In recent years, rapid advancements in the field of Artificial Intelligence (AI) have brought significant impact to the world, transforming and disrupting established industries. Within medicine in particular, AI is already supporting and outperforming established infrastructure for improved patient outcomes. While still in the early stages of adoption — 9% of healthcare organizations have used AI models for at least five years as of 2021 — AI is already earning its seal of approval as a valuable part of the clinical framework.
In healthcare organizations, this technology is helping to boost patient care through predictive analytics and personalized treatment plans, while supporting providers through key aspects of their administrative and operative tasks. Already, algorithms are going toe-to-toe with radiologists to identify malignant tumors.
This guide will explore how AI is making present-day clinical operations smarter, while laying the groundwork to power the future of healthcare. We’ll be exploring the use cases of this structure, its application in data exchange, and what the AI-led medical landscape will look like in the years to come.
Artificial Intelligence refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, learning from experience, and making decisions. Using AI, systems are equipped with problem-solving and decision-making capabilities that won’t always require a human’s input.
In healthcare, AI integration is helping systems operate at a faster and more efficient pace to improve care delivery. This is largely due to the system’s power of analyzing large volumes of data to identify patterns that improve diagnosis, treatment, or even prevention. According to the research firm Frost & Sullivan, clinical support from AI has the potential to enhance outcomes by 30%-40%, while cutting treatment costs by 50%.
AI’s impact in healthcare is only just beginning, as computing powers, learning algorithms, and datasets derived from medical records and wearable devices are rapidly becoming more sophisticated.
AI’s impact cuts across different fields of healthcare, such as diagnostics, drug development, personalized medicine, plus patient monitoring applying these technologies to support the existing frameworks.
The following are common AI techniques adopted in healthcare:
Machine Learning (ML) is the most commonly applied AI structure. Under this technique, computers are trained from datasets to perform different tasks with little to no human intervention needed. In healthcare, ML is largely applied to precision medicine, helping to predict the most efficient treatment methods to accommodate individual needs or disease attributes. Through ML, the need for human input is reduced when performing tasks like classifying patients or recognizing key insights needed for treatment protocols.
ML uses different approaches to acquire the knowledge necessary for making predictions and analyses. Using supervised learning, the machine is taught from structured data sets to recognize patterns in data. Unsupervised learning takes place without human input, teaching the computer to discover patterns from unstructured data like images. Lastly, semi-supervised learning uses a mix of structured and unstructured data to train learning models.
Using Natural Language Processing (NLP), machines can speak the human language, or at least understand and interpret it. NLP algorithms ensure that systems can make sense of information contained in human records and articles. Familiar applications like speech recognition, text analysis, and translation all fall under NLP. Within healthcare, NLP has several applications, ranging from medical dictation, to verbal updates made to electronic health records by providers, to transcribing patient interactions, as well as conversational AI models used in telehealth services.
This is an early form of AI in healthcare, providing a knowledge bank made up of established rules to guide decision-making in healthcare settings. This system has been in use for decades, and is still widely adopted by Electronic Health Record manufacturers. Under rule-based expert systems, subject matter experts and knowledge engineers partner to create a body of rules to govern a knowledge area such as X-ray image interpretation.
At present, data has become an inevitable feature of modern healthcare operations. At Metriport, our API permits healthcare systems to interact with each other, so data needed for application development is easily accessible and retrievable.
Every year, health systems generate close to 2000 exabytes of data from patient care and treatment, medical imaging, drug management, EHRs, HIEs, and so on. This is why interoperability — the easy and secure exchange of health information — has become the primary focus of promoting collaboration between health organizations. AI could hold the key to more efficient health data exchange, as its application to Electronic Health Record systems has shown.
In comparison to standard patient record systems, AI-optimized clinical records can categorize and structure large amounts of patient data more efficiently, ensuring that providers can access and interpret information with ease. This improvement alone could be a big benefit to providers who spend close to 62% of patient time reviewing patient records.
As AI continues to advance, its potential for data usage in healthcare continues to evolve. By integrating this technology into structures like Health Information Exchanges (HIEs), traditionally manual processes like patient matching can be accelerated to close gaps in care. AI integration into HIEs ensures that data extraction, standardization, and exchange are streamlined for easier operations.
AI is currently leading the charge for modern development in healthcare. In day-to-day operations, this technology harnesses information from large datasets to encourage a patient-centric approach to healthcare.
Through AI, organizations now have insights into diagnostic and treatment options in a fraction of the time, with the benefit of improved accuracy. AI structures are also opening the door for true interoperability in healthcare, building on past efforts at data standardization to connect siloed health systems for a clearer picture of patient healthcare.
However, while these benefits exist, AI is presently functioning in a world racing to meet up with its requirements. As challenges in regulatory provisions and trust issues around data privacy, plus human biases in AI are refined, this structure is set to transform health operations in the years and decades to come.
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