The coronavirus disease (COVID-19) is a severe infectious disease that has been confirmed to lead to human-to-human transmission since December 2019 [1]. Considering the sudden outbreak of the disease as a challenging threat, it was brought into the Class B of infectious diseases defined in the Law of the People’s Republic of China on the Prevention and Control of Infectious Diseases, but management and policies were adopted according to the Class A of infectious diseases [2,3]. All of the challenges have brought great pressure on medical institutions and professionals, including the lack of medical equipment and the complexity of diagnosis. At the same time, the fear of the disease has a negative impact on both the psychological and physiological well-being of affected individuals.

Although, most cases of the disease occurred in mainland China in the beginning, other areas have also confirmed cases of the same disease, and the number of cases continues to increase. The World Health Organization (WHO) has declared the 2019-20 coronavirus outbreak to be a Public Health Emergency of International Concern [4]. So far, the United States is the most seriously affected country in the world. In addition to China, many cases were found in the Western Pacific Region, such as South Korea and Japan. Russia and Great Britain were relatively seriously affected areas in Europe [5] according to the latest data reported by the WHO.

Symptoms include fever, cough, or shortness of breath, and even pneumonia, multi-organ failure, and death in the most severe cases. The latent period can be between 1 and 14 days, and on average is between 3 and 7 days according to the epidemiological investigation. What is even worse is that some patients may be asymptomatic at the beginning, which results in some undetected errors [6]. Given the severity of the infectious disease and urgency of diagnosis and treatment, a large number of studies related to disease prevention and control have been carried out with the support of various countries according to the WHO [7].

Considering the rapid spread of the disease, the transferring of knowledge of diagnosis and treatment of the disease and newly updated achievements of research are important, especially from areas with improved epidemics like mainland China, the area where the epidemic had begun. Although some efforts have been made through teleconsultation and medical staff assistance, they are still limited due to a lack of experts. Using decision support tools is an efficient way to transfer the knowledge of experts to clinical practices. Epic (Epic Systems), which is a health care software company with electronic medical record software application, has sent out an update to its customers to detect potential cases of COVID-19 [8]. DIPS, which is a major openEHR vendor, released open source components to assist software developers creating apps to fight COVID-19 [9]. Although Epic provided a complete solution for medical information systems where the clinical decision support tools have already been embedded, most other systems still need to integrate with medical information systems to be used in clinical practice. The interoperability of disease-related data has become an important issue.

Many studies on terminology standardization have been conducted to improve the interoperability. Systematized Nomenclature of Human Medicine International has issued an interim release to promote the analysis with the most up-to-date terminology [10]. The Observational Health Data Sciences and Informatics has also committed vocabulary about COVID-19 into GitHub [11]. These efforts have been focused on the shared representation of the concepts related to COVID-19 but are not enough for data exchanging, with the shared data model being the key issue. It specifies not only the data structures but also the attributes of data elements. Although there exist many methods [12-15] to develop a data model for certain requirements, the rapidly updating knowledge for COVID-19 makes it a challenge to achieve a flexible model constantly with the requirement evolution.

An open, semantic-sharing, and collaborative-modeling framework is needed to meet the dynamic change of data requirements. openEHR specifications can be used to create standards and build information and interoperability solutions for health care as a multilevel modeling framework [16]. In the approach suggested by openEHR, the reference model (RM) focuses on the logic structures and attributes required to express data, so it is stable and provides basic components for building concrete medical information models. The archetype model is comprised of archetypes and templates. Based on the RM, archetypes can be developed to define all the attributes about specific clinical concepts. Different archetypes can be organized into context-specific data sets, templates that are mostly developed and used locally. Only the RM is implemented in apps, while clinical information (archetypes and templates) is independent of specific implementations. The approach allows data models to be flexible and extensible within the constraints of the RM, which can keep up with the development of the clinical knowledge and meet the requirements of the complicated clinical environment [17]. The knowledge obtained in clinical practice in mainland China can be sharable and beneficial for other countries and regions by being formalized as openEHR archetypes and templates.

As a goal, we developed an openEHR template to promote interoperability among clinical systems for the diagnosis and treatment of COVID-19. The remainder of this paper is organized as follows: the Methods section introduces the knowledge source and methodology we used to develop and review the template, the Results section illustrates our results step-by-step according to the proposed methodology, and the Discussion section discusses the contributions of this paper and limitations.

Based on the methodology previously described, 203 data items were extracted from the guideline in China, including 8 sections and 15 subsections (see Multimedia Appendix 1). After the classification and merge of these data elements, 16 domain concepts (16 leaf nodes in the mind map) were organized for diagnosis and treatment of COVID-19. The results in this step are illustrated in the mind map in Figure 3 (full results can be found in Multimedia Appendix 2).

Among these domain concepts, only 2 archetypes were classified into Instruction, and 3 of them were classified into Evaluation. The archetypes of Observation include 11 items. A total 22 archetypes have been developed to represent all data elements about COVID-19, and all of them can be referred to from the CKM directly. These archetypes found in the CKM, which are adapted to our requirements, are shown in Textbox 1. Finally, a template was developed with the constraint of these archetypes as shown in Figure 4.

In addition, it has been deployed in a hospital, which has accepted many cases of COVID-19, to support data sharing between CDRs and clinical decision support systems (CDSS). Because the CDR is developed based on openEHR, the storage structure is consistent with the template. Although there exist many storage implementations of openEHR [25,26], they are transparent for the invocation of data services as a result of openEHR two-level modeling. Data elements in the template can be uniquely identified by paths and attributes. In this way, data for each data element can be transferred in representational state transfer (REST)ful API. The rules in CDSS were also built based on the template. The template was used in both CDR and CDSS. The data from RESTful API was parsed, extracted, and used in the inference engine to produce the decision for the case. The user interface for the data view whose data was from openEHR-based CDRs is shown in Figure 5. Textbox 2 shows the part of the content of the data that is in the RESTful API from CDR.

In the end, the template (COVID-19 Pneumonia Diagnosis and Treatment [7th edition]) has been uploaded into the CKM [27].

Since the openEHR template developed in our study covered the contents related to clinical characteristics, diagnosis criteria, clinical classification, warning signs for severe and critical cases, differential diagnosis, diagnosis of suspected cases, treatment, and discharge from the latest guideline, it could be used for data exchanging among systems in different clinical scenarios such as screening patients in outpatient clinics, where the diagnosis of suspected cases will be the main focus; the routine round in the wards, where the diagnosis and warning signs for severe or critical cases will be more important; and the intensive care unit, where the treatment recommendation will be the most necessary. Although some hospital information system vendors [8] provided the monolithic solution, the applications for different scenarios with an integrated solution were still encouraged to fully use the expertise of different vendors, which is the norm in the health care institutions. For this reason, the data interoperability is an important issue, and the results of our study can play a significant role.

Furthermore, the results of our study can be used for purposes other than the diagnosis and treatment of COVID-19. It can help to develop scales according to severity at different levels and be used for risk assessment of COVID-19. Meanwhile, it is also significant for the prevention and control of the disease in the community. The questionnaires can be designed for people who are under closed management to monitor their physical conditions.

COVID-19 was a new threatening infectious disease that brought great pressure on medical systems around the world and with limited previous knowledge in the domain. The methods of diagnosis and treatment have been updated rapidly to reflect the achievement of the latest research since the outbreak of the disease, which sets the challenge for data exchange among systems. The openEHR modeling approach perfectly meets the requirement since the multilevel modeling is especially suitable for the knowledge evolution. In the openEHR ecosystem, when the knowledge of diagnosis and treatment has been updated, only the template needs to be updated and the apps can be kept unchanged. This enables the latest knowledge to be applied to clinical practice at the fastest speed.

In our study, once the latest guideline has been released, the new knowledge can be incorporated into the existing template according to the flowchart shown in Figure 6. Compared with starting from scratch as in the first round, only a few steps need to be performed when the knowledge has been updated, which is shown in the red box (Figure 6).

The purpose of the template needs to be refined before modeling since it will largely affect the final results. First, although the purpose of the study is to develop the template for diagnosis and treatment, it still needs to be refined based on whether it is for rule-based decision support tools only or general decision support. As an example, the guideline only mentioned that pregnancy status may affect the intervention without specifying the exact rules, so it is not necessary to be modeled if only for the rule-based decision support tool, but it will still be useful information for professionals to make the decision. Second, the refined purpose should also clarify whether it is used for exchanging the original data from EMR or the condition points for the final decision support. Data items extracted from the guideline usually did not exist in CDR, so they need to be abstracted from the existing data. For instance, the guideline may describe the “Two consecutive negative nucleic acid tests using respiratory tract samples (taken at least 24 hours apart)” as a condition point for discharge, but it has to be calculated from two data items of the nucleic acid test.

This study has used the guideline released by the National Health Commission of the People’s Republic of China as the only knowledge source for modeling since the authoritative knowledge was limited at the beginning of the outbreak. However, with the ever-increasing research results and experiences of diagnosis and treatment, there are and will be more knowledge sources, such as the handbook developed by the First Affiliated Hospital, Zhejiang University School of Medicine jointly sponsored by the Jack Ma Foundation and Alibaba Foundation [28], which could be taken as complementary to the guideline. Our modeling approach makes it much easier to apply new or updated knowledge in the template quickly.

Although the template has been reviewed and verified in our study, it still has limitations. First, due to the reason that most of the experienced medical professionals were prioritizing clinical care of patients with COVID 19, it was difficult to have the template reviewed by professionals. Second, since there are not many cases that have been conducted, there may exist some points not appropriate for specific cases (eg, the patient may need to be treated with extra intervention that is not represented in the template). Therefore, further case studies and reviews are necessary to improve the template.

The template can be easily deployed in the openEHR-based CDR as shown in this study. However, not all institution’s implemented systems are based on openEHR, so there will be a limitation for the use of the template in such scenarios. However, since the infrastructure of openEHR has been designed to be compatible with other existing industry standards, the template can be easily transferred to other popular accepted industry standards like JavaScript Object Notation (JSON), XML, and Health Level 7. To take JSON as an example, the template can be expressed in this format within the support of the JSON schema [29]. The JSON schema is similar to the XML schema to help describe the data format and provide the constraints for the data expression.

This paper developed and released the openEHR template based on the latest guidelines of COVID-19 in China. Most of the archetypes used in the template can be covered by existing archetypes in the CKM. This study proved that the openEHR approach has advantages in modeling a new medical application field and meeting the requirements of rapidly updating knowledge. The template developed in this study could be used to transfer the experience and knowledge achieved from China to other countries and regions as soon as possible from the perspective of improving data exchange among applications to defeat COVID-19.