Coronavirus disease 2019 (COVID-19) surveillance system: Development of COVID-19 minimum data set and interoperable reporting framework

Abstract

INTRODUCTION:

The 2019 coronavirus disease (COVID-19) is a major global health concern. Joint efforts for effective surveillance of COVID-19 require immediate transmission of reliable data. In this regard, a standardized and interoperable reporting framework is essential in a consistent and timely manner. Thus, this research aimed at to determine data requirements towards interoperability.

MATERIALS AND METHODS:

In this cross-sectional and descriptive study, a combination of literature study and expert consensus approach was used to design COVID-19 Minimum Data Set (MDS). A MDS checklist was extracted and validated. The definitive data elements of the MDS were determined by applying the Delphi technique. Then, the existing messaging and data standard templates (Health Level Seven-Clinical Document Architecture [HL7-CDA] and SNOMED-CT) were used to design the surveillance interoperable framework.

RESULTS:

The proposed MDS was divided into administrative and clinical sections with three and eight data classes and 29 and 40 data fields, respectively. Then, for each data field, structured data values along with SNOMED-CT codes were defined and structured according HL7-CDA standard.

DISCUSSION AND CONCLUSION:

The absence of effective and integrated system for COVID-19 surveillance can delay critical public health measures, leading to increased disease prevalence and mortality. The heterogeneity of reporting templates and lack of uniform data sets hamper the optimal information exchange among multiple systems. Thus, developing a unified and interoperable reporting framework is more effective to prompt reaction to the COVID-19 outbreak.

Introduction

In December 2019, a cluster of pneumonia cases of primary unknown etiology emerged in Wuhan City, Hubei Province, China. After extensive speculation, ultimately, a novel species of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was recognized as the causative pathogen of the disease. The disease name was initially called “2019 novel CoV” and later changed into CoV disease 2019 (COVID-19). The highly contagious nature of the disease and rapid increase of emerging new cases in China and many other countries have led the World Health Organization (WHO) on January 30, 2020, to declare the COVID-19 outbreak a global public health threat.[1,2,3,4,5,6,7,8]

Surveillance is the foundation of public health practice and research. To prepare for and deal with COVID-19 pandemic outbreak, a robust and responsive surveillance system should be considered, which provides a partnership cooperation among public health practitioners, clinicians, and policymakers to direct disease control and prevention efforts.[9,10] The effectiveness of COVID-19 Surveillance System (COVSS) depends on clinical data and reports from wide scattered public and hospital information system as data input (e.g., Hospital information systems (HIS), Iranian Electronic Health Record (so-called SEPAS), Iranian Integrated Health System (known as SIB), and other clinical information systems). In this sense, effective implementation of COVSS necessitates clear and coherent sets of data, along with unified standards for sharing this data rapidly, supporting e-health and P4-medicine (Predictive, Preventive, Personalized, and Participatory).[11,12] A modular methodology should be developed in the design and implementation of information systems that will increase their integrity and enterprise usefulness. Data standardization and harmonization is the first important step in the life cycle of the information system (known as System Development Life Cycle (SDLC)) and it should be achieved conforming to a proper plan.[13,14] Minimum Data Set (MDS) is one standard approach for data collection, providing accurate access to health data. In respect to the development Public Health Surveillance (PHS), MDS solution offers enhanced progresses in systematic collection, interpretation, comparison, and integration of data regarding health-related threats. However, data sharing may also be hindered if standardized methods are not used for coding and formatting data. The use of Information and Communication Technology may aid in enabling standardized, automated, and interoperable frameworks for data exchange between public and health information systems with heterogeneous platforms.[15,16,17,18,19] Thus, the present study was conducted to provide a comprehensive MDS as a template for implementing a COVSS and then presented designing an exchanging framework toward interoperability in the context of COVID-19.

Materials and Methods

This was a cross-sectional descriptive study conducted in 2020. Initially, to design the COVID-19 MDS, a combination of literature review and expert consensus approach was used. In this regard, a review of the literature was conducted to retrieve related data resources on COVID-19, while also applying guidelines and instructions issued from local, national, and international organizations, especially the WHO and Center for Disease Control. Literature review was limited to English languages between December 2019 and March 2020 in the full text along with valid sources available on PubMed, Scopus, Web of Science, Science direct, Embase, and Cochrane databases.

To confirm the COVID-19 MDS, the preliminary data list was evaluated through consensus of the selected experts after review and discussion. Thus, we brought together a multidisciplinary team of 40 samples with expertise in virology, epidemiology, public health practitioners, infectious diseases, and experience in health information management. A researcher-made questionnaire was created to validate data fields. The experts participating in the study were asked to review the initial draft of variables to score the items according to the importance perceived by them based on a 5-point Likert scale (ranging from 1:“very slightly important” to 5:”highly important”.[1,2,3,4,5]

The content validity of the questionnaire was evaluated using the comments from medical informatics and health information technology experts (a total of six persons, consisting of three experts in each field). For the reliability of the questionnaire, the test–retest method was used by 10 infectious disease specialists. Through decision Delphi technique in two rounds, decisions on included data fields were made based on the agreement level. Specifically, data fields with <50% agreement were excluded in the first round, while those with more than 75% agreement were included in the primary round. Those with 50%–75% agreement were surveyed in the second round, and if there was 75% consensus over a subject, it was regarded as a final data field. Further, if any experts intended to change, delete, or add a variable for a specific purpose, they were asked to write an acceptable reason. The collected data were analyzed by SPSS 16 where Spearman's rank correlation coefficient was used to evaluate the reliability of the questionnaire, which showed a coefficient of 85%.

To determine the corresponding information content of data fields, a complete COVID-19 patient record sample in the Ayatollah Taleghani Hospital (focal center of COVID-19, Abadan, Iran) was selected and its contents were extracted by a checklist. Then, the information content was coded using selected classification or nomenclature systems.

In the next step, all scattered codes were mapped to Systematized Nomenclature of Medicine–Clinical Terms (SNOMED-CT) reference codes using NPEX SNOMED-CT online browser (https://snomedbrowser.com/). This process was visualized through MindMaple Lite 1.71 software as a graphic user interface representing thesaurus mapping across multiple medical terminologies [Figure 1]. Finally, SNOMED-CT codes were structured into Health Level Seven-Clinical Document Architecture (HL7-CDA) standard framework to provide the message syntax. Finally, the Extensive Markup Language (XML) hierarchical rules were defined for standardization of the message structure. XML provides a comprehensive and unified human- and machine-readable resource which formally defines and represents CDA information as a set of concepts in a given domain. Overall, the CDA schema was designed based on coded and structured title and body (CDA, level two and three) through SNOMED-CT reference codes and XML structure.

Figure 1.

MindMaple Lite1.71 routes

Results

After the literature review, the proposed COVID-19 MDS was divided into administrative and clinical data categories. Each of the categories contained three and eight data class and 52 and 85 data field, respectively. The administrative data category included demographical, admission, and report ID data classes. The second category was clinical data involving clinical presentation, exposure to casual factors, physical examination, signs and symptoms, laboratory findings, CT results, treatment plan, and discharge outcome. Then, Delphi surveys were used to finalize the primary MDS. The results of two Delphi rounds are presented in Table 1.

Table 1.

Administrative and clinical data classes for a minimum data set for coronavirus disease-19 reporting

Data classes	Total number of fields	First round of Delphi			Second round of Delphi			Final
		<50%	50%-75%	75%<	<50%	50%-75%	75%<
Administrative data category
Demographical	27	6	12	9	6	0	6	15
Admission	12	4	3	5	2	0	1	6
Report ID	13	3	5	5	2	0	3	8
Clinical data category
Clinical presentation	8	3	3	2	2	0	1	3
Exposure	5	3	2	0	1	0	1	1
Physical examination	13	4	3	6	2	0	1	7
Sign and symptom	6	2	1	3	0	0	1	3
Laboratory	21	7	6	8	3	0	3	11
Imaging CT	10	4	3	3	2	0	1	4
Treatment plan	8	3	2	3	1	0	1	4
Discharge outcome	14	4	5	5	3	0	2	7
Total	137	43	45	49	24	0	21	69

CT=Computed tomography

After the second round of Delphi [Table 1], 45 data fields for clinical and 23 fields for the administrative category were excluded from primary MDS [Table 1]. Overall, the ultimate data fields for administrative and clinical categories were 29 and 40, respectively. In the next stage, for each finalized data field, their corresponding content was extracted from real patient medical records. After defining the information content for the fields, they were coded using selected classification or nomenclature systems (preferred codes). Then, all scattered codes were mapped to integrated codes at SNOMED-CT through MindMaple software. Tables 2 and 3 report the data classes, fields, corresponding content, data format, content definition, as well as preferred and reference codes for clinical and administrative data categories.

Table 2.

Administrative minimum data set description for information exchange of coronavirus disease-19

Required data elements			Real case definition
Data classes/items	Content definition	Response format	Case sample	Vocab code	Preferred codes	Reference codes
A. Demographical data
Name, surname	First/middle/last name	String	Patient name		XaLva	371484003
Father name	First name	String	Person name		XaLva	734006007
Age (years)	Infant: x <1 year*, child: 1 year < x <5 years*, teenage: 5 years< x <17 years*, young: 17 years< x <34 years*, middle age: 34 years < x <65 years*, aged: x >65 years*	Integer	Middle age: 58 years	RCC	X24Ai	28288005
Sex	Male*, female*	Force choice	F	RCC	X768C	703118005
National ID	Numbers range from two to ten digits with two separator dash	Integer	National ID: xx to xxx- xxxxxx-x	RCC	XE2Hj	422549004
Date of birth	yyyy/mm/dd	Date	1962/10/17	RCC	9155	184099003
Place of birth	Geographical location: Province, city, village	Forced choice and string	Iran/Tehran	RCC	XaG3t	315446000
Marital status	Single*, married*, widow*, other*	Force choice	Married	RCC	XE0oa	87915002
Employment status	Unemployed*, employed*, retired*, student*, other*	Force choice	Employed	RCC	Ua0TB	224363007
Occupation	Free text	String	EMS nurse	RCC	XaBrW	106292003
Educational level	Illiterate*, under diploma*, diploma*, bachelor*, master of science or above*, unspecified*	Forced choice and string	Received university education	RCC	Ua0Rt	224300008
Race/nationality	Iranian: Persian*, Kurdish*, Turkish*, other*	Forced choice and string	Iranian/Persia	RCC	Xa6g5	297553001
Home address	Province-city-street- alley-house no	String	Tehran	RCC	134Z	433178008
			City-street-alley-house no	RCC	9153	184097001
Postal/zip code	Ten digit with dash	Integer	xxxxx-xxxxx	RCC	9158	184102003
Phone number	Ten digit with + 98	Integer	xxxxx-xxxxx	RCC	9158	824551000000105
B. Admission data
Admission date	yyyy/mm/dd	Date	2020/2/5	RCC	Xa0cK	399423000
Reason for admission	Free text	String	Influenza-like symptoms	ICD10	R68.8	315642008
Medical record number	Six digit with two separator dashes	Integer	MRN: xx-xx-xx	RCC	Xn73J	398225001
Social security number	Nine digit with two separator dash	Integer	SSN: XXX-XX-XXXX	RCC	XagCD	398093005
Physician ID	Numbers range from two to eight digits	Integer	phys. id: xx to xxxxxxxx	RCC	Xabhz	713578002
Insurance ID	Eight digit number	Integer	Ins. id: xxxxxxxx	RCC	XE2Hj	456281000000100
C. Report Identification data
Report heading	COVID-19 reporting	String	Unstructured free text	RCC	Xa4H9	716931000000107
Report ID	rep. id: xxx-x-xx	Integer	Six digit with two dash	RCC	Xbn9Z	439272007
Report Date	yyyy/mm/dd	Date	yyyy/mm/dd	RCC	Uc35Z	399651003
Reporter user ID	Personnel id: xxxx	Integer	Numbers range from three to eight digits	RCC	Xabhz	713578002
Recipient user ID	Personnel id: xxxx	Integer	Numbers range from three to eight digits	RCC	Xabhz	713578002
Reporting organization ID	Hospital ref. no: xxxx	Integer	Numbers range from two to eight digits	RCC	9R6K	185975009
Recipient organization ID	Public health no. xxx	Integer	Numbers range from two to eight digits	RCC	XaC8K	719051000000105
Sample ID	Sample id no. xx-xx	Integer	Four digit with a separator dash	RCC	4j33	719051000000105

RCC=Renal cell carcinoma, COVID=Coronavirus disease

Table 3.

Clinical minimum data set description for information exchange of coronavirus disease-19

Required data elements			Real case definition
Data classes/items	Content definition	Response format	Case sample	Vocabcode	Preferredcodes	Reference codes
D. Clinical presentation
Current existing condition	Hypertension Chronic respiratory diseases (specify type)	Select all that apply and string	Mild COPD	ICD10	J44.8	313296004
	Diabetes
	Coronary heart disease (specify type)
	Cerebrovascular diseases (specify type)
	Mental diseases (specify type)
	Cancer (specify type)
	HIV/AIDS infection
	Renal diseases (specify type)
	Liver disease
	Other
	Pregnancy status (if patient is a woman)	Force choice	Not pregnant	RCC	X76Qu	60001007
Days from exposure to symptom onset	<2 days*, 2-4 days*, 4-7 days*, 1-2 weeks*, 2-4 weeks*, 1-3 months*, 3-6 months*, 6-12 months*, 1 year*<	Integer	10 days	RCC	XaB8B	307474000
Days from illness onset to treatment		Integer	2 days	RCC	XaB8B	307474000
E. Exposure to casual factors
Exposure history	Contact/bitten with sick domestic or wild animal	Select all that apply and string	Contact with suspicious person outside wards	ICD10 CM	Z03.818	506901000000103
	Contact with suspicious person outside wards
	Contact with patients in isolation wards
	Contact with specimens
	Exposure to contaminated surfaces Other
F. Physical examination
Body mass index (kg/m2)	<18.5*, between 18.5 and 24.9*, between 25 and 29.9*, >30*, unknown*	Force choice and integer	Body mass index 25-29, overweight	ICD10	E66.9	162863004
Respiratory rate	≤24 breaths per min* >24 breaths per min*	Force choice and integer	18 breath per minute	ICD10	R06.89	289100008
Temperature (°C)	<37.3*, 37.3-38*, 38.1-39*, >39.0*	Force choice and integer	Body temperature above 39	ICD10	R50.9	50177009
Heart rate (bit/min)	<60*, between 60 and 100*, >100*, unknown*	Force choice and integer	Normal heart rate	RCC	Xa7s1	76863003
Blood group	RH positive: A, B, AB, O RH negative: A, B, AB, O	Force choice and string	Blood group B Rh (D) positive	RCC	Xa0dT	278150003
Blood pressure (mmHg)	<120*, between 120 and 129*, between 130 and 139*, >140*, unknown*	Force choice and integer	Normal BP, 120-129	RCC	Ua1fM	2004005
Lung examination	Clear or normal*, rales*, decreased breath sounds or dullness*, rhonchi*, wheezing*, other*	Select all that apply and string	Rhonchi present	ICD10	R09.8	268929007
G. Signs and symptoms
Asymptomatic	Yes*, no*	Force choice	Symptomatic disease	RCC	XC0v5	264931009
If asymptomatic response is “NO,” the symptom is:	Fever Cough Dyspnea	Select all that apply and string	Dry cough Dyspnea Fever	ICD10	R06.2 R06.8 R50.9	49727002 230145002 722892007 8579004
	weakness		Weakness		R11
	Myalgia
	Chest tightness or pain
	Expectoration
	Headache
	Sore throat
	Diarrhea
	Anorexia
	Nausea
	Abdominal pain
	Hemoptysis
	Other
Symptom onset date	yyyy/mm/dd	Date	2020/1/28	RCC	XaR6r	520191000000103
H. Laboratory findings
Sample type	Nasopharyngeal swab	Select all that apply and	Nasopharyngeal swab	RCC	412B	168141000
	Oropharyngeal swab	string
	Broncho alveolar lavage
	Nasopharyngeal aspirate
	Sputum
	Tissue (lung) biopsy
	Serum
	Whole blood test
	Stool
	Urine
	Other
CBC	White blood cell count	Integer	CBC routine test	LOINC	24317-0	26604007
	Lymphocyte count
	Platelet count, hemoglobin Neutrophil count
Coagulation profiles	Prothrombin time APTT	Integer	Coagulation/bleeding tests normal	RCC	42Q1	165562007
	D-dimer
Blood lipids and electrolytes	Triglyceride Total cholesterol	Integer	Serum triglycerides borderline high	RCC	44Q3 44I2	442193004 166685005
	Low-density lipoprotein		Electrolytes normal
	Serum potassium
	Serum sodium
Blood gases analysis	PaO2	Integer	Normal blood gases	RCC	X7702	250544002
	PaO2/FiO2
	Lactic acid
	PaCO
Liver and renal function	Creatinine Aspartate aminotransferase	Integer	Serum creatinine raised	ICD10	R79.8	166717003
	Albumin
	Alanine aminotransferase
Specialty LAB	Elisa test*, real-time PCR*, virus culture*, Other*	Select all that apply and string	Analysis using real time PCR	LOINC	76581-8	444076003
Sampling time	yyyy/mm/dd	Date	2020/2/3	RCC	4I32	168149003
Test time	yyyy/mm/dd	Date	2020/2/4	RCC	X77Vk	252127002
Sampling location	Nasal*, pharyngeal*, mouth*, lung*, blood vessel*, other*	Select all that apply and string	Nasopharyngeal	RCC	Xa0GE	71836000
Test result	Positive CoV*, negative CoV*	Force choice	Positive COVID-19	ICD10	R84.5	13320001000004109
I. Imaging CT
Chest CT-scan	Unilateral*, bilateral*	Force choice	Bilateral chest CT-scan	ICD9 CM	87.41	426827002
CT features	GGO Consolidation interlobular septal thickening	Select all that apply and string	Lung consolidation	ICD10	J18.1	95436008
	Crazy paving pattern
	Air bronchogram
	Spider web sign
	Subpleuoral line
	Bronchial wall thickening
	Lymph node enlargement
	Pericardial effusion
	Plural effusion Other
Lung segment involvement	Average lung Dorsal of right lower	Select all that apply and	Right lower zone pneumonia	ICD10	J18.1	301001009
	Lateral basal of right lower	string
	Posterior basal of right lower
	Dorsal of left lower
	Posterior basal of left lower
	Other
Distribution	Sub pleural diffuse	Force choice	Pleural effusion	ICD10	J11.1	81075000
	Per bronchial
	Peri bronchovascular
	Mixed
J. Treatment plan
Oxygen therapy	Noninvasive mechanical ventilator*, Invasive mechanical ventilator*, ECMO*, other*	Select all that apply and string	Noninvasive ventilation therapy	ICD9 CM	93.90	784821000000105
Drug therapy	Antibiotic treatment*, antifungal treatment*, antiviral treatment*, glucocorticoids*, intravenous immunoglobulin therapy*, other*	Select all that apply and string	Corticosteroid	RX- NORM	C0010137	79440004
Complementary therapy	Yes*, no*, if yes, mention the procedure type*	Select all that apply and string	Respiratory rehabilitation	ICD9 CM	93.99	790841000000106
Consultation program	Mental*, occupational*, family*, social*, other*	Force choice	Mental counseling	ICD9 CM	89.08	313080005
K. Discharge outcome
Discharge date	yyyy/mm/dd	Date	2020/2/9	RCC	XaZuU	442864001
Discharge status	Death*, full recovery*, partial recovery*, other*	Force choice	Postdischarge follow-up	RCC	Xaat1	406151001
If death, underlying cause of death	Related to current disease*, unrelated to current disease*, not applicable*, unknown*	Force choice	Not applicable	RCC	X90ca	385432009
If death, date of death	yyyy/mm/dd*	Date	Not applicable	RCC	X90ca	385432009
Discharge location	Home*, hospital*, other care facilities*: 1- quarantine centers, 2- nursing facility, 3- hospice care, 4-rehabilitation facility	Forced choice	Discharge to home	RCC	XaApt	306689006
Discharge Prescribed drugs	Drug name	String	Naproxen 200 mgtetracycline 250 mg	RX-NORM	C0027396 C0974349	416821000 324012004
Date of follow up	yyyy/mm/dd	Date	2020/2/14	RCC	8H8Z	183616001

COPD=Chronic obstructive pulmonary disease, RCC=Renal cell carcinoma, BP=Blood pressure, CBC=Complete blood count, APTT=Activated partial thromboplastin time, PCR=Polymerase chain reaction, COVID=Coronavirus disease, CoV=Coronavirus, CT=Computed tomography, GGO=Ground-glass opacity, ECMO=Extracorporeal membrane oxygenation, LAB=Laboratory

XML schemas

XML schemas of COVID-19 provide a tools of defining the structure, content and semantics of exchange reports. The report template is divided into administrative and clinical sections. In Figure 2 presents XML based CDA framework related to COVID-19 reporting [Figure 2].

Figure 2.

Extensive Markup Language-based Clinical Document Architecture hierarchical framework related to COVID-19 disease reporting

The HL7-CDA standard was used for standardization of the message syntax. In the CDA structure, the data field related to identification of entities was pasted into the document heading, while the CDA body contained detailed information about clinical findings [Figure 3].

Figure 3.

Free-text Health Level Seven-Clinical Document Architecture framework for information exchange of COVID-19 reporting

Discussion

With the widespread outbreak of COVID-19, Iran Ministry of Health and Medical Education has focused on the coordination of care and highlights the need to standardized data collection to streamline and improve the surveillance capabilities of Iranian Health system in response to this pandemic. In this regard, developing a unified and interoperable reporting framework is most effective to prompt detection and tracking of cases, investigate causes, and control a disease outbreak.[20,21,22] The purpose of MDS is to standardize the collection and reporting of a minimal amount of data as a basis for implementing any electronic systems for clinical, research, surveillance, and management purposes.[23,24,25,26] The developed MDS in this study primarily focused on PHS, whoever can be used for other applications. In this regard, we initially defined an MDS required for unified data reporting of COVID-19. Then, the structure and semantics of COVID-19 disease reporting were standardized according to HL7-CDA for the purpose of information exchange.

The quality of surveillance systems can be limited due to poor uptake or unreliable data entry process. Manual data entry is time-consuming and suffers from the inconsistent and poor-quality data structured forms. Furthermore, reports are inadequate and data are input into incorrect or erroneous fields. Thus, a reliable and friendly data entry process is crucial for capturing high quality data. Each data field should also be comprehensive so that it can be recorded in a few clicks. From a health-care provider's perspective, it is easier to analyze the data fields that are compulsory options rather than free-text data.[27,28] To compliance with data quality criteria such as data consistency and comparability in COVSS, not only a COVID-19 MDS but also more detailed categories (levels) and data formats for data capturing were defined.

New improvements in data collection instruments support the findability, accessibility, interoperability, and reusability (FAIR) of data, emphasizing the need for uniform data that can be integrated from distributed databases.[29,30,31] In this regard, this study therefore provides exchange, aggregate, and proper data management to reach FAIR data regarding COVID-19.[32]

Given the prevalence of COVID-19 in Iran,[33,34,35] the current study determined the national COVSS MDS, to collect, analyze, and report COVID-19 indicators. Each data element was mapped to common coding standards and terminologies to facilitate interoperability between various health systems at local, national, and global levels.

The COVSS MDS can be used in other countries as a main prerequisite to the implementation of the COVID-19 surveillance system. This study also highlights the benefits of standardization of COVID-19 data exchange processes which can be useful to other public health domains. Interoperable reporting for COVID-19 provides timely and reliable clinical data for measuring disease trends, efficiently applying control and prevention actions, detecting high-risk inhabitants or geographic zones, and keeping the clinical community informed through warnings, recommendations, notifies, and guidelines.[36,37,38]

Our study method had three major strengths. First of all, the proposed COVSS MDS was gathered through an extensive literature review combined with a two-round Delphi survey that benefits from evidence based and expert's wisdom in determining data elements. Second, the adoption of standard nomenclature such as SNOMED-CT is suggested for the Electronic Health Record (EHR) as it captures clinical information at the level of details required by clinicians for care provision in most health-care disciplines and settings. Finally, we leveraged HL7-CDA, as a standard for the exchange of clinical documents, which should be readable by computers and humans. HL7 CDA is an XML-based standard which has a simple and very flexible text format for structuring and exchanging information on the Web environment.[39,40]

Given some of the unfamiliar aspects of this novel outbreak, we recommend the development of conceptual models of surveillance systems and conducting a pilot study including a further Delphi stage prior to refine some data categories. In addition, this MDS may need to be appraised from the perspectives of a greater group of clinical and public health professionals to be applicable in a nationwide. Further, this study provides COVID-19 interoperable reporting framework from a data management perspective, but its technological aspects need to be resolved which are beyond our discussions in this article.

Conclusion

An effective COVID-19 surveillance system requires complete and timely information to guide fully informed decisions to reduce the further spread of disease by taking early preventive measures. The template presented in this study can enable interoperability across many clinical and public health information systems that populate the COVID-19 surveillance system. The main output of the proposed template supports collaborations among various healthcare providers and public health agencies in patient care management as well as research or public health purposes. Given some of the unfamiliar aspects of this novel outbreak, we recommend the development of conceptual models of surveillance systems and conducting a pilot study including a further Delphi stage prior to refine some data categories.

Financial support and sponsorship

This research project has been financially supported by Abadan Faculty of Medical Sciences (Iran) under contract number of 98U749.

Conflicts of interest

There are no conflicts of interest.