An empirical study and simulation of EHR software in light of COVID-19

Abstract

The COVID-19 pandemic has highlighted the need for attention directed towards accurate and accessible disease surveillance. As eighty-five percent of all health data is now processed in electronic form, the healthcare industry is increasingly becoming dependent upon patient healthcare data to facilitate well-coordinated and efficient decisions in a timely manner. Electronic health records can be crucial in unearthing the health disparities found among disadvantaged communities in terms of treatment and patient care. By creating a cloud-based software solution, electronic health records will not only be able to share patient health information to multiple healthcare settings, but also provide earlier disease detection and intervention. While implementing telemedicine is proving to be advantageous in reducing physical contact and maintaining social distancing guidelines, much of the dismay from clinicians has been towards the challenges with clinical documentation and patient flow. The CDC has stressed the importance of sending electronic health record case reports to public health officials on countless occasions. The software that vendors create are by no means perfect. Therefore, it is of utmost importance to focus on minimizing disruptions and COVID-19 related errors when using the software. In order to devise software aimed at mining sufficient data and providing tools solely directed at patient care, medical practitioners and software vendors are in endless communication. Implementing the necessary features best suited to support the general population requires eradicating any sort of configuration that can contribute to patient harm. This research will look into the role of EHRs in improving data tracking and collection and whether or not this software can be relied upon in the current climate.