Electronic data technologies, in particular, artificial intelligence (AI) and machine learning, have been fully integrated into healthcare systems, creating not only opportunities but also difficulties. On one hand, such innovations can only pave the way for a medical future that is prone to revolutionizing patient care and medical research, while, on the other hand, these innovations face numerous barriers, for instance, the practices surrounding privacy and ethics regulations.
Healthcare is considered a profession of the utmost significance, but it has failed to take advantage of the latest electronic data technology and apply it where the greatest needs occur. The time being spent is due to the privacy laws involving medical information and the ethical considerations behind their handling. Different sectors, especially the financial and retail industries where data usage has been widely propagated in their operations, have to apply data utilization with great care because of the risks associated with breaching privacy faced in the healthcare sector (Paxton, 2023). Therefore, healthcare practitioners and administrators who tread this path must be prepared to address the many regulatory and ethical complexities that arise in this environment.
While the COVID-19 crisis has served as a catalyst propelling the healthcare industry into the adoption of digital data technologies, there are still several factors that the healthcare industry must take into consideration as new technologies emerge. The toppling this pandemic could entail on global health was unignorable, but it also turned out to be the much-sought-after catalyst that casts light on the need for creative approaches to control infectious disease and the improvement of healthcare delivery. For instance, health services could utilize electronic health records (EHR) and pertinent data as a key tool in communicating COVID-19 vaccines to the public (Merminet al., 2023). Through such endeavors, people got a chance to perceive how technological data means, in dubs, could break down the boundaries and red tape and thus lead to the smooth operational flow of medical assets.
The growth and evolution will be inevitable and hold expectations for a positive future of healthcare data management. Advancements in cloud computing technology will work wonders to peel off the cards of state boundaries and integrate the various healthcare systems so that all information works smoothly, even when the systems operate differently. This combination of ecosystems can greatly help us automate and simplify workflows such as field mapping, monitoring, patient surveillance, and scientific research (Paxton, 2023). Furthermore, the achievements of using predictive analytics and statistical modeling that one can mark as a speedy diagnosis of the patient’s disease in the near future will have a deep influence on the doctor’s life when he curates interpersonal health solutions for every patient.
Not only the language translation, but there are some outstanding outputs in the data generation, too. An essential part of this would be readily accessible medical information in different languages for the policy to have an international impact, without which effective communication between patients and providers of health care would not be possible (Paxton, 2023). AI-powered machine translation tools could be a powerful weapon to overcome language barriers and an indirect approach to reaching the highest level of medical care services, regardless of the patient’s nationality. Along with the usage of SQL and NoSQL databases, data integration becomes relatively simple, and efficient health records collection, storage, and retrieval are implemented, which makes it possible to process and analyze data in real time (Sen & Mukherjee, 2024).
Finally, integrating electronic data technologies into healthcare brings forth an overall change encapsulated with a big impact on patient care, medical research, and the delivery of healthcare, among other things. Despite these issues, the underlying advantages of AI, machine learning, and cloud computing are immense; thus, one cannot understate the power they hold. The COVID-19 outbreak has greatly magnified the world’s eagerness to use health data technologies to surmount healthcare challenges, enabling a future in which data-driven ideas lead innovations and enhance global health outcomes.
References
Mermin-Bunnell, K., Zhu, Y., Hornback, A., Damhorst, G., Walker, T., Robichaux, C., … & Anderson, B. (2023). Use of natural language processing of patient-initiated electronic health record messages to identify patients with COVID-19 infection. JAMA network open, 6(7), e2322299-e2322299.
Paxton, N. C. (2023). Navigating the intersection of 3D printing, software regulation and quality control for point-of-care manufacturing of personalized anatomical models. 3D printing in medicine, 9(1), 9.
Sen, P. S., & Mukherjee, N. (2024). An ontology-based approach to designing a NoSQL database for semi-structured and unstructured health data. Cluster Computing, 271-959-976.