key: cord-0719920-l8ae5zla authors: Kazemi-Arpanahi, Hadi; Moulaei, Khadijeh; Shanbehzadeh, Mostafa title: Design and development of a web‐based registry for Coronavirus (COVID‐19) disease date: 2020-06-25 journal: Med J Islam Repub Iran DOI: 10.34171/mjiri.34.68 sha: b855380604e9d1149c2307af45dde212bd418ad6 doc_id: 719920 cord_uid: l8ae5zla Background: The 2019 coronavirus (COVID-19) is a highly contagious disease associated with a high morbidity and mortality worldwide. The accumulation of data through a prospective clinical registry enables public health authorities to make informed decisions based on real evidence obtained from surveillance of COVID-19. This registry is also fundamental to providing robust infrastructure for future research surveys. The purpose of this study was to design a registry and its minimum data set (MDS), as a valid and reliable data source for reporting and benchmarking COVID-19. Methods: This cross sectional and descriptive study provides a template for the required MDS to be included in COVID-19 registry. This was done by an extensive literature review and 2 round Delphi survey to validate the content, which resulted in a web-based registry created by Visual Studio 2019 and a database designed by Structured Query Language (SQL). Results: The MDS of COVID-19 registry was categorized into the administrative part with 3 sections, including 30 data elements, and the clinical part with 4 sections, including 26 data elements. Furthermore, a web-based registry with modular and layered architecture was designed based on final data classes and elements. Conclusion: To the best of our knowledge, COVID-19 registry is the first designed instrument from information management perspectives in Iran and can become a homogenous and reliable infrastructure for collecting data on COVID-19. We hope this approach will facilitate epidemiological surveys and support policymakers to better plan for monitoring patients with COVID-19. In December 2019, a series of cases of pneumonia with unknown etiology occurred in Wuhan, Hubei Province, China. On January 7, 2020, the novel coronavirus (COVID- 19) , previously known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or 2019-nCoV), was identified as the causative organism (1, 2) . It is classified as a type of RNA virus that belongs to the family of coronaviruses, which primarily leads to a respiratory sys-tem infection (3) . COVID-19 is highly contagious that rapidly spread to other countries. The World Health Organization (WHO) has recently declared the COVID-19 a public health emergency (4) . Given the significant burdens associated with COVID-19, decision was made to adopt information technology and data infrastructures to bolster efficient research, surveillance, and treatment of this emerging outbreak. Clinihttp://mjiri.iums.ac.ir Med J Islam Repub Iran. 2020 (25 Jun); 34:68. 2 cal registry is one of such information platforms that standardize the collection of highly generalizable data and reinforce research infrastructure (5, 6) . Clinical registries have a great potential for epidemiological surveillance, evaluating health-care delivery patterns, tracking clinical outcomes, describing disease natural progression, evidence-based therapy, and comparing the effectiveness of different interventions and post marketing drug surveillance. Moreover, clinical registries allow the study of the designed parameters and recruitment of participants for clinical trials (7) (8) (9) (10) . The COVID-19 registry will serve as a data source to standardize collection of comprehensive data related to many unclear aspects of COVID-19, such as transmission patterns, severity, clinical phenotype, prognostic factors, therapeutics' plans effectiveness and complications, survival estimation, incidence and prevalence of disease across country, and thereby allowing collaboration on research and surveillance of COVID- 19. However, despite the advantages afforded by clinical registries, some considerations need to be addressed from a data management perspective: the design of an effective data capture system and determination of the required data elements and validity of their corresponding values (11) (12) (13) . Therefore, in this study, the required data elements for COVID-19 were defined and a clinical registry platform that met these requirements was designed. This was a cross-sectional and descriptive study in 2020 that was conducted in two phases: in the first one, the aim was to identify required data elements and validated data capture template to be included in the COVID-19 registry, and the second one was designing a registry system for COIVD-19 on the web platform. Literature review First, an extensive literature review to identify the COVID-19 MDS data elements was performed. In the first step to retrieve related resources, the Web of Science, ScienceDirect, Embase, Scopus, Elsevier, Cochran, Pub-Med and Google Scholar were reviewed, and the follow-ing search terms were used: (designed using English MeSH keywords and Emtree terms): "COVID-19" or "Novel coronavirus 2019" or "2019 nCoV" and "clinical characteristics" or "para clinical characteristics" or "epidemiological characteristics". After selecting the advance search interface in the mentioned databases using title, title/abstract, title/abstract/keyword and topic fields, and setting up Boolean operators (AND, OR) and implementing the input and output criteria (full text English articles from Dec 2019-Mar 2020), 18 articles were included in the study (3, (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) . Data were extracted from the related retrieved resources and entered into the checklist with 2 administrative and clinical sections. Questionnaire development A questionnaire was developed using the data elements of the checklist and included 5 columns: "very important", "important" "neutral", "low important", and "very important" for each data item (eg, patient name, visit number, vital sign, exposure and etc.). To add necessary data elements by experts, a blank row was provided at the end of the questionnaire. The content validity of the questionnaire was assessed by an expert panel, including 2 infectious specialists and 3 health information management (HIM) experts. Also, test-retest was used to evaluate the reliability of the questionnaire. Delphi phase The initial MDS content was validated by Delphi technique using 2 rounds by a group of multidisciplinary experts working in hospitals affiliated to Ilam University of Medical Sciences (west of Iran). Table 1 shows the demographic characteristics of these experts. The experts were asked to review the initial data list to score each item according to their importance perceived by them based on a 5-point Likert scale, ranging from 1 to 5, where 1 indicated "not important" for inclusion and 5 indicated "highly important" for inclusion. Agreement was reached for data elements based on experts' agreement level. After initial ranking, items with less than 50% agreement were deleted, those with more than 75% agreement excluded from the second round, and those with 50% to 75% agreement were surveyed in the second round. The checklists were individually presented 3 to the experts who were blind to the scores of other experts, and if there was 75% consensus over a subject, it was included into the final MDS. We used Visual Studio 2019 to design COVID-19 webbased registry because of its numerous benefits (eg, costeffectiveness, scalability and accessibility, user friendliness, fast and convenience, custom search, improved intellicode, clipboard and refactoring attributes) (31, 32).The proposed system was implemented with cascading style sheets (CSS) technology as a web-based program. CSS, along with the Hypertext Markup Language (HTML), was used to describe the presentation of documents and set the document syntax, layout, display format, and visual effects (eg, font type, color, spacing, and sizes). The code was written in Java script language for designing the website. Finally, Structured Query Language (SQL) was used to create the relational database (RDB). SQL provides efficient and systematic storage of data with high performance, availability, scalability, flexibility, management, and security (33) . The results of this study are divided into 3 phases: The proposed COVID-19 MDS was divided into the nonclinical section with 4 data classes, including 43 data elements, and the clinical data category with 4 data classes, including 44 data items. The nonclinical section includes sociodemographic, identification number, and patient disposition classes, and the clinical category includes diagnostic, exposure, physical examination, and medical / diagnostic procedure. The potential participants who determined the final data elements of the MDS of the COVID-19 registry were 25 medical specialists. However, 2 specialists did not participate in the study. Table 1 shows the demographic characteristics of the experts. The results of the 2 Delphi rounds Sociodemographic characteristics 18 3 10 5 1 0 4 14 Identification 10 3 4 3 3 0 1 4 Patient disposition 15 2 10 3 1 0 2 12 Clinical data category Diagnostic 15 3 9 3 2 0 1 10 Exposure 8 3 2 3 2 0 1 3 Physical examination 11 3 4 4 2 0 2 6 Medical procedures 10 1 6 3 2 0 1 7 Total 87 18 45 24 12 0 12 56 Tables 2 and 3 . The experts participated in 2 rounds by completing the questionnaire. At the end of the first Delphi round, 18 data elements were deleted (< 50%), 45 moved to the next round (50%-75%), and 24 marked as definitive (75% <). In addition, no new data elements were suggested by the experts. After the second round, in general, 13 data elements for the nonclinical and 18 elements for the clinical category were excluded from the report template. Therefore, the experts agreed on 30 data elements from 43 data elements of the nonclinical category. The second category was the clinical data involving 4 data classes with 26 data elements. The ultimate data elements for the nonclinical and clinical categories were 30 and 26, respectively ( Table 2 ). The results of weighing of data elements after the second round of Delphi are displayed in Table 3 . In Table 4 , data classes, elements, and their formats and standard contents (recording template) were defined for 2 nonclinical and clinical data categories. In the development of the software, our focus was on accessibility and user-friendliness of the system to expe-dite reporting time. Our designed system uses an advanced search capability to enable custom search, contact to site administrator, provide useful news about disease (prevention, self-care, treatment information, etc.), rendering daily statistics and multimedia instructions. Access to the registry is provided to registered members on the system home page (user name & password boxes). Each user has a unique identification password and username to log into the system. Figures 1 and 2 display the designed webbased registry screen of COVID-19. The lesson learned from previous global pandemics and the widespread prevalence of zoonotic viral diseases (eg, SARS and MERS), highlights the importance of patient registries in the field of new emerging outbreaks such as COVID-19 (34, 35) . In this regard, for proper implementation of a public health surveillance system (PHSs), clini- 6 cal registries offer enhanced progresses in systematic collection, analysis, comparison, and integration of population-based data. Clinical registries allow ongoing monitoring and benchmarking of clinical management and treatment outcomes, and thus are considered among the most effective strategies for quality healthcare improvement. They are powerful and comprehensive tools for conducting research and detecting eligible subjects to contribute in a particular study or clinical trial (5, (36) (37) (38) . COVID-19 registry has been developed on the web platform enabling scientific teamwork in the field of COVID-19 and purposes to achieve a collaborative multi setting research study with a flexible registry structure. Moreover, no extra onsite software installation, configuration, or hardware is needed because of the web-based platform. One of the basic steps in building a registry was determining a minimum and yet inclusive required data set that would be standardized across organizations and could pave the way for collaboration between researchers (39) . To identify the necessary data on COVID-19 across clinical and public health information systems, initially a list of potential data elements gathered through conducting extensive literature review. Also, the number of data elements was reduced after the expert panel's discussion and vote, and ultimately the MDS were finalized for inclusion in the COVID-19 registry. The COVID-MDS aims to harmonize the collection process, and increase the comparability of clinical care data across COVID-19 registries and databases, and facilitate pooled analyses to address clinical research questions. The quality of research results depends on generalizable and high quality data (40) . As Ieva et al (2014) stated, "when reliable data capture system recording data regarding a disease natural history progression, researchers can design studies more creditably and identify suitable subjects." (41) Also, the International Conference on Harmonization (ICH) guideline E6 on good clinical practice (GCP) necessitates that clinical trials be conducted based precise, comprehensive and verifiable data to guarantee patient safety and data quality. Thus, the credibility of registry is emphasized by researchers (42) . Studies derived from well-designed and well-implemented registries provide a more realistic view of clinical procedures, patient outcomes, safety and efficacy, and measurable effec- 7 tiveness, and support the decision-making and evidencebased design process (43) . The quality of clinical registers can be restricted due to poor uptake or unreliable data entry process. The manual data entry is time-consuming for clinical staff and is vulnerable to documentation errors, such as inaccuracies and omissions (9, 44) . In COVID-19 registry, an electronic web-based data entry is provided to automatically reject incorrect values or those that are outside the range; furthermore, the manual entry of data is avoided as much as possible. Furthermore, to comply with other data quality criteria, such as data consistency and comparability in COVID-19 registry, first, most required data elements and their values were determined for reporting COVID-19 in a consistent manner across Iran's health system. COVID-19 registry is comprehensive and can provide an in-depth description of specific patient cohorts rather than delivering epidemiological data. Another key feature for any registry is interoperability with other health information systems that can be helpful to avoid duplication of data entry and reduce the workload on care givers. Bergin et al (2016) also recognized some possible challenges in developing a registry that hinder comprehensive and accurate data capture, including the increased workload of health care providers and proper integration of data capture into daily clinical workflow (45) . Therefore, it is valuable to harmonize data elements, data descriptions, and process for uniform capturing of each item (46, 47) . Thus, in this study, both COVID19 MDS and detailed categories (levels) and data formats for data capturing were defined. For future studies, working on technical aspects of data exchanging to automated pool data in the registry is the next challenge. Given some unfamiliar aspects of the COVID-19, further development and adjustments are required; thus, conducting a pilot study, including a further Delphi step to refine the MDS, is recommended. Moreover, this MDS may need to be evaluated from the perspectives of larger group of medical and public health experts to be applicable at the national level. Further, we used the Delphi consensus approach to reach an agreement on COVID-19 MDS. This technique has been demonstrated to be suitable for assessment information systems requirements (48) . However, one of its restrictions is that most views are marginalized. Despite the aforementioned limitations, this registry provides a standardized and agreed dataset on COVID-19 to accumulate patients, so gradually larger cohorts will be available in the future. In addition, this registry can collect large volumes of data from multiple settings and lay the foundation to conduct in-depth analyses by the artificial intelligence (AI) technique on many unfamiliar aspects of COVID-19. In addition, it is expected to push quickly towards better scientific collaboration for COVID-19. Registry implementation also allowed us to evaluate the quality of care and to help inform best practice in controlling COVID-19. This study represents a fundamental effort towards building a national registry that uses information management approaches to improve accuracy, completeness, comparability, and interoperability of data about COVID-19 across the health care sector. This registry helps to conduct surveys to study various aspects of COVID 19 using a set of variables that were included in the registry according to the experts' opinions. 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The authors declare that they have no competing interests.