A near real-time electronic health record-based COVID-19 surveillance system: An experience from a developing country

Abstract

Context Access to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making. Aim To demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran. Method This COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients’ data, as well as a pool of health data for analytical and decision-making purposes. Outcomes Using this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres. Conclusion Electronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19. Implications Improved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems.

Evaluating the success of Iran Electronic Health Record System (SEPAS) based on the DeLone and McLean model: a cross-sectional descriptive study

“…This secure channel is provided using the SHAMS network, a national private health information network. Transferring health data using the SHAMS network guarantees the confidentiality of patients' data [7]. SEPAS is not used by physicians or nurses currently, but when healthcare providers are supposed to use SEPAS, quick response and stability are more critical.…”

“…The software infrastructure instances are installed in several locations as SEPAS nodes, hosted by each medical university in the country. Hospitals which are under the supervision of the associated medical universities exchange health data through their corresponding SEPAS nodes [6,7]. Management Centre of Statistics and Information Technology of the ministry is accountable for its development.…”

Creating rare epilepsy cohorts using keyword search in electronic health records

“…[SNOMED-CT]), and other text search software (e.g., EMERSE, Elasticsearch). 8,9,13 A traditional method of cohort creation is using administrative codes. Administrative codes are commonly used for clinical research because they are standardized diagnostic codes, widely adopted, and integrated in EHRs.…”

“…More work is needed to compare the tools available for creating rare epilepsy cohorts, including regular expression text search, administrative codes (e.g., ICD, systematized nomenclature of medical clinical terms [SNOMED‐CT]), and other text search software (e.g., EMERSE, Elasticsearch) 8,9,13 . A traditional method of cohort creation is using administrative codes.…”

Designing a comprehensive minimum dataset for patients with COVID-19 in Iranian hospital information systems

“…Various studies have highlighted the importance of registries and EHRs in managing COVID-19 data (Kazemi-Arpanahi et al, 2020; Khorrami et al, 2020; Sheikhtaheri et al, 2022; Zarei et al, 2021). For instance, a web-based registry specifically designed for COVID-19 aimed to provide a reliable data source for reporting and benchmarking the disease (Kazemi-Arpanahi et al, 2020).…”