focus on those efforts related to the representation of clinical medical knowledge required for capturing diagnoses and findings from a wide range of general to specialty clinical perspectives (e.g., internists to pathologists). Standardized medical terminology and the usage of structured reporting have been shown to improve the usage of medical information in secondary activities, such as research, public health, and case studies. The impact of standardization and structured reporting is not limited to secondary activities; standardization has been shown to have a direct impact on patient healthcare.

Danish Prostate Cancer Registry - methodology and early results from a novel national database

Background

Systematized Nomenclature of Medicine (SNOMED) codes are computer-processable medical terms used to describe histopathological evaluations. SNOMED codes are not readily usable for analysis. We invented an algorithm that converts prostate SNOMED codes into an analyzable format. We present the methodology and early results from a new national Danish prostate database containing clinical data from all males who had evaluation of prostate tissue from 1995 to 2011.

Materials and methods

SNOMED codes were retrieved from the Danish Pathology Register. A total of 26,295 combinations of SNOMED codes were identified. A computer algorithm was developed to transcode SNOMED codes into an analyzable format including procedure (eg, biopsy, transurethral resection, etc), diagnosis, and date of diagnosis. For validation, ~55,000 pathological reports were manually reviewed. Prostate-specific antigen, vital status, causes of death, and tumor-node-metastasis classification were integrated from national registries.

Results

Of the 161,525 specimens from 113,801 males identified, 83,379 (51.6%) were sets of prostate biopsies, 56,118 (34.7%) were transurethral/transvesical resections of the prostate (TUR-Ps), and the remaining 22,028 (13.6%) specimens were derived from radical prostatectomies, bladder interventions, etc. A total of 48,078 (42.2%) males had histopathologically verified prostate cancer, and of these, 78.8% and 16.8% were diagnosed on prostate biopsies and TUR-Ps, respectively.

Future perspectives

A validated algorithm was successfully developed to convert complex prostate SNOMED codes into clinical useful data. A unique database, including males with both normal and cancerous histopathological data, was created to form the most comprehensive national prostate database to date. Potentially, our algorithm can be used for conversion of other SNOMED data and is available upon request.

Representation of information about family relatives as structured data in electronic health records

BACKGROUND

The ability to manage and leverage family history information in the electronic health record (EHR) is crucial to delivering high-quality clinical care.

OBJECTIVES

We aimed to evaluate existing standards in representing relative information, examine this information documented in EHRs, and develop a natural language processing (NLP) application to extract relative information from free-text clinical documents.

METHODS

We reviewed a random sample of 100 admission notes and 100 discharge summaries of 198 patients, and also reviewed the structured entries for these patients in an EHR system's family history module. We investigated the two standards used by Stage 2 of Meaningful Use (SNOMED CT and HL7 Family History Standard) and identified coverage gaps of each standard in coding relative information. Finally, we evaluated the performance of the MTERMS NLP system in identifying relative information from free-text documents.

RESULTS

The structure and content of SNOMED CT and HL7 for representing relative information are different in several ways. Both terminologies have high coverage to represent local relative concepts built in an ambulatory EHR system, but gaps in key concept coverage were detected; coverage rates for relative information in free-text clinical documents were 95.2% and 98.6%, respectively. Compared to structured entries, richer family history information was only available in free-text documents. Using a comprehensive lexicon that included concepts and terms of relative information from different sources, we expanded the MTERMS NLP system to extract and encode relative information in clinical documents and achieved a corresponding precision of 100% and recall of 97.4%.

CONCLUSIONS

Comprehensive assessment and user guidance are critical to adopting standards into EHR systems in a meaningful way. A significant portion of patients' family history information is only documented in free-text clinical documents and NLP can be used to extract this information.