Abstract
The extensive use of clinical decision support systems (CDSS) and electronic health records (EHR) has significantly altered the landscape of healthcare. Medical professionals now have access to priceless tools that transform patient data management and help them make wise clinical judgments. However, as we seamlessly incorporate artificial intelligence (AI) solutions into EHR and CDSS, a new era of healthcare is beginning. The incorporation of AI technologies is thoroughly explored in this chapter, shedding light on how they might improve clinical operations and patient outcomes. The chapter opens by emphasizing the crucial role played by EHR in centralizing medical records, digitizing patient data, and enabling effective data sharing between healthcare providers. The chapter conducts an in-depth exploration of how machine learning algorithms are applied to unearth patterns in patient data, identify disease risks, and provide personalized treatment recommendations.
Top1. Introduction
Within the ever-evolving healthcare landscape, technological advancements serve as pivotal catalysts, profoundly influencing the manner in which medical professionals engage with patient data. Central to this transformation is the concept of Electronic Health Records (EHRs) and Clinical Decision Support Systems (CDSS). As we venture further into the era of data-driven healthcare, it becomes pivotal to understand these systems and the transformative potential of integrating Artificial Intelligence (AI) into them.
Electronic Health Records (EHRs): EHRs, at their core, are digital versions of a patient's paper chart. They encapsulate a patient's medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory and test results. Gone are the days where patient files were stacked in towering shelves; modern healthcare necessitates instant access to patient information to deliver timely and effective care (R. Kishore Kumar and M. S. C. Senthil Kamalesh, 2023). EHRs streamline this process, allowing healthcare providers to swiftly make decisions based on comprehensive patient data. Moreover, they enhance the continuity of care since the data is easily shared across different healthcare settings, from specialists to labs, and even pharmacies(B. Ram Vishal and K. A. M, 2023).
Electronic Health Records (EHRs) digitally catalog patient or population health data in a systematic manner. They serve as lifelong health documentation, aggregating diverse forms of health-related information for easy access. EHRs encompass data from various healthcare sectors like administration, pharmacy, clinical, radiology, laboratory, and nursing, as illustrated in Figure.1. The information within an EHR can be neatly organized, semi-organized, freeform, or a mix of these categories(D. Palanikkumar, A. Y. Begum, 2023). For instance, EHR content might include plain text, basic data types like state variables or Boolean values, timestamps, coded information, embeddable data like multimedia or readable content, quantified measurements with their respective units, URLs, and grouping structures like sets or lists.
Figure 1.
Block diagram of electronic health records
In the past, health records primarily existed in paper form, comprised of various prescriptions, X-ray findings, and laboratory results (A. Nareshkumar and B. Gururaj, 2022). These paper-based records, however, are cumbersome to manage. EHRs provide a streamlined solution by housing comprehensive patient health histories electronically. Furthermore, EHRs simplify the electronic transfer of medical information between hospitals, alleviating the need for patients to transport physical documents and reports. To truly harness the advantages of EHRs, institutions should implement an information system adept at storing, organizing, and transmitting these electronic records(R. Kavitha, and V. Hariram, 2023). Yet, a proficient information system must also be paired with a robust data management strategy. This strategy should address and rectify the challenges inherent in EHRs, which we'll discuss in the subsequent section.
Key Terms in this Chapter
Interoperability: The capacity of various devices, applications, or information systems to communicate, share information, and work together in a coordinated way.
Electronic Health Records (EHR): A digital copy of a patient's medical history that is kept up to date over time by the provider and contains all of the essential clinical information necessary for that person to receive care from that particular provider, such as demographics, progress notes, issues, medications, vital signs, previous medical history, immunisations, laboratory results, and radiology reports.
Machine Learning (ML): A branch of AI that focuses on the use of statistical models and techniques to enable computers to become better at tasks over time.
Health Informatics: The multidisciplinary study of how IT-based innovations are designed, developed, adopted, and used in the management, planning, and provision of healthcare services.
Natural Language Processing (NLP): A branch of artificial intelligence that focuses on how people and computers interact, particularly on how to teach computers to handle and analyse large volumes of natural language input.
Clinical Decision Support Systems (CDSS): Computer programmes that assist in decision-making in healthcare settings, interpreting clinical data and offering advice to medical personnel.
Artificial Intelligence (AI): A field in computer science that aims to build robots that can replicate cognitive processes like language comprehension, learning, reasoning, and problem-solving.
Predictive Analytics: Estimating future outcomes' probability using data, statistical algorithms, and machine learning methods based on past performance.