AI is poised to revolutionize healthcare by providing efficiencies in daily clinical and administrative operations. It is important that hospitals have the right foundation in place for embracing this new technology.
Gen AI could automate routine tasks like completing patient discharge summaries and care coordination notes; synthesize physician shift-notes; and create lists of medication orders for each patient visit. It could also help catch diseases such as pulmonary hypertension and cardiac amyloidosis earlier, which can be fatal.
1. Automated Decision-Making
For healthcare systems, AI offers the promise of dramatically increasing efficiency and accuracy across a variety of administrative tasks. AI can help automate the many manual processes that are commonplace in healthcare – from data entry, to the creation of clinical pathways and patient education materials.
The ability of AI to take in large, diverse datasets enables it to perform better than humans on a range of tasks including risk stratification, population health and integrates various healthcare services. This has significant implications for patient outcomes, as well as for medical research and drug development.
However, it’s important to note that these applications are not foolproof. Depending on the context and dataset, they may be vulnerable to biases. Furthermore, they often require extensive human intervention to ensure that results are accurate and trustworthy.
While the use of AI in healthcare appears inevitable, the exact nature of how we implement it will determine whether these tools truly disrupt or just augment existing workflows. A key question is: does AI actually provide a benefit that is greater than the cost of implementation? This requires a comprehensive evaluation of three dimensions: statistical validity, clinical utility and economic utility.
A number of healthcare organisations have begun to pilot AI tools in order to make healthcare more efficient and improve outcomes. Examples include the identification of COVID-19 cases from X-rays, automated support for patients with questions outside of business hours and the augmentation of providers through intelligent chatbots.
These technologies are not just proving to be highly effective; they are also helping to alleviate pressure on clinicians and surrogate decision-makers who have to make high stakes decisions that impact the wellbeing of their patients. AI can support these professionals by providing them with the confidence that they are using an accurate tool.
2. Artificial Intelligence vs. Human Intelligence
Many surveyed healthcare leaders have a concern about AI replacing, or even augmenting, human clinicians. This concern is well-founded: Depending on the specific AI technique being employed, it is likely that human tasks and skills will change, and that the work environment will need to evolve along with it.
Nevertheless, there are also cases where AI is poised to significantly improve healthcare services. For example, generative AI can help to streamline administrative workflows by enabling healthcare workers to use voice commands or the touch screen of a mobile phone to record a patient visit and have that information automatically transcribed into structured notes that can be easily uploaded to a medical chart. These kinds of tools can free up time for physicians to spend with patients and to complete other tasks.
Another way that AI is being used to improve healthcare services is through analytics platforms that allow for more informed treatment decisions based on the most up-to-date research findings. These types of systems can make recommendations about what to do for a particular patient based on the results of diagnostic tests, medical history and other factors such as social and economic conditions that might influence health outcomes.
However, these applications raise other concerns for the general public. For instance, when it comes to a person’s personal relationship with their doctor, 57% say they would be uncomfortable if their provider relied on AI to diagnose diseases and recommend treatments. Another area of concern is the potential for unintended bias in AI algorithms. These algorithms are shaped by the data that is fed to them, and there is some concern that AI will continue to perpetuate existing racial and gender biases in healthcare.
3. Artificial Intelligence vs. Machine Learning
As the demand for AI in healthcare continues to grow, we’re seeing more healthcare organizations using it for administrative workflows and allowing human clinicians to spend more time on face-to-face patient care. This can help reduce staff burnout and cognitive overload, enabling them to provide superior customer service, which can also improve medical outcomes.
For example, healthcare systems may use AI to help track trends in a large data set and identify patterns in symptoms or signs of disease. This can make it easier to identify and predict where COVID-19 outbreaks are likely to occur, which can aid in better prevention strategies.
Another benefit of AI is the ability to process huge amounts of information more quickly than humans. This can allow researchers and healthcare professionals to connect previously unconnected data points in minutes that would have taken years to analyze with traditional methods. This can speed up the development of new drugs, preventative healthcare and even diagnostics.
Some healthcare providers are integrating rule-based artificial intelligence into clinical workflows and EHR systems to provide diagnosis and treatment recommendations. However, this has yet to reach widespread adoption due to the difficulty of implementing complex AI technology within existing systems. It is also challenging for the tech industry to provide solutions that are fully integrated into existing workflows, approved by regulators, easily adopted and embedded within EHR systems, taught to clinical professionals, taught to recognize complex conditions such as sepsis, and standardised to a degree that allows vendors to provide a consistent experience for users.
Fortunately, entrepreneurs are developing business models that are leveraging the power of AI to transform healthcare. One example is a digital consultant app called Babylon that uses voice recognition to listen to user reports of symptoms and compares them against a database of diseases and illnesses. It also combines historical data to determine the likelihood of a certain diagnosis.
4. Artificial Intelligence vs. Natural Language Processing
Many forms of AI are currently in use and many have healthcare applications, but the field is complex. Several different technologies are involved, with the most common being machine learning and deep learning. Deep learning is a specific application of artificial intelligence that uses algorithms to train models on data, making the model ‘learn’ by analyzing patterns and trends in the information and then using these findings to make decisions.
Another example of a healthcare AI application involves the use of natural language processing to help clinicians better understand patient communication and deliver care that aligns with their preferences. In addition to improving the quality of patient interactions, these tools can free up valuable human resources to focus on more complex and important tasks.
A number of healthcare companies are implementing AI in order to help them improve the accuracy of their clinical decisions and to enhance the efficiency of patient care. For example, the company Komodo Health uses AI to analyze and interpret de-identified real-world patient data to identify trends and patterns that are often overlooked by humans. This helps providers develop more accurate patient profiles and allows them to factor social inequities into a patient’s history.
AI is also being used to streamline administrative processes such as answering phone calls and processing insurance claims. This can save medical establishments precious productivity hours, which can allow them to invest more in compassionate face-to-face professional care for patients.
However, it is vital that healthcare professionals remain in the loop when it comes to AI technology and how it is applied. This will ensure that the right balance is achieved between patient needs and healthcare provider expertise. The best way to do this is through collaboration that includes researchers, developers, and healthcare practitioners.
5. Artificial Intelligence vs. Deep Learning
With its ability to analyze massive volumes of data, AI can identify hidden patterns that may not be obvious to human eyes. This is particularly useful for identifying underlying causes of disease, such as genetic predispositions and environmental factors. It can also help predict disease onset or progression and inform treatment plans. For example, an AI system could identify a specific variant in a patient’s genes that predisposes them to autoimmune diseases and then develop personalized treatments to address the condition. This reduces the risk of side effects associated with generic medications and improves the efficacy of treatments.
Medical AI technology can also enhance patient engagement and support medical workflows. For instance, digital consultant apps such as Babylon allow users to report symptoms to a virtual doctor who analyzes them and compares the information against known medical knowledge. Then, it recommends a course of action to the user. In addition, AI-powered content management tools can help hospitals optimize revenue cycles by automatically submitting claims for insurance coverage and simplifying coding tasks that often lead to errors in medical records.
Finally, AI can accelerate drug discovery and development by performing large-scale data analysis, identifying new leads for potential drugs or drug combinations and reducing the time it takes to bring them to market. In fact, some biopharmaceutical companies are using AI to shave two years off the process of developing a new drug.
Despite these exciting uses of AI in healthcare, several challenges still remain. For one, ensuring that AI systems are tested under real-world conditions and that they function as expected is crucial. This is especially important in healthcare, where the consequences of faulty or biased AI can be so severe that they pose risks to patients’ lives and well-being.