Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR

Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability

Background

Semantic interoperability facilitates the exchange of and access to health data that are being documented in electronic health records (EHRs) with various semantic features. The main goals of semantic interoperability development entail patient data availability and use in diverse EHRs without a loss of meaning. Internationally, current initiatives aim to enhance semantic development of EHR data and, consequently, the availability of patient data. Interoperability between health information systems is among the core goals of the European Health Data Space regulation proposal and the World Health Organization's Global Strategy on Digital Health 2020-2025.

Objective

To achieve integrated health data ecosystems, stakeholders need to overcome challenges of implementing semantic interoperability elements. To research the available scientific evidence on semantic interoperability development, we defined the following research questions: What are the key elements of and approaches for building semantic interoperability integrated in EHRs? What kinds of goals are driving the development? and What kinds of clinical benefits are perceived following this development?

Methods

Our research questions focused on key aspects and approaches for semantic interoperability and on possible clinical and semantic benefits of these choices in the context of EHRs. Therefore, we performed a systematic literature review in PubMed by defining our study framework based on previous research.

Results

Our analysis consisted of 14 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. All articles reported clinical benefits of the selected approach to enhancing semantic interoperability. We identified 3 main categories: increasing the availability of data for clinicians (n=6, 43%), increasing the quality of care (n=4, 29%), and enhancing clinical data use and reuse for varied purposes (n=4, 29%). Regarding semantic development goals, data harmonization and developing semantic interoperability between different EHRs was the largest category (n=8, 57%). Enhancing health data quality through standardization (n=5, 36%) and developing EHR-integrated tools based on interoperable data (n=1, 7%) were the other identified categories. The results were closely coupled with the need to build usable and computable data out of heterogeneous medical information that is accessible through various EHRs and databases (eg, registers).

Conclusions

When heading toward semantic harmonization of clinical data, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of health care data. Instead of promoting a single approach, semantic interoperability should be assessed through several levels of semantic requirements A dual model or multimodel approach is possibly usable to address different semantic interoperability issues during development. The objectives of semantic interoperability are to be achieved in diffuse and disconnected clinical care environments. Therefore, approaches for enhancing clinical data availability should be well prepared, thought out, and justified to meet economically sustainable and long-term outcomes.

Clinical Source Data Production and Quality Control in Real-world Studies: Proposal for Development of the eSource Record System

BACKGROUND

An eSource generally includes the direct capture, collection, and storage of electronic data to simplify clinical research. It can improve data quality and patient safety and reduce clinical trial costs. There has been some eSource-related research progress in relatively large projects. However, most of these studies focused on technical explorations to improve interoperability among systems to reuse retrospective data for research. Few studies have explored source data collection and quality control during prospective data collection from a methodological perspective.

OBJECTIVE

This study aimed to design a clinical source data collection method that is suitable for real-world studies and meets the data quality standards for clinical research and to improve efficiency when writing electronic medical records (EMRs).

METHODS

On the basis of our group's previous research experience, TransCelerate BioPharm Inc eSource logical architecture, and relevant regulations and guidelines, we designed a source data collection method and invited relevant stakeholders to optimize it. On the basis of this method, we proposed the eSource record (ESR) system as a solution and invited experts with different roles in the contract research organization company to discuss and design a flowchart for data connection between the ESR and electronic data capture (EDC).

RESULTS

The ESR method included 5 steps: research project preparation, initial survey collection, in-hospital medical record writing, out-of-hospital follow-up, and electronic case report form (eCRF) traceability. The data connection between the ESR and EDC covered the clinical research process from creating the eCRF to collecting data for the analysis. The intelligent data acquisition function of the ESR will automatically complete the empty eCRF to create an eCRF with values. When the clinical research associate and data manager conduct data verification, they can query the certified copy database through interface traceability and send data queries. The data queries are transmitted to the ESR through the EDC interface. The EDC and EMR systems interoperate through the ESR. The EMR and EDC systems transmit data to the ESR system through the data standards of the Health Level Seven Clinical Document Architecture and the Clinical Data Interchange Standards Consortium operational data model, respectively. When the implemented data standards for a given system are not consistent, the ESR will approach the problem by first automating mappings between standards and then handling extensions or corrections to a given data format through human evaluation.

CONCLUSIONS

The source data collection method proposed in this study will help to realize eSource's new strategy. The ESR solution is standardized and sustainable. It aims to ensure that research data meet the attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available standards for clinical research data quality and to provide a new model for prospective data collection in real-world studies.

Prediction of COVID-19 diagnosis based on openEHR artefacts

Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems.

Semantic interoperability in health records standards: a systematic literature review

The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient’s history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.

Verifying the Feasibility of Implementing Semantic Interoperability in Different Countries Based on the OpenEHR Approach: Comparative Study of Acute Coronary Syndrome Registries

Background

The semantic interoperability of health care information has been a critical challenge in medical informatics and has influenced the integration, sharing, analysis, and use of medical big data. International standard organizations have developed standards, approaches, and models to improve and implement semantic interoperability. The openEHR approach—one of the standout semantic interoperability approaches—has been implemented worldwide to improve semantic interoperability based on reused archetypes.

Objective

This study aimed to verify the feasibility of implementing semantic interoperability in different countries by comparing the openEHR-based information models of 2 acute coronary syndrome (ACS) registries from China and New Zealand.

Methods

A semantic archetype comparison method was proposed to determine the semantics reuse degree of reused archetypes in 2 ACS-related clinical registries from 2 countries. This method involved (1) determining the scope of reused archetypes; (2) identifying corresponding data items within corresponding archetypes; (3) comparing the semantics of corresponding data items; and (4) calculating the number of mappings in corresponding data items and analyzing results.

Results

Among the related archetypes in the two ACS-related, openEHR-based clinical registries from China and New Zealand, there were 8 pairs of reusable archetypes, which included 89 pairs of corresponding data items and 120 noncorresponding data items. Of the 89 corresponding data item pairs, 87 pairs (98%) were mappable and therefore supported semantic interoperability, and 71 pairs (80%) were labeled as “direct mapping” data items. Of the 120 noncorresponding data items, 114 (95%) data items were generated via archetype evolution, and 6 (5%) data items were generated via archetype localization.

Conclusions

The results of the semantic comparison between the two ACS-related clinical registries prove the feasibility of establishing the semantic interoperability of health care data from different countries based on the openEHR approach. Archetype reuse provides data on the degree to which semantic interoperability exists when using the openEHR approach. Although the openEHR community has effectively promoted archetype reuse and semantic interoperability by providing archetype modeling methods, tools, model repositories, and archetype design patterns, the uncontrolled evolution of archetypes and inconsistent localization have resulted in major challenges for achieving higher levels of semantic interoperability.

Integrating multiple blockchains to support distributed personal health records

Blockchain technologies have evolved in recent years, as have the use of personal health record (PHR) data. Initially, only the financial domain benefited from Blockchain technologies. Due to efficient distribution format and data integrity security, however, these technologies have demonstrated potential in other areas, such as PHR data in the healthcare domain. Applying Blockchain to PHR data faces different challenges than applying it to financial transactions via crypto-currency. To propose and discuss an architectural model of a Blockchain platform named "OmniPHR Multi-Blockchain" to address key challenges associated with geographical distribution of PHR data. We analyzed the current literature to identify critical barriers faced when applying Blockchain technologies to distribute PHR data. We propose an architecture model and describe a prototype developed to evaluate and address these challenges. The OmniPHR Multi-Blockchain architecture yielded promising results for scenarios involving distributed PHR data. The project demonstrated a viable and beneficial alternative for processing geographically distributed PHR data with performance comparable with conventional methods. Blockchain's implementation tools have evolved, but the domain of healthcare still faces many challenges concerning distribution and interoperability. This study empirically demonstrates an alternative architecture that enables the distributed processing of PHR data via Blockchain technologies.

Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China

BACKGROUND

With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes. The crucial problem is how to improve the precision and resolve ambiguity in the archetype retrieval.

METHOD

Based on the query expansion technology and Word2Vec model in Nature Language Processing (NLP), we propose to find synonyms as substitutes for original search terms in archetype retrieval. Test sets in different medical professional level are used to verify the feasibility.

RESULT

Applying the approach to each original search term (n = 120) in test sets, a total of 69,348 substitutes were constructed. Precision at 5 (P@5) was improved by 0.767, on average. For the best result, the P@5 was up to 0.975.

CONCLUSIONS

We introduce a novel approach that using NLP technology and corpus to find synonyms as substitutes for original search terms. Compared to simply mapping the element contained in openEHR to an external dictionary, this approach could greatly improve precision and resolve ambiguity in retrieval tasks. This is helpful to promote the application of openEHR and advance EHR information sharing.