Health and healthcare are the most crucial part of a human’s life. The drift of healthcare from traditional methods to EMRs, EHRs and other AI in healthcare have been inevitable. The change has bought us close to paper-free systems and have introduced us to an easier approach. Eventhough these data models have a lot of advantages of their own. Some major challenges taht they have to face are mentioned below:
Healthcare Data – Healthcare data is too complicated, fragmented, and difficult to model within a unified data analytics model. The data are very sensitive and need to be secure making it harder to implement these models.
EMR Issues – Most existing EHR/EMR systems are not designed with usability and clinical workflow in mind. As a result, these systems become obstacles for physicians and their patients, forcing physicians to work outside of working hours to complete documentation. Traditional EMR systems have limited or no decision support features to assist physicians/doctors in their assessment and clinical process.
System Interoperability – The seamless, effective, and meaningful exchange of patient information is yet to be achieved across healthcare systems. The existing EMR systems do not use open standards, making it difficult for patients and clinics to integrate medical records from multiple sources, resulting in unnecessary tests and delayed diagnoses.