We live in an era where data is revered, and Electronic Health Records (EHRs) stand as the treasure trove of invaluable information capable of propelling medical research, enhancing outcomes, and shaping the future of healthcare. Artificial Intelligence (AI) is not here to replace anyone but rather to simplify tasks. It is rightly said that the synergy between EHRs and AI has the potential to revolutionize healthcare by streamlining processes, improving precision, and ultimately enhancing patient outcomes. In this article, let’s explore how EHR and AI collaborate seamlessly and investigate the transformative impact of AI on Electronic Health Records.
What are Electronic Health Records
When it comes to patients, their data is of utmost importance. Instead of the traditional method of saving patients’ records by pen on paper, EHRs serve as the digital version of a patient’s medical history. As of January 2022, the global value of Electronic Health Records was approximately $30.9 billion USD.
Let’s travel back in time and understand the journey of EHRs. The traditional means of saving patient records can be traced back to ancient civilizations like the Egyptians, Greeks, and Romans. After that, the records were maintained by the clergy. Cut to the 19th century, we saw a significant boom and development in this concept. In the 1970s, the earliest usage and implementation of EHRs took place. The Regenstrief Medical Record System (RMRS) was the earliest electronic medical records system, developed by Dr. Clem McDonald and his colleagues at the Regenstrief Institute in Indianapolis, Indiana. While the term “EHR” dates back to the 1960s, it gained popularity in the 1990s.
The Institute of Medicine (IOM) advocated for the adoption of EHRs in that same decade. In 2004, the Office of the National Coordinator for Health Information Technology (ONC) was formed to promote this concept and related technologies, and since then, there has been no looking back. With the widespread use of EHRs, programs were established to ensure meaningful utilization. In the early 2010s, The Center for Medicare & Medicaid Services introduced the Meaningful Use Program, further emphasizing the importance of EHRs. In 2015, this program transformed into the Medicare Access and CHIP Reauthorization Act (MACRA) and the Quality Payment Program (QPP). Post that, the HER industry has always been a boom owing to what it brings to the table.
Key Functions of EHRs
- Maintain Patient Records: EHRs serve as centralized repositories for patient information in a digital format, enhancing efficiency, security, and seamless access to critical data.
- Real-time Accessibility: Storing data alone is insufficient. In emergencies, timely access to a patient’s history is crucial for effective treatment. Authorized individuals can access this information in real time.
- Order Management: Healthcare providers can efficiently manage and place orders for a wide range of medical services through the EHR system.
Benefits of Electronic Health Records
EHRs have played a vital role in enhancing the quality of care, efficiency, and security, among other benefits. They provide easy and rapid access to patient information, streamlining processes.
- EHRs assist providers in effectively diagnosing and treating patients.
- They enhance the privacy and security of patient data.
- E-prescribing is now made easy and hassle-free.
- By automating tasks, EHRs have simplified the lives of administrators.
- They offer clinical decision support.
- EHRs improve patient engagement.
- They foster research and analytics.
- Reducing costs through paperwork reduction.
Introduction to Artificial Intelligence in Electronic Health Records
Technically, AI and EHRs have grown in tandem. Here’s an overview
- The early 1970s marked the foundational phase of EHRs in human history. Despite their computerized nature, these record systems were rudimentary, lacking the sophisticated functions that AI has since mastered.
- In the 1980s, Expert Systems, a branch of AI designed to mimic human decision-making, gained prominence. While a significant leap for the healthcare industry, it primarily laid the groundwork for EHRs in clinical decision-making
- In the subsequent decade, Knowledge-Based Systems began yielding results, prompting experts to integrate them into EHRs.
- The 2000s witnessed remarkable advancements in data storage and processing, providing a green light for AI to analyze valuable healthcare data that EHR has.
- By the early 2010s, with advancements in Machine Learning and Big Data, AI in EHRs started illuminating data analysis, predictive modeling, and clinical decision support.
- AI thrives on transparency, in 2014, the OpenNotes initiative blossomed, paving the way for the future application of AI in EHRs.
- A significant stride occurred in 2016 when Google’s DeepMind collaborated with the National Health Service (NHS) to develop Streams, an app utilizing AI to identify individuals at risk of acute kidney diseases. This highlighted the pivotal role of AI in clinical workflows and EHR systems.
- In 2018, a roadmap for the future of AI in EHRs, particularly in diagnostic imaging, took shape following the FDA’s approval of an AI algorithm in radiology.
- Since its inception, AI, especially in healthcare and mainly in EHRs, has experienced unstoppable integration and advancements, promising a bright future. The ongoing developments showcase the transformative journey of AI, turning a basic computerized record into a marvel.
Using AI to Improve EHR Systems
Data Management and organisations
EHRs generate a vast volume of data encompassing patients’ health, medical history, treatments, and outcomes. The role of AI is pivotal in managing this data by organizing it in a structured manner. Through the use of algorithms, it strategically structures the data, providing significant relief for healthcare providers when accessing and utilizing it efficiently.
Natural Language Processing (NLP) for Clinical Notes:
AI-powered Natural Language Processing (NLP) extracts information from unstructured clinical notes, transforming it into structured data for analysis. Additionally, it enhances the accuracy of documentation by converting spoken or written language into structured data.
Clinical decision support:
After analyzing the vast datasets, AI can identify patterns. AI-powered EHRs provide healthcare providers with recommendations, enabling well-informed decisions regarding patient health. Epic Systems Corporation is a renowned provider of Electronic Health Records (EHRs). Its widely utilized EHR platform, EpicCare, is prominent in hospitals. A notable feature of EpicCare is its Clinical Decision Support, aiding healthcare providers in making well-informed decisions.
Cybersecurity and Fraud detection
Wherever data exists, the risk of fraud emerges. AI algorithms play a crucial role in identifying fraudulent patterns by thoroughly analyzing data. Furthermore, these algorithms ensure that sensitive data is accessed only by authorized individuals, thereby enhancing security and safeguarding against unauthorized usage.
Patient engagement and remote monitoring
Leveraging AI in EHRs significantly improves patient engagement and remote monitoring. It enables tailored communication with patients through chatbots and virtual assistance. AI analyzes data from remote monitoring devices, wearables, and smart sensors, facilitating the early detection of health issues and tracking patients’ vital signs and adherence to treatment plans. Predictive analytics are employed for optimizing patient engagement.
Furthermore, AI plays a crucial role in analyzing patient behavior, including medication adherence, exercise routines, and lifestyle choices, providing valuable insights into overall well-being. Importantly, these AI-driven processes are conducted through secure transactions, ensuring the confidentiality and privacy of patient information.
Certainly, the integration of AI has revolutionized the EHR industry. However, the balance between technological advancement and ethical considerations must be carefully maintained.
Looking ahead, the future envisions continuous growth in AI, with the integration of more sophisticated algorithms to enhance the accessibility of EHRs. NLP will contribute to a deeper understanding of unstructured data. AI will play a pivotal role in overcoming interoperability challenges. The increasing popularity of AI-driven predictive analytics is expected in EHRs.
While the overall future of AI in EHRs is promising, it is imperative to address challenges associated with data privacy, security, and the ethical use of AI in healthcare. Balancing innovation with ethical standards ensures that the benefits of AI are maximized while safeguarding the integrity of patient information and maintaining trust within the healthcare ecosystem. Contact the App-Scoop Vancouver Mobile App Developers for further details.