Building a Coronavirus Disease 2019 (COVID-19) healthcare registry in an evolving pandemic

doi:10.21203/rs.3.rs-5002321/v1

Objective:

COVID-19 emerged in Wuhan, China in December 2019 and was declared a pandemic on March 11, 2020. As there were many unknowns about COVID-19, an immediate priority early in the pandemic was to collect high-quality data to understand disease characteristics and epidemiology to inform healthcare resource management. A COVID-19 registry was developed by Singapore Health Services (SingHealth) to support clinical, operational, and research needs. We describe the development process of the SingHealth COVID-19 registry.

Methods:

Patients diagnosed with COVID-19 and managed by the Singapore Health Services (SingHealth), the largest of three public healthcare clusters in Singapore that includes acute hospitals, national specialty centres, subacute hospitals, and primary health centres, were included in the registry. A combination of laboratory test results and nationally administered electronic disease tags were used to identify COVID-19 patients. A dashboard was built in the Electronic Health Intelligence System (eHIntS), the data repository within SingHealth, linking variables in a minimum data set (MDS) using patient identifiers. Healthcare utilization in different ward types was computed, and 19 comorbidities and 14 complications were derived and coded from raw data sources within eHints for hospitalized patients.

Results:

The COVID-19 registry contains more than 100 variables across 15 data domains, including raw data in long format (e.g. problem list, movement within hospital) and derived variables (e.g. comorbidities, complications, outcomes). As of December 31, 2023, the COVID-19 registry comprises of 156,262 unique patients of whom, 27,630 were admitted for COVID-19 at least once at one of the SingHealth hospitals. It has provided data for operational, clinical, and research purposes.

Conclusion:

The SingHealth COVID-19 registry is a model of how a low-cost, automated database can be developed amidst an evolving pandemic, leveraging on pandemic-necessitated standardized clinical workflows for pandemic operations, clinical management and research.

Biological sciences/Microbiology/Virology

Health sciences/Diseases/Infectious diseases/Viral infection

COVID-19

disease registry

automated surveillance

Key findings - This work demonstrates that electronic healthcare data can be leveraged to develop automated disease registries during a rapidly evolving global pandemic. Data from such registries can be utilized for clinical, operational and research needs and are an invaluable resource to inform policy decisions.

What this adds - International Classification of Diseases, 10^th Revision, Clinical Modification (ICD-10-CM) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED) codes were mapped to specific comorbidities and complications in order to build the comorbidities and complications database. Components for the minimum data set were determined by the registry team enabling rapid implementation. Methods for development and maintenance of the dynamic registry database and incorporation of nationally instituted electronic patient tags are described.

Implications – Methods described can be adapted to develop disease registries rapidly during a future pandemic/emerging disease.

COVID-19 emerged in Wuhan, China in December 2019. The beta-type of the coronavirus strain that caused COVID-19 was identified and named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus by the World Health Organization [1]. The disease was declared a pandemic on March 11, 2020, and had spread to 229 countries and territories, infecting more than 681 million individuals and claiming 6.8 million lives by March 9, 2023 [2].

As there were many unknowns about COVID-19, an immediate priority early in the pandemic was to collect high-quality data to understand rates of infection, hospitalization, intensive care unit admission, disease characteristics including incubation period and outcomes such as case-fatality rate, in order to inform healthcare resource management and infection prevention strategies [3] [4]. A number of early studies found that the incubation period of COVID-19 was longer than common respiratory viral infections [3] [5] [6] and this enabled the development of effective containment strategies.

As the pandemic progressed, gaps in knowledge remained and new questions arose, such as those related to emerging variants, vaccine regimens, and long COVID-19 syndromes. The SARS-CoV-2 variant, Omicron, contained more mutations (60 mutations) than pre-Omicron variants [7], and its sub-variants were more transmissible and had better immune evasion [8] [9] compared to pre-Omicron sub-variants. Omicron subvariants continued to be monitored [10], and their risks of posing additional threats to public health remained a question. Although COVID-19 vaccination programs had been extensively implemented worldwide, clinical studies had shown a reduction in vaccine effectiveness against the Omicron variant compared to other variants [11-13], creating a dilemma regarding the need for booster doses [14]. Separately, more than 65 million individuals globally were estimated to be living with long COVID-19 syndrome, a multisystemic condition following COVID-19 [15]. However, the exact characteristics and impacts of long COVID-19 on both individuals and the healthcare systems were not fully understood [16].

Systematic and standardized data collection on a large scale in a timely manner is essential in outbreak management. The first three years of the pandemic witnessed the development of COVID-19 registries in several countries, including Europe [17], Brazil [18], India [19], Kuwait [20], Sweden [21], and Denmark [17]. These registries had brought timely and regionally relevant data for epidemiological research and had contributed to obtaining a more precise picture of the outcomes of COVID-19. However, most of them had focused on either specific aspects of the disease or specific populations. For example, the COVID-19 registry created by the European Academy of Neurology provided data on the spectrum of neurological symptoms following COVID-19 infections [22], and the Kuwaiti registry included pediatric COVID-19 cohorts. Singapore has had very low COVID-19 fatality rates, largely owing to the rapid and adaptive governmental response throughout the pandemic [23]. Data from other national COVID-19 registries elsewhere had limited generalizability to the Singapore cohort due to differences in demographics, population size, and healthcare system.

Therefore, a COVID-19 registry focusing on the Singapore cohort was developed by Singapore Health Services (SingHealth) to help provide an overall picture of the epidemiological characteristics and outcomes of COVID-19 in Singapore. We describe the development process of the SingHealth COVID-19 registry.

Health system and electronic health records

Healthcare is provided by both the public and private sector in Singapore. The public sector covers about 80% of tertiary acute care but the converse is true for primary care, of which about 80% is covered by the private sector [24]. SingHealth, the largest of three public healthcare clusters in Singapore, is a network of acute hospitals, national specialty centres, subacute hospitals, and primary care polyclinics [25]. At the beginning of the COVID-19 pandemic, SingHealth institutions included four acute hospitals (Singapore General Hospital (SGH, 1796 beds), Changi General Hospital (CGH, 1071 beds), Sengkang General Hospital (SKH, 799 beds), and KK Women’s and Children’s Hospital (KKH, 848 beds)), five specialty centres (the National Cancer Centre Singapore (NCCS), National Heart Centre Singapore (NHCS), Singapore National Eye Centre (SNEC), National Dental Centre Singapore (NDCS), and National Neurosciences Institute (NNI)), three subacute hospitals (Sengkang Community Hospital (233-beds), Outram Community Hospital (99 beds) and Bright Vision Hospital (276 beds)), and eight of 23 polyclinics in the country providing primary care under the public sector [26].

SingHealth uses a comprehensive electronic medical records (EMR) system across all the care settings. In Singapore, information technology for the public healthcare sector is centrally managed by Synapxe (formerly, the Integrated Health Information Systems, IHiS). Synapxe also administers the SingHealth-Synapxe Electronic Health Intelligence System (eHints), an enterprise data repository that integrates information from multiple healthcare transactional systems including clinical, administrative and ancillary systems within SingHealth to serve analysis and reporting functions for operational and clinical purposes [27].

COVID-19 registry governance

The COVID-19 registry was developed through a collaborative effort among infectious disease clinicians and epidemiologists from the Department of Infection Prevention and Epidemiology (DIPE, SGH), health services researchers from the Health Services Research Centre (SingHealth), and data system experts from Synapxe (referred hereafter as the core development team). The SingHealth disease registry policy provided the framework for registry development and governance. The COVID-19 registry is governed by a steering committee with representatives from every SingHealth institution that received COVID-19 patients. Registry data can be accessed according to SingHealth’s disease registry data use policy after approval from the steering committee. Data requests for research are required to have prior ethics approval. Informed patient consent was not required as per the SingHealth disease registry policy.

COVID-19 registry data integration

The COVID-19 Registry is fronted by a dashboard which displays variables linked from other subject areas in eHIntS using patient identifiers (National Registration Identity Card (NRIC) number and/or case number, which are unique to each individual and visit, respectively) (see “COVID-19 registry development”). There was no manual data entry specifically for the registry.

COVID-19 diagnostics and case patient identification

Suspect case criteria defined by the Ministry of Health, Singapore (MOH) for the novel respiratory infection on January 21, 2020 was (a) a person with clinical signs and symptoms suggestive of pneumonia or severe respiratory infection with breathlessness AND travel to mainland China within last 14 days; or (b) a person with an acute respiratory illness of any degree of severity who, within 14 days before onset of illness, had been to a hospital in mainland China or had close contact with a case of 2019 novel coronavirus infection [28]. As the pandemic evolved rapidly, individual institutions modified case definitions and clinical workflows according to the epidemiological situation [29]. All hospitals maintained a manual listing of suspected and confirmed cases for reporting and contact tracing purposes.

In-house laboratory PCR testing for this novel coronavirus was available at SGH on January 23, 2020, the same day that the first patient with laboratory-confirmed COVID-19 was identified. Between January 2020 and April 15, 2020, the COVID-19 registry included all patients meeting institutional/national epidemiological case definitions regardless of laboratory test results, as testing was limited by laboratory testing capacity. Between April 15, 2020 and October 9, 2021, SARS-CoV-2 PCR test was widely accessible and subsidized and hence was utilized for all suspected cases and for active surveillance amongst high-risk persons such as healthcare workers and dormitory residents [30]. Only laboratory-confirmed SARS-CoV-2 PCR-positive patients were included in the COVID-19 registry during this period.

As of October 8, 2021, 84.7% of Singapore’s population had completed their COVID-19 vaccination [31]. Full vaccination was found to reduce the risk of severe COVID-19 and death by ten-fold (0.12% versus 1.59% respectively) [30]. At this stage, national COVID-19 control measures transitioned from containment to mitigation. COVID-19 diagnosis and surveillance testing changed from a primarily PCR-based test to an Antigen Rapid Test (ART) based method from October 10, 2021 [32]. As this included ART self-tests, the COVID-19 registry incorporated national electronic COVID-19 tagging for case patient identification between October 10, 2021 and May 8, 2022.

Electronic patient tagging

Several COVID-19-related electronic tags were introduced throughout the pandemic. These tags managed centrally at a national level, appeared as alerts on the individual patient health record. The first group of tags were for persons issued the Quarantine Order (QO) and Stay Home Notice (SHN) following return from international travel. The second tag was introduced to identify patients who were COVID positive, either through ART self-test, ART at a primary care facility that declared their COVID-19 status in a centrally managed COVID-19 portal, or a laboratory-based SARS-CoV-2 PCR test, between October 10, 2021 and May 8, 2022. The second electronic tag was utilized to identify COVID-19 cases for the COVID-19 registry. From May 9, 2022, identification of COVID-19 patients for the COVID-19 registry reverted to laboratory-confirmed cases through PCR and/or ART within SingHealth institutions.

COVID-19 registry development

Phase 1: Information needs assessment

The core development team performed information needs assessment through internal discussions and engagement with clinicians who had COVID-19-related research questions and identified eight general areas to be included in the registry. These areas broadly included population-level transmission dynamics and clinical disease characteristics and their modifiers (Table 1).

Phase 2: Identification of data elements

Next, the team identified the data elements for each of the areas determined in phase 1. A variety of resources including scientific and media publications related to COVID-19, expert opinions, COVID-19 specific data collection tools, national health information system (e.g. COVID-19 tag from national database), hospital electronic health record system and the eHints data dictionary were consulted (Table 2).

Phase 3: Selection of a minimum data set (MDS) and data types

The minimum data set (MDS) is a defined set of data variables in a health information system (in this case, eHints) containing the variables essential for meeting the needs of most users in our healthcare system. By precisely defining data elements, the MDS serves as a data dictionary that promotes transparency and clarity in data collection, analysis, and reporting.

As the COVID-19 pandemic progressed, clinical workflows became protocolized and new templates with variables relevant to bed allocation (such as admission to dedicated respiratory surveillance wards for COVID-19 suspect patients or COVID-19 isolation wards for COVID-19 confirmed patients) and management were collected electronically and became available in eHints. These were later added to the MDS.

Two clinicians from the study team mapped the International Classification of Diseases, 10^th Revision, Clinical Modification (ICD-10-CM) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED) codes to the specific comorbidities and complications in order to build the comorbidities and complications database. Algorithms were created by combining diagnostic codes and other structured clinical data to define comorbidities and COVID-19 complications in eHints. Pre-existing conditions prior to COVID-19 diagnosis were considered to be comorbidities and conditions that developed following COVID-19 diagnosis were considered complications. As COVID-19 emerged as a novel disease, literature reviews were conducted regularly in order to better understand the disease characteristics and to incorporate more appropriate symptoms and conditions in the registry database.

Specific subject areas and variables relevant to the identified data elements were selected from eHints. Three types of data utilized by the registry are (1) structured data such as demographics, visit details, ICD-10-CM diagnostic codes and COVID-19 electronic tags, (2) unstructured data such as discharge summaries and radiology reports, and (3) specific comorbidities and COVID-19 complications coded from raw data such as ICD diagnostic and SNOMED codes.

Phase 4: Validation of data variables

All data variables in the COVID-19 registry were included in the internal validation process – demographics, laboratory results, vital signs, in-hospital patient movement, comorbidities and complications, therapeutics, vaccinations and outcome measures, to ensure that the data linked in eHints were correct.Ten percent of all patients with at least one variable, from each institution in the registry were sampled. The gold standard reference was manual review of the EMR for each of the nineteen comorbidities and fourteen complications.

Phase 5: Evaluation and modification of MDS

Throughout the COVID-19 pandemic, MDS was modified to align with evolving information and public health and clinical measures such as the introduction of electronic tagging systems, incorporation of therapeutics, and implementation of vaccination strategies.

Ethics

SingHealth Centralized Institutional Review Board (CIRB Ref: 2021/2027) determined that ethical deliberation was not required for the project as data from the registry would be de-identified prior to analysis for research. All methods were performed in accordance with the relevant SingHealth disease registry guidelines and regulations.

The COVID-19 registry contains more than 100 variables across 15 data domains, including raw data in long format (e.g. problem list, movement within hospital) and derived variables (e.g. comorbidities, complications, outcomes). It holds information on patient demographics, comorbidities, vital signs, laboratory results, treatment and medications, complications, outcomes and vaccination status. The MDS as of December 31, 2022 is shown in Table 3.

As of December 31, 2023, data from three of four SingHealth acute hospitals (SGH, SKH, KKH) and one specialty centre (NHC) had been collated in the registry. The fourth acute hospital had structurally different EHR data in eHints and could not be incorporated at time of writing. The COVID-19 registry comprises of 156,262 unique patients of whom, 27,630 were admitted for COVID-19 at least once at one of the SingHealth hospitals. Of these inpatients, there were 14,829 males (54.5%) and the mean age was 45.5 (SD=30.1)years. Majority were of Chinese race (58.7%) and Singapore nationality (76.1%). 672 (2.5%) had been admitted to ICU and mortality for hospitalized COVID-19 patients was 2.6%. 12,018 (44.9%) patients had at least one comorbidity and 6,925 (25.1%) had at least one complication following COVID-19. The number of infections and COVID-related admissions over time is shown in Figure 1. Between October 10^th, 2021 and May 8^th, 2022, the national electronic COVID-19 tag allowed inclusion of COVID-19 who were not admitted to the SingHealth institutions.

The SingHealth COVID-19 registry has provided data for operational, clinical, and research purposes. For operations, between 2020 and 2021, the registry provided data on the estimated length of hospital stay, the percentage of patients requiring oxygen supplementation, and parameters for risk prediction for hospital admission. Between 2020 and 2022, eleven data requests were received for research projects. Incubation periods of emerging variants were estimated [33], COVID-19 was characterized [34] and COVID-19 outcomes in vaccinated hematopoietic stem cell transplant recipients were reviewed based on the registry cohort data [35].

We successfully built a COVID-19 registry for this emerging infectious disease that was novel and that was being continually characterized amidst the evolving pandemic, despite several challenges faced during the building process. First, since epidemiology was rapidly evolving and the prevention strategies were rapidly changing, the registry-building effort needed to be versatile and dynamic. Second, the building process had to be carried out without affecting the operational demands of concomitant surveillance, pandemic management, and reporting. Third, as the pandemic kept evolving and a range of SARS-CoV-2 variants, point-of-care diagnostics, vaccines, and vaccination regimes emerged, the methodologies and variables of the registry needed to be modified in a timely manner to maintain the currency of the registry.

In the registry, we created a MDS to serve as a data dictionary to promote transparency and clarity in data collection, analysis, and reporting. The MDS is a well-known tool that is used to identify data variables for disease registration and reporting [36]. It helps improve the quality of data, especially for the clinical data component that is often unstandardized. The clinical data component, generally including diagnosis, prior medical history, laboratory and radiological results, therapeutics, disease progress, and outcomes, varies according to the disease. While MDS design may involve surveys to gather requirements [36], our MDS was determined by a core development team, and variables were limited to those within our electronic data repository eHints so as to automate the registry database.

EHRs are known to connect inefficiently and require extensive processing prior to being incorporated into disease registries [37], as data in EHRs often lack structure and standardization. The rapid evolution of the COVID-19 pandemic resulted in the development and implementation of highly protocolized clinical workflows supported by templates in EHR, to facilitate the right siting of patients, testing, and treatment regimes. This allowed streamlining of data capture for the COVID-19 registry.

Interoperability has been central to effective healthcare data exchange [38]. Implementation of standards in healthcare data has improved syntactic or structural interoperability but semantic or process interoperability remains significantly limited. Standards such as Health Level 7 (HL7) guide message structuring and facilitate syntactic interoperability whereby the meaning of data is unaltered. Semantic interoperability that allows for data interpretation in addition to exchange, however, needs improvement. In developing the COVID-19 registry, we attempted to normalize the data by mapping ICD-10 and Snomed codes to enable semantic interoperability. As COVID-19 is an acute respiratory illness and a large proportion of the patients had a limited number of comorbidities, this level of normalization was sufficient. However, this may not be applicable to more complex disease registries.

There are some limitations of the registry. First, the variables included were restricted to those that are readily available in the existing data repository. Incompleteness was due to the presence of two different EHR systems within SingHealth system.The reduced granularity of complication and comorbidity variables was due to limitations in semantic interoperability. Second, when data from the registry is extracted, substantial effort is required for cleaning and data engineering. However, it is a large database that is automated and that continues to accumulate patient data with minimal running cost and it is readily accessible for extraction and analysis. Lastly, the method for case identification varied over the course of the pandemic, requiring continuous alignment of the source database. However, bulk of the information in the MDS are for those who were hospitalized and these patients can be confidently enumerated and characterized.

The SingHealth COVID-19 registry is a model of how a low-cost, automated database can be developed amidst an evolving pandemic, leveraging on pandemic-necessitated standardized clinical workflows for pandemic operations, clinical management and research.

Acknowledgments

We would like to acknowledge the COVID-19 steering committee members who have overseen the administration of this registry. We also thank Mr Ee Chern Kwek for data cleaning and exploration and Ms Anita Oei for eHINTS registry development and data extraction for analysis.

Conflicts of Interest

Nil declared.

Funding

This work was funded by the SingHealth Academic Medicine General Fund, Ref: 03/FY2020/G2/01-A92.

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Table 1: Information needs assessment for the COVID-19 registry

Questions addressed for needs assessment

What information do clinicians, researchers, administrators and policymakers need for the future?

According to those requirements, what information on COVID-19 should be recorded?
What data is available regarding the specific information needed for the COVID-19 registry?
What resources can identify the data?
What are the minimum data elements?
What methods can be used to determine the minimum data set (MDS)?
Given the evolving epidemiology, disease characterization, therapeutics, vaccination and laboratory modalities, how can the registry database remain versatile to incorporate new information?

General areas identified as needed for the COVID-19 registry

COVID-19 incidence and prevalence
COVID-19 outbreaks and associated factors
Role of lifestyle (e.g. dwelling, occupation, etc) in COVID-19 risk
Symptoms, signs and abnormal medical imaging and laboratory findings of COVID-19
Natural history of COVID-19 and clinical outcomes
Impact of comorbidities, vaccination and therapeutics on COVID-19 clinical outcomes
Long COVID-19 syndromes
ISARIC (risk) score variables

ISARIC: International Severe Acute Respiratory and emerging Infection Consortium

Table 2: Information sources utilized for COVID-19 registry database building

Resources	Data collection methods	Remarks
Publications – scientific and media	Medical literature review, internet search engines, social media, print media, others	In the rapidly evolving early phase of the pandemic, Chinese language media carried essential information. Statistical reporting in media was generally more prompt that official sources
Expert opinions	Formal and informal professional networks	Disease characterization early in pandemic primarily was based on informal information sharing through personal communications
COVID-19 specific data collection tools	Guidelines developed by international organizations such as the World Health Organization (WHO)	Adapted for local epidemiological situation
National health information system (COVID-19 tag from national database)	On-site review of information systems	Continued to evolve with initial tagging of acute COVID-19 patients and later, tagging of incoming travelers, at risk contacts, persons under quarantine
Electronic Medical Records (EMR)	Document review	Structured medical records in SingHealth institutions are electronically held in information systems such as iHIS

Table 3: MDS for the COVID-19 registry

Data domains	Variables	Remarks
Demographics	Patient identifier Age Gender Nationality Country of residence Postal code Smoking	Available for all patients in registry
COVID diagnosis	Date of first known positive swab in SingHealth
Presenting signs and symptoms	Shortness of breath Sore throat Runny nose Fever Loss of smell or taste Cough Body soreness Blocked nose Others
Laboratory results	Order location Specimen collection date/time Specimen status description Laboratory test description Reported date Result value Unit of measurement Reference range Abnormal flag Result comments Source name* Procedure description* Microorganism description* Antibiotic code* Sensitivity* Sensitivity procedure*	*applicable only for microbiological tests
Vital signs	Temperature Pulse Blood pressure Weight Height Acute respiratory symptoms Body surface area Glasgow coma score Secretions Oxygen therapy (including type) APACHE score (for ICU admissions) Pain score Respiratory rate Oxygen saturation (SpO₂)	Available entries with date and time
Problem list	Problem onset date Problem description Problem status Problem type Coding scheme Active indicator Chronic indicator	SNOMED
Medical documents	Admission note Discharge summary
Treatments	Ventilator Extracorporeal membrane oxygenation Chest X-rays Computed tomography (CT) scans Magnetic resonance imaging (MRI) scans Doppler Hemodialysis Peritoneal dialysis	If ordered, with start date, end date and quantity
Medications	Date Medication name Generic drug name Duration Quantity Route Dosage Instructions	Inpatient and discharge medications
Patient movements within hospital	Institution Movement sequence number Admit date/time Admit source Admit specialty Admit accommodation category Discharge date/time
Discharge diagnosis	Diagnosis date Diagnosis record type (primary/secondary) Diagnosis catalogue code Diagnosis code Diagnosis description	ICD-10
Comorbidities	Pregnancy Immunodeficiency excluding HIV HIV Previous stroke or transient ischemic attack Chronic neurological or neuromuscular disease excluding stroke Hypertension Congestive heart failure Previous acute myocardial infarction Cardiomyopathy Arrhythmia Peripheral vascular disease Diabetes (and type) Liver disorder Dialysis Malignancy Asthma COPD Chronic lung disease excluding asthma and COPD Previous surgery	Coded from problem list with problem codes in SNOMED
Complications	Prone (and duration prone) Healthcare associated infection Acute kidney injury Acute respiratory failure Acute respiratory distress syndrome Shock Cardiomyopathy Myocarditis Myocardial infarction Stroke Deep vein thrombosis Pulmonary embolism Arrhythmia Sepsis Adverse events (including pneumonia) Adverse drug reaction Required oxygen Required dialysis (and type)	Coded from discharge diagnoses (ICD-10) and problem list (SNOMED)
Outcomes	Length of stay* ICU admission* ICU length of stay* ICA admission* ICA length of stay* Discharge status Date of last positive swab Date of first negative swab Death date Death in hospital	*Derived from raw data in movements within hospital
Vaccination	Date of first dose

APACHE: Acute Physiology and Chronic Health Evaluation, COPD: chronic obstructive pulmonary disease, HIV: Human Immunodeficiency virus, ICA: intensive care area, ICU: intensive care unit, ICD: International Classification of Diseases, SNOMED: Standardized Medical Nomenclature for Medicine

No competing interests reported.

Building a Coronavirus Disease 2019 (COVID-19) healthcare registry in an evolving pandemic

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Abstract

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What is new

Introduction

Methods

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Discussion

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Additional Declarations

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Version 1