Large language models (LLMs) have emerged as a frontier technology in Artificial Intelligence (AI) systems that can perform complex tasks like text translation summarization and retrieval of information. These developments are driven by the availability of large-scale training datasets and advances in computational capabilities.

The federal healthcare system is unique, and it requires a specialized approach to ensure accuracy and quality. One way to achieve this is by using agency-specific data sources in combination with traditional LLMs such as GPT-3. In this article, we explore the concept and role of RAG in the federal healthcare space.

Artificial Intelligence (AI) has undergone a remarkable evolution in recent years, propelling us into an era where machines are not just learning but making informed decisions. On the most basic level, AI refers to the power of computers to follow the tasks normally performed by humans, like recognizing objects, making decisions, and making translations between languages. Initially, AI research was concentrated on the design of systems that could compute, learn, and solve difficult problems. In the modern AI era, progress is traceable to the new techniques in machine learning (ML) and deep learning (DL)

Artificial intelligence and machine learning offer varied advantages to the learners and trainees. However, it is important to note that just like any other technology in the world, artificial intelligence and machine learning have their challenges. This is because some of the people are bound to take advantage of this technology to exploit others. With a positive mind and necessary approaches, it will be possible to overcome these challenges.

FHIR is revolutionizing the healthcare industry by providing a standard for exchanging health information across different EHRs and medical apps. By defining the architecture of exchangeable packets of health data, FHIR is able to achieve interoperability using over 150 resources. These resources reflect typical terms commonly used in healthcare settings, including disease, practitioner, location, and scheduling. This article explores the potential impact of FHIR on improving data security, quality, and velocity.

CMS is expecting that the evolution of medical technologies and other products, such as innovative analytics, artificial intelligence (AI), data interoperability (DI), and machine learning (ML), to deliver evidence-based services to clinicians and patients at the point of care. Most importantly, cloud computing has played an important role in modernizing health-associated technological tools. Let’s take a look at how Microsoft technology stack can assist…

The private sector denotes any section of the economy that is operated primarily by individuals and corporation for the purpose of profit and is also not controlled by the state. Hence, it comprises all the for-profit enterprises that are not run or not under the ownership of the government. Currently all evidence points to the fact that the private sector is progressively surpassing the public sector in various aspect of development as well as product and service delivery.

This concept paper presents a research proposal for the study of cloud computing and its impact upon the healthcare community. The proposed research intends to study the interoperability of healthcare systems, cloud IT solutions and the potential security risks; Furthermore, this research will examine the evident reluctance in making use of cloud computing technology and the competitive pressure versus the concerns and doubts faced by those considering this move.

For many years, multiple organizations across all sectors of the economy had unintegrated data systems. With time, there was a realization that unintegrated data caused a lot of pain, struggles, and inconsistencies. It was discovered that it was not possible to look across an organization’s data and make an informed decision. This led to a rise in the concept of data warehousing, which required that the disparate systems in an organization that held different types of data be integrated.

The digitization of healthcare records in the U.S. healthcare system has tremendously evolved. The process allows easy availability, accessibility, and interpretation of patients’ data within the healthcare environment. The digitization of healthcare data also ensures data structuring and standardization, fostering automated clinical decision-making and various programmed processes central to optimal patient outcomes. Health Level Seven International (HL7) has demonstrated great commitment to addressing healthcare records digitization challenges by developing healthcare data transfer and data modeling principles for the past two decades. FHIR (Fast Healthcare Interoperability Resources) is HL7’s new healthcare information exchange standard founded on novel industry techniques but guided by decades of experiences gained through the implementation of preceding standards

Fast Healthcare Interoperability Resources have a significant role in interoperability; therefore, it is set to revolutionize how health information is collected and analyzed used in decision making and stored for future reference.